Project 1 Investigate No-Show Appointment Dataset.ipynb

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    "# Project: Investigate a Dataset - NoShow Appointments\n",
    "\n",
    "## Table of Contents\n",
    "* <a href=\"#intro\">1. Introduction</a>\n",
    "  * <a href=\"#sec1.1\"> 1.1. Dataset Description </a>\n",
    "  * <a href=\"#sec1.2\"> 1.2. Question(s) for Analysis </a>\n",
    "  * <a href=\"#sec1.3\"> 1.3. Import Packages </a>\n",
    "\n",
    "* <a href=\"#wrangling\">2. Data Wrangling</a>\n",
    "  \n",
    "* <a href=\"#cleaning\">3. Data Cleaning</a>\n",
    "  \n",
    "* <a href=\"#feature engineering\">4. Feature Engineering</a> \n",
    "\n",
    "* <a href=\"#eda\">5. Exploratory Data Analysis</a>\n",
    "  * <a href=\"#EDA_question1\">5.1. Were there patients who missed appointments?</a>   \n",
    "  * <a href=\"#EDA_question2\">5.3. What was the distribution like for appointment waiting times?</a>\n",
    "  * <a href=\"#EDA_question3\">5.4. What areas had the shortest and longest wait times?</a>\n",
    "  * <a href=\"#EDA_question4\">5.5. Which timeframes with the highest and lowest appointments?</a>\n",
    "  * <a href=\"#EDA_question5\">5.6. Is there a correlation between the waiting time and no-show ups?</a>\n",
    "  * <a href=\"#EDA_question6\">5.7. What gender was most likely to miss and of what age?</a>\n",
    "  * <a href=\"#EDA_question7\">5.8. Which area was hit headest by patients who didn't show up?</a>\n",
    "  * <a href=\"#EDA_question8\">5.9. Which areas had the most and least appointments?</a>\n",
    "  * <a href=\"#EDA_question9\">5.2. Did the patients who received an sms(es) more likely show up?</a>\n",
    "\n",
    "* <a href=\"#conclusions\">6. Conclusions</a>\n",
    "  * <a href=\"#limitations\"> 6.1. Limitations</a>\n",
    "  * <a href=\"#references\"> 6.2. References</a>"
   ]
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   "source": [
    "<a id='intro'></a>\n",
    "## Introduction"
   ]
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   "metadata": {},
   "source": [
    "<a id='sec1.1'></a>\n",
    "### Dataset Description \n",
    "\n",
    "> This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are\n",
    "included in each row.\n",
    "\n",
    "1. PatientId: Uniqe identification of each patient\n",
    "2. AppointmentID: Unique identification of each appointment\n",
    "3. Gender: Male or Female\n",
    "4. ScheduledDay: The day the patient set up their appointment.\n",
    "6. Age: How old the patient is.\n",
    "7. Neighborhood: The location of the hospital.\n",
    "8. Scholarship: Indicates whether or not the patient is enrolled in Brasilian welfare program [Bolsa Família](https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia).\n",
    "9. Hypertension: Whether a patient has it(True or False).\n",
    "10. Diabetes: Whether a patient has it(True or False).\n",
    "11. Alcoholism: Whether a patient has it(True or False).\n",
    "12. Handicap; Whether a patient is(True or False).\n",
    "13. SmsReceived: Messages sent to the patient.\n",
    "14. NoShow: Shows whether a patient showed up or not. Says ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up.\n",
    "\n",
    "[Click here to view the dataset source](https://www.kaggle.com/datasets/joniarroba/noshowappointments). "
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sec1.2'></a>\n",
    "\n",
    "### Questions for Analysis\n",
    "My questions for this dataset are split into two main areas of concern. How **TIME** and **POPULATION** play in this dataset.\n",
    "\n",
    "> **Demography**: \n",
    "1. Were there patients who missed appointments?\n",
    "2. What was the distribution like for appointment waiting times?\n",
    "3. What areas had the shortest and longest wait times?\n",
    "4. Timeframes with the highest and lowest appointments?\n",
    "5. Is there a correlation between the waiting time and no-show ups?\n",
    "6. What gender was most likely to miss and of what age?\n",
    "7. Which area was hit headest by patients who didn't show up?\n",
    "8. Which areas had the most and least appointments?\n",
    "9. Did the patients who received an sms(es) more likely show up? \n",
    "10. What factors correlate?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sec1.3'></a>\n",
    "### Import the Necessary Modules"
   ]
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   "cell_type": "code",
   "execution_count": 152,
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   "source": [
    "# Importing necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style('darkgrid')\n"
   ]
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      "  \u001b[31m   \u001b[0m copying pandas/plotting/_core.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_misc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/arrays/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/arrays\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/api\n",
      "  \u001b[31m   \u001b[0m copying pandas/api/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/api\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/errors\n",
      "  \u001b[31m   \u001b[0m copying pandas/errors/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/errors\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/config.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/display.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/localization.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/dates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/compat/numpy\n",
      "  \u001b[31m   \u001b[0m copying pandas/compat/numpy/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/compat/numpy\n",
      "  \u001b[31m   \u001b[0m copying pandas/compat/numpy/function.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/compat/numpy\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/tile.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/merge.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/util.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/melt.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/pivot.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/tools/timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/tools/datetimes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/tools/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/tools/numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/util/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/util/hashing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/util\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/cast.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/inference.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/grouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/arrays.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/blocks.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/managers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/check.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/align.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/pytables.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/engines.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/expressions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/eval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/scope.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/expr.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/datetimes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/numpy_.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/_ranges.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/integer.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/sparse.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/array_.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/ops/roperator.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/ops/missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/ops/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/ops/docstrings.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/scipy_sparse.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/accessors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/datetimes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/multi.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/frozen.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/category.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/msgpack/_version.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/msgpack/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/msgpack/exceptions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/msgpack\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/console.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/style.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/html.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/excel.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/css.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/csvs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/latex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/printing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_openpyxl.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_xlrd.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_util.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_odfreader.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_xlwt.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_xlsxwriter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/json/_json.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/json/_normalize.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/json/_table_schema.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/json/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/sas7bdat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/sas_constants.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/sasreader.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/sas_xport.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboard/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboard/clipboards.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboard/exceptions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboard/windows.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_block_internals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_alter_axes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_timeseries.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_constructors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_operators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_subclass.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_io.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_quantile.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_datetime_values.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_explode.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_internals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_repr.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_ufunc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_validate.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_duplicates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_asof.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_rank.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_replace.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_combine_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_cut.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_union_categoricals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_qcut.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_pivot.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_util.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_melt.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tools/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tools/test_numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tools\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_external_block.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_sparse.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_integer.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_numpy.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_resampler_grouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_timedelta.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_period_index.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_datetime_index.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_time_grouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_resample_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_validate_args_and_kwargs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_series_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_safe_import.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_frame_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_interval_array_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_util.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_move.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_index_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_validate_args.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_validate_kwargs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_categorical_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_numpy_array_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_extension_array_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_deprecate.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_hashing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_produces_warning.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_deprecate_kwarg.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_almost_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/config\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/config/test_localization.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/config\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/config/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/config\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/config/test_config.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/config\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_parquet.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_compression.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_date_converters.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_pickle.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_clipboard.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_stata.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_html.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_gcs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_sql.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_gbq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_feather.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_spss.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/generate_legacy_storage_files.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_s3.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_packers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_block_internals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_alter_axes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_axis_select_reindex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_convert_to.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_timeseries.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_constructors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_mutate_columns.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_query_eval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_join.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_sort_values_level_as_str.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_operators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_subclass.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_quantile.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_explode.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_repr_info.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_nonunique_indexes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_validate.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_duplicates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_asof.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_rank.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_replace.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_combine_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_to_csv.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_inference.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_timegrouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_grouping.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_counting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_value_counts.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_transform.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_nth.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_bin_groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_function.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_filters.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_rank.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_index_as_string.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_whitelist.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/internals/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/internals/test_internals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/internals\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/computation/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/computation/test_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/computation/test_eval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/computation\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_backend.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_misc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_boxplot_method.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_hist_method.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_converter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_expanding.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_rolling.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_ewm.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_moments.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_grouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_window.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_pairwise.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_timeseries_window.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_array.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_integer.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_datetimes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_numpy.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_datetime64.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_timedelta64.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_object.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/api\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/api/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/api\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/api/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/api\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/api/test_types.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/api\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/test_frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/test_generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/test_label_or_level_utils.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/test_series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_parsing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_ccalendar.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_conversion.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_array_to_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_liboffsets.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_libfrequencies.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_parse_iso8601.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_normalize_date.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_period_asfreq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_pivot.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_combine_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_indexing_slow.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_ix.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_chaining_and_caching.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_callable.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_iloc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_loc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_timedelta.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_floats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_coercion.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_scalar.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_partial.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_indexing_engines.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reductions\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reductions/test_stat_reductions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reductions\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reductions/test_reductions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reductions\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reductions/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reductions\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/test_nat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_frozen.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_category.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_numpy_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_alter_index.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_callable.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_boolean.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_iloc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_loc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_merge_index_as_string.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_merge_asof.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_join.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_merge.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_multi.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_merge_ordered.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/decimal\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/decimal/test_decimal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/decimal\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/decimal/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/decimal\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/decimal/array.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/decimal\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/arrow\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/arrow/bool.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/arrow\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/arrow/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/arrow\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/arrow/test_bool.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/arrow\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/json/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/json/test_json.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/json/array.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/json\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/reshaping.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/methods.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/reduce.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/setitem.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/dtype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/io.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/interface.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/getitem.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/casting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/constructors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/printing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_extension.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_subtype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_buffer.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_unpack.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_pack.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_unpack_raw.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_except.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_case.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_read_size.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_seq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_limits.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_obj.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_newspec.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_sequnpack.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_console.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_to_latex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_to_html.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_eng_formatting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_printing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_style.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_css.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_to_excel.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_to_csv.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_readers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_xlwt.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_xlsxwriter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_writers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_odf.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_openpyxl.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_xlrd.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_style.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_comment.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_multi_thread.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_compression.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_converters.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_parse_dates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_c_parser_only.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_dialect.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_skiprows.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_read_fwf.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_unsupported.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_mangle_dupes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_python_parser_only.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_textreader.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_network.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_na_values.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_usecols.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_quoting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_header.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_index_col.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_compression.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_json_table_schema.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_readlines.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_ujson.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_pandas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_normalize.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/sas/test_sas7bdat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/sas/test_sas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/sas/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/sas/test_xport.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/pytables/test_pytables_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/pytables/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/pytables/test_pytables.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/pytables/test_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/test_observance.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/test_federal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/test_calendar.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/test_holiday.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/frequencies/test_inference.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/frequencies/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/frequencies/test_to_offset.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/frequencies/test_freq_code.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_offsets_properties.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_fiscal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_yqm_offsets.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_ticks.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_offsets.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_convert_objects.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_construct_object_arr.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_promote.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_construct_from_scalar.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_find_common_type.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_upcast.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_infer_dtype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_infer_datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_construct_ndarray.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_downcast.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/aggregate/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/aggregate/test_cython.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/aggregate/test_aggregate.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/aggregate/test_other.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/interval/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/interval/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/interval/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/interval\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_warnings.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_constructors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_operators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_subclass.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_algos.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_repr.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_libsparse.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_accessor.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_array.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_arithmetics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_dtype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/series/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/series/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/series/test_series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/series\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_to_from_scipy.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_to_csv.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/interval/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/interval/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/interval/test_interval_new.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/interval\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_indexing_slow.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_multiindex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_ix.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_chaining_and_caching.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_xs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_getitem.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_sorted.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_setitem.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_iloc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_loc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_slice.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_set_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_partial.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/interval/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/interval/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/interval/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/interval\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/test_timedelta.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/test_formats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/period/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/period/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/period/test_asfreq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/period\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_timestamp.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_comparisons.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_rendering.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_unary_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_interval_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_interval_new.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_interval_tree.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_names.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_join.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_copy.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_integrity.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_constructor.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_conversion.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_equivalence.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_get_set.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_monotonic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_drop.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_duplicates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_set_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_contains.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_partial_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_reindex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_scalar_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_formats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_partial_slicing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_tools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_asfreq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_period_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_misc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_date_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_scalar_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_formats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_partial_slicing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_tools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_timedelta.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_scalar_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_formats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_partial_slicing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_timedelta_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_tools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/_libs/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/_libs/tslibs/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_libs/tslibs\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/misc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/hist.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/boxplot.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/style.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/tools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/converter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/core.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/timeseries.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/api/types\n",
      "  \u001b[31m   \u001b[0m copying pandas/api/types/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/api/types\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/api/extensions\n",
      "  \u001b[31m   \u001b[0m copying pandas/api/extensions/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/api/extensions\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats/templates\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/templates/html.tpl -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats/templates\n",
      "  \u001b[31m   \u001b[0m UPDATING build/lib.macosx-10.9-x86_64-3.8/pandas/_version.py\n",
      "  \u001b[31m   \u001b[0m set build/lib.macosx-10.9-x86_64-3.8/pandas/_version.py to '0.25.0'\n",
      "  \u001b[31m   \u001b[0m running build_ext\n",
      "  \u001b[31m   \u001b[0m building 'pandas._libs.algos' extension\n",
      "  \u001b[31m   \u001b[0m creating build/temp.macosx-10.9-x86_64-3.8\n",
      "  \u001b[31m   \u001b[0m creating build/temp.macosx-10.9-x86_64-3.8/pandas\n",
      "  \u001b[31m   \u001b[0m creating build/temp.macosx-10.9-x86_64-3.8/pandas/_libs\n",
      "  \u001b[31m   \u001b[0m gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/Steve/opt/anaconda3/include -arch x86_64 -I/Users/Steve/opt/anaconda3/include -arch x86_64 -DNPY_NO_DEPRECATED_API=0 -Ipandas/_libs/src/klib -Ipandas/_libs/src -I/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/numpy/core/include -I/Users/Steve/opt/anaconda3/include/python3.8 -c pandas/_libs/algos.c -o build/temp.macosx-10.9-x86_64-3.8/pandas/_libs/algos.o -Wno-unused-function\n",
      "  \u001b[31m   \u001b[0m xcrun: error: invalid active developer path (/Library/Developer/CommandLineTools), missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun\n",
      "  \u001b[31m   \u001b[0m error: command '/usr/bin/gcc' failed with exit code 1\n",
      "  \u001b[31m   \u001b[0m \u001b[31m[end of output]\u001b[0m\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "\u001b[31m  ERROR: Failed building wheel for pandas\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[?25h  Running setup.py clean for pandas\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed to build pandas\n",
      "Installing collected packages: pandas\n",
      "  Attempting uninstall: pandas\n",
      "    Found existing installation: pandas 1.2.3\n",
      "    Uninstalling pandas-1.2.3:\n",
      "      Successfully uninstalled pandas-1.2.3\n",
      "  Running setup.py install for pandas ... \u001b[?25lerror\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mRunning setup.py install for pandas\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m \u001b[31m[955 lines of output]\u001b[0m\n",
      "  \u001b[31m   \u001b[0m /private/var/folders/rg/fbkfsqpd1dx3_sq7w45ylk1w0000gp/T/pip-install-xdrxdjiw/pandas_5ef5e8f6c6bf42e5a9f12db5558bde06/setup.py:487: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  \u001b[31m   \u001b[0m   LooseVersion(python_target) < \"10.9\"\n",
      "  \u001b[31m   \u001b[0m running install\n",
      "  \u001b[31m   \u001b[0m /Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
      "  \u001b[31m   \u001b[0m   warnings.warn(\n",
      "  \u001b[31m   \u001b[0m running build\n",
      "  \u001b[31m   \u001b[0m running build_py\n",
      "  \u001b[31m   \u001b[0m creating build\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas\n",
      "  \u001b[31m   \u001b[0m copying pandas/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas\n",
      "  \u001b[31m   \u001b[0m copying pandas/_typing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas\n",
      "  \u001b[31m   \u001b[0m copying pandas/_version.py -> build/lib.macosx-10.9-x86_64-3.8/pandas\n",
      "  \u001b[31m   \u001b[0m copying pandas/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas\n",
      "  \u001b[31m   \u001b[0m copying pandas/testing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/compat\n",
      "  \u001b[31m   \u001b[0m copying pandas/compat/_optional.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/compat\n",
      "  \u001b[31m   \u001b[0m copying pandas/compat/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/compat\n",
      "  \u001b[31m   \u001b[0m copying pandas/compat/chainmap.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/compat\n",
      "  \u001b[31m   \u001b[0m copying pandas/compat/pickle_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/compat\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/accessor.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/nanops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/algorithms.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/resample.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/window.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/index.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/config_init.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/strings.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/_depr_module.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/_test_decorators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/_validators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/_print_versions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/_decorators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/_doctools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/testing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/_exceptions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/util/_tester.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/util\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/feather_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/parquet.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/gcs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/pytables.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/html.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboards.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/parsers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/date_converters.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/pickle.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sql.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/s3.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/packers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/stata.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/gbq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/spss.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tseries/plotting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tseries/converter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tseries/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tseries/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tseries/offsets.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tseries/frequencies.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tseries/holiday.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tseries\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_optional_dependency.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_expressions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_register_accessor.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_downstream.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_errors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_join.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_lib.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_nanops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_take.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_algos.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_multilevel.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_strings.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/test_base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/_libs\n",
      "  \u001b[31m   \u001b[0m copying pandas/_libs/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_libs\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_core.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_misc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/arrays/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/arrays\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/api\n",
      "  \u001b[31m   \u001b[0m copying pandas/api/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/api\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/errors\n",
      "  \u001b[31m   \u001b[0m copying pandas/errors/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/errors\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/config.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/display.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/localization.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m copying pandas/_config/dates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_config\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/compat/numpy\n",
      "  \u001b[31m   \u001b[0m copying pandas/compat/numpy/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/compat/numpy\n",
      "  \u001b[31m   \u001b[0m copying pandas/compat/numpy/function.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/compat/numpy\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/tile.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/merge.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/util.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/melt.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/reshape/pivot.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/reshape\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/tools/timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/tools/datetimes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/tools/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/tools/numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/tools\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/util/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/util/hashing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/util\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/cast.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/inference.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/dtypes/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/dtypes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/grouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/groupby/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/groupby\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/arrays.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/blocks.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/internals/managers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/internals\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/check.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/align.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/pytables.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/engines.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/expressions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/eval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/scope.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/computation/expr.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/computation\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/datetimes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/numpy_.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/_ranges.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/integer.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/sparse.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/array_.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/arrays/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/arrays\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/ops/roperator.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/ops/missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/ops/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/ops/docstrings.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/ops\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/scipy_sparse.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/sparse/frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/sparse\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/accessors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/datetimes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/multi.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/frozen.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/core/indexes/category.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/core/indexes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/msgpack/_version.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/msgpack/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/msgpack/exceptions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/msgpack\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/console.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/style.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/html.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/excel.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/css.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/csvs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/latex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/printing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_openpyxl.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_xlrd.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_util.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_odfreader.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_xlwt.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/excel/_xlsxwriter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/excel\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/json/_json.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/json/_normalize.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/json/_table_schema.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/json/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/json\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/sas7bdat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/sas_constants.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/sasreader.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/sas/sas_xport.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/sas\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboard/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboard/clipboards.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboard/exceptions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/clipboard/windows.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/clipboard\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_block_internals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_alter_axes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_timeseries.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_constructors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_operators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_subclass.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_io.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_quantile.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_datetime_values.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_explode.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_internals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_repr.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_ufunc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_validate.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_duplicates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_asof.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_rank.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_replace.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_combine_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_cut.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_union_categoricals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_qcut.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_pivot.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_util.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_melt.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/test_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tools/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tools\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tools/test_numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tools\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_external_block.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_sparse.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_integer.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/test_numpy.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_resampler_grouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_timedelta.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_period_index.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_datetime_index.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_time_grouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/resample/test_resample_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/resample\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_validate_args_and_kwargs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_series_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_safe_import.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_frame_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_interval_array_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_util.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_move.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_index_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_validate_args.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_validate_kwargs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_categorical_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_numpy_array_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_extension_array_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_deprecate.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_hashing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_produces_warning.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_deprecate_kwarg.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/util/test_assert_almost_equal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/util\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/config\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/config/test_localization.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/config\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/config/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/config\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/config/test_config.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/config\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_parquet.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_compression.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_date_converters.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_pickle.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_clipboard.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_stata.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_html.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_gcs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_sql.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_gbq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_feather.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_spss.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/generate_legacy_storage_files.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_s3.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/test_packers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_block_internals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_alter_axes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_axis_select_reindex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_convert_to.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_timeseries.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_constructors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_mutate_columns.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_query_eval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_join.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_sort_values_level_as_str.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_operators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_subclass.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_quantile.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_explode.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_repr_info.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_nonunique_indexes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_validate.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_duplicates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_asof.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_rank.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_replace.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_combine_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/frame/test_to_csv.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/frame\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_inference.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_timegrouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_grouping.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_counting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_value_counts.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_transform.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_nth.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_bin_groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_function.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_filters.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_rank.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_index_as_string.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/test_whitelist.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/internals/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/internals\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/internals/test_internals.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/internals\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/computation/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/computation/test_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/computation\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/computation/test_eval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/computation\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_backend.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_misc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_boxplot_method.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_hist_method.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_converter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/plotting/test_series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/plotting\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_expanding.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_rolling.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_ewm.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_moments.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_grouper.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_window.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_pairwise.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/window/test_timeseries_window.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/window\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_array.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_integer.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_datetimes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/test_numpy.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_datetime64.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_timedelta64.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arithmetic/test_object.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arithmetic\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/api\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/api/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/api\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/api/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/api\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/api/test_types.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/api\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/test_frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/test_generic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/test_label_or_level_utils.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/generic/test_series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/generic\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_parsing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_ccalendar.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_conversion.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_array_to_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_timedeltas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_liboffsets.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_libfrequencies.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_parse_iso8601.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_normalize_date.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tslibs/test_period_asfreq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tslibs\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_pivot.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_combine_concat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/test_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_indexing_slow.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_ix.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_chaining_and_caching.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_callable.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_categorical.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_iloc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_loc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_timedelta.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_floats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_coercion.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_scalar.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_partial.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/test_indexing_engines.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reductions\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reductions/test_stat_reductions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reductions\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reductions/test_reductions.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reductions\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reductions/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reductions\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/test_nat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_frozen.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_category.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/test_numpy_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_alter_index.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_callable.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_boolean.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_iloc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_loc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_numeric.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/series/indexing/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/series/indexing\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_merge_index_as_string.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_merge_asof.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_join.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_merge.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_multi.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/reshape/merge/test_merge_ordered.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/reshape/merge\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/decimal\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/decimal/test_decimal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/decimal\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/decimal/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/decimal\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/decimal/array.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/decimal\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/arrow\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/arrow/bool.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/arrow\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/arrow/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/arrow\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/arrow/test_bool.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/arrow\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/json/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/json/test_json.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/json/array.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/json\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/reshaping.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/methods.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/reduce.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/setitem.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/dtype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/io.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/interface.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/getitem.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/casting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/groupby.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/base.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/constructors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/extension/base/printing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/extension/base\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_extension.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_subtype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_buffer.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_unpack.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_pack.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_unpack_raw.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_except.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_case.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_read_size.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_seq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_limits.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_obj.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_newspec.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/msgpack/test_sequnpack.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/msgpack\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_console.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_to_latex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_to_html.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_eng_formatting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_printing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_style.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_css.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_to_excel.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/formats/test_to_csv.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/formats\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_readers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_xlwt.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_xlsxwriter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_writers.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_odf.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_openpyxl.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_xlrd.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/excel/test_style.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/excel\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_comment.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_multi_thread.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_compression.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_converters.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_parse_dates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_c_parser_only.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_dialect.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_skiprows.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_read_fwf.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_unsupported.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_mangle_dupes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_python_parser_only.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_textreader.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_network.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_na_values.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_usecols.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_quoting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_header.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_index_col.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/parser/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/parser\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_compression.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_json_table_schema.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_readlines.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_ujson.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_pandas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/json/test_normalize.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/json\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/sas/test_sas7bdat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/sas/test_sas.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/sas/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/sas/test_xport.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/sas\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/pytables/test_pytables_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/pytables/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/pytables/test_pytables.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/io/pytables/test_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/io/pytables\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/test_observance.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/test_federal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/test_calendar.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/holiday/test_holiday.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/holiday\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/frequencies/test_inference.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/frequencies/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/frequencies/test_to_offset.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/frequencies/test_freq_code.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/frequencies\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_offsets_properties.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_fiscal.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_yqm_offsets.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_ticks.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/tseries/offsets/test_offsets.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/tseries/offsets\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_convert_objects.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_construct_object_arr.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_promote.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_construct_from_scalar.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_find_common_type.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_upcast.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_infer_dtype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_infer_datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_construct_ndarray.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/dtypes/cast/test_downcast.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/dtypes/cast\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/aggregate/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/aggregate/test_cython.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/aggregate/test_aggregate.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/groupby/aggregate/test_other.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/groupby/aggregate\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/interval/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/interval/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/interval/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/interval\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_warnings.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_constructors.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_operators.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_subclass.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_algos.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/common.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_repr.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_api.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/categorical/test_dtypes.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/categorical\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_libsparse.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_accessor.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_array.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_arithmetics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/arrays/sparse/test_dtype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/arrays/sparse\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/series/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/series/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/series\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/series/test_series.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/series\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_frame.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_to_from_scipy.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_apply.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/sparse/frame/test_to_csv.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/sparse/frame\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/interval/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/interval/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/interval/test_interval_new.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/interval\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_indexing_slow.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_multiindex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_ix.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_chaining_and_caching.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_xs.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_getitem.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_sorted.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_setitem.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_iloc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_loc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_slice.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_set_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexing/multiindex/test_partial.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexing/multiindex\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/interval/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/interval/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/interval/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/interval\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/test_timedelta.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/test_formats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timedelta/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timedelta\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/period/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/period/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/period/test_asfreq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/period\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_timestamp.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_comparisons.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_rendering.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_unary_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/scalar/timestamp/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/scalar/timestamp\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_interval_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_interval.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_interval_new.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/interval/test_interval_tree.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/interval\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_names.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/conftest.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_sorting.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_join.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_copy.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_integrity.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_reshape.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_analytics.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_constructor.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_conversion.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_equivalence.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_get_set.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_monotonic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_drop.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_duplicates.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_set_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_contains.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_partial_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_format.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/multi/test_reindex.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/multi\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_period.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_scalar_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_formats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_partial_slicing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_tools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_asfreq.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/period/test_period_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/period\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_misc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_missing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_date_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_timezones.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_datetimelike.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_scalar_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_formats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_datetime.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_partial_slicing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/datetimes/test_tools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/datetimes\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_timedelta.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_scalar_compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_indexing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_astype.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_formats.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_setops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_construction.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_partial_slicing.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_ops.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_arithmetic.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_timedelta_range.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m copying pandas/tests/indexes/timedeltas/test_tools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/tests/indexes/timedeltas\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/_libs/tslibs\n",
      "  \u001b[31m   \u001b[0m copying pandas/_libs/tslibs/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/_libs/tslibs\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/misc.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/hist.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/compat.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/boxplot.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/style.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/tools.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/converter.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/core.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m copying pandas/plotting/_matplotlib/timeseries.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/plotting/_matplotlib\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/api/types\n",
      "  \u001b[31m   \u001b[0m copying pandas/api/types/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/api/types\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/api/extensions\n",
      "  \u001b[31m   \u001b[0m copying pandas/api/extensions/__init__.py -> build/lib.macosx-10.9-x86_64-3.8/pandas/api/extensions\n",
      "  \u001b[31m   \u001b[0m creating build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats/templates\n",
      "  \u001b[31m   \u001b[0m copying pandas/io/formats/templates/html.tpl -> build/lib.macosx-10.9-x86_64-3.8/pandas/io/formats/templates\n",
      "  \u001b[31m   \u001b[0m UPDATING build/lib.macosx-10.9-x86_64-3.8/pandas/_version.py\n",
      "  \u001b[31m   \u001b[0m set build/lib.macosx-10.9-x86_64-3.8/pandas/_version.py to '0.25.0'\n",
      "  \u001b[31m   \u001b[0m running build_ext\n",
      "  \u001b[31m   \u001b[0m pandas._libs.algos: -> [['pandas/_libs/algos.c']]\n",
      "  \u001b[31m   \u001b[0m Traceback (most recent call last):\n",
      "  \u001b[31m   \u001b[0m   File \"<string>\", line 2, in <module>\n",
      "  \u001b[31m   \u001b[0m   File \"<pip-setuptools-caller>\", line 34, in <module>\n",
      "  \u001b[31m   \u001b[0m   File \"/private/var/folders/rg/fbkfsqpd1dx3_sq7w45ylk1w0000gp/T/pip-install-xdrxdjiw/pandas_5ef5e8f6c6bf42e5a9f12db5558bde06/setup.py\", line 807, in <module>\n",
      "  \u001b[31m   \u001b[0m     setup(\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/__init__.py\", line 87, in setup\n",
      "  \u001b[31m   \u001b[0m     return distutils.core.setup(**attrs)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/core.py\", line 148, in setup\n",
      "  \u001b[31m   \u001b[0m     return run_commands(dist)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/core.py\", line 163, in run_commands\n",
      "  \u001b[31m   \u001b[0m     dist.run_commands()\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/dist.py\", line 967, in run_commands\n",
      "  \u001b[31m   \u001b[0m     self.run_command(cmd)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/dist.py\", line 1214, in run_command\n",
      "  \u001b[31m   \u001b[0m     super().run_command(command)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/dist.py\", line 986, in run_command\n",
      "  \u001b[31m   \u001b[0m     cmd_obj.run()\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/command/install.py\", line 68, in run\n",
      "  \u001b[31m   \u001b[0m     return orig.install.run(self)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/command/install.py\", line 664, in run\n",
      "  \u001b[31m   \u001b[0m     self.run_command('build')\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/cmd.py\", line 313, in run_command\n",
      "  \u001b[31m   \u001b[0m     self.distribution.run_command(command)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/dist.py\", line 1214, in run_command\n",
      "  \u001b[31m   \u001b[0m     super().run_command(command)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/dist.py\", line 986, in run_command\n",
      "  \u001b[31m   \u001b[0m     cmd_obj.run()\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/command/build.py\", line 135, in run\n",
      "  \u001b[31m   \u001b[0m     self.run_command(cmd_name)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/cmd.py\", line 313, in run_command\n",
      "  \u001b[31m   \u001b[0m     self.distribution.run_command(command)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/dist.py\", line 1214, in run_command\n",
      "  \u001b[31m   \u001b[0m     super().run_command(command)\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/dist.py\", line 986, in run_command\n",
      "  \u001b[31m   \u001b[0m     cmd_obj.run()\n",
      "  \u001b[31m   \u001b[0m   File \"/Users/Steve/opt/anaconda3/lib/python3.8/site-packages/setuptools/_distutils/command/build_ext.py\", line 339, in run\n",
      "  \u001b[31m   \u001b[0m     self.build_extensions()\n",
      "  \u001b[31m   \u001b[0m   File \"/private/var/folders/rg/fbkfsqpd1dx3_sq7w45ylk1w0000gp/T/pip-install-xdrxdjiw/pandas_5ef5e8f6c6bf42e5a9f12db5558bde06/setup.py\", line 407, in build_extensions\n",
      "  \u001b[31m   \u001b[0m     self.check_cython_extensions(self.extensions)\n",
      "  \u001b[31m   \u001b[0m   File \"/private/var/folders/rg/fbkfsqpd1dx3_sq7w45ylk1w0000gp/T/pip-install-xdrxdjiw/pandas_5ef5e8f6c6bf42e5a9f12db5558bde06/setup.py\", line 397, in check_cython_extensions\n",
      "  \u001b[31m   \u001b[0m     raise Exception(\n",
      "  \u001b[31m   \u001b[0m Exception: Cython-generated file 'pandas/_libs/algos.c' not found.\n",
      "  \u001b[31m   \u001b[0m                 Cython is required to compile pandas from a development branch.\n",
      "  \u001b[31m   \u001b[0m                 Please install Cython or download a release package of pandas.\n",
      "  \u001b[31m   \u001b[0m \n",
      "  \u001b[31m   \u001b[0m \u001b[31m[end of output]\u001b[0m\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "\u001b[33m  WARNING: No metadata found in /Users/Steve/opt/anaconda3/lib/python3.8/site-packages\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[?25h  Rolling back uninstall of pandas\n",
      "  Moving to /Users/Steve/opt/anaconda3/lib/python3.8/site-packages/pandas-1.2.3.dist-info/\n",
      "   from /Users/Steve/opt/anaconda3/lib/python3.8/site-packages/~andas-1.2.3.dist-info\n",
      "  Moving to /Users/Steve/opt/anaconda3/lib/python3.8/site-packages/pandas/\n",
      "   from /Users/Steve/opt/anaconda3/lib/python3.8/site-packages/~andas\n",
      "\u001b[1;31merror\u001b[0m: \u001b[1mlegacy-install-failure\u001b[0m\n",
      "\n",
      "\u001b[31m×\u001b[0m Encountered error while trying to install package.\n",
      "\u001b[31m╰─>\u001b[0m pandas\n",
      "\n",
      "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
      "\u001b[1;36mhint\u001b[0m: See above for output from the failure.\n"
     ]
    }
   ],
   "source": [
    "# Upgrade pandas to use dataframe.explode() function. \n",
    "!pip install --upgrade pandas==0.25.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> In this section of the report, I am loading in the data, checking for cleanliness and a brief overview to get a look at what the data looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the raw dataset\n",
    "df_raw = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "\n",
    "# Assign the raw dataset to another df ensuring I am not editing the raw dataset\n",
    "df_noshow = df_raw\n",
    "\n",
    "# Quick look into the first 5 rows of the df\n",
    "df_noshow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of the dataset: No of Columns and Rows\n",
    "df_noshow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         float64\n",
       "AppointmentID       int64\n",
       "Gender             object\n",
       "ScheduledDay       object\n",
       "AppointmentDay     object\n",
       "Age                 int64\n",
       "Neighbourhood      object\n",
       "Scholarship         int64\n",
       "Hipertension        int64\n",
       "Diabetes            int64\n",
       "Alcoholism          int64\n",
       "Handcap             int64\n",
       "SMS_received        int64\n",
       "No-show            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the df data-types\n",
    "df_noshow.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# More detailed view of the shape of the dataset: size of columns, NaN values and datatypes\n",
    "df_noshow.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical summary of the dataset\n",
    "df_noshow.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No-show\n",
       "No     88208\n",
       "Yes    22319\n",
       "dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show count of Patients before data editing\n",
    "Before_edit = df_noshow.groupby(['No-show']).size()\n",
    "Before_edit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1296 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the data before cleaning \n",
    "\n",
    "ax = df_noshow.hist(figsize=(20,18),bins=20, grid=True, color='#86bf91', zorder=2, rwidth=0.9)\n",
    "ax = ax[0]\n",
    "\n",
    "def hist_plot():\n",
    "    for x in ax:\n",
    "        x.spines['right'].set_visible(False)\n",
    "        x.spines['top'].set_visible(False)\n",
    "        x.spines['left'].set_visible(False)\n",
    "\n",
    "    # Switch off ticks\n",
    "        x.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "\n",
    "    # Draw horizontal axis lines\n",
    "        vals = x.get_yticks()\n",
    "        for tick in vals:\n",
    "            x.axhline(y=tick, linestyle='dashed', alpha=0.4, color='#eeeeee', zorder=1)\n",
    "            \n",
    "    # Format y-axis label\n",
    "        x.yaxis.set_major_formatter(StrMethodFormatter('{x:,g}'))\n",
    "        ax = plt.subplot(1,1,1)\n",
    "        ax.hist(x)\n",
    "        ax.set_xlabel(xlabel)\n",
    "        ax.set_ylabel(ylabel)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cleaning'></a>\n",
    "## Data Cleaning\n",
    "> The Goal here is to tidy up the data to ensure the information is accurate and consistent.\n",
    "- Check for Missing Values \n",
    "- Check for typos and correct that\n",
    "- Identify outliers\n",
    "- Identify variables with unique values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_noshow.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Editing header typos & formatting some of the headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>NoShow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hypertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   Diabetes  Alcoholism  Handicap  SmsReceived NoShow  \n",
       "0         0           0         0            0     No  \n",
       "1         0           0         0            0     No  \n",
       "2         0           0         0            0     No  \n",
       "3         0           0         0            0     No  \n",
       "4         1           0         0            0     No  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_noshow.rename(columns = {'Hipertension':'Hypertension','SMS_received':'SmsReceived', 'Handcap':'Handicap','No-show':'NoShow'},inplace=True)\n",
    "df_noshow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for unique values for specific columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['F', 'M'], dtype=object),\n",
       " array([ 62,  56,   8,  76,  23,  39,  21,  19,  30,  29,  22,  28,  54,\n",
       "         15,  50,  40,  46,   4,  13,  65,  45,  51,  32,  12,  61,  38,\n",
       "         79,  18,  63,  64,  85,  59,  55,  71,  49,  78,  31,  58,  27,\n",
       "          6,   2,  11,   7,   0,   3,   1,  69,  68,  60,  67,  36,  10,\n",
       "         35,  20,  26,  34,  33,  16,  42,   5,  47,  17,  41,  44,  37,\n",
       "         24,  66,  77,  81,  70,  53,  75,  73,  52,  74,  43,  89,  57,\n",
       "         14,   9,  48,  83,  72,  25,  80,  87,  88,  84,  82,  90,  94,\n",
       "         86,  91,  98,  92,  96,  93,  95,  97, 102, 115, 100,  99,  -1]),\n",
       " array([0, 1]),\n",
       " array([1, 0]),\n",
       " array([0, 1]),\n",
       " array([0, 1]),\n",
       " array([0, 1]),\n",
       " array([0, 1, 2, 3, 4]),\n",
       " array([0, 1]))"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns: Scholarship,Hypertension,Diabetes,Alcoholism,Handicap,SmsReceived\n",
    "df_noshow.Gender.unique(),df_noshow.Age.unique(),df_noshow.Scholarship.unique(), df_noshow.Hypertension.unique(), df_noshow.Diabetes.unique(), df_noshow.Diabetes.unique(), df_noshow.Alcoholism.unique(), df_noshow.Handicap.unique(), df_noshow.SmsReceived.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle the -1 values in Age\n",
    "\n",
    "In the age column, there is a -1 which makes no sense as the age value can only start from 0 year. Check how many rows equate to this value and if minimal, drop the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>NoShow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99832</th>\n",
       "      <td>4.659432e+14</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06T08:58:13Z</td>\n",
       "      <td>2016-06-06T00:00:00Z</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "99832  4.659432e+14        5775010      F  2016-06-06T08:58:13Z   \n",
       "\n",
       "             AppointmentDay  Age Neighbourhood  Scholarship  Hypertension  \\\n",
       "99832  2016-06-06T00:00:00Z   -1         ROMÃO            0             0   \n",
       "\n",
       "       Diabetes  Alcoholism  Handicap  SmsReceived NoShow  \n",
       "99832         0           0         0            0     No  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking rows containing -1 in the Age column\n",
    "df_noshow.loc[df_noshow['Age'] == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>NoShow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hypertension, Diabetes, Alcoholism, Handicap, SmsReceived, NoShow]\n",
       "Index: []"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the age rows equaling -1\n",
    "df_noshow.drop(df_noshow.index[df_noshow['Age'] == -1], inplace=True)\n",
    "\n",
    "#Check whether row still exist\n",
    "df_noshow.loc[df_noshow['Age'] == -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Values DTypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         object\n",
       "AppointmentID     object\n",
       "Gender            object\n",
       "ScheduledDay      object\n",
       "AppointmentDay    object\n",
       "Age                int64\n",
       "Neighbourhood     object\n",
       "Scholarship        int64\n",
       "Hypertension       int64\n",
       "Diabetes           int64\n",
       "Alcoholism         int64\n",
       "Handicap           int64\n",
       "SmsReceived        int64\n",
       "NoShow            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the patientid and appointmentid dtypes from float and int to str dtype\n",
    "df_noshow['PatientId'] = df_noshow['PatientId'].astype('str')\n",
    "df_noshow['AppointmentID'] = df_noshow['AppointmentID'].astype('str')\n",
    "\n",
    "df_noshow.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         object\n",
       "AppointmentID     object\n",
       "Gender            object\n",
       "ScheduledDay      object\n",
       "AppointmentDay    object\n",
       "Age                int64\n",
       "Neighbourhood     object\n",
       "Scholarship         bool\n",
       "Hypertension        bool\n",
       "Diabetes            bool\n",
       "Alcoholism          bool\n",
       "Handicap            bool\n",
       "SmsReceived         bool\n",
       "NoShow            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting specific column value datatypes from int to bool as the columns capture true or false values\n",
    "df_noshow['Scholarship'] = df_noshow['Scholarship'].astype('bool')\n",
    "df_noshow['Hypertension'] = df_noshow['Hypertension'].astype('bool')\n",
    "df_noshow['Diabetes'] = df_noshow['Diabetes'].astype('bool')\n",
    "df_noshow['Alcoholism'] = df_noshow['Alcoholism'].astype('bool')\n",
    "df_noshow['Handicap'] = df_noshow['Handicap'].astype('bool')\n",
    "df_noshow['SmsReceived'] = df_noshow['SmsReceived'].astype('bool')\n",
    "\n",
    "df_noshow.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert scheduledday and appointmentday datatypes from string to datetime\n",
    "df_noshow['ScheduledDay'] = pd.to_datetime(df_noshow['ScheduledDay']).dt.date.astype('datetime64[ns]')\n",
    "df_noshow['AppointmentDay'] = pd.to_datetime(df_noshow['AppointmentDay']).dt.date.astype('datetime64[ns]')\n",
    "\n",
    "#df_noshow['ScheduledDay'] = df_noshow['ScheduledDay'].apply(np.datetime64)\n",
    "#df_noshow['AppointmentDay'] = df_noshow['AppointmentDay'].apply(np.datetime64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='feature engineering'></a>\n",
    "## Feature Engineering\n",
    "\n",
    "> In this section, I will be creating some new dataset features based on the existing dataset. This will enable me to have an even deeper look into the dataset in the Explorations section.\n",
    "\n",
    "1. WaitingTime: The difference in days between the date scheduled and date of appointment\n",
    "2. WtCat: Categorize the waiting times in groups to drill down    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>NoShow</th>\n",
       "      <th>WaitingTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29872499824296.0</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>558997776694438.0</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4262962299951.0</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>867951213174.0</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8841186448183.0</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PatientId AppointmentID Gender ScheduledDay AppointmentDay  Age  \\\n",
       "0   29872499824296.0       5642903      F   2016-04-29     2016-04-29   62   \n",
       "1  558997776694438.0       5642503      M   2016-04-29     2016-04-29   56   \n",
       "2    4262962299951.0       5642549      F   2016-04-29     2016-04-29   62   \n",
       "3     867951213174.0       5642828      F   2016-04-29     2016-04-29    8   \n",
       "4    8841186448183.0       5642494      F   2016-04-29     2016-04-29   56   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "0    JARDIM DA PENHA        False          True     False       False   \n",
       "1    JARDIM DA PENHA        False         False     False       False   \n",
       "2      MATA DA PRAIA        False         False     False       False   \n",
       "3  PONTAL DE CAMBURI        False         False     False       False   \n",
       "4    JARDIM DA PENHA        False          True      True       False   \n",
       "\n",
       "   Handicap  SmsReceived NoShow  WaitingTime  \n",
       "0     False        False     No            0  \n",
       "1     False        False     No            0  \n",
       "2     False        False     No            0  \n",
       "3     False        False     No            0  \n",
       "4     False        False     No            0  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column(WaitingTime) and fill-in the data\n",
    "df_noshow['WaitingTime'] = df_noshow['AppointmentDay'] - df_noshow['ScheduledDay']\n",
    "\n",
    "# Extract the day value from datetime \n",
    "df_noshow['WaitingTime'] = df_noshow['WaitingTime'].dt.days\n",
    "\n",
    "df_noshow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for Min WaitingTime\n",
    "df_noshow.WaitingTime.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for Max WaitingTime\n",
    "df_noshow.WaitingTime.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>NoShow</th>\n",
       "      <th>WaitingTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27033</th>\n",
       "      <td>7839272661752.0</td>\n",
       "      <td>5679978</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>38</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55226</th>\n",
       "      <td>7896293967868.0</td>\n",
       "      <td>5715660</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-18</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>19</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64175</th>\n",
       "      <td>24252258389979.0</td>\n",
       "      <td>5664962</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>22</td>\n",
       "      <td>CONSOLAÇÃO</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71533</th>\n",
       "      <td>998231581612122.0</td>\n",
       "      <td>5686628</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>81</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72362</th>\n",
       "      <td>3787481966821.0</td>\n",
       "      <td>5655637</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>7</td>\n",
       "      <td>TABUAZEIRO</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               PatientId AppointmentID Gender ScheduledDay AppointmentDay  \\\n",
       "27033    7839272661752.0       5679978      M   2016-05-10     2016-05-09   \n",
       "55226    7896293967868.0       5715660      F   2016-05-18     2016-05-17   \n",
       "64175   24252258389979.0       5664962      F   2016-05-05     2016-05-04   \n",
       "71533  998231581612122.0       5686628      F   2016-05-11     2016-05-05   \n",
       "72362    3787481966821.0       5655637      M   2016-05-04     2016-05-03   \n",
       "\n",
       "       Age  Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "27033   38    RESISTÊNCIA        False         False     False       False   \n",
       "55226   19  SANTO ANTÔNIO        False         False     False       False   \n",
       "64175   22     CONSOLAÇÃO        False         False     False       False   \n",
       "71533   81  SANTO ANTÔNIO        False         False     False       False   \n",
       "72362    7     TABUAZEIRO        False         False     False       False   \n",
       "\n",
       "       Handicap  SmsReceived NoShow  WaitingTime  \n",
       "27033      True        False    Yes           -1  \n",
       "55226      True        False    Yes           -1  \n",
       "64175     False        False    Yes           -1  \n",
       "71533     False        False    Yes           -6  \n",
       "72362     False        False    Yes           -1  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking waitingtime that is below 0 Days\n",
    "df_noshow.loc[df_noshow['WaitingTime'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 5 records with WaitingTime below 0 Days and having a second look at the record features, we can see the AppointmentDays are earlier than the ScheduledDays. Also, the patients did not show up for their appointments.\n",
    "\n",
    "I have chosen to drop the five records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>NoShow</th>\n",
       "      <th>WaitingTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hypertension, Diabetes, Alcoholism, Handicap, SmsReceived, NoShow, WaitingTime]\n",
       "Index: []"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the age rows equaling -1\n",
    "df_noshow.drop(df_noshow.index[df_noshow['WaitingTime'] < 0], inplace=True)\n",
    "\n",
    "#Check whether row still exist\n",
    "df_noshow.loc[df_noshow['Age'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a waiting-time category column based on conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This a breakdown of the waiting times based on category.\n",
    "\n",
    "- Same day = 0 Days\n",
    "- Short Wait = 1 to 3 Days\n",
    "- 1 Week = 4 to 7 Days\n",
    "- 2 Weeks = 8 to 15 Days\n",
    "- A Month = 16 to 30 Days\n",
    "- Quarter = 31 to 90 Days\n",
    "- Long Wait = 91 to 180 Days\n",
    "- Extreme Long Wait = 181 Days and Over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>NoShow</th>\n",
       "      <th>WaitingTime</th>\n",
       "      <th>CategoryWT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29872499824296.0</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Same day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>558997776694438.0</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Same day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4262962299951.0</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Same day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>867951213174.0</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Same day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8841186448183.0</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Same day</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PatientId AppointmentID Gender ScheduledDay AppointmentDay  Age  \\\n",
       "0   29872499824296.0       5642903      F   2016-04-29     2016-04-29   62   \n",
       "1  558997776694438.0       5642503      M   2016-04-29     2016-04-29   56   \n",
       "2    4262962299951.0       5642549      F   2016-04-29     2016-04-29   62   \n",
       "3     867951213174.0       5642828      F   2016-04-29     2016-04-29    8   \n",
       "4    8841186448183.0       5642494      F   2016-04-29     2016-04-29   56   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "0    JARDIM DA PENHA        False          True     False       False   \n",
       "1    JARDIM DA PENHA        False         False     False       False   \n",
       "2      MATA DA PRAIA        False         False     False       False   \n",
       "3  PONTAL DE CAMBURI        False         False     False       False   \n",
       "4    JARDIM DA PENHA        False          True      True       False   \n",
       "\n",
       "   Handicap  SmsReceived NoShow  WaitingTime CategoryWT  \n",
       "0     False        False     No            0   Same day  \n",
       "1     False        False     No            0   Same day  \n",
       "2     False        False     No            0   Same day  \n",
       "3     False        False     No            0   Same day  \n",
       "4     False        False     No            0   Same day  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a list of conditions\n",
    "conditions = [\n",
    "    (df_noshow['WaitingTime'] == 0),\n",
    "    (df_noshow['WaitingTime'] >= 1) & (df_noshow['WaitingTime'] <= 3),\n",
    "    (df_noshow['WaitingTime'] >= 4) & (df_noshow['WaitingTime'] <= 7),\n",
    "    (df_noshow['WaitingTime'] >= 8) & (df_noshow['WaitingTime'] <= 15),\n",
    "    (df_noshow['WaitingTime'] >= 16) & (df_noshow['WaitingTime'] <= 30),\n",
    "    (df_noshow['WaitingTime'] >= 31) & (df_noshow['WaitingTime'] <= 90),\n",
    "    (df_noshow['WaitingTime'] >= 91) & (df_noshow['WaitingTime'] <= 180),\n",
    "    (df_noshow['WaitingTime'] >= 181)\n",
    "     ]\n",
    "               \n",
    "# Creating a list of the category values to assign\n",
    "values = ['Same day','Short Wait','1 Week','2 Weeks','A Month','Quarter','Long Wait','Extreme Long Wait']\n",
    "               \n",
    "#Create the waittime category\n",
    "df_noshow['CategoryWT'] = np.select(conditions,values)  \n",
    "df_noshow.head()\n",
    "                                                                                                                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoryWT\n",
       "1 Week        17510\n",
       "2 Weeks       13528\n",
       "A Month       15868\n",
       "Long Wait       217\n",
       "Quarter       10161\n",
       "Same day      38562\n",
       "Short Wait    14675\n",
       "Name: AppointmentID, dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_noshow.groupby('CategoryWT').count()['AppointmentID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>NoShow Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CategoryWT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 Week</th>\n",
       "      <td>13097</td>\n",
       "      <td>4413</td>\n",
       "      <td>33.694739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Weeks</th>\n",
       "      <td>9362</td>\n",
       "      <td>4166</td>\n",
       "      <td>44.499039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A Month</th>\n",
       "      <td>10709</td>\n",
       "      <td>5159</td>\n",
       "      <td>48.174433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Long Wait</th>\n",
       "      <td>161</td>\n",
       "      <td>56</td>\n",
       "      <td>34.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <td>6792</td>\n",
       "      <td>3369</td>\n",
       "      <td>49.602473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Same day</th>\n",
       "      <td>36770</td>\n",
       "      <td>1792</td>\n",
       "      <td>4.873538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Short Wait</th>\n",
       "      <td>11316</td>\n",
       "      <td>3359</td>\n",
       "      <td>29.683634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow         No   Yes  NoShow Rate\n",
       "CategoryWT                          \n",
       "1 Week      13097  4413    33.694739\n",
       "2 Weeks      9362  4166    44.499039\n",
       "A Month     10709  5159    48.174433\n",
       "Long Wait     161    56    34.782609\n",
       "Quarter      6792  3369    49.602473\n",
       "Same day    36770  1792     4.873538\n",
       "Short Wait  11316  3359    29.683634"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group categorywt and noshow values\n",
    "wt_ns = df_noshow.groupby(by=['CategoryWT','NoShow'])\n",
    "wt_ns = wt_ns.count()['PatientId'].unstack()\n",
    "\n",
    "wt_ns.fillna(value=0, inplace=True)\n",
    "wt_ns.reset_index(drop=False, inplace=True)\n",
    "#wt_ns.info()\n",
    "\n",
    "# Grouping the dataset by the waiting categories, returning the sum of all instances:\n",
    "wt_ns = wt_ns.groupby('CategoryWT').sum()\n",
    "\n",
    "# Creating a new attribute, \"NoShow Rate\", relating how many patients did not show up against those who did.\n",
    "wt_ns['NoShow Rate'] = (wt_ns.Yes / wt_ns.No)*100\n",
    "\n",
    "wt_ns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "> After cleaning the dataset and creating some new features. In this section, I am exploring the dataset through **Computing statistics** and **Creating visualizations** with the goal of addressing the research questions that posed in the Introduction section.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the Totals of Patients and Appointments by Gender, No-Show Ups, CategoryWT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71836\n",
       "M    38685\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the number of Male and Female patients\n",
    "Gender_distr =df_noshow['Gender'].value_counts()\n",
    "Gender_distr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "F    71836\n",
       "M    38685\n",
       "Name: AppointmentID, dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the number of Male and Female appointments\n",
    "Gender_App =df_noshow.groupby('Gender').count()['AppointmentID']\n",
    "Gender_App"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing the Dataset before and after editing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoShow\n",
       "No     88207\n",
       "Yes    22314\n",
       "dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show count of Patients after data editing\n",
    "After_edit = df_noshow.groupby(['NoShow']).size()\n",
    "After_edit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>self</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>88208</td>\n",
       "      <td>88207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>22319</td>\n",
       "      <td>22314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      self  other\n",
       "No   88208  88207\n",
       "Yes  22319  22314"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare equality of the dataset versions\n",
    "Before_edit.compare(After_edit,keep_equal=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question1'></a>\n",
    "### Question 1: Were there patients who missed appointments?\n",
    "\n",
    "To check whether there were patients who missed their appointments, we visualize the count of appointments by status of No-Show. No-Show captures where a patient showed up or not for their appointment. \"Yes\" means they didn't show up while \"No\" means they did show up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the ratio\n",
    "df_noshow['NoShow'].value_counts().plot(kind='pie',autopct='%1.0f%%');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see 20% of the patients did not show up for their appointments while the rest 80% did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question2'></a>\n",
    "### Question 2: What was the distribution like for appointment waiting times?\n",
    "\n",
    "After categorizing the waiting times into different timeframes like same day, short wait, 1 week, etc; here we will visualize the total count of appointments that fell into category and see how they all compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Waiting Times Categories')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution\n",
    "df_noshow['CategoryWT'].value_counts().plot(kind='bar',figsize=(15,8));\n",
    "plt.ylabel('Count of Appointments')\n",
    "plt.xlabel('Category of Waiting Time')\n",
    "plt.title('Distribution of Waiting Times Categories')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Same day      34.891107\n",
       "1 Week        15.843143\n",
       "A Month       14.357452\n",
       "Short Wait    13.278020\n",
       "2 Weeks       12.240208\n",
       "Quarter        9.193728\n",
       "Long Wait      0.196343\n",
       "Name: CategoryWT, dtype: float64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the % breakdown of the waiting time category by dividing the len of the appointments\n",
    "catWT_per = df_noshow['CategoryWT'].value_counts()/len(df_noshow['AppointmentID'])*100\n",
    "catWT_per"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the bar chart we can see \"Same day\" appointments had the highest total count while \"Long Wait\" had the lowest total count of appointments. You can refer to the timeframe category breakdown here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question3'></a>\n",
    "### Question 3: What areas had the shortest and longest wait times?\n",
    "\n",
    "As the question clearly states, we are looking to find which areas had the longest waiting time and those that had the shortest waiting time too. Here, we will first find the max and min waiting times and then match these waiting times with areas that had these exact times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the max waiting time value\n",
    "lwt = df_noshow['WaitingTime'].max()\n",
    "lwt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the min waiting time value\n",
    "swt = df_noshow['WaitingTime'].min()\n",
    "swt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MARUÍPE', 'SÃO CRISTÓVÃO'], dtype=object)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find areas with the max waiting times using panda query\n",
    "a_lwt = df_noshow.query('WaitingTime == 179')\n",
    "a_lwt.Neighbourhood.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['JARDIM DA PENHA', 'MATA DA PRAIA', 'PONTAL DE CAMBURI',\n",
       "       'ANDORINHAS', 'DA PENHA', 'TABUAZEIRO', 'BENTO FERREIRA',\n",
       "       'SÃO PEDRO', 'SANTA MARTHA', 'SÃO CRISTÓVÃO', 'CONQUISTA',\n",
       "       'NOVA PALESTINA', 'JARDIM CAMBURI', 'JABOUR', 'RESISTÊNCIA',\n",
       "       'MONTE BELO', 'BONFIM', 'SANTO ANTÔNIO', 'BELA VISTA',\n",
       "       'SANTA HELENA', 'MARIA ORTIZ', 'ITARARÉ', 'INHANGUETÁ',\n",
       "       'UNIVERSITÁRIO', 'REDENÇÃO', 'SANTA CLARA', 'CENTRO',\n",
       "       'PARQUE MOSCOSO', 'DO MOSCOSO', 'MARUÍPE', 'SANTOS DUMONT',\n",
       "       'ARIOVALDO FAVALESSA', 'CARATOÍRA', 'ILHA DO FRADE', 'GURIGICA',\n",
       "       'ILHA DE SANTA MARIA', 'JOANA D´ARC', 'PRAIA DO CANTO',\n",
       "       'REPÚBLICA', 'SÃO BENEDITO', 'SANTO ANDRÉ', 'SANTA LÚCIA',\n",
       "       'BARRO VERMELHO', 'SANTA LUÍZA', 'FONTE GRANDE', 'SÃO JOSÉ',\n",
       "       'JESUS DE NAZARETH', 'ROMÃO', 'PIEDADE', 'ILHA DAS CAIEIRAS',\n",
       "       'VILA RUBIM', 'FORTE SÃO JOÃO', 'GRANDE VITÓRIA', 'SANTA TEREZA',\n",
       "       'DO CABRAL', 'MÁRIO CYPRESTE', 'ESTRELINHA', 'HORTO', 'DO QUADRO',\n",
       "       'SOLON BORGES', 'SEGURANÇA DO LAR', 'CRUZAMENTO',\n",
       "       'ANTÔNIO HONÓRIO', 'PRAIA DO SUÁ', 'CONSOLAÇÃO', 'FRADINHOS',\n",
       "       'GOIABEIRAS', 'ILHA DO PRÍNCIPE', 'BOA VISTA', 'DE LOURDES',\n",
       "       'JUCUTUQUARA', 'COMDUSA', 'SANTOS REIS', 'ENSEADA DO SUÁ',\n",
       "       'ILHA DO BOI', 'SANTA CECÍLIA', 'NAZARETH', 'MORADA DE CAMBURI',\n",
       "       'AEROPORTO', 'PARQUE INDUSTRIAL'], dtype=object)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find areas with the min waiting times using panda query\n",
    "a_lwt = df_noshow.query('WaitingTime == 0')\n",
    "a_lwt['Neighbourhood'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the total count of areas that had the shortest waiting time\n",
    "a_lwt['Neighbourhood'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the max waiting time is 179 days and the shortest was 0 days. MARUÍPE and SÃO CRISTÓVÃO had the longest waiting times of 179 days while 80 other areas the shortest waiting times of 0 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question4'></a>\n",
    "### Question 4: Timeframes with the most and least appointments?\n",
    "\n",
    "Here we want to find the total count of appointments that fell in each waiting time category. After getting the total count, look which category had the most and least appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Same day      38562\n",
       "1 Week        17510\n",
       "A Month       15868\n",
       "Short Wait    14675\n",
       "2 Weeks       13528\n",
       "Quarter       10161\n",
       "Long Wait       217\n",
       "Name: CategoryWT, dtype: int64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find time categories with most appointments \n",
    "# Check value count of time categories\n",
    "ma = df_noshow['CategoryWT'].value_counts()\n",
    "ma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Same day\" appointments had the highest count at 38562 while \"Long Wait\" appointments had the lowest at 217.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question5'></a>\n",
    "\n",
    "### Question 5: Is there a correlation between the waiting time and no-show ups? \n",
    "\n",
    "In this question, we are looking at whether there is a correlation between the length of the waiting time and the rate of showing up or not showing up for appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>NoShow</th>\n",
       "      <th>CategoryWT</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>NoShow Rate</th>\n",
       "      <th>NoShow %</th>\n",
       "      <th>NoShowing Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 Week</td>\n",
       "      <td>13097</td>\n",
       "      <td>4413</td>\n",
       "      <td>33.694739</td>\n",
       "      <td>33.69%</td>\n",
       "      <td>16847.369627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 Weeks</td>\n",
       "      <td>9362</td>\n",
       "      <td>4166</td>\n",
       "      <td>44.499039</td>\n",
       "      <td>44.50%</td>\n",
       "      <td>22249.519333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Month</td>\n",
       "      <td>10709</td>\n",
       "      <td>5159</td>\n",
       "      <td>48.174433</td>\n",
       "      <td>48.17%</td>\n",
       "      <td>24087.216360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Long Wait</td>\n",
       "      <td>161</td>\n",
       "      <td>56</td>\n",
       "      <td>34.782609</td>\n",
       "      <td>34.78%</td>\n",
       "      <td>17391.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quarter</td>\n",
       "      <td>6792</td>\n",
       "      <td>3369</td>\n",
       "      <td>49.602473</td>\n",
       "      <td>49.60%</td>\n",
       "      <td>24801.236749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Same day</td>\n",
       "      <td>36770</td>\n",
       "      <td>1792</td>\n",
       "      <td>4.873538</td>\n",
       "      <td>4.87%</td>\n",
       "      <td>2436.769105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Short Wait</td>\n",
       "      <td>11316</td>\n",
       "      <td>3359</td>\n",
       "      <td>29.683634</td>\n",
       "      <td>29.68%</td>\n",
       "      <td>14841.816896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow  CategoryWT     No   Yes  NoShow Rate NoShow %  NoShowing Rate\n",
       "0           1 Week  13097  4413    33.694739   33.69%    16847.369627\n",
       "1          2 Weeks   9362  4166    44.499039   44.50%    22249.519333\n",
       "2          A Month  10709  5159    48.174433   48.17%    24087.216360\n",
       "3        Long Wait    161    56    34.782609   34.78%    17391.304348\n",
       "4          Quarter   6792  3369    49.602473   49.60%    24801.236749\n",
       "5         Same day  36770  1792     4.873538    4.87%     2436.769105\n",
       "6       Short Wait  11316  3359    29.683634   29.68%    14841.816896"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copying wt_ns\n",
    "cpwt_ns = wt_ns.copy()\n",
    "\n",
    "# Making the index as a column in order to be plotted\n",
    "cpwt_ns.reset_index(drop=False, inplace=True)\n",
    "\n",
    "# Transforming the NoShow Rate into strings with the % values\n",
    "cpwt_ns['NoShow %'] = cpwt_ns['NoShow Rate'].apply(lambda x: '{0:.2f}%'.format(x))\n",
    "\n",
    "# Multiplying the rate values by 500 times for easier plotting\n",
    "cpwt_ns['NoShowing Rate'] = cpwt_ns['NoShow Rate']*500\n",
    "\n",
    "cpwt_ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define graph size\n",
    "fig1, ax = plt.subplots(figsize=[10,10]) \n",
    "fig1.subplots_adjust(top=0.72)\n",
    "plt.title('Appointments distribution by waiting time categories')\n",
    "\n",
    "# Graph colors\n",
    "colors = ['tab:blue', 'tab:green', 'tab:red'] \n",
    "\n",
    "# Set the y-axis color and label\n",
    "ax.set_ylabel('Count of Events', color=colors[0], fontsize=12)\n",
    "ax.tick_params(axis='y', labelcolor=colors[0])\n",
    "\n",
    "# Plotting the line chart:\n",
    "cpwt_ns[['CategoryWT', 'NoShowing Rate']].plot(x='CategoryWT', linestyle='-', marker='o', ax=ax, color=colors[2])\n",
    "\n",
    "#Setting the line chart marker labels by getting the x-axis ticks to plot the label\n",
    "x = ax.get_xticks()  \n",
    "for a,b,c in zip(x,cpwt_ns['NoShowing Rate'], cpwt_ns['NoShow %']):\n",
    "    plt.text(a,b+1500,c, color='red', fontsize=14)\n",
    "    \n",
    "# Plotting the bar chart:\n",
    "cpwt_ns[['CategoryWT', 'No', 'Yes']].plot(x='CategoryWT', kind='bar', ax=ax, color=colors[0:2],)\n",
    "\n",
    "#Set the y-axis color and label\n",
    "ax.set_xlabel('Categories of Waiting Times', fontsize=12)  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see as the length of the waiting time increases so does the rate of patients missing their appointments increase. However, the rate of missing appointments for the \"Long Wait\" drops opposite to the patterns of the other timeframes. This needs further investigation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question6'></a>\n",
    "\n",
    "### Question 6: What gender was most likely to miss their appointments?\n",
    "\n",
    "Looking at the appointments, the goal here is to break them down by gender and find which gender was most likely to miss their appointment by getting the total count of missed appointments by each gender. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>WaitingTime</th>\n",
       "      <th>CategoryWT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>NoShow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
       "      <th>No</th>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "      <td>57245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "      <td>14591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
       "      <th>No</th>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "      <td>7723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               PatientId  AppointmentID  ScheduledDay  AppointmentDay    Age  \\\n",
       "Gender NoShow                                                                  \n",
       "F      No          57245          57245         57245           57245  57245   \n",
       "       Yes         14591          14591         14591           14591  14591   \n",
       "M      No          30962          30962         30962           30962  30962   \n",
       "       Yes          7723           7723          7723            7723   7723   \n",
       "\n",
       "               Neighbourhood  Scholarship  Hypertension  Diabetes  Alcoholism  \\\n",
       "Gender NoShow                                                                   \n",
       "F      No              57245        57245         57245     57245       57245   \n",
       "       Yes             14591        14591         14591     14591       14591   \n",
       "M      No              30962        30962         30962     30962       30962   \n",
       "       Yes              7723         7723          7723      7723        7723   \n",
       "\n",
       "               Handicap  SmsReceived  WaitingTime  CategoryWT  \n",
       "Gender NoShow                                                  \n",
       "F      No         57245        57245        57245       57245  \n",
       "       Yes        14591        14591        14591       14591  \n",
       "M      No         30962        30962        30962       30962  \n",
       "       Yes         7723         7723         7723        7723  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the df by Gender and NoShow status\n",
    "g_ns = df_noshow.groupby([\"Gender\",\"NoShow\"]).count()\n",
    "g_ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting for the above grouped df: Gender & NoShow\n",
    "g_ns[\"PatientId\"].unstack().plot(kind=\"bar\",stacked=False,figsize=(10,6),grid=True)\n",
    "plt.title(\"Gender and NoShow\") \n",
    "plt.legend([\"Didn't Show Up\", \"Showed Up\"])\n",
    "plt.ylabel(\"Patient Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the numbers we can see we had more female patients compared to their male counterparts who had appointments. Breaking down further, we can also see more female patients missed their appointments compared to male patients. Further investigation is needed into this as we can't tell by this, whether it is because there are more female patients or there are more reasons behind this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question7'></a>\n",
    "### Question 7: Which area was hit headest by patients who didn't show up?\n",
    "\n",
    "Here we are trying to figure out which areas had the highest cases of patients not showing up for their appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SmsReceived</th>\n",
       "      <th>WaitingTime</th>\n",
       "      <th>CategoryWT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>NoShow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AEROPORTO</th>\n",
       "      <th>No</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ANDORINHAS</th>\n",
       "      <th>No</th>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "      <td>1741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTÔNIO HONÓRIO</th>\n",
       "      <th>No</th>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TABUAZEIRO</th>\n",
       "      <th>Yes</th>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">UNIVERSITÁRIO</th>\n",
       "      <th>No</th>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">VILA RUBIM</th>\n",
       "      <th>No</th>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        PatientId  AppointmentID  Gender  ScheduledDay  \\\n",
       "Neighbourhood   NoShow                                                   \n",
       "AEROPORTO       No              7              7       7             7   \n",
       "                Yes             1              1       1             1   \n",
       "ANDORINHAS      No           1741           1741    1741          1741   \n",
       "                Yes           521            521     521           521   \n",
       "ANTÔNIO HONÓRIO No            221            221     221           221   \n",
       "...                           ...            ...     ...           ...   \n",
       "TABUAZEIRO      Yes           572            572     572           572   \n",
       "UNIVERSITÁRIO   No            120            120     120           120   \n",
       "                Yes            32             32      32            32   \n",
       "VILA RUBIM      No            710            710     710           710   \n",
       "                Yes           141            141     141           141   \n",
       "\n",
       "                        AppointmentDay   Age  Scholarship  Hypertension  \\\n",
       "Neighbourhood   NoShow                                                    \n",
       "AEROPORTO       No                   7     7            7             7   \n",
       "                Yes                  1     1            1             1   \n",
       "ANDORINHAS      No                1741  1741         1741          1741   \n",
       "                Yes                521   521          521           521   \n",
       "ANTÔNIO HONÓRIO No                 221   221          221           221   \n",
       "...                                ...   ...          ...           ...   \n",
       "TABUAZEIRO      Yes                572   572          572           572   \n",
       "UNIVERSITÁRIO   No                 120   120          120           120   \n",
       "                Yes                 32    32           32            32   \n",
       "VILA RUBIM      No                 710   710          710           710   \n",
       "                Yes                141   141          141           141   \n",
       "\n",
       "                        Diabetes  Alcoholism  Handicap  SmsReceived  \\\n",
       "Neighbourhood   NoShow                                                \n",
       "AEROPORTO       No             7           7         7            7   \n",
       "                Yes            1           1         1            1   \n",
       "ANDORINHAS      No          1741        1741      1741         1741   \n",
       "                Yes          521         521       521          521   \n",
       "ANTÔNIO HONÓRIO No           221         221       221          221   \n",
       "...                          ...         ...       ...          ...   \n",
       "TABUAZEIRO      Yes          572         572       572          572   \n",
       "UNIVERSITÁRIO   No           120         120       120          120   \n",
       "                Yes           32          32        32           32   \n",
       "VILA RUBIM      No           710         710       710          710   \n",
       "                Yes          141         141       141          141   \n",
       "\n",
       "                        WaitingTime  CategoryWT  \n",
       "Neighbourhood   NoShow                           \n",
       "AEROPORTO       No                7           7  \n",
       "                Yes               1           1  \n",
       "ANDORINHAS      No             1741        1741  \n",
       "                Yes             521         521  \n",
       "ANTÔNIO HONÓRIO No              221         221  \n",
       "...                             ...         ...  \n",
       "TABUAZEIRO      Yes             572         572  \n",
       "UNIVERSITÁRIO   No              120         120  \n",
       "                Yes              32          32  \n",
       "VILA RUBIM      No              710         710  \n",
       "                Yes             141         141  \n",
       "\n",
       "[160 rows x 14 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group and then find count of NoShow by Neighbourhood\n",
    "a_ns = df_noshow.groupby(['Neighbourhood','NoShow']).count()\n",
    "a_ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting for the above grouped df: Gender & NoShow\n",
    "a_ns[\"PatientId\"].unstack().plot(kind=\"barh\",figsize=(15,25))\n",
    "plt.title(\"Neighborhood and NoShow\") \n",
    "plt.legend([\"Showed Up\",\"Didn't Show Up\"])\n",
    "plt.xlabel(\"Neighborhood\")\n",
    "plt.xlabel(\"Patient Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jardim Camburi had the highest number of patients missing their appoinments but also had the highest number of patient appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question8'></a>\n",
    "\n",
    "### Question 8: Which areas had the most and least appointments? \n",
    "\n",
    "Looking at the number of appointments, we are trying to figure out which areas had the most and least number of patient appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JARDIM CAMBURI                 7717\n",
       "MARIA ORTIZ                    5805\n",
       "RESISTÊNCIA                    4430\n",
       "JARDIM DA PENHA                3877\n",
       "ITARARÉ                        3514\n",
       "                               ... \n",
       "ILHA DO BOI                      35\n",
       "ILHA DO FRADE                    10\n",
       "AEROPORTO                         8\n",
       "ILHAS OCEÂNICAS DE TRINDADE       2\n",
       "PARQUE INDUSTRIAL                 1\n",
       "Name: Neighbourhood, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check count of appointments by Neighbourhood\n",
    "Neigh_appoint = df_noshow['Neighbourhood'].value_counts()\n",
    "Neigh_appoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7717"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check max value\n",
    "Neigh_appoint.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check min value\n",
    "Neigh_appoint.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JARDIM CAMBURI had the most patient appointments with 7717 appointments and PARQUE INDUSTRIAL had the least with 1 appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question9'></a>\n",
    "### Question 9: Did patients who received an sms turn up compared to those who didn't receive one?\n",
    "\n",
    "In this case, we are trying to figure out whether sending a reminder sms to patients who had appointments had an influence on whether the patient showed up for their appointment or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35482"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count patients who turned up and received an sms\n",
    "sms_ns1 = df_noshow[df_noshow[\"SmsReceived\"] == 1][\"NoShow\"].count()\n",
    "sms_ns1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75039"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count patients who did show up but didn't received an sms\n",
    "sms_ns0 = df_noshow[df_noshow[\"SmsReceived\"] == 0][\"NoShow\"].count()\n",
    "sms_ns0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see more patients turned up but didn't receive an sms. This needs further investigation as we would think the oppposite would be true."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA_question10'></a>\n",
    "\n",
    "### Question 10: What factors correlate?\n",
    "\n",
    "Here we want to take a look at the dataset's features and how they relate, to what degree and in which direction they do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "            background-color:  #e3eef8;\n",
       "            color:  #000000;\n",
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       "            background-color:  #eff6fc;\n",
       "            color:  #000000;\n",
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       "            background-color:  #e4eff9;\n",
       "            color:  #000000;\n",
       "        }#T_783e5_row6_col2,#T_783e5_row6_col3,#T_783e5_row6_col4{\n",
       "            background-color:  #f5f9fe;\n",
       "            color:  #000000;\n",
       "        }#T_783e5_row6_col7{\n",
       "            background-color:  #8cc0dd;\n",
       "            color:  #000000;\n",
       "        }#T_783e5_row7_col6{\n",
       "            background-color:  #8fc2de;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_783e5_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Age</th>        <th class=\"col_heading level0 col1\" >Scholarship</th>        <th class=\"col_heading level0 col2\" >Hypertension</th>        <th class=\"col_heading level0 col3\" >Diabetes</th>        <th class=\"col_heading level0 col4\" >Alcoholism</th>        <th class=\"col_heading level0 col5\" >Handicap</th>        <th class=\"col_heading level0 col6\" >SmsReceived</th>        <th class=\"col_heading level0 col7\" >WaitingTime</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_783e5_level0_row0\" class=\"row_heading level0 row0\" >Age</th>\n",
       "                        <td id=\"T_783e5_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_783e5_row0_col1\" class=\"data row0 col1\" >-0.092469</td>\n",
       "                        <td id=\"T_783e5_row0_col2\" class=\"data row0 col2\" >0.504599</td>\n",
       "                        <td id=\"T_783e5_row0_col3\" class=\"data row0 col3\" >0.292398</td>\n",
       "                        <td id=\"T_783e5_row0_col4\" class=\"data row0 col4\" >0.095811</td>\n",
       "                        <td id=\"T_783e5_row0_col5\" class=\"data row0 col5\" >0.083827</td>\n",
       "                        <td id=\"T_783e5_row0_col6\" class=\"data row0 col6\" >0.012629</td>\n",
       "                        <td id=\"T_783e5_row0_col7\" class=\"data row0 col7\" >0.034813</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_783e5_level0_row1\" class=\"row_heading level0 row1\" >Scholarship</th>\n",
       "                        <td id=\"T_783e5_row1_col0\" class=\"data row1 col0\" >-0.092469</td>\n",
       "                        <td id=\"T_783e5_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_783e5_row1_col2\" class=\"data row1 col2\" >-0.019738</td>\n",
       "                        <td id=\"T_783e5_row1_col3\" class=\"data row1 col3\" >-0.024899</td>\n",
       "                        <td id=\"T_783e5_row1_col4\" class=\"data row1 col4\" >0.035019</td>\n",
       "                        <td id=\"T_783e5_row1_col5\" class=\"data row1 col5\" >-0.009068</td>\n",
       "                        <td id=\"T_783e5_row1_col6\" class=\"data row1 col6\" >0.001182</td>\n",
       "                        <td id=\"T_783e5_row1_col7\" class=\"data row1 col7\" >-0.030435</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_783e5_level0_row2\" class=\"row_heading level0 row2\" >Hypertension</th>\n",
       "                        <td id=\"T_783e5_row2_col0\" class=\"data row2 col0\" >0.504599</td>\n",
       "                        <td id=\"T_783e5_row2_col1\" class=\"data row2 col1\" >-0.019738</td>\n",
       "                        <td id=\"T_783e5_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_783e5_row2_col3\" class=\"data row2 col3\" >0.433082</td>\n",
       "                        <td id=\"T_783e5_row2_col4\" class=\"data row2 col4\" >0.087967</td>\n",
       "                        <td id=\"T_783e5_row2_col5\" class=\"data row2 col5\" >0.084949</td>\n",
       "                        <td id=\"T_783e5_row2_col6\" class=\"data row2 col6\" >-0.006285</td>\n",
       "                        <td id=\"T_783e5_row2_col7\" class=\"data row2 col7\" >-0.017236</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_783e5_level0_row3\" class=\"row_heading level0 row3\" >Diabetes</th>\n",
       "                        <td id=\"T_783e5_row3_col0\" class=\"data row3 col0\" >0.292398</td>\n",
       "                        <td id=\"T_783e5_row3_col1\" class=\"data row3 col1\" >-0.024899</td>\n",
       "                        <td id=\"T_783e5_row3_col2\" class=\"data row3 col2\" >0.433082</td>\n",
       "                        <td id=\"T_783e5_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_783e5_row3_col4\" class=\"data row3 col4\" >0.018471</td>\n",
       "                        <td id=\"T_783e5_row3_col5\" class=\"data row3 col5\" >0.059204</td>\n",
       "                        <td id=\"T_783e5_row3_col6\" class=\"data row3 col6\" >-0.014561</td>\n",
       "                        <td id=\"T_783e5_row3_col7\" class=\"data row3 col7\" >-0.027200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_783e5_level0_row4\" class=\"row_heading level0 row4\" >Alcoholism</th>\n",
       "                        <td id=\"T_783e5_row4_col0\" class=\"data row4 col0\" >0.095811</td>\n",
       "                        <td id=\"T_783e5_row4_col1\" class=\"data row4 col1\" >0.035019</td>\n",
       "                        <td id=\"T_783e5_row4_col2\" class=\"data row4 col2\" >0.087967</td>\n",
       "                        <td id=\"T_783e5_row4_col3\" class=\"data row4 col3\" >0.018471</td>\n",
       "                        <td id=\"T_783e5_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_783e5_row4_col5\" class=\"data row4 col5\" >0.003715</td>\n",
       "                        <td id=\"T_783e5_row4_col6\" class=\"data row4 col6\" >-0.026154</td>\n",
       "                        <td id=\"T_783e5_row4_col7\" class=\"data row4 col7\" >-0.038527</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_783e5_level0_row5\" class=\"row_heading level0 row5\" >Handicap</th>\n",
       "                        <td id=\"T_783e5_row5_col0\" class=\"data row5 col0\" >0.083827</td>\n",
       "                        <td id=\"T_783e5_row5_col1\" class=\"data row5 col1\" >-0.009068</td>\n",
       "                        <td id=\"T_783e5_row5_col2\" class=\"data row5 col2\" >0.084949</td>\n",
       "                        <td id=\"T_783e5_row5_col3\" class=\"data row5 col3\" >0.059204</td>\n",
       "                        <td id=\"T_783e5_row5_col4\" class=\"data row5 col4\" >0.003715</td>\n",
       "                        <td id=\"T_783e5_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_783e5_row5_col6\" class=\"data row5 col6\" >-0.025148</td>\n",
       "                        <td id=\"T_783e5_row5_col7\" class=\"data row5 col7\" >-0.020885</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_783e5_level0_row6\" class=\"row_heading level0 row6\" >SmsReceived</th>\n",
       "                        <td id=\"T_783e5_row6_col0\" class=\"data row6 col0\" >0.012629</td>\n",
       "                        <td id=\"T_783e5_row6_col1\" class=\"data row6 col1\" >0.001182</td>\n",
       "                        <td id=\"T_783e5_row6_col2\" class=\"data row6 col2\" >-0.006285</td>\n",
       "                        <td id=\"T_783e5_row6_col3\" class=\"data row6 col3\" >-0.014561</td>\n",
       "                        <td id=\"T_783e5_row6_col4\" class=\"data row6 col4\" >-0.026154</td>\n",
       "                        <td id=\"T_783e5_row6_col5\" class=\"data row6 col5\" >-0.025148</td>\n",
       "                        <td id=\"T_783e5_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_783e5_row6_col7\" class=\"data row6 col7\" >0.398128</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_783e5_level0_row7\" class=\"row_heading level0 row7\" >WaitingTime</th>\n",
       "                        <td id=\"T_783e5_row7_col0\" class=\"data row7 col0\" >0.034813</td>\n",
       "                        <td id=\"T_783e5_row7_col1\" class=\"data row7 col1\" >-0.030435</td>\n",
       "                        <td id=\"T_783e5_row7_col2\" class=\"data row7 col2\" >-0.017236</td>\n",
       "                        <td id=\"T_783e5_row7_col3\" class=\"data row7 col3\" >-0.027200</td>\n",
       "                        <td id=\"T_783e5_row7_col4\" class=\"data row7 col4\" >-0.038527</td>\n",
       "                        <td id=\"T_783e5_row7_col5\" class=\"data row7 col5\" >-0.020885</td>\n",
       "                        <td id=\"T_783e5_row7_col6\" class=\"data row7 col6\" >0.398128</td>\n",
       "                        <td id=\"T_783e5_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13f0447f0>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_noshow.corr().style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the correlation heatmap, the darker the color the greater the correlation. Features that greatly correlate include Hypertension and Age, Hypertension and Diabetes, SmsReceived and WaitingTime, and  Diabetes and Age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> About 20% of the appointments made ended with the patients not turning up.\n",
    "\n",
    "> Looking at the waiting times, majority of appointments that happened on the scheduling day had the highest number of show up rates at about 34.9%. Same day appointments\n",
    "\n",
    "> MARUÍPE and SÃO CRISTÓVÃO had the longest waiting time.\n",
    "\n",
    "> As the waiting time increased so did the number of patients who didn't show up increase. However, the longest waiting time(between 90 and 180 days) did have an decrease in patients who didn't show up and this needs further investigation as to why this is the case.\n",
    "\n",
    "> More female patients were likely to miss showing up considered to their male counterparts.\n",
    "\n",
    "> The highest correlation was concetrated around hypertension and age, diabetes and hypertension, waiting time and sms received, diabetes and age.\n",
    "\n",
    "> Patients who received an sms were less likely to show up compared to those who didn't receive one according to the data. This needs some further investigation as we would think the opposite would be true since they are being reminded of their appointments. Also to check whether the same day appointment had an effect on this.\n",
    "\n",
    "> The neighborhood of Jardim Camburi recorded the highest missed appointments, the highest compared to the other neighborhoods.\n",
    "\n",
    "<a id='limitations'></a>\n",
    "## Limitations \n",
    "\n",
    "From the dataset provided, there are two columns that did seem swapped based on the dataset description. I am tempted to believe that the Handicap and SmsReceived values have been swapped based on boolean datatype in the description. However, due to the limited time I have to work on this I will not be investigating this further.\n",
    "\n",
    "To make more meaning of the dataset, I think the dataset needed more features to further explain why the patients behaved the way they did.\n",
    "\n",
    "Due to my limited expertise and time, I was not able to do some preditions using machine learning. I had some ideas on just using machine learning to do the analysis for me by predicting show-up rates and factors that carried heavy influence in predicting this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='references'></a>\n",
    "### References\n",
    "[UsageError: Line magic function `%` not found. Jupyter Notebook](https://stackoverflow.com/questions/52874240/usageerror-line-magic-function-not-found-jupyter-notebook)\n",
    "\n",
    "[Pandas – Delete rows based on column values](https://datascienceparichay.com/article/pandas-delete-rows-based-on-column-values/)\n",
    "\n",
    "[How to Solve Python TypeError: ‘method’ object is not subscriptable](https://researchdatapod.com/python-typeerror-method-object-is-not-subscriptable/#:~:text=of%20this%20tutorial!-,The%20error%20%E2%80%9CTypeError%3A%20'method'%20object%20is%20not%20subscriptable,a%20list%20with%20square%20brackets.)\n",
    "\n",
    "[Is the Handcap and SMS_received columns swapped?](https://www.kaggle.com/datasets/joniarroba/noshowappointments/discussion/202717)\n",
    "\n",
    "[Finding top 10 in a dataframe in Pandas](https://stackoverflow.com/questions/43859416/finding-top-10-in-a-dataframe-in-pandas)\n",
    "\n",
    "[How to Create a Seaborn Correlation Heatmap in Python?](https://medium.com/@szabo.bibor/how-to-create-a-seaborn-correlation-heatmap-in-python-834c0686b88e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This application is used to convert notebook files (*.ipynb)\n",
      "        to various other formats.\n",
      "\n",
      "        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.\n",
      "\n",
      "Options\n",
      "=======\n",
      "The options below are convenience aliases to configurable class-options,\n",
      "as listed in the \"Equivalent to\" description-line of the aliases.\n",
      "To see all configurable class-options for some <cmd>, use:\n",
      "    <cmd> --help-all\n",
      "\n",
      "--debug\n",
      "    set log level to logging.DEBUG (maximize logging output)\n",
      "    Equivalent to: [--Application.log_level=10]\n",
      "--show-config\n",
      "    Show the application's configuration (human-readable format)\n",
      "    Equivalent to: [--Application.show_config=True]\n",
      "--show-config-json\n",
      "    Show the application's configuration (json format)\n",
      "    Equivalent to: [--Application.show_config_json=True]\n",
      "--generate-config\n",
      "    generate default config file\n",
      "    Equivalent to: [--JupyterApp.generate_config=True]\n",
      "-y\n",
      "    Answer yes to any questions instead of prompting.\n",
      "    Equivalent to: [--JupyterApp.answer_yes=True]\n",
      "--execute\n",
      "    Execute the notebook prior to export.\n",
      "    Equivalent to: [--ExecutePreprocessor.enabled=True]\n",
      "--allow-errors\n",
      "    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.\n",
      "    Equivalent to: [--ExecutePreprocessor.allow_errors=True]\n",
      "--stdin\n",
      "    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'\n",
      "    Equivalent to: [--NbConvertApp.from_stdin=True]\n",
      "--stdout\n",
      "    Write notebook output to stdout instead of files.\n",
      "    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]\n",
      "--inplace\n",
      "    Run nbconvert in place, overwriting the existing notebook (only \n",
      "            relevant when converting to notebook format)\n",
      "    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]\n",
      "--clear-output\n",
      "    Clear output of current file and save in place, \n",
      "            overwriting the existing notebook.\n",
      "    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]\n",
      "--no-prompt\n",
      "    Exclude input and output prompts from converted document.\n",
      "    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]\n",
      "--no-input\n",
      "    Exclude input cells and output prompts from converted document. \n",
      "            This mode is ideal for generating code-free reports.\n",
      "    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]\n",
      "--allow-chromium-download\n",
      "    Whether to allow downloading chromium if no suitable version is found on the system.\n",
      "    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]\n",
      "--disable-chromium-sandbox\n",
      "    Disable chromium security sandbox when converting to PDF..\n",
      "    Equivalent to: [--WebPDFExporter.disable_sandbox=True]\n",
      "--show-input\n",
      "    Shows code input. This flag is only useful for dejavu users.\n",
      "    Equivalent to: [--TemplateExporter.exclude_input=False]\n",
      "--embed-images\n",
      "    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.\n",
      "    Equivalent to: [--HTMLExporter.embed_images=True]\n",
      "--log-level=<Enum>\n",
      "    Set the log level by value or name.\n",
      "    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']\n",
      "    Default: 30\n",
      "    Equivalent to: [--Application.log_level]\n",
      "--config=<Unicode>\n",
      "    Full path of a config file.\n",
      "    Default: ''\n",
      "    Equivalent to: [--JupyterApp.config_file]\n",
      "--to=<Unicode>\n",
      "    The export format to be used, either one of the built-in formats\n",
      "            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']\n",
      "            or a dotted object name that represents the import path for an\n",
      "            ``Exporter`` class\n",
      "    Default: ''\n",
      "    Equivalent to: [--NbConvertApp.export_format]\n",
      "--template=<Unicode>\n",
      "    Name of the template to use\n",
      "    Default: ''\n",
      "    Equivalent to: [--TemplateExporter.template_name]\n",
      "--template-file=<Unicode>\n",
      "    Name of the template file to use\n",
      "    Default: None\n",
      "    Equivalent to: [--TemplateExporter.template_file]\n",
      "--theme=<Unicode>\n",
      "    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed\n",
      "    as prebuilt extension for the lab template)\n",
      "    Default: 'light'\n",
      "    Equivalent to: [--HTMLExporter.theme]\n",
      "--writer=<DottedObjectName>\n",
      "    Writer class used to write the \n",
      "                                        results of the conversion\n",
      "    Default: 'FilesWriter'\n",
      "    Equivalent to: [--NbConvertApp.writer_class]\n",
      "--post=<DottedOrNone>\n",
      "    PostProcessor class used to write the\n",
      "                                        results of the conversion\n",
      "    Default: ''\n",
      "    Equivalent to: [--NbConvertApp.postprocessor_class]\n",
      "--output=<Unicode>\n",
      "    overwrite base name use for output files.\n",
      "                can only be used when converting one notebook at a time.\n",
      "    Default: ''\n",
      "    Equivalent to: [--NbConvertApp.output_base]\n",
      "--output-dir=<Unicode>\n",
      "    Directory to write output(s) to. Defaults\n",
      "                                  to output to the directory of each notebook. To recover\n",
      "                                  previous default behaviour (outputting to the current \n",
      "                                  working directory) use . as the flag value.\n",
      "    Default: ''\n",
      "    Equivalent to: [--FilesWriter.build_directory]\n",
      "--reveal-prefix=<Unicode>\n",
      "    The URL prefix for reveal.js (version 3.x).\n",
      "            This defaults to the reveal CDN, but can be any url pointing to a copy \n",
      "            of reveal.js. \n",
      "            For speaker notes to work, this must be a relative path to a local \n",
      "            copy of reveal.js: e.g., \"reveal.js\".\n",
      "            If a relative path is given, it must be a subdirectory of the\n",
      "            current directory (from which the server is run).\n",
      "            See the usage documentation\n",
      "            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)\n",
      "            for more details.\n",
      "    Default: ''\n",
      "    Equivalent to: [--SlidesExporter.reveal_url_prefix]\n",
      "--nbformat=<Enum>\n",
      "    The nbformat version to write.\n",
      "            Use this to downgrade notebooks.\n",
      "    Choices: any of [1, 2, 3, 4]\n",
      "    Default: 4\n",
      "    Equivalent to: [--NotebookExporter.nbformat_version]\n",
      "\n",
      "Examples\n",
      "--------\n",
      "\n",
      "    The simplest way to use nbconvert is\n",
      "\n",
      "            > jupyter nbconvert mynotebook.ipynb --to html\n",
      "\n",
      "            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].\n",
      "\n",
      "            > jupyter nbconvert --to latex mynotebook.ipynb\n",
      "\n",
      "            Both HTML and LaTeX support multiple output templates. LaTeX includes\n",
      "            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and \n",
      "            'classic'. You can specify the flavor of the format used.\n",
      "\n",
      "            > jupyter nbconvert --to html --template lab mynotebook.ipynb\n",
      "\n",
      "            You can also pipe the output to stdout, rather than a file\n",
      "\n",
      "            > jupyter nbconvert mynotebook.ipynb --stdout\n",
      "\n",
      "            PDF is generated via latex\n",
      "\n",
      "            > jupyter nbconvert mynotebook.ipynb --to pdf\n",
      "\n",
      "            You can get (and serve) a Reveal.js-powered slideshow\n",
      "\n",
      "            > jupyter nbconvert myslides.ipynb --to slides --post serve\n",
      "\n",
      "            Multiple notebooks can be given at the command line in a couple of \n",
      "            different ways:\n",
      "\n",
      "            > jupyter nbconvert notebook*.ipynb\n",
      "            > jupyter nbconvert notebook1.ipynb notebook2.ipynb\n",
      "\n",
      "            or you can specify the notebooks list in a config file, containing::\n",
      "\n",
      "                c.NbConvertApp.notebooks = [\"my_notebook.ipynb\"]\n",
      "\n",
      "            > jupyter nbconvert --config mycfg.py\n",
      "\n",
      "To see all available configurables, use `--help-all`.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `NbConvertApp`.\n",
      "[NbConvertApp] WARNING | pattern 'Investigate_a_Dataset.ipynb' matched no files\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}