wrangle_act_def.ipynb

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    "# Project: Wrangling and Analyze Data"
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   "source": [
    "## Data Gathering\n",
    "In the cell below, gather **all** three pieces of data for this project and load them in the notebook. **Note:** the methods required to gather each data are different.\n",
    "1. Directly download the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)"
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    "2. Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
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    "3. Use the Tweepy library to query additional data via the Twitter API (tweet_json.txt)"
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    "## Assessing Data\n",
    "In this section, detect and document at least **eight (8) quality issues and two (2) tidiness issue**. You must use **both** visual assessment\n",
    "programmatic assessement to assess the data.\n",
    "\n",
    "**Note:** pay attention to the following key points when you access the data.\n",
    "\n",
    "* You only want original ratings (no retweets) that have images. Though there are 5000+ tweets in the dataset, not all are dog ratings and some are retweets.\n",
    "* Assessing and cleaning the entire dataset completely would require a lot of time, and is not necessary to practice and demonstrate your skills in data wrangling. Therefore, the requirements of this project are only to assess and clean at least 8 quality issues and at least 2 tidiness issues in this dataset.\n",
    "* The fact that the rating numerators are greater than the denominators does not need to be cleaned. This [unique rating system](http://knowyourmeme.com/memes/theyre-good-dogs-brent) is a big part of the popularity of WeRateDogs.\n",
    "* You do not need to gather the tweets beyond August 1st, 2017. You can, but note that you won't be able to gather the image predictions for these tweets since you don't have access to the algorithm used.\n",
    "\n"
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    "### Quality issues\n",
    "1.\n",
    "\n",
    "2.\n",
    "\n",
    "3.\n",
    "\n",
    "4.\n",
    "\n",
    "5.\n",
    "\n",
    "6.\n",
    "\n",
    "7.\n",
    "\n",
    "8."
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    "### Tidiness issues\n",
    "1.\n",
    "\n",
    "2."
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    "## Cleaning Data\n",
    "In this section, clean **all** of the issues you documented while assessing. \n",
    "\n",
    "**Note:** Make a copy of the original data before cleaning. Cleaning includes merging individual pieces of data according to the rules of [tidy data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html). The result should be a high-quality and tidy master pandas DataFrame (or DataFrames, if appropriate)."
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   "source": [
    "# Make copies of original pieces of data\n"
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   "source": [
    "### Issue #1:"
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   "source": [
    "#### Define:"
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   "source": [
    "#### Code"
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    "#### Test"
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   "source": [
    "### Issue #2:"
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   "source": [
    "#### Define"
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   "source": [
    "#### Code"
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    "#### Test"
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    "## Storing Data\n",
    "Save gathered, assessed, and cleaned master dataset to a CSV file named \"twitter_archive_master.csv\"."
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   "source": [
    "## Analyzing and Visualizing Data\n",
    "In this section, analyze and visualize your wrangled data. You must produce at least **three (3) insights and one (1) visualization.**"
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    "### Insights:\n",
    "1.\n",
    "\n",
    "2.\n",
    "\n",
    "3."
   ]
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   "source": [
    "### Visualization"
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