Smoking-Log-Analysis/smoking_log_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### workbook using sql with pandas to analyze an activity log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we want to analyze an activity log that contains a timestamp for each time the activity takes place (in this case, smoking). we will want to know the number of unique days the activity took place for each subject (user), and the difference between the number of times it happened on the first day vs the last day for each subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandasql import sqldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pysqldf = lambda q: sqldf(q, globals())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "smoking_log_df = pd.read_csv(\"smoking_log.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [
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      "text/plain": [
       "    id  user_id      time_stamp\n",
       "0    1        1  6/7/14 12:38AM\n",
       "1    2        1  6/8/14 12:39AM\n",
       "2    3        1  6/8/14 12:40AM\n",
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     "execution_count": 11,
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   ],
   "source": [
    "# data (fabricated, not real) showing user id and logged time for each activity as a timestamp\n",
    "smoking_log_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# we only want dates.  Note we use sqlite syntax with pandasql\n",
    "df_dates = pysqldf(\"SELECT id, user_id, SUBSTR(time_stamp, 0, 7) AS date FROM smoking_log_df\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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   "outputs": [
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      "text/plain": [
       "    id  user_id    date\n",
       "0    1        1  6/7/14\n",
       "1    2        1  6/8/14\n",
       "2    3        1  6/8/14\n",
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    "df_dates"
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   "outputs": [],
   "source": [
    "# log the activity count per day\n",
    "num_cig_per_day = pysqldf(\"SELECT user_id, date, count(user_id) as num_cig \\\n",
    "    FROM df_dates GROUP BY user_id, date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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      "text/plain": [
       "    user_id    date  num_cig\n",
       "0         1  6/7/14        1\n",
       "1         1  6/8/14        2\n",
       "2         1  6/9/14        1\n",
       "3         2  6/7/14        2\n",
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    "num_cig_per_day"
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  {
   "cell_type": "code",
   "execution_count": 17,
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    "collapsed": false
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   "outputs": [],
   "source": [
    "# query the unique days the activity took place, per user\n",
    "uniq_user_days = pysqldf(\"SELECT DISTINCT user_id, date FROM df_dates\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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      "text/plain": [
       "    user_id    date\n",
       "0         1  6/7/14\n",
       "1         1  6/8/14\n",
       "2         1  6/9/14\n",
       "3         2  6/7/14\n",
       "4         2  6/8/14\n",
       "5         2  6/9/14\n",
       "6         3  6/8/14\n",
       "7         3  6/9/14\n",
       "8         3  6/7/14\n",
       "9         4  6/8/14\n",
       "10        4  6/9/14"
      ]
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     "execution_count": 18,
     "metadata": {},
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   ],
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    "uniq_user_days"
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  {
   "cell_type": "code",
   "execution_count": 19,
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   "source": [
    "# how many unique days did the activity take place for each user \n",
    "count_uniq_days = pysqldf(\"SELECT user_id, COUNT(date) FROM uniq_user_days GROUP BY user_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>COUNT(date)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  COUNT(date)\n",
       "0        1            3\n",
       "1        2            3\n",
       "2        3            3\n",
       "3        4            2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_uniq_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get the first and last day the activity occurred for each user\n",
    "min_dates = pysqldf(\"select user_id, min(date) AS min_date \\\n",
    "    FROM num_cig_per_day group by user_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "max_dates = pysqldf(\"select user_id, max(date) AS max_date \\\n",
    "    FROM num_cig_per_day group by user_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>min_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6/7/14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6/7/14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6/7/14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6/8/14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id min_date\n",
       "0        1   6/7/14\n",
       "1        2   6/7/14\n",
       "2        3   6/7/14\n",
       "3        4   6/8/14"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>max_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6/9/14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6/9/14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6/9/14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6/9/14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id max_date\n",
       "0        1   6/9/14\n",
       "1        2   6/9/14\n",
       "2        3   6/9/14\n",
       "3        4   6/9/14"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO: what is the difference between the number of times the activity took place on the first\n",
    "# day it happened and the last day it occurred?\n",
    "# we didn't have time to do this, if you want to try it, please send in a pull request!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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