FPII_user.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json \n",
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy/lib/arraysetops.py:463: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "#Load the data frame file\n",
    "userDf = pd.read_csv('userDfFile.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.183362e+06</td>\n",
       "      <td>1.183362e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.711835e+00</td>\n",
       "      <td>2.372686e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.105231e+00</td>\n",
       "      <td>8.050645e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.130000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.890000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.570000e+00</td>\n",
       "      <td>1.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.165600e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_stars  review_count\n",
       "count   1.183362e+06  1.183362e+06\n",
       "mean    3.711835e+00  2.372686e+01\n",
       "std     1.105231e+00  8.050645e+01\n",
       "min     1.000000e+00  0.000000e+00\n",
       "25%     3.130000e+00  2.000000e+00\n",
       "50%     3.890000e+00  5.000000e+00\n",
       "75%     4.570000e+00  1.600000e+01\n",
       "max     5.000000e+00  1.165600e+04"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_stars    float64\n",
       "review_count       int64\n",
       "user_id           object\n",
       "yelping_since     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>yelping_since</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.80</td>\n",
       "      <td>272</td>\n",
       "      <td>lsSiIjAKVl-QRxKjRErBeg</td>\n",
       "      <td>2010-07-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.94</td>\n",
       "      <td>2559</td>\n",
       "      <td>om5ZiponkpRqUNa3pVPiRg</td>\n",
       "      <td>2006-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.72</td>\n",
       "      <td>277</td>\n",
       "      <td>-lGwMGHMC_XihFJNKCJNRg</td>\n",
       "      <td>2014-10-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.76</td>\n",
       "      <td>436</td>\n",
       "      <td>D-ydMTPGWXTVm4_jjp0k9g</td>\n",
       "      <td>2013-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.23</td>\n",
       "      <td>921</td>\n",
       "      <td>PcvbBOCOcs6_suRDH7TSTg</td>\n",
       "      <td>2012-08-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_stars  review_count                 user_id yelping_since\n",
       "0           3.80           272  lsSiIjAKVl-QRxKjRErBeg    2010-07-13\n",
       "1           3.94          2559  om5ZiponkpRqUNa3pVPiRg    2006-01-18\n",
       "2           4.72           277  -lGwMGHMC_XihFJNKCJNRg    2014-10-31\n",
       "3           3.76           436  D-ydMTPGWXTVm4_jjp0k9g    2013-04-01\n",
       "4           4.23           921  PcvbBOCOcs6_suRDH7TSTg    2012-08-16"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>yelping_since</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1183357</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>OME6i0Xwqbx_xWMKgKqAjw</td>\n",
       "      <td>2009-05-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183358</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3-y8vkrcLwEAXLPxPbr49Q</td>\n",
       "      <td>2016-10-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183359</th>\n",
       "      <td>3.42</td>\n",
       "      <td>22</td>\n",
       "      <td>ZjAfWpPFq9edGphegQXUfQ</td>\n",
       "      <td>2012-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183360</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>yC7UhCwzzteD_mpfA8mryw</td>\n",
       "      <td>2014-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183361</th>\n",
       "      <td>3.57</td>\n",
       "      <td>6</td>\n",
       "      <td>9iUkIqYh84eAOXBh-NPheA</td>\n",
       "      <td>2010-06-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         average_stars  review_count                 user_id yelping_since\n",
       "1183357           1.00             1  OME6i0Xwqbx_xWMKgKqAjw    2009-05-09\n",
       "1183358           2.00             1  3-y8vkrcLwEAXLPxPbr49Q    2016-10-21\n",
       "1183359           3.42            22  ZjAfWpPFq9edGphegQXUfQ    2012-01-13\n",
       "1183360           1.00             1  yC7UhCwzzteD_mpfA8mryw    2014-05-30\n",
       "1183361           3.57             6  9iUkIqYh84eAOXBh-NPheA    2010-06-18"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a new column \"activity\" to measure how active a user is. \n",
    "\n",
    "We find the most recent date among all \"yelping_since\", call it *most_recent*.\n",
    "\n",
    "Then *activity=review_count/(most_recent+1-yelping_since)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First covert string in 'yelping_since' column all to datetime format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_stars           float64\n",
       "review_count              int64\n",
       "user_id                  object\n",
       "yelping_since    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf['yelping_since']=pd.to_datetime(userDf['yelping_since'])\n",
    "userDf.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the most_recent yelping_since datetime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-07-26 00:00:00\n"
     ]
    }
   ],
   "source": [
    "most_recent=userDf['yelping_since'].max()\n",
    "print(most_recent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now calculate *activity* and store all values in a new column *activity*.\n",
    "\n",
    "Since the value is usually very small, we multiply it by 1000.\n",
    "\n",
    "Thus, *activity* means the number of reviews the user writes per 1000 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "userDf['activity']=userDf.apply(lambda row: round(row.review_count*1000/((most_recent-row.yelping_since).days+1)),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.80</td>\n",
       "      <td>272</td>\n",
       "      <td>lsSiIjAKVl-QRxKjRErBeg</td>\n",
       "      <td>2010-07-13</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.94</td>\n",
       "      <td>2559</td>\n",
       "      <td>om5ZiponkpRqUNa3pVPiRg</td>\n",
       "      <td>2006-01-18</td>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.72</td>\n",
       "      <td>277</td>\n",
       "      <td>-lGwMGHMC_XihFJNKCJNRg</td>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.76</td>\n",
       "      <td>436</td>\n",
       "      <td>D-ydMTPGWXTVm4_jjp0k9g</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.23</td>\n",
       "      <td>921</td>\n",
       "      <td>PcvbBOCOcs6_suRDH7TSTg</td>\n",
       "      <td>2012-08-16</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_stars  review_count                 user_id yelping_since  activity\n",
       "0           3.80           272  lsSiIjAKVl-QRxKjRErBeg    2010-07-13       106\n",
       "1           3.94          2559  om5ZiponkpRqUNa3pVPiRg    2006-01-18       608\n",
       "2           4.72           277  -lGwMGHMC_XihFJNKCJNRg    2014-10-31       277\n",
       "3           3.76           436  D-ydMTPGWXTVm4_jjp0k9g    2013-04-01       276\n",
       "4           4.23           921  PcvbBOCOcs6_suRDH7TSTg    2012-08-16       510"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf['activity'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13500"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf['activity'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1068980"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf['activity'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average_stars                      3.65\n",
       "review_count                         54\n",
       "user_id          duDYbWuTmgxzAh5gK0f6Tw\n",
       "yelping_since       2017-07-23 00:00:00\n",
       "activity                          13500\n",
       "Name: 1068980, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.loc[1068980]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.183362e+06</td>\n",
       "      <td>1.183362e+06</td>\n",
       "      <td>1.183362e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.711835e+00</td>\n",
       "      <td>2.372686e+01</td>\n",
       "      <td>1.484975e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.105231e+00</td>\n",
       "      <td>8.050645e+01</td>\n",
       "      <td>5.064215e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.130000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.890000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.570000e+00</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.165600e+04</td>\n",
       "      <td>1.350000e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_stars  review_count      activity\n",
       "count   1.183362e+06  1.183362e+06  1.183362e+06\n",
       "mean    3.711835e+00  2.372686e+01  1.484975e+01\n",
       "std     1.105231e+00  8.050645e+01  5.064215e+01\n",
       "min     1.000000e+00  0.000000e+00  0.000000e+00\n",
       "25%     3.130000e+00  2.000000e+00  2.000000e+00\n",
       "50%     3.890000e+00  5.000000e+00  5.000000e+00\n",
       "75%     4.570000e+00  1.600000e+01  1.200000e+01\n",
       "max     5.000000e+00  1.165600e+04  1.350000e+04"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1183357</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>OME6i0Xwqbx_xWMKgKqAjw</td>\n",
       "      <td>2009-05-09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183358</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3-y8vkrcLwEAXLPxPbr49Q</td>\n",
       "      <td>2016-10-21</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183359</th>\n",
       "      <td>3.42</td>\n",
       "      <td>22</td>\n",
       "      <td>ZjAfWpPFq9edGphegQXUfQ</td>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183360</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>yC7UhCwzzteD_mpfA8mryw</td>\n",
       "      <td>2014-05-30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183361</th>\n",
       "      <td>3.57</td>\n",
       "      <td>6</td>\n",
       "      <td>9iUkIqYh84eAOXBh-NPheA</td>\n",
       "      <td>2010-06-18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         average_stars  review_count                 user_id yelping_since  \\\n",
       "1183357           1.00             1  OME6i0Xwqbx_xWMKgKqAjw    2009-05-09   \n",
       "1183358           2.00             1  3-y8vkrcLwEAXLPxPbr49Q    2016-10-21   \n",
       "1183359           3.42            22  ZjAfWpPFq9edGphegQXUfQ    2012-01-13   \n",
       "1183360           1.00             1  yC7UhCwzzteD_mpfA8mryw    2014-05-30   \n",
       "1183361           3.57             6  9iUkIqYh84eAOXBh-NPheA    2010-06-18   \n",
       "\n",
       "         activity  \n",
       "1183357         0  \n",
       "1183358         4  \n",
       "1183359        11  \n",
       "1183360         1  \n",
       "1183361         2  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define users with activity equal to greater than the 75% benchmark as active users, and users with acitivity equal to or smaller than the 25% benchmark as inactive users.\n",
    "\n",
    "Therefore *active users have activity>=12* and *inactive uers have activity<=2*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "activeUserDf=userDf[userDf['activity']>=12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "inactiveUserDf=userDf[userDf['activity']<=2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299882, 5)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activeUserDf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(376641, 5)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inactiveUserDf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299882.000000</td>\n",
       "      <td>299882.000000</td>\n",
       "      <td>299882.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.766665</td>\n",
       "      <td>76.951718</td>\n",
       "      <td>47.239978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.678757</td>\n",
       "      <td>147.226163</td>\n",
       "      <td>93.224085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.450000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.820000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.170000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>11656.000000</td>\n",
       "      <td>13500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_stars   review_count       activity\n",
       "count  299882.000000  299882.000000  299882.000000\n",
       "mean        3.766665      76.951718      47.239978\n",
       "std         0.678757     147.226163      93.224085\n",
       "min         1.000000       1.000000      12.000000\n",
       "25%         3.450000      17.000000      16.000000\n",
       "50%         3.820000      36.000000      24.000000\n",
       "75%         4.170000      77.000000      46.000000\n",
       "max         5.000000   11656.000000   13500.000000"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activeUserDf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>376641.000000</td>\n",
       "      <td>376641.000000</td>\n",
       "      <td>376641.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.649186</td>\n",
       "      <td>2.067000</td>\n",
       "      <td>1.344246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.456919</td>\n",
       "      <td>1.334665</td>\n",
       "      <td>0.607845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.670000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_stars   review_count       activity\n",
       "count  376641.000000  376641.000000  376641.000000\n",
       "mean        3.649186       2.067000       1.344246\n",
       "std         1.456919       1.334665       0.607845\n",
       "min         1.000000       0.000000       0.000000\n",
       "25%         2.670000       1.000000       1.000000\n",
       "50%         4.000000       2.000000       1.000000\n",
       "75%         5.000000       3.000000       2.000000\n",
       "max         5.000000      11.000000       2.000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inactiveUserDf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there a correlation between user activity and average_stars?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm=sm.OLS.from_formula('activity~average_stars',userDf)\n",
    "result=lm.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               activity   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                     251.1\n",
      "Date:                Sun, 26 Nov 2017   Prob (F-statistic):           1.54e-56\n",
      "Time:                        21:55:10   Log-Likelihood:            -6.3234e+06\n",
      "No. Observations:             1183362   AIC:                         1.265e+07\n",
      "Df Residuals:                 1183360   BIC:                         1.265e+07\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept        12.3727      0.163     75.853      0.000      12.053      12.692\n",
      "average_stars     0.6673      0.042     15.845      0.000       0.585       0.750\n",
      "==============================================================================\n",
      "Omnibus:                  3821584.597   Durbin-Watson:                   1.888\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):    3979123702046.129\n",
      "Skew:                          53.012   Prob(JB):                         0.00\n",
      "Kurtosis:                    8985.770   Cond. No.                         14.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tells matplotlib to display images inline instead of a new window\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b260080>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m7/pdVkMFiIT6nti1rS3Z7Rdz8eqmQf8Mg1kGIpfv7W+ZiGm15bQluo+gKvSb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170ea748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot('activity', 'average_stars', userDf, line_kws = {\"color\":\"r\"}, ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two data points on the right most are certainly outliers, so we try discarding them and rerun the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "newUserDf=userDf.drop(userDf['activity'].idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12000"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newUserDf['activity'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "newUserDf=newUserDf.drop(newUserDf['activity'].idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newUserDf['activity'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm=sm.OLS.from_formula('activity~average_stars',newUserDf)\n",
    "result=lm.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b2e1fd0>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KmrhnBhXfj092Mbmmgsba1I+s3YdPE/MyfzWEg25y5ayKgOCk/dJITLwqZh45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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1fcf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot('activity', 'average_stars', newUserDf, line_kws = {\"color\":\"r\"}, ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84804</th>\n",
       "      <td>3.88</td>\n",
       "      <td>8</td>\n",
       "      <td>ucTsDuaXmzplLM8LaD9EfQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_stars  review_count                 user_id yelping_since  \\\n",
       "84804           3.88             8  ucTsDuaXmzplLM8LaD9EfQ    2017-07-26   \n",
       "\n",
       "       activity  \n",
       "84804      8000  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.loc[userDf['activity']==8000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newUserDf['activity'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102408</th>\n",
       "      <td>4.0</td>\n",
       "      <td>12</td>\n",
       "      <td>WqlMM_aK-YZIbZ8uqu7gTg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        average_stars  review_count                 user_id yelping_since  \\\n",
       "102408            4.0            12  WqlMM_aK-YZIbZ8uqu7gTg    2017-07-26   \n",
       "\n",
       "        activity  \n",
       "102408     12000  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.loc[userDf['activity']==12000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>average_stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>yelping_since</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3107</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2PQq3hSlLGT1ysBXTf6aDg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33564</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NP70UyuODLULhSC9CrDKnw</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38212</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5rL5_6MeAUPhCkRbtTPZSQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49124</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>9hj8HvSoa0uxLY7Al6EoBA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84804</th>\n",
       "      <td>3.88</td>\n",
       "      <td>8</td>\n",
       "      <td>ucTsDuaXmzplLM8LaD9EfQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102408</th>\n",
       "      <td>4.00</td>\n",
       "      <td>12</td>\n",
       "      <td>WqlMM_aK-YZIbZ8uqu7gTg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135931</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>IhI0RF9_PEF9RyQhzElhCQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156109</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0Qq7TvEe40l8H0MU6F9_PA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179607</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>C2mKJ7tJFunXZHHcujbafA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185438</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>JCTlqAKBDVrf9ySd6VeCRQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198141</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>q8tDxQW3hmxhwFdY_ydn_Q</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214514</th>\n",
       "      <td>3.33</td>\n",
       "      <td>3</td>\n",
       "      <td>1k0uJ5NLvREoJo3Qz15ouA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216746</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>Aoo7KRDUSWdZCMqFZJTBew</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219215</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>pGMlDfstUyXADh0BHt6YyQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236279</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>fXgUtoKguzGUqEDdKyG5rg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266115</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>x41uBPVa2jGpC7Jo4Xz4pw</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295020</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>FhK4Sq-kciFApRZI3EkVMQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303523</th>\n",
       "      <td>5.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1T1zKMXN9SBoyezhcIZMjg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308371</th>\n",
       "      <td>4.00</td>\n",
       "      <td>3</td>\n",
       "      <td>96ueMWf7Exsij7fI3Cxmhg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310920</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3MuktxyapUjSAj6ZGhNcJg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317633</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-jZeQl6u5cpwTNQXpLXkqg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344102</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>c8iqzC6vcY69NyDxoOMTMQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383936</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>wPsC-ykuxSRXGtvMkQPHsg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391050</th>\n",
       "      <td>3.67</td>\n",
       "      <td>3</td>\n",
       "      <td>cMFir5k4KTsTylQFgOAq6Q</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393884</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>JMGkmFHsufOvIEu3g6MGXA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410961</th>\n",
       "      <td>5.00</td>\n",
       "      <td>3</td>\n",
       "      <td>uscRPgdyYR2uN7NnHEEMyQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419005</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>Aw5ZrZWe9xLvts9odG5N6w</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421664</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>f_m-q9L3SHZBda9R_35Meg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425840</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>ExgcJuogc3tCJF7vIPq90Q</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430788</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>pPyNd5TFvc3M3XRqsDR4_w</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103088</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>rVKVkz_OLVDlD6KszhvvMA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104937</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1AP2ZB8WcbFoW7jo0dy7AQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104943</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>es21pOb2B6BuqCcpEw8KVg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106500</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>lgd6zk43gPK1UIXeaIv55g</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115254</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>569hWIYc_Si05TZmNoB83g</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127305</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>AsVOlAN9FtTZUFHR1XNYrw</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128471</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>fd6JIac7Qw-djQzK3RdEhA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128645</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>WAHgeCkv8Gew0ducBI96wQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128822</th>\n",
       "      <td>3.00</td>\n",
       "      <td>2</td>\n",
       "      <td>IawHNXWc1ciu7axnLCzpMg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129987</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>r85HVCE5lwUveXIwi3rxIg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130753</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>AsU1Ky4ifqf603NMaEOdpw</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137875</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>4cbNL5Rz73rL6hUJQlBVUA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138344</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>oQJkWLQz6-vIx63ZLXxn1A</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138525</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5D_ytw4QMM4YmVh0aBmM2A</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139109</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>78ivsqghc9-F8t5hmvZvZQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140024</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>bBMuRWEa3UjOgD5fTshRjA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142620</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>I0nnD3l34OuInqKZNC_Rsw</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143072</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>xogcA23GYpM8Ukhmoz7Tgg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149152</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>GZ32_5YLJxgcoyzxQEVTyA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156072</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>PXaFzh8MvjcelY1wTNeAmg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1157947</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>mnqcL3y3cVRcFcNqwJ_eCg</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159845</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>GI-6PMNsNgNS71GGZfCssQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167270</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>C7EdUMSC2nsy51M7Ep7zDQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168484</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>BNhcQaY5evS6fZ-6ppB4IQ</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171635</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>9R_cuq3CBhnayBRgLjPUNA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1172591</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>TViwuoPKysuih5Xvh0cKaA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1173136</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>FcyL6q4aNI1h4m5qW46CgA</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179675</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>ZTjQ0ZBzwvDce3rY3X2s9A</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179849</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>EPuG9oUEmeVa_pn40EeDSw</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181601</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>oFvFilGBYgyetsz4S5kZhw</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         average_stars  review_count                 user_id yelping_since  \\\n",
       "3107              1.00             2  2PQq3hSlLGT1ysBXTf6aDg    2017-07-26   \n",
       "33564             5.00             1  NP70UyuODLULhSC9CrDKnw    2017-07-26   \n",
       "38212             1.00             1  5rL5_6MeAUPhCkRbtTPZSQ    2017-07-26   \n",
       "49124             1.00             1  9hj8HvSoa0uxLY7Al6EoBA    2017-07-26   \n",
       "84804             3.88             8  ucTsDuaXmzplLM8LaD9EfQ    2017-07-26   \n",
       "102408            4.00            12  WqlMM_aK-YZIbZ8uqu7gTg    2017-07-26   \n",
       "135931            5.00             1  IhI0RF9_PEF9RyQhzElhCQ    2017-07-26   \n",
       "156109            5.00             1  0Qq7TvEe40l8H0MU6F9_PA    2017-07-26   \n",
       "179607            2.00             1  C2mKJ7tJFunXZHHcujbafA    2017-07-26   \n",
       "185438            5.00             1  JCTlqAKBDVrf9ySd6VeCRQ    2017-07-26   \n",
       "198141            5.00             1  q8tDxQW3hmxhwFdY_ydn_Q    2017-07-26   \n",
       "214514            3.33             3  1k0uJ5NLvREoJo3Qz15ouA    2017-07-26   \n",
       "216746            5.00             1  Aoo7KRDUSWdZCMqFZJTBew    2017-07-26   \n",
       "219215            5.00             1  pGMlDfstUyXADh0BHt6YyQ    2017-07-26   \n",
       "236279            5.00             1  fXgUtoKguzGUqEDdKyG5rg    2017-07-26   \n",
       "266115            1.00             1  x41uBPVa2jGpC7Jo4Xz4pw    2017-07-26   \n",
       "295020            5.00             1  FhK4Sq-kciFApRZI3EkVMQ    2017-07-26   \n",
       "303523            5.00             3  1T1zKMXN9SBoyezhcIZMjg    2017-07-26   \n",
       "308371            4.00             3  96ueMWf7Exsij7fI3Cxmhg    2017-07-26   \n",
       "310920            5.00             1  3MuktxyapUjSAj6ZGhNcJg    2017-07-26   \n",
       "317633            5.00             1  -jZeQl6u5cpwTNQXpLXkqg    2017-07-26   \n",
       "344102            5.00             1  c8iqzC6vcY69NyDxoOMTMQ    2017-07-26   \n",
       "383936            1.00             1  wPsC-ykuxSRXGtvMkQPHsg    2017-07-26   \n",
       "391050            3.67             3  cMFir5k4KTsTylQFgOAq6Q    2017-07-26   \n",
       "393884            5.00             1  JMGkmFHsufOvIEu3g6MGXA    2017-07-26   \n",
       "410961            5.00             3  uscRPgdyYR2uN7NnHEEMyQ    2017-07-26   \n",
       "419005            5.00             1  Aw5ZrZWe9xLvts9odG5N6w    2017-07-26   \n",
       "421664            1.00             1  f_m-q9L3SHZBda9R_35Meg    2017-07-26   \n",
       "425840            5.00             1  ExgcJuogc3tCJF7vIPq90Q    2017-07-26   \n",
       "430788            5.00             1  pPyNd5TFvc3M3XRqsDR4_w    2017-07-26   \n",
       "...                ...           ...                     ...           ...   \n",
       "1103088           1.00             1  rVKVkz_OLVDlD6KszhvvMA    2017-07-26   \n",
       "1104937           1.00             1  1AP2ZB8WcbFoW7jo0dy7AQ    2017-07-26   \n",
       "1104943           5.00             1  es21pOb2B6BuqCcpEw8KVg    2017-07-26   \n",
       "1106500           1.00             1  lgd6zk43gPK1UIXeaIv55g    2017-07-26   \n",
       "1115254           5.00             1  569hWIYc_Si05TZmNoB83g    2017-07-26   \n",
       "1127305           5.00             1  AsVOlAN9FtTZUFHR1XNYrw    2017-07-26   \n",
       "1128471           5.00             1  fd6JIac7Qw-djQzK3RdEhA    2017-07-26   \n",
       "1128645           1.00             1  WAHgeCkv8Gew0ducBI96wQ    2017-07-26   \n",
       "1128822           3.00             2  IawHNXWc1ciu7axnLCzpMg    2017-07-26   \n",
       "1129987           5.00             1  r85HVCE5lwUveXIwi3rxIg    2017-07-26   \n",
       "1130753           5.00             1  AsU1Ky4ifqf603NMaEOdpw    2017-07-26   \n",
       "1137875           1.00             1  4cbNL5Rz73rL6hUJQlBVUA    2017-07-26   \n",
       "1138344           5.00             1  oQJkWLQz6-vIx63ZLXxn1A    2017-07-26   \n",
       "1138525           5.00             1  5D_ytw4QMM4YmVh0aBmM2A    2017-07-26   \n",
       "1139109           5.00             1  78ivsqghc9-F8t5hmvZvZQ    2017-07-26   \n",
       "1140024           5.00             1  bBMuRWEa3UjOgD5fTshRjA    2017-07-26   \n",
       "1142620           5.00             1  I0nnD3l34OuInqKZNC_Rsw    2017-07-26   \n",
       "1143072           5.00             1  xogcA23GYpM8Ukhmoz7Tgg    2017-07-26   \n",
       "1149152           1.00             1  GZ32_5YLJxgcoyzxQEVTyA    2017-07-26   \n",
       "1156072           5.00             1  PXaFzh8MvjcelY1wTNeAmg    2017-07-26   \n",
       "1157947           5.00             1  mnqcL3y3cVRcFcNqwJ_eCg    2017-07-26   \n",
       "1159845           1.00             1  GI-6PMNsNgNS71GGZfCssQ    2017-07-26   \n",
       "1167270           5.00             1  C7EdUMSC2nsy51M7Ep7zDQ    2017-07-26   \n",
       "1168484           1.00             1  BNhcQaY5evS6fZ-6ppB4IQ    2017-07-26   \n",
       "1171635           5.00             1  9R_cuq3CBhnayBRgLjPUNA    2017-07-26   \n",
       "1172591           2.00             1  TViwuoPKysuih5Xvh0cKaA    2017-07-26   \n",
       "1173136           5.00             1  FcyL6q4aNI1h4m5qW46CgA    2017-07-26   \n",
       "1179675           2.00             1  ZTjQ0ZBzwvDce3rY3X2s9A    2017-07-26   \n",
       "1179849           5.00             1  EPuG9oUEmeVa_pn40EeDSw    2017-07-26   \n",
       "1181601           2.00             1  oFvFilGBYgyetsz4S5kZhw    2017-07-26   \n",
       "\n",
       "         activity  \n",
       "3107         2000  \n",
       "33564        1000  \n",
       "38212        1000  \n",
       "49124        1000  \n",
       "84804        8000  \n",
       "102408      12000  \n",
       "135931       1000  \n",
       "156109       1000  \n",
       "179607       1000  \n",
       "185438       1000  \n",
       "198141       1000  \n",
       "214514       3000  \n",
       "216746       1000  \n",
       "219215       1000  \n",
       "236279       1000  \n",
       "266115       1000  \n",
       "295020       1000  \n",
       "303523       3000  \n",
       "308371       3000  \n",
       "310920       1000  \n",
       "317633       1000  \n",
       "344102       1000  \n",
       "383936       1000  \n",
       "391050       3000  \n",
       "393884       1000  \n",
       "410961       3000  \n",
       "419005       1000  \n",
       "421664       1000  \n",
       "425840       1000  \n",
       "430788       1000  \n",
       "...           ...  \n",
       "1103088      1000  \n",
       "1104937      1000  \n",
       "1104943      1000  \n",
       "1106500      1000  \n",
       "1115254      1000  \n",
       "1127305      1000  \n",
       "1128471      1000  \n",
       "1128645      1000  \n",
       "1128822      2000  \n",
       "1129987      1000  \n",
       "1130753      1000  \n",
       "1137875      1000  \n",
       "1138344      1000  \n",
       "1138525      1000  \n",
       "1139109      1000  \n",
       "1140024      1000  \n",
       "1142620      1000  \n",
       "1143072      1000  \n",
       "1149152      1000  \n",
       "1156072      1000  \n",
       "1157947      1000  \n",
       "1159845      1000  \n",
       "1167270      1000  \n",
       "1168484      1000  \n",
       "1171635      1000  \n",
       "1172591      1000  \n",
       "1173136      1000  \n",
       "1179675      1000  \n",
       "1179849      1000  \n",
       "1181601      1000  \n",
       "\n",
       "[168 rows x 5 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf.loc[userDf['yelping_since']=='2017-07-26']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are very curious about those outliers. \n",
    "\n",
    "#### Why did they write so many reviews once they sign up as a yelp user?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#Load the data frame file\n",
    "reviewDf = pd.read_csv('reviewDfFile4.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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",
       "        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>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>676447</th>\n",
       "      <td>rBko32y5vK13SD_sW3AG_g</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>M5a64uFULsiubm0y4DoW0g</td>\n",
       "      <td>4.0</td>\n",
       "      <td>One of the cutest cafes in Pittsburgh! The caf...</td>\n",
       "      <td>duDYbWuTmgxzAh5gK0f6Tw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   business_id        date               review_id  stars  \\\n",
       "676447  rBko32y5vK13SD_sW3AG_g  2017-07-25  M5a64uFULsiubm0y4DoW0g    4.0   \n",
       "\n",
       "                                                     text  \\\n",
       "676447  One of the cutest cafes in Pittsburgh! The caf...   \n",
       "\n",
       "                       user_id  \n",
       "676447  duDYbWuTmgxzAh5gK0f6Tw  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviewDf.loc[reviewDf['user_id']=='duDYbWuTmgxzAh5gK0f6Tw']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "reviewDf = pd.read_csv('reviewDfFile8.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>467464</th>\n",
       "      <td>rBko32y5vK13SD_sW3AG_g</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>M5a64uFULsiubm0y4DoW0g</td>\n",
       "      <td>4.0</td>\n",
       "      <td>One of the cutest cafes in Pittsburgh! The caf...</td>\n",
       "      <td>duDYbWuTmgxzAh5gK0f6Tw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   business_id        date               review_id  stars  \\\n",
       "467464  rBko32y5vK13SD_sW3AG_g  2017-07-25  M5a64uFULsiubm0y4DoW0g    4.0   \n",
       "\n",
       "                                                     text  \\\n",
       "467464  One of the cutest cafes in Pittsburgh! The caf...   \n",
       "\n",
       "                       user_id  \n",
       "467464  duDYbWuTmgxzAh5gK0f6Tw  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviewDf.loc[reviewDf['user_id']=='duDYbWuTmgxzAh5gK0f6Tw']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We searched all the review data frames and discovered that althought user 'duDYbWuTmgxzAh5gK0f6Tw' has a review_count of 54, only 2 of this user's reviews are available in the review datasets. This shows that the review datasets don't perfectly align with the user dataset. Since it makes no sense to analyze users with more than 80% of their reviews missing from the review datasets, we need to map user_id in userDfFile.csv to corresponding review_id s in reviewDfFile*.csv, and then drop all users with less than 80% of reviews. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sidenote: Another question that might be interesting: On which day does the most people sign up to become a yelp user?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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