so-data-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import math\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "basepath = os.getcwd() + '/assignment2'\n",
    "tagsFilepath = basepath + '/input/TrainTags.csv'\n",
    "tagsFeaturesFilePath = basepath + '/SOPrediction/generated_data/tags_features.csv'\n",
    "usersFilePath = basepath + '/SOPrediction/generated_data/users.csv'\n",
    "ansFilepath = basepath + '/input/TrainAnswers.csv'\n",
    "qnsFilepath = basepath + '/input/TrainQuestions.csv'\n",
    "tagsData = pd.read_csv(tagsFilepath, encoding='latin1')\n",
    "tagsFeaturesData = pd.read_csv(tagsFeaturesFilePath, encoding='latin1')\n",
    "qnsData = pd.read_csv(qnsFilepath, encoding='latin1')\n",
    "ansData = pd.read_csv(ansFilepath, encoding='latin1')\n",
    "usersData = pd.read_csv(usersFilePath, encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "qnsData = qnsData[:50000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Note response time (in hours) of the first answer given\n",
    "# If no answer posted, return -1\n",
    "def get1stResponseTime(qnid):\n",
    "    # Get answers for qn\n",
    "    answers = ansData[ansData['ParentId'] == qnid]\n",
    "\n",
    "    # If no answers return -1\n",
    "    if len(answers) == 0:\n",
    "        return -1\n",
    "\n",
    "    # Get the datetime of the 1st answered qn\n",
    "    ansDate = min(answers['CreationDate'])\n",
    "    # Get when the qn was asked\n",
    "    qnDate = qnsData[qnsData['Id'] == qnid]['CreationDate'].values[0]\n",
    "    ansDate_dt = datetime.strptime(ansDate , \"%Y-%m-%dT%H:%M:%SZ\")\n",
    "    qnDate_dt = datetime.strptime(qnDate , \"%Y-%m-%dT%H:%M:%SZ\")\n",
    "    delta = ansDate_dt - qnDate_dt\n",
    "    return delta.seconds/3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 3.6166666666666667 minutes ---\n"
     ]
    }
   ],
   "source": [
    "# runs 10 mins for 1mil rows\n",
    "start_time = datetime.now()\n",
    "qnsData['FirstResponse'] = qnsData['Id'].apply(get1stResponseTime)\n",
    "tdelta = (datetime.now() - start_time).seconds/60\n",
    "print(\"--- %s minutes ---\" % tdelta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(qnsData[qnsData['FirstResponse'] == -1])\n",
    "# replace -1 with max value\n",
    "maxResponseTime = qnsData['FirstResponse'].max()\n",
    "qnsData['FirstResponse'] = qnsData['FirstResponse'].replace(-1, 2*maxResponseTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "responseTime_by_user = qnsData.groupby('OwnerUserId')['FirstResponse'].mean()\n",
    "users_response_df =  pd.DataFrame({'userid':responseTime_by_user.index, \n",
    "                                   'mean_response_time':responseTime_by_user.values})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getUserAcceptRate(userid):\n",
    "    acceptrate = usersData[usersData['User Id']==userid]['Accept Rate']\n",
    "    if len(acceptrate) == 0:\n",
    "        return -1\n",
    "    return acceptrate.values[0]\n",
    "\n",
    "start_time = datetime.now()\n",
    "users_response_df['Accept Rate'] = users_response_df['userid'].apply(getUserAcceptRate)\n",
    "tdelta = (datetime.now() - start_time).seconds/60\n",
    "print(\"--- %s minutes ---\" % tdelta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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EZmf7zCzjKqHtk6e3Dbi/jVkr5UkiPgH8EDiT56dDFgMnk5pkmVnGVULbJ09vG3B/G7NW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1082a6358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ACCEPTANCE RATE VS RESPONSE TIME\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "df = users_response_df[users_response_df['mean_response_time'] != -1].dropna()\n",
    "df1 = df[df['Accept Rate'] <= 10]['mean_response_time'].values\n",
    "df2 = df[df['Accept Rate'].between(11, 20, inclusive=True)]['mean_response_time'].values\n",
    "df3 = df[df['Accept Rate'].between(21, 30, inclusive=True)]['mean_response_time'].values\n",
    "df4 = df[df['Accept Rate'].between(31, 40, inclusive=True)]['mean_response_time'].values\n",
    "df5 = df[df['Accept Rate'].between(41, 50, inclusive=True)]['mean_response_time'].values\n",
    "df6 = df[df['Accept Rate'].between(51, 60, inclusive=True)]['mean_response_time'].values\n",
    "df7 = df[df['Accept Rate'].between(61, 70, inclusive=True)]['mean_response_time'].values\n",
    "df8 = df[df['Accept Rate'].between(71, 80, inclusive=True)]['mean_response_time'].values\n",
    "df9 = df[df['Accept Rate'].between(81, 90, inclusive=True)]['mean_response_time'].values\n",
    "df10 = df[df['Accept Rate'].between(91, 100, inclusive=True)]['mean_response_time'].values\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.boxplot([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10],0,'')\n",
    "labels = ['0-10%', '10-20%', '20-30%', '30-40%', '40-50%', '50-60%', '60-70%', '70-80%', '80-90%', '90-100%']\n",
    "plt.xticks(list(range(1,10)), labels,rotation='vertical')\n",
    "plt.xlabel('Acceptance Rate of Asker')\n",
    "plt.ylabel('Response Time To Askers qn(in hours)')\n",
    "plt.ylim([-3,35])\n",
    "plt.subplots_adjust(bottom=0.2)\n",
    "plt.show()\n",
    "fig.savefig('assignment2/ACCEPTANCE RATE VS RESPONSE TIME.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 9.45 minutes ---\n"
     ]
    }
   ],
   "source": [
    "weekdays = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']\n",
    "def getTimeOfDay(ansid):\n",
    "    creationDate = ansData[ansData['Id']==ansid]['CreationDate'].values[0]\n",
    "    creationDate_dt = datetime.strptime(creationDate, \"%Y-%m-%dT%H:%M:%SZ\")\n",
    "    hourOfDay = creationDate_dt.hour\n",
    "    periodOfDay = int(hourOfDay/4)\n",
    "    # Monday is 0 and Sunday is 6\n",
    "    dayOfWeek = creationDate_dt.weekday()\n",
    "    return hourOfDay,periodOfDay,dayOfWeek\n",
    "\n",
    "start_time = datetime.now()\n",
    "ansData['HourOfDay'],ansData['PeriodOfDay'],ansData['DayOfWeek'] = \\\n",
    "    zip(*ansData['Id'].map(getTimeOfDay))\n",
    "tdelta = (datetime.now() - start_time).seconds/60\n",
    "print(\"--- %s minutes ---\" % tdelta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1e+03 ns, sys: 0 ns, total: 1e+03 ns\nWall time: 7.15 µs\n"
     ]
    },
    {
     "data": {
      "image/png": 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UJCPA2NjYGCMjIws9HUmSlozx8XFGR0cBRqtqfLo6V24kSVKvGG4kSVKvGG4k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\nkiT1iuFGkiT1iuFGkiT1yv8Pzm2qdr3/4FwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107306320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ANSWER POSTED TIME - VS - ANSWER SCORE\n",
    "%time\n",
    "sumScore = ansData['Score'].sum()\n",
    "boxplotData = []\n",
    "for i in range(6):\n",
    "    df = ansData[ansData['PeriodOfDay'] == i]['Score']\n",
    "    df = df / sumScore\n",
    "    boxplotData.append(df.values)\n",
    "\n",
    "plt.boxplot(boxplotData,0,'')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAnswersCount(qnid):\n",
    "    return len(ansData[ansData['ParentId'] == qnid])\n",
    "\n",
    "qnsData['AnsCount'] = qnsData['Id'].apply(getAnswersCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 1.3166666666666667 minutes ---\n"
     ]
    }
   ],
   "source": [
    "def getQnTimeOfDay(qnid):\n",
    "    creationDate = qnsData[qnsData['Id']==qnid]['CreationDate'].values[0]\n",
    "    creationDate_dt = datetime.strptime(creationDate, \"%Y-%m-%dT%H:%M:%SZ\")\n",
    "    hourOfDay = creationDate_dt.hour\n",
    "    # Monday is 0 and Sunday is 6\n",
    "    dayOfWeek = creationDate_dt.weekday()\n",
    "    return hourOfDay,dayOfWeek\n",
    "\n",
    "start_time = datetime.now()\n",
    "qnsData['HourOfDay'],qnsData['DayOfWeek'] = \\\n",
    "    zip(*qnsData['Id'].map(getQnTimeOfDay))\n",
    "tdelta = (datetime.now() - start_time).seconds/60\n",
    "print(\"--- %s minutes ---\" % tdelta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OhwAfBkhyMXBbVb2+X/+jwOYkNwLXA0+iN7rwF3sPCJIkaZIGDglVdVl/IuI59E473Aic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\nGRIkSVKTIUGSJDUZEiRJUtP/Bx8jlrW0gqCyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1078ab048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QN POSTED TIME - VS - ANSWER COUNT\n",
    "boxplotData = []\n",
    "X = []\n",
    "Y = []\n",
    "sumCount = qnsData['AnsCount'].sum()\n",
    "for i in range(24):\n",
    "    df = qnsData[(qnsData['HourOfDay'] == i) & (qnsData['AnsCount'] < 100)]['AnsCount']\n",
    "    df = df / sumCount\n",
    "    boxplotData.append(df.values)\n",
    "    val = qnsData[qnsData['HourOfDay'] == i]['AnsCount'].mean()\n",
    "    # val = qnsData[qnsData['DayOfWeek'] == i]['AnsCount'].mean()\n",
    "    X.append(i)\n",
    "    Y.append(val)\n",
    "\n",
    "# plt.boxplot(boxplotData,0,'')\n",
    "# plt.scatter(qnsData['DayOfWeek'],qnsData['AnsCount'])\n",
    "# df = qnsData[qnsData['AnsCount'] < 100]\n",
    "# plt.scatter(df['DayOfWeek'],df['AnsCount'])\n",
    "plt.bar(X,Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t2sDtt8Muu4SBmy+9BOusk3QqkYZp8JgKM3vPzNZtyjAicUilYMMNYdddk04i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\n8l4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1072ac860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QN POSTED TIME - VS - FIRST ANSWER RESPONSE TIME\n",
    "df1 = qnsData[qnsData['HourOfDay'].between(0, 3, inclusive=True)]['FirstResponse'].mean()\n",
    "df2 = qnsData[qnsData['HourOfDay'].between(3, 6, inclusive=True)]['FirstResponse'].mean()\n",
    "df3 = qnsData[qnsData['HourOfDay'].between(6, 9, inclusive=True)]['FirstResponse'].mean()\n",
    "df4 = qnsData[qnsData['HourOfDay'].between(9, 12, inclusive=True)]['FirstResponse'].mean()\n",
    "df5 = qnsData[qnsData['HourOfDay'].between(12, 15, inclusive=True)]['FirstResponse'].mean()\n",
    "df6 = qnsData[qnsData['HourOfDay'].between(15, 18, inclusive=True)]['FirstResponse'].mean()\n",
    "df7 = qnsData[qnsData['HourOfDay'].between(18, 21, inclusive=True)]['FirstResponse'].mean()\n",
    "df8 = qnsData[qnsData['HourOfDay'].between(21, 24, inclusive=True)]['FirstResponse'].mean()\n",
    "\n",
    "fig = plt.figure()\n",
    "# plt.boxplot([df1,df2,df3,df4,df5,df6,df7,df8],0,'')\n",
    "X = list(range(1,9))\n",
    "plt.scatter(X,[df1,df2,df3,df4,df5,df6,df7,df8])\n",
    "plt.plot(X,[df1,df2,df3,df4,df5,df6,df7,df8])\n",
    "\n",
    "labels = ['0-3', '3-6', '6-9', '9-12', '12-15', '15-18', '18-21', '21-24']\n",
    "plt.xticks(X, labels)\n",
    "plt.xlabel('Hour of Qn creation time')\n",
    "plt.ylabel('Avg Response Time(in hours)')\n",
    "plt.subplots_adjust(bottom=0.2)\n",
    "plt.show()\n",
    "fig.savefig('assignment2/QnHourVsFirstResponse.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TAG POPULARITY - VS - ANSWER COUNT\n",
    "def getTagPopularity(qnid):\n",
    "    df = tagsFeaturesData[tagsFeaturesData['qnid'] == qnid]\n",
    "    return df['avg_popularity'].values[0]\n",
    "\n",
    "qnsData['AvgPopularity'] = qnsData['Id'].map(getTagPopularity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5H9m8eVO/hzGh+fN3Z926EYOJJGnGGEoq1ASS84Gl/R7KOEbYtGmY0dFRQ4kkacYYSqq1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\nCiWrRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d0f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(qnsData['AvgPopularity'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MzMysteRpiXgv8L1snIibgMcqF0bEDUUEZmZmZuWWp4j4D+C5wA8r5gVt3LHSzMzMni5P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\nH6X8S1gDHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1078def98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = qnsData[qnsData['AvgPopularity'].between(0.00, 0.01, inclusive=True)]['FirstResponse']\n",
    "df2 = qnsData[qnsData['AvgPopularity'].between(0.01, 0.02, inclusive=True)]['FirstResponse']\n",
    "df3 = qnsData[qnsData['AvgPopularity'].between(0.02, 0.03, inclusive=True)]['FirstResponse']\n",
    "df4 = qnsData[qnsData['AvgPopularity'].between(0.03, 0.04, inclusive=True)]['FirstResponse']\n",
    "df5 = qnsData[qnsData['AvgPopularity'].between(0.04, 0.05, inclusive=True)]['FirstResponse']\n",
    "df6 = qnsData[qnsData['AvgPopularity'].between(0.05, 0.06, inclusive=True)]['FirstResponse']\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.boxplot([df1,df2,df3,df4,df5,df6],0,'')\n",
    "labels = ['0-0.01', '0.01-0.02', '0.02-0.03', '0.03-0.04', '0.04-0.05', '0.05-0.06']\n",
    "plt.xticks(list(range(1,7)), labels,rotation='vertical')\n",
    "plt.xlabel('Average Qn Popularity (by Tags)')\n",
    "plt.ylabel('Response Time(in hours)')\n",
    "plt.subplots_adjust(bottom=0.3)\n",
    "plt.margins(0.05)\n",
    "plt.show()\n",
    "fig.savefig('assignment2/TagPopVsFirstResponse.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = qnsData[qnsData['AvgPopularity'].between(0.00, 0.01, inclusive=True)]['AnsCount'].mean()\n",
    "df2 = qnsData[qnsData['AvgPopularity'].between(0.01, 0.02, inclusive=True)]['AnsCount'].mean()\n",
    "df3 = qnsData[qnsData['AvgPopularity'].between(0.02, 0.03, inclusive=True)]['AnsCount'].mean()\n",
    "df4 = qnsData[qnsData['AvgPopularity'].between(0.03, 0.04, inclusive=True)]['AnsCount'].mean()\n",
    "df5 = qnsData[qnsData['AvgPopularity'].between(0.04, 0.05, inclusive=True)]['AnsCount'].mean()\n",
    "df6 = qnsData[qnsData['AvgPopularity'].between(0.05, 0.06, inclusive=True)]['AnsCount'].mean()\n",
    "df7 = qnsData[qnsData['AvgPopularity'].between(0.06, 0.07, inclusive=True)]['AnsCount'].mean()\n",
    "df8 = qnsData[qnsData['AvgPopularity'].between(0.07, 0.08, inclusive=True)]['AnsCount'].mean()\n",
    "\n",
    "fig = plt.figure()\n",
    "X = list(range(1,9))\n",
    "Y = [df1,df2,df3,df4,df5,df6,df7,df8]\n",
    "plt.scatter(X,Y)\n",
    "plt.plot(X,Y)\n",
    "labels = ['0-0.01', '0.01-0.02', '0.02-0.03', '0.03-0.04', '0.04-0.05',\n",
    "          '0.05-0.06','0.06-0.07','0.07-0.08']\n",
    "plt.xticks(X, labels,rotation='vertical')\n",
    "plt.xlabel('Average Qn Popularity (by Tags)')\n",
    "plt.ylabel('Avg # of Answers received')\n",
    "plt.subplots_adjust(bottom=0.3)\n",
    "plt.margins(0.05)\n",
    "plt.show()\n",
    "fig.savefig('assignment2/TagPopVsAnsCount.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Takes 5 mins\n",
    "def getReputation(userid):\n",
    "    user = usersData[usersData['User Id']==userid]\n",
    "    if len(user) == 0:\n",
    "        return 0\n",
    "    return user['Reputation'].values[0]\n",
    "\n",
    "users_response_df['Reputation'] = users_response_df['userid'].apply(getReputation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GTVUKZ+VJInYAto+IpyXVbr8XeEUrghrE0oh4bBTv38zMbEyVvXBWntkZy2eX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\na+QfxyQLVvbkr+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a21d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = users_response_df[users_response_df['Reputation'] != 0]\n",
    "\n",
    "df1 = df[df['Reputation'].between(0, 200, inclusive=True)]['mean_response_time']\n",
    "df2 = df[df['Reputation'].between(200, 400, inclusive=True)]['mean_response_time']\n",
    "df3 = df[df['Reputation'].between(400, 600, inclusive=True)]['mean_response_time']\n",
    "df4 = df[df['Reputation'].between(600, 800, inclusive=True)]['mean_response_time']\n",
    "df5 = df[df['Reputation'].between(800, 1000, inclusive=True)]['mean_response_time']\n",
    "df6 = df[df['Reputation'].between(1000, 1200, inclusive=True)]['mean_response_time']\n",
    "df7 = df[df['Reputation'].between(1200, 1400, inclusive=True)]['mean_response_time']\n",
    "df8 = df[df['Reputation'].between(1400, 1600, inclusive=True)]['mean_response_time']\n",
    "df9 = df[df['Reputation'].between(1600, 1800, inclusive=True)]['mean_response_time']\n",
    "df10 = df[df['Reputation'].between(1800, 2000, inclusive=True)]['mean_response_time']\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "X = list(range(1,10))\n",
    "Y = [df1,df2,df3,df4,df5,df6,df7,df8,df10]\n",
    "plt.boxplot(Y,0,'')\n",
    "labels = ['0-200', '200-400', '400-600', '600-800', '800-1000',\n",
    "          '1000-1200','1200-1400','1400-1600','1600-1800']\n",
    "plt.xticks(X, labels,rotation='vertical')\n",
    "plt.xlabel('Reputation of beginners')\n",
    "plt.ylabel('Response Time(in hours)')\n",
    "plt.subplots_adjust(bottom=0.3)\n",
    "plt.margins(0.05)\n",
    "plt.show()\n",
    "fig.savefig('assignment2/ReputationVsResponseTime.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "qnFeatureFilepath = basepath + '/input/X_ling_feat.txt'\n",
    "qnFeatureData = pd.read_table(qnFeatureFilepath,sep=' ')\n",
    "qnFeatureData = qnFeatureData[:50000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "qnsData['error_ratio_title'] = qnFeatureData['error_ratio_title'].values\n",
    "qnsData['error_ratio_body'] = qnFeatureData['error_ratio_body'].values\n",
    "qnsData['isimage'] = qnFeatureData['isimage'].values\n",
    "qnsData['iscode'] = qnFeatureData['iscode'].values\n",
    "qnsData['islink'] = qnFeatureData['islink'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f/x7aZUWJP5HUiFInYjDwg7u/kPj+NOBk4F3gNHevSfaciUW9mvvV0q+diOSs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\nIAiCIAiqIoyIIAiCIAiq4v8BoiH23lPVrCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109aed160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = qnsData\n",
    "df1 = df[df['error_ratio_body'].between(0, 0.1, inclusive=True)]['FirstResponse'].mean()\n",
    "df2 = df[df['error_ratio_body'].between(0.1, 0.2, inclusive=True)]['FirstResponse'].mean()\n",
    "df3 = df[df['error_ratio_body'].between(0.2, 0.3, inclusive=True)]['FirstResponse'].mean()\n",
    "df4 = df[df['error_ratio_body'].between(0.3, 0.4, inclusive=True)]['FirstResponse'].mean()\n",
    "df5 = df[df['error_ratio_body'].between(0.4, 0.5, inclusive=True)]['FirstResponse'].mean()\n",
    "df6 = df[df['error_ratio_body'].between(0.5, 0.6, inclusive=True)]['FirstResponse'].mean()\n",
    "df7 = df[df['error_ratio_body'].between(0.6, 0.7, inclusive=True)]['FirstResponse'].mean()\n",
    "df8 = df[df['error_ratio_body'].between(0.7, 0.8, inclusive=True)]['FirstResponse'].mean()\n",
    "df9 = df[df['error_ratio_body'].between(0.8, 0.9, inclusive=True)]['FirstResponse'].mean()\n",
    "\n",
    "fig = plt.figure()\n",
    "X = list(range(1,10))\n",
    "Y = [df1,df2,df3,df4,df5,df6,df7,df8,df9]\n",
    "plt.scatter(X,Y)\n",
    "plt.plot(X,Y)\n",
    "labels = ['0 - 0.1', '0.1 - 0.2', '0.2 - 0.3','0.3 - 0.4','0.4 - 0.5','0.5 - 0.6',\n",
    "          '0.6 - 0.7','0.7 - 0.8','0.8 - 0.9']\n",
    "plt.xticks(X, labels,rotation='vertical')\n",
    "plt.xlabel('Spelling mistakes / Qn Word Count')\n",
    "plt.ylabel('Avg Response time(in hours)')\n",
    "plt.subplots_adjust(bottom=0.3)\n",
    "plt.margins(0.05)\n",
    "plt.show()\n",
    "fig.savefig('assignment2/SpellingmistakeVsResponseTime.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.79209769712\n11.0632061728\n8.38074993216\n11.1482001832\n9.91066676858\n8.54794877699\n"
     ]
    },
    {
     "data": {
      "image/png": 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ERKQJxBnO+AxhC+7HzWxJ1DYNeBXY2MxmD73Q3bWFpoiISEHFSSKuSTwKERER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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1082b3b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meanRT_noimage = qnsData[qnsData['isimage'] == 0]['FirstResponse']\n",
    "meanRT_image = qnsData[qnsData['isimage'] == 1]['FirstResponse']\n",
    "\n",
    "meanRT_nolink = qnsData[qnsData['islink'] == 0]['FirstResponse']\n",
    "meanRT_link = qnsData[qnsData['islink'] == 1]['FirstResponse']\n",
    "\n",
    "meanRT_nocode = qnsData[qnsData['iscode'] == 0]['FirstResponse']\n",
    "meanRT_code = qnsData[qnsData['iscode'] == 1]['FirstResponse']\n",
    "\n",
    "print(meanRT_noimage.mean())\n",
    "print(meanRT_image.mean())\n",
    "print(meanRT_nolink.mean())\n",
    "print(meanRT_link.mean())\n",
    "print(meanRT_nocode.mean())\n",
    "print(meanRT_code.mean())\n",
    "\n",
    "fig = plt.figure()\n",
    "X = [1,2,3,4,5,6]\n",
    "Y = [meanRT_noimage,meanRT_image,meanRT_nolink,meanRT_link,meanRT_nocode,meanRT_code]\n",
    "# Y = [meanRT_noimage.mean(),meanRT_image.mean(),meanRT_nolink.mean(),meanRT_link.mean()\n",
    "#         ,meanRT_nocode.mean(),meanRT_code.mean()]\n",
    "\n",
    "box = plt.boxplot(Y,0,'',patch_artist=True)\n",
    "\n",
    "colors = ['lightblue','blue', 'lightgreen', 'green', 'lightyellow','yellow']\n",
    "for patch, color in zip(box['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "labels = ['Without image', 'With image', 'Without links','With links','Without Code','With code']\n",
    "plt.xticks(X, labels,rotation='vertical')\n",
    "plt.ylabel('Response time to Qn(in hours)')\n",
    "plt.subplots_adjust(bottom=0.3)\n",
    "plt.margins(0.05)\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('assignment2/QnFeaturesVsAvgResponseTime.png')"
   ]
  },
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.019125</td>\n",
       "      <td>0.033056</td>\n",
       "      <td>0</td>\n",
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       "      <th>iscode</th>\n",
       "      <th>isimage</th>\n",
       "      <th>islink</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.007652</td>\n",
       "      <td>0.201389</td>\n",
       "      <td>6</td>\n",
       "      <td>8767810</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075758</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.019125</td>\n",
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       "      <td>9072820</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.003814</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qnsData[qnsData.columns.difference(['DayOfWeek','Title','ClosedDate','CreationDate','Body','Unnamed: 0','OwnerUserId'])][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tagsData[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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kRMRLgRuA84Af1ZUvBc4FRlJKd6SUvgV8EPjFiDilBe2VJEkd0uxIwnrg5pTS1xrKTya7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\npEKGBEmSVOj/A8+iOSffHiVzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1435096a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qnScores = qnsData['Score']\n",
    "# plt.hist(qnScores)\n",
    "plt.hist(qnScores, 30, range=[50, 200], facecolor='gray', align='mid')\n",
    "# plt.scatter(qnsData['Score'].index,qnsData['Score'].values)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}