EDA - Number of unique visitors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author : Isha Patil**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is used for Exploratory Data Analysis (EDA) to find the number of unique visitors in the Art Gallery based on unique mac ids provided in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "from pandas import Series\n",
    "from matplotlib import pyplot\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = [12.0,8.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading first 1 lakh rows of the dataset\n",
    "data=pd.read_csv(\"kiana_data.csv\", nrows=100000)\n",
    "\n",
    "# Grouping by Mac Addresses and sorting dates\n",
    "data_date=data.groupby('ClientMacAddr').apply(lambda x: x.sort_values('just_date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a new data frame with data for mac id 0\n",
    "df_0=data_date.loc[data_date['ClientMacAddr']==0]\n",
    "\n",
    "# Making a new dataframe with a particular date for mac id 0 \n",
    "df_date=df_0.loc[df_0['just_date']=='2018-07-07']\n",
    "\n",
    "# Finding min and max time of the particular date\n",
    "max=(df_date.iloc[:,4]).max()\n",
    "min=(df_date.iloc[:,4]).min()\n",
    "\n",
    "# Function to calculate the total time spent in the gallery by mac id 0 on a particular date\n",
    "from datetime import datetime\n",
    "s1 = min\n",
    "s2 = max\n",
    "FMT = '%H:%M:%S.%f'\n",
    "tdelta = datetime.strptime(s2, FMT) - datetime.strptime(s1, FMT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(seconds=32050, microseconds=373000)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=pd.read_csv(\"histogram.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.columns = ['dates', 'freq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MrOJcCMzMKs7TR4egVk9p9BRJqzUYplwO9P/ZgTYN3S0CM7OKcyEwM6s4FwIzs4rrSiGQ9FZJ90j6k6TDupGDmZmljhcCScOB44BdgEnA3pImdToPMzNL3WgRbA38KSLuj4hngZ8B7+hCHmZmRncKwdrAX2ruzynbzMysCwbseQSSpgPTy90nJN3TZKhVgb+3JqvG4n2qDTEb0LX33cWYznEAxev1/z8gc6w3ZgOf5briNaLJ1+7x6np+qBuF4GFgnZr748u2l4iIGcCM/r6YpJkRMbW/cdoVb7DEdI4DM147YjrHgRmvnbrRNXQTsIGk9SQtB/wHcF4X8jAzM7rQIoiIRZIOBi4GhgMnR8Sdnc7DzMxSV8YIIuJC4MIOvVy/u5faHG+wxHSOAzNeO2I6x4EZr20UEd3OwczMushLTJiZVZwLgZlZxbkQ2JAjSd3OYVkGQ4492pGrpJbve1qd52DIsVWG1BiBpK2AjYDlyqbrI+KuFsZfBdgAeBp4FblUxl/7EW85Mt95wArAIxGxsAI5Kvr4x1vS9n7GHBYRzzvHumOOJv/OGwFPRsStzcTpFXMssBq5vMxDEfG7FsRsaZ6DIcd2GjKFQNJU4FvkDusP5E5wZeA+4ISIeKKf8UcBp5PLYTwA/A34F3B7RPysyXgnkgvv/Qu4veT6+4i4aajmWBN7ZfKD9xrg3ojo9yWlJK0NbEwWwutbsHOoXI6Sjgc2BO4pcQVcBpwVEY82U2QknV3iPAhMAcYC5wMzIuLhJmO2NM/BkGM7DaVCcCIwJyK+ImkMsDr5R3g78CTwhYh4uh/xPwLsGhG7SRpHfpg3B6YCc4EjIuKZBuO9LSLeLmlN4P8B04DXAtcAX4+IxUMtxxL3tcC3geeBu8md4kLgXOD8shhhozE3JYvW7SXWvwHPkYXxtEYPBKqYo6TXAycBW5Jn/I8ENgXeSB4lf6eReCXmlsCPImKz8rl8FtgK+HdgEfD5Rv4n25HnYMix7SJiSNzIP9rJwJq9tq8C/Bp4Swvi/wgY3Wv7huSZ0Ts2GO8/yZZK7+1TgV8Bk4dijuX5PwK+SB4hrQO8DvggcAKwS5Mxv08WOsiuwRXIg4AfAgc4x7rivQ04pde20eRO8TfAB5uIuR3ws17bBKxB7ig/2+08B0OO7b4NpcHiS8k/3omSviBpe0mjI+IR8sj4H/2MfwX5wT1O0r6SNpU0MrIpLmBCg/HOBcZJ+rKkbSWNL/FmkkcPU4ZojpDddw9G+ktE3FFe63LgM5I2byLmg+S4CBHxbEQ8SZ69fhywl6R/c47LdBmwkqSrJO1ZPj9PRXYDXk/2dTckIq4EHpZ0i6TpklYo+f6N/H2s0WjMVuc5GHJstyHTNdRD0vZk82s9YAvgEeAvEfGhFsU/EJhInpU9DlgJeCXw9oj4V4OxXgkcTrZa5pI761eT/yQ7NBpvsOQoaTPgTOA24AcRcVnNYzcD+0XE7Q3GXBs4jXzPx0TEz3vF3D8a6I+vao7lue8FdiS7NBYANwAfAg6MiBsajVdi7gJ8gDx4eJDsWtwdmF4OLJqJ2dI8B0OO7TLkCgG8MMi5CrmW0arkYOlz/Yi3NrAP8Efg3hJzRbJ/d1Xgmoh4qIF4Y4BdyWsx3Ez2uW9OTucdQQ4i/mGo5dgr/ool3zeSH5K/AA+R3VdbNxlzeIm5J7BZyfsvwOsiYnvnuNQ4q5B/44XkRIunybGHbYB1aWJNMEnrk63xEWS/+93AE2S3yRjgVxHxlyVHaH+egyHHThiShaCVlFNSvwNcR/bvPU3OSjo/mphiJmkL4GhywPAtwGLgAuCkaHLGx2DIsSb2JuSspueAAB4ni8tbyR3YxRExv4F4w8hB7PHAKOCvJe8gr3x3LzArGpjyWrUc9fLZZn/lxdlmZ9WbU6+Yo8lxpOeBO8rmFYE/RsRRTcZsaZ6DIcdOcSFYBklHAY9FxFfL/fXIo7n9yCW1D4iIRQ3EOwZ4NCKOKPc3Bg4E3g38MCK+MhRzLHHeSy47viowC3iG3IFdHhG/bzLm+4G9yZlhN5OzxUYDlzW5c6hcjn3MNutp/U0F5tPcrJkPA7tFzjhbkXzPG5AzpUYDh0Xjs6RamudgyLFjeo8e+/ay0f9PkddV3gAY2eux04BpDcb7JnAU2WRUzfZVyCOJLYZijuX5NwLblu/XBnYCDiMHTN/fZMzrgTfX5LcpWQTPBD7qHOuK19LZZuW5bwZ+DKzaa/vqwNnA+7qd52DIsVO3oTRrqF2+B/yZPLp+k6R1ypx6gNeTTfNGfA1YHjiYHNCmZnbTFuRg7JDLUdII4CpgqqTlIuLhiLgkIr4OfAPYoybnemOKnCWzXU9+EXF7RJwN/C+wu6QNneMytXq2GWSxWgycoZzFt42kERExD3gF+f/VqFbnORhy7Ah3DS2B9OJZf5KWJ7tGdiZnIT1Kztn+W0Ts00S8VwOHkEdy95D9+ZOAMRGx61DKsVf8Lcid1UyyS+P28gFB0gJg3Yh4qsGYGwHHkNODrwN+FxGzymPzgQnRwMymquZYntuy2WY1Md9CzsJZn5xp9gy5892zHzFbmudgyLHdXAiWoMzueAPZXzgGOCMiblOeHTqOvM7ygoh4tM54w8gd6WYl3sXkDI83k8sD3EpOc33Z9ZsHc459vMbqwPvIU+5HApPJWRoPRsTHmow5kpzmN4n88G1JLoXx54g41DkuNU5LZ5uVmD0ntt1BTjl+DvgnOYnh1cBV0eB6VW2YFTfgc+wkF4IlkLQ/sD8vzgB4b3noWHLAtNF/kvcBHwZ+T85S2JH84B4H/CKaW6phwOdY4q5AHnHtTp5VuYBcy2UUefQlYGaDR8VjS367kM3xR8nB2L+XeCPII+W6cq5ijq2ebVZivoFs/VxMzpKCfL83R8QZTcZs9ay4AZ9jp7kQLIGki4HvR8S5Ndu2BD5Ofti+12C8a4CvRcQFpd/4FeSR9l7AhaXPuNEcLyGXgBiwOZa4x5OzMBaSrY3HydbFWdHk6rCSTiKb7/PIo+Jh5I7xt1FzYpVzXGq8ls42KzG+Tq60+ZVyfy1ghxJzMfDuiPhnN/McDDl2XLtGoQfzjTyy+ixlXZhej60OXA1s2UC84WSf7oG9ti9Hzi+/EpjURI6fbnGOXyPPbm1JjjUx/gCsVHN/M3Kl2Adocs0Vsm/8VTX3X0MWwHuAzznHuuK1dLZZed7eZOtnGjCq12OnADt1O0+y5Tygc+z0resJDNQbObhzNvBbchB2eNm+Xvkgj24w3trkLIVrqZmWRjbt7+39D1lHvFHk4NbZZae/f9mZi+yDbibHLch57lcC+9RsX7GZHGue+xPgS7x8QcDXlPzHNhhzNDlT6jRynnbtFNcx5MlvK3c5x+UHQY4jyZlLXyVbfuv0xC7/P1s3+vcuzz2ELFD7lZ3txmX7fcAbmoi3HPA/wBHkAnH9zhP4GDlNulU5jgSOLHm2JMdO3tw11IcyK+M15CyPNcj1RyaRa488BcyLBgb4JE0nB5/ukfTvwB7kP8t1ZF/ikxFxUIM5nkKuPPkw2Wf8VXIH82tyR7kgIv6rgXj7kUVpPtkltBd5xHkleer9c43mWBP7NcBngD+RRWsuOfYwkRzg3qCJmCsC/03+/u4kz6adTw6S/ywi1msix0+WHK8tOT5JFtuGcuyZfVVyPJQcLLyrRTkeSu5YrqHJ32OrZ5v1jlnu70PuYBeTs2U2Ba6NiE/UG7NX/Im8uA7QfLJbbNMm8lw3Ih4qf5s9eXE69PLAJv3McRT593kj+bt8rMRsKMducCHoRdIU8khhEbnTvzciPqs8S3AyudOZG3X+4iRNIo+yryMHpK4gTyz5BnBhuf/3aGDt+DIwdWpETCp9+W8mB6jeSs7y+TR5ZnC9g5BbkkskX0oWvJPIi/q8mRw0PpJcQ73hNfhrXmMdcoezNblDfBXZb/6TiDi5yZgrkTNnXkeexr9JyffkaO6M3f9Hdm1MJBcdG0seCDSUY5nNtU5EzJa0AVmoNyKL1iTyf6uhHJXnDDyrXBvnELIL4i808Xts9WyzmphTySVJFpBLwo8gPzMLyZ3iE9FA37ukV5AHTedHrtTaU7imka2E+8jPTr2z4kaRXT+fjpqZb8rzOJ4i/y4Le16rzpgjeXHtqN9ExLHKcz02Jj9D82jwd9kNLgS9SDoWuD8ivi1pNeB44MyIOFu5ENu7IuLUOmP1HBl+gfwnu4n8oGxPfuA+Rq4xM7fBHD8FbBgRB5XWxl7AqeRR0vbAt6OxqXTHkwXvO5I+Tx4dXkWuk7IeeQGaRxrJscTdkGxV7EheLe1ksghMJnfYf6WBglVirk8eFb6JXFLh/FIQJgKzySPQx6POSz9K2oFsQf2mJ4/yd9+U3IHNJgcBGxmMfE/J8bBeR8kbkjvZkSXHeg8mtiYXQdse+GREzCw7oI3JdYbmk5cQrbfwt3S2WYn5XmA6ufzy6uT/U0OTFfqIuQvZhTaTXAzuB+Q4yQcj4rtNxPsoeYb3uyWNB3YjT5r8NVmY72ki5ofIInAM+Tf6DfCRkucJ0eCCdd3iM4tfbgo5fZLIRbtOJ5eNhdxx170Gf80H/SxywPUVEXEY+cG9iVy7551N5HgW0HPS11rkgPGPI+I88ijkXQ3GmwNMKK2e/cgd/+fJC6ZMJI/KmnEs2QV2OVn4ric/1BsAd0bEgkaKQPFNcjrmn4F/l7RdRDwWuUzwPyLiMXLnWK8Pki20ayUdV1ouO5AHSTeVHBud7fFJ4MpyELCjpJMk3UbucEdExD/qLQLFkeQR9fHAnpI+DvyObAHOjoj5Df4e3wMcFREnRMTnI2J9cme2Ofn3b8Z04EuRs2bOAPaWtBuA8ozd6U3EnAXMAH5Jdq19h/y7f6YU60aNJ1u9AJ8ju38/QnZdHVe6ixr1HuC4iLiQHBc4gLww0DrA10srZODrxEDEYLmRg63bks362u0/Bw4id2jNrrOzPtksfRNwP3kkthKwQhOxxIt9xTPJD8sK5bFZjeZILo17fnnuceW9LlceuxGY0kSOa5JHhb23v53cUbyxiZivJufdQw6WH0i2XF5Vtn2hifc+BfgpWZC/QZ4M9Dy5XkxDg68l3ksGgsmuxJ5W4Pn0MctrGfHG97zncv8f5e+zK3AR8J9N/O+0bEZced6qZGF6Rc22vcgVUCEPpvZt9HdZE/t4YNNy/67ye3ycXNytkVhvIgvBJmRX6MSax5pZq2gkWZzeQxbReb1iXgJs18z77vSt6wkMxBsvzhAaVr5uQA4izmoyXk+c3cim489blOcawCfIueR/IruHvtWPeKPJtVBOI1sdFwDnNRlr5fIB3rOPx95ZPiTDG4y5D3kx8dptx/Hi5R9vprmZTW8nLxQD2Zc/myz+91MKYoPxDiGPCqfW5lt2HDPpNb1wGbEmkGM2byDHfm6reWxDcuyp7njlea2eEfcKsjtxYrnfcw7KDOD95e/SUMxe8XcGvlx2trXvf1gTsfYHziHH575GHlxsSS4L3nCO5W98a/lfP41ygEOO3dzSn/fdydsI7GWiNLMj4nlJwyPiPkk/Iyt+M/F6+qsvJAc1H4McYIsmz9Ytcf8GfBf4bjmLddWI+HM/4j0FzJZ0OLlYncgurGZiPSrpPOAISR8EToyIX0lajjIjq4n3fh3wqKTl48WzZ08APi3pq2R309P1BiuDeoqIX0t6denb3xQ4PSI+p7xkYTMD5CeTO64vAyuU/vPrySPHe6OxiyQ9SPaPn0juwK4peT1JtjLuayRezYy4Gbw4I+5/ysmET5GFv9F1iiaQLaEJ5JIZATwjqedg4qRGYyovszkZmBMRF5fxlZvJvvie5VDq7l6TNJkcr3qILKxvI8euxpLjSj9uMse1yNb5DWQr+ExJj5EHE7Oa+F12hQeL61T+8Wp36lYHSa8i+2H3o1zZjDwyPj76cep9maWiiFgk6Uvk/PrtIuLqJuOtRe5k3gVMjojbms2txFuBXJJ4d3Lp6VvJMbkfRxOXPSzxngY+CvwXebQ5DPhuRFxSZ4yWzoirifl/vLhQ26PAR3oKtaRzyYOAixqM+Q1y2vLz5HTtA5TXdL4tIh7rPV21zniLSp73R8SnyiSDV5PFtO6ZQjUxv0n8AZ6hAAAGyElEQVS+56fIwf/9ytjF+8iW1RUxABeY61O3myS+Db0b2V31DWDzXtvHk4PHzXS39MR8XR+PTSIHZxuN9zVgk17bp5WvzXQTfAL4OqU/u2b7ar1fp5+/x9eQZ7Ju1GC8Y4FP1eR0DqXrjjyi36+JHGtjjiPPrt2j3F8J2L0FMc8iZ+tBtmIaGm/o432fTS4j0ZNjf9/3auV998QcThPXMujmzbOGrB0OJ4+0fijpKkmHSlonIuaQfac79yPmjyRdIekQ5WqPkE3y/20i3nrAKSXepyWtHRHXSnoXOcDbTI4TgJPL+/5Med/zgQ17ZtE0GK/n93iFpE+VeH8i+7QbPRGvZTPilhBzAXmxnQ+Xxz5Kdr/0N+YZ5KwkyFleU/sRbz45OeCA8thH6P/7nk++756Y/02e1zN4dLsS+Ta0buRg66/IKXmrkNMSTyd3XGeTYyRbtTDmWWRzv+5T+JcQ76cl3pnkGcWtzrGhmMuIdw7ZHdFIvJbPiBsMMQdDjgPh5jECa7meudNRM3Bbxgq+Qi7o9dpux6xwjsMjYrGkYZGTITYgZ7I9HhFbNhpvsMQcDDl2k2cNWctFr5k7ZWBvoaRFZDO/6zErnGNLZ8QNlpiDIcducovAOqacCf1oNLjWeydjVjHHdsyIGwwxB0OOneJCYGZWcZ41ZGZWcS4EZmYV50JgZlZxLgTWFpImSLqj17YjJH1mGc+bKumYNuc1p2dQr2b7rZJeL+kgSe9fyvN3l3RY+f6dygsPtSPHp0pOd0n6fu98+xF7O0m/bkUsGzo8fdQGlMh1eBpei6eB+LMlPQRsQy5hjaSNgTERcQO5eNjSnn8euWQx5CqqvyaXRq6LpBFR3/UN/hwRk5UL4/22vNYv6ogvchLIoJq1Yt3lFoF1haQrJX1D0o2S7pW0Tdn+whGrpFUkXSLpTkk/lPSgpFV7tzbKUg5HlO8nSrpI0ixJvys7+d7OAP6j5v5/kGvFvKTVIunj5Yj8tjI/HEkfkHSs8rKWuwP/V47cJ0qaLOn68vPnKhc163mv35E0E/iEpD0l3SHpD5KWukheKRq/J9cXQrlcx03lNb5ctk2QdI+k04A7yIui1P6u3yrpbkk3U3PRIklbS7pO0i2Sfi9po7L9auVqnT0/d41ypU0bolwIrJtGRMTW5Pr9X+rj8S8B10TEJsC55AV0lmUG8LFyZudnyHXiezsLeGc52oZcHrqvE7QOI5cK2IxcOuAFEfF7smVwaERMjlz++zTgs+Xnb+/1npaLiKkRcRTwRWDniNicLCZLpLxG7w7A7ZJ2ItcX2ppcLXRLSduWH92AXNF1k4h4sOb5o8hLPO5Grru/Rk34u4FtImKLktORZftJ5PLUPZfXHBURf1hanja4uRBYuyzpBJXa7T1dHbPIxdp62xb4CUBEXEC5jsOSSHoleUnQsyXdSq7hv+bLEoiYRx4571COfBdFxB29fw64DThd0vvIJYyX9tpjgRUj4qqy6dSSf48za76/llzs7kBy3Zq+TCzv4Vrggoj4DbBTud1Crs2/MS8uPPdgRFzfR5yNgQci4r7Ik4Z+UvPYWPJ3dQdwNHnlLsg1od6uvC7y/uSV9WwI8xiBtcsj5BK/tVYGHqi5/0z5upjG/hcX8dKDmJ7rwg4jL3gz+eVPeZme7qF5LHm5hreRO/PdgM9J2rSBHHt7Yb37iDhI0utL/FmStoyIR3r9/J/7eB8CvhYRJ75kozShNn4DvkKumb9HiXFlye9fki4F3kFecnJQrZtjjXOLwNoiIp4A5kraHkDSyuTF1q9pIMzV5AXfkbQLLxaWecBqZQzhFeSlJomIhcADkvYsz9FS+rZ/QV739z2U8YFaZZbOOhFxBXmN37HkSqC1/kmu409EPA481jPWAexLGYzuI/bEiLghIr4ILKBXn/5SXAzsX1o+SFpby76I+93ABEkTy/29ax4bCzxcvv9Ar+f9kLxQz00RsdSWmA1+bhFYO70fOE7St8v9L0djl9L8MnCGpDvJAdOHACLiOUn/A9xI7sjurnnOPsAJkj5PXgntZ8DL+rcj4h+SrgPWiIj7+3jt4cBPSpePyGsa/yMn5bzgZ8APJH0ceDd5Fbbvl379+8m18/vyf8qVKkUuWVxX/3tEXCLptcB1JY8nyKthLfGSnxHxtKTpwAWS/kVeZH5MefibwKnld3VBr+fNkrQQ+FE9udng5rWGbNCQNBuYGhF/73YuQ53y0p1XAht7KurQ564hM3sJ5Ql1NwCfcxGoBrcIzMwqzi0CM7OKcyEwM6s4FwIzs4pzITAzqzgXAjOzinMhMDOruP8PxAfymdbx4m4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data for better visualization\n",
    "d.plot.hist(bins=30, alpha=0.5)\n",
    "pyplot.xticks(list(range(0,30000,2000)),rotation=70)\n",
    "pyplot.xlabel(\"Unique Visitors Per day\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This plot is saved in the home folder as histogram.jpeg for future reference\n",
    "pyplot.savefig(\"histogram.jpeg\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}