screen shot data.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "paths = glob.glob('/Users/wickes1/Desktop/oldscreenshots/*.png')"
   ]
  },
  {
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   },
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    {
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      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-03-21 at 10.20.45 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-03-21 at 10.24.40 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-03-22 at 4.36.28 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-03-24 at 11.06.34 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-03-24 at 9.33.25 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-03-24 at 9.38.31 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-03-25 at 4.51.24 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-03-29 at 4.59.54 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 10.05.20 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 10.20.09 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 10.38.28 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 10.42.37 AM (2).png\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 10.42.37 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 10.42.39 AM (2).png\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 10.42.39 AM 1.png\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 10.42.39 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 11.38.54 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 3.00.06 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 3.00.11 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-03 at 3.02.06 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-05 at 6.40.36 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-07 at 11.02.25 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-08 at 7.54.37 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-08 at 8.42.20 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-10 at 1.12.48 PM.png\n",
      "PM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-10 at 11.10.00 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-10 at 7.08.40 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-10 at 7.08.48 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-10 at 8.53.12 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-11 at 11.03.26 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-12 at 11.49.30 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-12 at 7.22.12 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-13 at 9.44.45 AM.png\n",
      "AM\n",
      "/Users/wickes1/Desktop/oldscreenshots/Screen Shot 2017-04-13 at 9.44.51 AM.png\n",
      "AM\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "\n",
    "for path in paths:\n",
    "    print(path)\n",
    "    split = path.split()\n",
    "    date = split[2]\n",
    "    hour, minute = split[4].split(\".\")[0:2]\n",
    "    ampm = split[-1].split(\".\")[0]\n",
    "    if not ampm in ['AM', 'PM']:\n",
    "        continue\n",
    "    sub = [date, ampm + hour.zfill(2)]\n",
    "    print(ampm)\n",
    "    data.append(sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data, columns = ['date', 'hour'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2015,          date  hour\n",
       "  0  2015-06-23  AM09\n",
       "  1  2015-06-24  AM09\n",
       "  2  2015-06-25  PM12\n",
       "  3  2015-07-02  PM02\n",
       "  4  2015-07-07  PM01\n",
       "  5  2015-07-07  AM11\n",
       "  6  2015-07-07  PM02\n",
       "  7  2015-07-07  PM06\n",
       "  8  2015-07-08  PM06\n",
       "  9  2015-07-09  AM10\n",
       "  10 2015-07-09  AM10\n",
       "  11 2015-07-10  AM11\n",
       "  12 2015-07-10  PM02\n",
       "  13 2015-07-12  PM09\n",
       "  14 2015-07-13  AM08\n",
       "  15 2015-07-14  AM11\n",
       "  16 2015-07-21  PM02\n",
       "  17 2015-07-23  AM11\n",
       "  18 2015-07-23  AM11\n",
       "  19 2015-07-27  AM11\n",
       "  20 2015-07-27  AM11\n",
       "  21 2015-08-01  PM02\n",
       "  22 2015-08-01  PM02\n",
       "  23 2015-08-01  PM02\n",
       "  24 2015-08-01  PM02\n",
       "  25 2015-08-04  AM10\n",
       "  26 2015-08-05  PM07\n",
       "  27 2015-08-06  PM02\n",
       "  28 2015-08-06  PM03\n",
       "  29 2015-08-06  PM03\n",
       "  ..        ...   ...\n",
       "  66 2015-12-03  PM01\n",
       "  67 2015-12-03  PM01\n",
       "  68 2015-12-03  AM11\n",
       "  69 2015-12-03  AM11\n",
       "  70 2015-12-03  AM11\n",
       "  71 2015-12-04  PM12\n",
       "  72 2015-12-04  PM12\n",
       "  73 2015-12-04  PM03\n",
       "  74 2015-12-04  PM03\n",
       "  75 2015-12-07  AM07\n",
       "  76 2015-12-08  PM01\n",
       "  77 2015-12-08  PM09\n",
       "  78 2015-12-08  PM09\n",
       "  79 2015-12-09  AM10\n",
       "  80 2015-12-10  AM11\n",
       "  81 2015-12-11  PM12\n",
       "  82 2015-12-15  PM01\n",
       "  83 2015-12-15  AM10\n",
       "  84 2015-12-15  AM09\n",
       "  85 2015-12-15  AM09\n",
       "  86 2015-12-17  PM02\n",
       "  87 2015-12-18  AM08\n",
       "  88 2015-12-21  PM02\n",
       "  89 2015-12-21  PM02\n",
       "  90 2015-12-22  PM03\n",
       "  91 2015-12-23  PM04\n",
       "  92 2015-12-23  PM04\n",
       "  93 2015-12-23  PM04\n",
       "  94 2015-12-23  PM04\n",
       "  95 2015-12-30  AM11\n",
       "  \n",
       "  [96 rows x 2 columns]), (2016,           date  hour\n",
       "  96  2016-01-04  PM01\n",
       "  97  2016-01-04  PM01\n",
       "  98  2016-01-04  PM01\n",
       "  99  2016-01-04  PM01\n",
       "  100 2016-01-04  PM01\n",
       "  101 2016-01-04  PM01\n",
       "  102 2016-01-04  PM01\n",
       "  103 2016-01-04  PM01\n",
       "  104 2016-01-04  AM11\n",
       "  105 2016-01-04  AM11\n",
       "  106 2016-01-04  AM11\n",
       "  107 2016-01-04  AM11\n",
       "  108 2016-01-04  PM12\n",
       "  109 2016-01-04  PM12\n",
       "  110 2016-01-04  PM12\n",
       "  111 2016-01-04  PM12\n",
       "  112 2016-01-04  PM03\n",
       "  113 2016-01-04  PM03\n",
       "  114 2016-01-04  PM03\n",
       "  115 2016-01-04  PM03\n",
       "  116 2016-01-04  PM03\n",
       "  117 2016-01-04  PM03\n",
       "  118 2016-01-04  PM03\n",
       "  119 2016-01-04  PM03\n",
       "  120 2016-01-04  PM03\n",
       "  121 2016-01-04  PM03\n",
       "  122 2016-01-06  PM01\n",
       "  123 2016-01-06  PM02\n",
       "  124 2016-01-06  PM02\n",
       "  125 2016-01-06  PM04\n",
       "  ..         ...   ...\n",
       "  446 2016-12-01  PM09\n",
       "  447 2016-12-01  PM09\n",
       "  448 2016-12-08  AM11\n",
       "  449 2016-12-08  AM11\n",
       "  450 2016-12-08  PM04\n",
       "  451 2016-12-09  PM08\n",
       "  452 2016-12-13  PM01\n",
       "  453 2016-12-13  AM09\n",
       "  454 2016-12-13  AM09\n",
       "  455 2016-12-14  AM11\n",
       "  456 2016-12-14  AM11\n",
       "  457 2016-12-17  PM02\n",
       "  458 2016-12-20  AM08\n",
       "  459 2016-12-20  AM08\n",
       "  460 2016-12-20  AM08\n",
       "  461 2016-12-21  PM03\n",
       "  462 2016-12-21  PM03\n",
       "  463 2016-12-22  PM02\n",
       "  464 2016-12-22  PM02\n",
       "  465 2016-12-27  AM10\n",
       "  466 2016-12-28  PM01\n",
       "  467 2016-12-28  PM01\n",
       "  468 2016-12-28  PM01\n",
       "  469 2016-12-28  PM01\n",
       "  470 2016-12-28  PM01\n",
       "  471 2016-12-28  PM01\n",
       "  472 2016-12-28  PM01\n",
       "  473 2016-12-28  PM01\n",
       "  474 2016-12-28  PM12\n",
       "  475 2016-12-28  PM12\n",
       "  \n",
       "  [380 rows x 2 columns]), (2017,           date  hour\n",
       "  476 2017-01-06  AM10\n",
       "  477 2017-01-16  PM02\n",
       "  478 2017-01-18  AM10\n",
       "  479 2017-01-18  PM05\n",
       "  480 2017-01-19  PM07\n",
       "  481 2017-01-19  PM07\n",
       "  482 2017-01-27  PM02\n",
       "  483 2017-01-27  PM02\n",
       "  484 2017-01-27  PM02\n",
       "  485 2017-01-27  AM08\n",
       "  486 2017-01-27  AM09\n",
       "  487 2017-02-02  AM10\n",
       "  488 2017-02-06  PM03\n",
       "  489 2017-02-06  PM07\n",
       "  490 2017-02-06  PM07\n",
       "  491 2017-02-06  PM07\n",
       "  492 2017-02-06  PM08\n",
       "  493 2017-02-06  PM08\n",
       "  494 2017-02-06  PM09\n",
       "  495 2017-02-06  PM09\n",
       "  496 2017-02-06  PM09\n",
       "  497 2017-02-07  PM01\n",
       "  498 2017-02-07  PM04\n",
       "  499 2017-02-07  PM04\n",
       "  500 2017-02-07  PM04\n",
       "  501 2017-02-07  PM04\n",
       "  502 2017-02-08  AM06\n",
       "  503 2017-02-08  PM09\n",
       "  504 2017-02-08  PM09\n",
       "  505 2017-02-08  PM09\n",
       "  ..         ...   ...\n",
       "  542 2017-03-21  AM10\n",
       "  543 2017-03-22  PM04\n",
       "  544 2017-03-24  AM11\n",
       "  545 2017-03-24  AM09\n",
       "  546 2017-03-24  AM09\n",
       "  547 2017-03-25  PM04\n",
       "  548 2017-03-29  PM04\n",
       "  549 2017-04-03  AM10\n",
       "  550 2017-04-03  AM10\n",
       "  551 2017-04-03  AM10\n",
       "  552 2017-04-03  AM10\n",
       "  553 2017-04-03  AM10\n",
       "  554 2017-04-03  AM11\n",
       "  555 2017-04-03  PM03\n",
       "  556 2017-04-03  PM03\n",
       "  557 2017-04-03  PM03\n",
       "  558 2017-04-05  AM06\n",
       "  559 2017-04-07  AM11\n",
       "  560 2017-04-08  PM07\n",
       "  561 2017-04-08  PM08\n",
       "  562 2017-04-10  PM01\n",
       "  563 2017-04-10  AM11\n",
       "  564 2017-04-10  AM07\n",
       "  565 2017-04-10  AM07\n",
       "  566 2017-04-10  AM08\n",
       "  567 2017-04-11  AM11\n",
       "  568 2017-04-12  AM11\n",
       "  569 2017-04-12  AM07\n",
       "  570 2017-04-13  AM09\n",
       "  571 2017-04-13  AM09\n",
       "  \n",
       "  [96 rows x 2 columns])]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.groupby(df.date.dt.year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116aad2e8>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RHey4crYIIc0LxQUhAf5UVOZcVA86F4Q0PxQXhAQwLFIbKC4IaX4oLggJoLioDRQXhDQ/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115f23470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('date').size().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dayOfWeek={0:'Monday', 1:'Tuesday', 2:'Wednesday', 3:'Thursday', 4:'Friday', 5:'Saturday', 6:'Sunday'}\n",
    "\n",
    "df['year'] = df['date'].dt.year\n",
    "df['month'] = df['date'].dt.month\n",
    "df['dayofweek'] = df['date'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11838d048>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dwLsKaY4HjpR0I3ArsBC4HTizwbLMzMysTRoK+BFxpaR9gc8AHwNuAQ6PiNML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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1181d02e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['hour']).size().plot(kind = 'bar')\\\n",
    "    .set_title(\"Screenshots from May 2015 - April 2017 by hour of day\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}