analysis_time_hist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "import time\n",
    "import json\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "from pylab import hist, show, xticks\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#data = pd.read_csv(\"fcc_comments.csv\",header=0,low_memory=False,usecols=[\"date_submission\"])\n",
    "data = pd.DataFrame(json.load(open(\"ecfs_17-108_0.json\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#read in submission date and reception data\n",
    "dates = data[\"date_submission\"].tolist()\n",
    "subdates = data[\"date_received\"].tolist()\n",
    "startdate = datetime(2017,4,27,0,0,0) #set startdate to be April 27, 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "testvals = dates\n",
    "testvals2 = subdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#convert times to forms datetime can work with\n",
    "testtime = datetime.strptime(testvals[0][0:19],\"%Y-%m-%dT%H:%M:%S\")\n",
    "testvals = [datetime.strptime(testval[0:19],\"%Y-%m-%dT%H:%M:%S\") for testval in testvals]\n",
    "\n",
    "testtime2 = datetime.strptime(testvals2[0][0:19],\"%Y-%m-%dT%H:%M:%S\")\n",
    "testvals2 = [datetime.strptime(testval2[0:19],\"%Y-%m-%dT%H:%M:%S\") for testval2 in testvals2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14400.0\n"
     ]
    }
   ],
   "source": [
    "tdelta = testvals[0]-startdate\n",
    "print(tdelta.total_seconds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get seconds between startdate and submission time\n",
    "testvals = [(testval-startdate).total_seconds() for testval in testvals]\n",
    "testvals2 = [(testval2-startdate).total_seconds() for testval2 in testvals2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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cT2fqmM+Zzmd9+gMdtj2/ut+T5oVx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\nvefDZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f395a775320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#submission\n",
    "fig = plt.figure(figsize=[25,15])\n",
    "ax = fig.add_subplot(1,1,1,)\n",
    "\n",
    "plt.hist(testvals)\n",
    "labels_sec = range(0,2000001,200000)\n",
    "labels = [str(timedelta(seconds=labelsecs)+startdate)[0:10] for labelsecs in labels_sec]\n",
    "ax.axes.set_xticklabels(labels)\n",
    "plt.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(direction='out', length=15, width=4)\n",
    "plt.xlabel(\"Date\",fontsize=36)\n",
    "plt.ylabel(\"# of comments\",fontsize=36);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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lSifDBxb5usbCfE51PgfV5LXVj1ad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\nMVB8mdyhE5QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f395a8128d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reception\n",
    "fig = plt.figure(figsize=[25,15])\n",
    "ax = fig.add_subplot(1,1,1,)\n",
    "\n",
    "plt.hist(testvals2);#,bins=20);\n",
    "labels_sec = range(0,2000001,200000)\n",
    "labels = [str(timedelta(seconds=labelsecs)+startdate)[0:10] for labelsecs in labels_sec]\n",
    "ax.axes.set_xticklabels(labels)\n",
    "plt.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(direction='out', length=15, width=4)\n",
    "plt.xlabel(\"Date\",fontsize=36)\n",
    "plt.ylabel(\"# of comments\",fontsize=36);"
   ]
  }
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