day1.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:0bd7da1f754f9a0dbae5b7b4cd36dd8f3d8e22ab059325a84863bbe78c7623d3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import collections as col\n",
      "import datetime\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Read the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('donations_sample.csv', index_col=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Organize data for plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "obadonations = col.defaultdict(lambda: 0)\n",
      "macdonations = col.defaultdict(lambda: 0)\n",
      "for i in range(len(df)):\n",
      "    \n",
      "    if \"Obama\" in df.ix[i, 'cand_nm']:\n",
      "        datestr = df.ix[i, 'contb_receipt_dt']\n",
      "        date = datetime.datetime.strptime(datestr, '%d-%b-%y')\n",
      "        \n",
      "        money = float(df.ix[i, 'contb_receipt_amt'])\n",
      "        obadonations[date] += float(df.ix[i, 'contb_receipt_amt'])\n",
      "        \n",
      "    if \"McCain\" in df.ix[i, 'cand_nm']:\n",
      "        datestr = df.ix[i, 'contb_receipt_dt']\n",
      "        date = datetime.datetime.strptime(datestr, '%d-%b-%y')\n",
      "        money = float(df.ix[i, 'contb_receipt_amt'])\n",
      "        macdonations[date] += float(df.ix[i, 'contb_receipt_amt'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Make into two lists"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sorted_by_date =  sorted(obadonations.items(), key=lambda (k,v): k)\n",
      "xdate, amt = zip(* sorted_by_date)\n",
      "\n",
      "asorted_by_date =  sorted(macdonations.items(), key=lambda (k,v): k)\n",
      "adate, aamt = zip(* asorted_by_date)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "plt.figure(figsize=(12,5))\n",
      "plt.plot(xdate, np.cumsum(amt).tolist())\n",
      "plt.plot(adate, np.cumsum(aamt).tolist())\n",
      "plt.legend(['Obama', 'McCain'], loc='upper center')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "<matplotlib.legend.Legend at 0x10cf8d3d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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m7L0+Bi73HV8JLLTWZlhrM4BFOEFdRERERFxUXAyffgrXXut2JfVHZXqqXwIeAbxl2lpY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\n16nH4PSilj0u77zKXPPp9k9ZvHux8zd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2zSQ9Bj7T41exeTb/CODvrZRzJfBpRrOF+Bti\nPM8S1+fWTJ4G0mzkFcA8YEAmbQDwVrX1KaHZcNIksGaS/9kjGX2GAM96/1mKT3AjGbObvS8uBg7b\nXTWkduzdZOD+autV1Mbc2zzgl6TBzCLSSbt7tXXL7Fvu7B3pxt9drlkzcFO1dWqlvbm3eXnVkRqw\nd6SJoTMz+/HTcm2Kj78EQQncNeVPZja62vuyKyNpLvBrM1tQ7X0Jgt2VsHc7j7B5uy415f4RBDsL\nSVNIbilXVntfgiAI2pOwd0HQNsSd6iAIgiAIgiCokLhTHQRBEARBEAQVEoPqIAiCIAiCIKiQGFQH\nQRAEQRAEQYXEoDoIgiAIgiAIKiQG1UEQBEEQBEFQITGoDoIgCIIgCIIK+T+zCghsCFs9RwAAAABJ\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cfc9550>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}