Data_Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at political contribution to better understand distribution of giving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#connect to database\n",
    "connection = sqlite3.connect(\"contributions.db\")\n",
    "cursor = connection.cursor()\n",
    "\n",
    "#start will all individual donations to members of congress\n",
    "cursor.execute(\"select amount from contrib_legislators\")\n",
    "results = cursor.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3949590\n"
     ]
    }
   ],
   "source": [
    "#put results (first item of returned tuples) into a list\n",
    "res_list = []\n",
    "for res in results:\n",
    "    res_list.append(res[0])\n",
    "print len(res_list)\n",
    "\n",
    "res_sorted = sorted(res_list)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  1208.07391172\n",
      "Median:  500.0\n",
      "25th pct: 250.000000\n",
      "50th pct: 500.000000\n",
      "75th pct: 1000.000000\n",
      "90th: 2400.000000\n",
      "95th: 2500.000000\n",
      "99th: 5000.000000\n",
      "Min: -207732\n",
      "Max: 5000000\n"
     ]
    }
   ],
   "source": [
    "# get mean, median, percentiles\n",
    "\n",
    "a = np.array(res_list)\n",
    "\n",
    "me = np.mean(a)\n",
    "print \"Mean: \", me\n",
    "\n",
    "md = np.median(a)\n",
    "print \"Median: \", md\n",
    "\n",
    "pct_25=np.percentile(a, 25)\n",
    "pct_50=np.percentile(a, 50)\n",
    "pct_75=np.percentile(a, 75)\n",
    "pct_90=np.percentile(a, 90)\n",
    "pct_95=np.percentile(a, 95)\n",
    "pct_99=np.percentile(a, 99)\n",
    "\n",
    "print \"25th pct: %f\\n50th pct: %f\\n75th pct: %f\\n90th: %f\\n95th: %f\\n99th: %f\" % (pct_25, pct_50, pct_75, pct_90, pct_95, pct_99)\n",
    "\n",
    "print \"Min: %d\" % (np.min(a))\n",
    "print \"Max: %d\" % (np.max(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Info on just individual contributions to Members of Congress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get indiv. donations to legislators\n",
    "cursor.execute(\"select contrib_legislators.amount from contrib_legislators JOIN contributors USING (contrib_id) where contributors.contrib_type = 'I'\")\n",
    "results = cursor.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2988349\n"
     ]
    }
   ],
   "source": [
    "#put results (first item of returned tuples) into a list\n",
    "indiv_list = []\n",
    "for res in results:\n",
    "    indiv_list.append(res[0])\n",
    "print len(indiv_list)\n",
    "\n",
    "indiv_sorted = sorted(indiv_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  779.667195498\n",
      "Median:  500.0\n",
      "25th pct: 250.000000\n",
      "50th pct: 500.000000\n",
      "75th pct: 1000.000000\n",
      "90th: 2000.000000\n",
      "95th: 2400.000000\n",
      "99th: 2600.000000\n",
      "Min: -150000\n",
      "Max: 2000000\n"
     ]
    }
   ],
   "source": [
    "# get mean, median, percentiles\n",
    "\n",
    "a = np.array(indiv_list)\n",
    "\n",
    "me = np.mean(a)\n",
    "print \"Mean: \", me\n",
    "\n",
    "md = np.median(a)\n",
    "print \"Median: \", md\n",
    "\n",
    "pct_25=np.percentile(a, 25)\n",
    "pct_50=np.percentile(a, 50)\n",
    "pct_75=np.percentile(a, 75)\n",
    "pct_90=np.percentile(a, 90)\n",
    "pct_95=np.percentile(a, 95)\n",
    "pct_99=np.percentile(a, 99)\n",
    "\n",
    "print \"25th pct: %f\\n50th pct: %f\\n75th pct: %f\\n90th: %f\\n95th: %f\\n99th: %f\" % (pct_25, pct_50, pct_75, pct_90, pct_95, pct_99)\n",
    "\n",
    "print \"Min: %d\" % (np.min(a))\n",
    "print \"Max: %d\" % (np.max(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "#count how many 1 million +\n",
    "great_mil = []\n",
    "\n",
    "for item in indiv_list:\n",
    "    if item >= 1000000:\n",
    "        great_mil.append(item)\n",
    "        \n",
    "print len(great_mil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "769\n"
     ]
    }
   ],
   "source": [
    "#find how many donations $10 thousand +\n",
    "great_tenthou = []\n",
    "\n",
    "for item in indiv_list:\n",
    "    if item >= 10000:\n",
    "        great_tenthou.append(item)\n",
    "        \n",
    "print len(great_tenthou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7111\n"
     ]
    }
   ],
   "source": [
    "#count how many 5 thousand +\n",
    "great_fivethou = []\n",
    "\n",
    "for item in indiv_list:\n",
    "    if item >= 5000:\n",
    "        great_fivethou.append(item)\n",
    "        \n",
    "print len(great_fivethou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1127285\n"
     ]
    }
   ],
   "source": [
    "#count how many one thousand +\n",
    "great_onethou = []\n",
    "\n",
    "for item in indiv_list:\n",
    "    if item >= 1000:\n",
    "        great_onethou.append(item)\n",
    "        \n",
    "print len(great_onethou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2545255\n"
     ]
    }
   ],
   "source": [
    "#count how many one thousand and below\n",
    "less_onethou = []\n",
    "\n",
    "for item in indiv_list:\n",
    "    if item <= 1000:\n",
    "        less_onethou.append(item)\n",
    "        \n",
    "print len(less_onethou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1Obe92tra1NraqqmpKVVWVqqiomL+ZwcAmHcuc/L53EVmdHRUVVVVusT/gHIuyJ+x7HA6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1114c4f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot histogram of individual contributions.  \n",
    "sns.set_style(\"white\")\n",
    "sns.despine(left=True)\n",
    "\n",
    "plt.hist(great_fivethou, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}