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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Analyzing State-by-State Changes In Earthquake Frequency"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from matplotlib import pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Load and clean up data\n",
      "\n",
      "The data was obtained from USGS here: http://earthquake.usgs.gov/earthquakes/search/. It was then run through a PostGIS database to determine the location of the epicenters by state, since USGS does provide specific state locations in its data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_quakes = pd.DataFrame.from_csv(\"../data/earthquake_states.csv\", index_col=None, parse_dates=[\"time\", \"updated\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some earthquakes within the U.S. bounding box don't have epicenters outside any state (e.g., the ocean)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "us_quakes = all_quakes.dropna(subset=[\"state\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_counts = pd.DataFrame(us_quakes.state.value_counts())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_counts.columns = [\"total_number_of_earthquakes\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_counts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>total_number_of_earthquakes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 18108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td> 12326</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nevada</th>\n",
        "      <td>  1975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Idaho</th>\n",
        "      <td>  1231</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Washington</th>\n",
        "      <td>   973</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oklahoma</th>\n",
        "      <td>   899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Montana</th>\n",
        "      <td>   726</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Wyoming</th>\n",
        "      <td>   714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hawaii</th>\n",
        "      <td>   640</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Utah</th>\n",
        "      <td>   559</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oregon</th>\n",
        "      <td>   376</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td>   174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Mexico</th>\n",
        "      <td>   165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td>   147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <td>   143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
        "      <td>   130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Missouri</th>\n",
        "      <td>    92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tennessee</th>\n",
        "      <td>    81</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kansas</th>\n",
        "      <td>    61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Illinois</th>\n",
        "      <td>    61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maine</th>\n",
        "      <td>    36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td>    35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
        "      <td>    35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kentucky</th>\n",
        "      <td>    33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>South Carolina</th>\n",
        "      <td>    32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nebraska</th>\n",
        "      <td>    29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>South Dakota</th>\n",
        "      <td>    29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Virginia</th>\n",
        "      <td>    28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td>    22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Indiana</th>\n",
        "      <td>    20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>North Carolina</th>\n",
        "      <td>    17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pennsylvania</th>\n",
        "      <td>    14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Georgia</th>\n",
        "      <td>    14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Hampshire</th>\n",
        "      <td>    13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Massachusetts</th>\n",
        "      <td>    10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Jersey</th>\n",
        "      <td>    10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mississippi</th>\n",
        "      <td>     9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>West Virginia</th>\n",
        "      <td>     8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Connecticut</th>\n",
        "      <td>     5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Louisiana</th>\n",
        "      <td>     5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Minnesota</th>\n",
        "      <td>     5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>North Dakota</th>\n",
        "      <td>     3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Michigan</th>\n",
        "      <td>     3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maryland</th>\n",
        "      <td>     2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Florida</th>\n",
        "      <td>     1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Iowa</th>\n",
        "      <td>     1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                total_number_of_earthquakes\n",
        "California                            18108\n",
        "Alaska                                12326\n",
        "Nevada                                 1975\n",
        "Idaho                                  1231\n",
        "Washington                              973\n",
        "Oklahoma                                899\n",
        "Montana                                 726\n",
        "Wyoming                                 714\n",
        "Hawaii                                  640\n",
        "Utah                                    559\n",
        "Oregon                                  376\n",
        "Arizona                                 174\n",
        "New Mexico                              165\n",
        "Arkansas                                147\n",
        "Colorado                                143\n",
        "Texas                                   130\n",
        "Missouri                                 92\n",
        "Tennessee                                81\n",
        "Kansas                                   61\n",
        "Illinois                                 61\n",
        "Maine                                    36\n",
        "Alabama                                  35\n",
        "New York                                 35\n",
        "Kentucky                                 33\n",
        "South Carolina                           32\n",
        "Nebraska                                 29\n",
        "South Dakota                             29\n",
        "Virginia                                 28\n",
        "Ohio                                     22\n",
        "Indiana                                  20\n",
        "North Carolina                           17\n",
        "Pennsylvania                             14\n",
        "Georgia                                  14\n",
        "New Hampshire                            13\n",
        "Massachusetts                            10\n",
        "New Jersey                               10\n",
        "Mississippi                               9\n",
        "West Virginia                             8\n",
        "Connecticut                               5\n",
        "Louisiana                                 5\n",
        "Minnesota                                 5\n",
        "North Dakota                              3\n",
        "Michigan                                  3\n",
        "Maryland                                  2\n",
        "Florida                                   1\n",
        "Iowa                                      1"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Charting Earthquake Activity Over Time, By State"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"Oklahoma\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot()\n",
      "ax.set_title(\"Oklahoma Earthquake Count By Year\")\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rYcIE+Lu/gw8+6H75ZcvC6W4///l0c6hmLiLSA+7wve+FvewZM+Bb3wo/zf/Z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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111c41350>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"California\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot()\n",
      "ax.set_title(\"California Earthquake Count By Year\")\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tceHaohGnthA7s8bvgnnyIjISGAxsgPnvFwIPA/cB3wKmAQer6oKw/3nAscBK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\ndOtmjUr33VeaplSDazYOOcR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WR2U/4I6w2x00lsMw7Md7hapOwwLpoEqUXbm0pZ2ynN/T\nkrSp6leq+jywLP08lfrMFUtNB/kcvC0iqZ62BwGbZtnnYGBkxro7wiPg+dXUpapjsMA5C5gGXKmq\nC7AP2/S042dQQueyCmtLUekyy6kNmAjsJyKtRaQ3sD3QkwSUWw5t6Z/DipWbiPTCnjheBrqp6pyw\naQ7QLcxvwupllOq4mLm+rGXXBG2bpC1XpOyK1JYis6Gzmp+5NWiJQf5Y4CQRGY89gi1P3ygig4Cv\nVHVy2urDVbU/9ki+m4gcWS1dInIEZoV0B3oDvw2BoZpE0VaNMsupDbgN+2KNB64GXgBWUWSnvJi0\nQQXLTUTWAx4ATlPVRenb1HyE2LIwyqStImWX5HIrhhYX5FX1XVXdW1V3wCyZDzN2ORS4N+OYmeHv\n4rBtxyro+iBs2gV4UFVXhUf757Ga33SsZpqiJ1ZDKDslatshHFPxMsuh7cOwfpWqnqGqA1V1f6Az\n8B7WRyOuciukrWLlJiJtsEB1l6o+FFbPEZGNw/buwNywfgarP1n0xD5vM6hA2ZVB2wyoTNmVqC0X\nFSm3YmlxQV5ENgx/WwHnAzelbWuFPVb/X9q61qlW+vAP/wkwiTKTRdfNYdMUYI+wbV1gJ2CKqs7G\nvPlBIiLAkcBDa5y4+treqVaZ5dB2U1heJ2hCRH4IrFDVKao6i/jKLa+2SpVbuM9bgcmqek3aplHA\n0WH+aBrLYRRwqFgWUG+gL/BKJT5z5dJWibKLoO2bQ9MXqvmZy0q1WnjjmDBffSb2mPwp9vh8KtZK\n/i7wx4z964AXMta1xx6rJwJvYY/XUi1dwNrA3dgH9m3gN2nbtg/rPwCurXaZ5dKGNcKWtcwiaOuF\n/QhNBp4ENk1QuWXVVsFy+z7QgGWlvBGmocD6WIPwe0FH57RjzgvlMwXYu1JlVy5tlSi7iNqmAZ8D\ni8LnYKtKfeaKnbwzlOM4Tg3T4uwax3GcloQHecdxnBrGg7zjOE4N40HecRynhvEg7ziOU8N4kHcc\nx6lhPMg7LRoR6SQiJ4b57iLyr7g1OU458Tx5p0UTBp56RFW3jVmK41SEqr3+z3ESyhVAHxF5A3gf\n2FpVtxWR4dhwsO2xrvNXAe2wYaiXAT9S1fki0ge4HhsX/ivgeFV9t/q34TjZcbvGaemcDXyoqgOB\nMzO2bQP8FPge8AfgS1XdDngROCrscwtwitogZGcCN1ZFteMUidfknZaO5JgHGKeqS4AlIrIAeCSs\nnwR8JwwytgvwLxt3CoC2lRTrOKXiQd5xcpP+hp+GtOUG7LvTCpgfngIcJ5G4XeO0dBYBHUo8RgDU\nXiAxVUR+BjY0rYSXvztOUvAg77RoVPVz4HkRmYS9oDmVbpb5xp/M+dTy4cBxIjIBG+J2v8oqdpzS\n8BRKx3GcGsZr8o7jODWMB3nHcZwaxoO84zhODeNB3nEcp4bxIO84jlPDeJB3HMepYTzIO47j1DAe\n5B3HcWqY/wcEBSKAS/TFCgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1116d3190>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"Texas\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot()\n",
      "ax.set_title(\"Texas Earthquake Count By Year\")\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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v3gAdl/vUUk8ul2PixIkAn8TLMGIVOorIIOBZVR3uTe8PfF9VjwysY4WORqKs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\nLJEs3Ft1D9iq8NWvwje+Ubr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fiV+zkQhrs57/AQ4AdgOmBea9CBzgfT8N+FGB7UYC/w1M\nTwZ2j3rcOJ9ytALNwCTv+8bA28BQb/oFYG/v+z+AsSnWmrbzeg7OKgMYCLwU2CZt57WY1lqc10HA\naO/7JrjyoR2Bq4HvefMvAq7yvu8ETAV6AI3ATNrflhM9t1XWmui5jaG1FzAG+Abwq7x9JX7NRvlE\nzrBV9SlgSd7sT3nzAR4HvlRg0xNwDWuCJNpvWpla5wO9xbXe7A2sBj4QkS2BPqrq93F3G1D1Pueq\noTWwXZrO6464GxJVXQgsFZG9UnpeC2kN1lBO+ry+p6pTve8rgP/g2jUcDdzqrXYr7efpC7gH9xpV\nnY0LgvvU4txWS2tgl4md23K1quoqVZ0CdOiQtlbXbBQq7fzpNRHxWzh+GShU2/krQH6D6Fu9V6Af\nVnj8ciioVVUfwQW9+cBs4BpVXYr7x84LbP8OMRoH1UirT2rOK/Bv4GgR6S4iw4E9gK1J4XkN0Rq8\nlmt2XkWkEfdm8Dywhaq2eotagS2871vR8Rz6Ddfy5yd6bivQulVguibnNqJWn/yCvXpesx2oNGCf\nDpwtIi/hXjk6NHoWkX2AVar6emD2iao6EvfKeoCInFyhhoq0ishJOHthS2A4cKF309aTOFpTdV6B\nm3EX+UvAL4BngHVUqdFVTMrVCjU8ryKyCfBn4Nuqujy4TN27eGpqCFRJa03ObZbOaykqCtiq+qaq\nHq6qe+Jsj/zOF8cDd+Vt8673d4W3rCbDdBbQOtNbtB/wV1Vd570OT8FlWPNwGaHP1rgna9q07ult\nk5bzOsubv05Vz1fV3VT1GKABmIGrt5+W81pKa83Oq4j0wAWV21X1fm92q4gM8pZvCSzw5r9DxzeA\nrXHX6zvU4NxWQes7UJtzW6bWMGpyXqNQUcAWkYHe327AD4HfBpZ1w7123h2Y190vCfZO5FHANGpA\nAa2/8xa9ARzsLesN7Au8oarv4bzsfUREgJOB+9fbcf21/idl5/W33vTGnkZE5DBgjaq+oarzSc95\nLaq1VufVOw83Aa+r6nWBRX8DTvW+n0r7efobMF5c7ZbhwKeAF2pxzVZLay3ObQytn2wanKjnNbse\nUUsncT70u7jXyBbc6+W5uJLXN4Gf5K3fBDyTN68X7rXz38B03OunxCktrZZWYEPgDtzF8hpwQWDZ\nHt78mcAvq62zWlpxBZBpO6+NuAfM68CjwJAUn9eCWmt4XvcH2nC1KV7xPmOBzXCFozM8XQ2BbSZ4\n5+8N4PBandtqaa3FuY2pdTawCFjuXTc71OqajfKxhjOGYRgZoVMOEWYYhtEZsYBtGIaRESxgG4Zh\nZAQL2IZhGBnBArZhGEZGsIBtGIaRESxgG50GEeknImd537cUkXvrrckwqonVwzY6DV4HPw+q6qg6\nSzGMRIg1RJhhpJSrgBEi8grwX2BHVR0lIs247jB74ZpG/xzYCNf178fA51V1iYiMAH6N6xN7FfB1\nVX2z9j/DMApjlojRmbgImKWquwHfzVu2M3AssBdwJfCBqu4OPAuc4q1zI/AtdZ1DfRf4TU1UG0ZE\nLMM2OhMS8h1gsqquBFaKyFLgQW/+NGAXr/On/YB7Xf8+APRMUqxhlIsFbKOrEBxFpC0w3Ya7D7oB\nS7zs3DBSiVkiRmdiOdCnzG0EQF3H9m+LyHHguuYUb+Bow0gLFrCNToOqLgKmiBsp/WraRxLJH1Uk\n/7s/fSLwVRGZiuvy8+hkFRtGeVi1PsMwjIxgGbZhGEZGsIBtGIaRESxgG4ZhZAQL2IZhGBnBArZh\nGEZGsIBtGIaRESxgG4ZhZAQL2IZhGBnh/wOA8QSZX7u5mwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d549c50>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"Ohio\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot(color=\"b\")\n",
      "ax.set_title(\"Ohio Earthquake Count By Year\")\n",
      "ax.set_ylim([0,5])\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L4rbDfNRH/gMDvR+TNWvCaIx+x/SnwtDe+ddrRd9wQxg2U3x556AbUOpxp5zY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\nAzez8QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d2c3b50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"Colorado\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot()\n",
      "ax.set_title(\"Colorado Earthquake Count By Year\")\n",
      "ax.set_ylim([0,25])\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9dli1CrrVKN11D9txHKdK2tpg001t8MzSpY3REHTAbma/KxRC1weuMStcYzYU0rh0qQXr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\nPGA7juM0CR6wHcdxmgQP2I7jOE3C/weeo58kDxgn7gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e7af490>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"Tennessee\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot()\n",
      "ax.set_title(\"Tennessee Earthquake Count By Year\")\n",
      "ax.set_ylim([0,10])\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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m/2VRuc8sGhsFbOi2ReL64k5KUKMx0S+l44+3n8tbt4bvp0xLpFU97DBbq6oc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\npUXQgK0oitIi/B/Dp5oYFq0BJQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f319610>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"Kentucky\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot()\n",
      "ax.set_title(\"Kentucky Earthquake Count By Year\")\n",
      "ax.set_ylim([0,10])\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/dPs3Wvstf3FBJXbe2TU+NmsypOQNK1nLzkvWkY7QXEsk+UQxapTVstNQXqkZ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\nNgzDaBH+P04RT3CL0xtUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f53d2d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"Kansas\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot()\n",
      "ax.set_title(\"Kansas Earthquake Count By Year\")\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7ulYiUkt0WF8exo8PDfUFF8A550CPHnDzzXDHHfDXv+a3jSTlwSqtlmIFxVvt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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f73cc10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = us_quakes[us_quakes[\"state\"] == \"Arkansas\"].set_index(\"time\")[\"id\"].resample(\"A\", how=\"count\").plot()\n",
      "ax.set_title(\"Arkansas Earthquake Count By Year\")\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lwYLcXtVXLIOd8SW8KRgK3OB9HwzcAQxMlrGmpobKykoAKioqqKqqWhPHil9t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\ngcMPT1/HNde4IVrZeGJxhg9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mJR85EJLGdb4dM1Fyg62qX6nqr1V1T1zY45uELCcBjyWU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\nwB+JAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f8b0110>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Calculating Percentage Change Decade Over Decade\n",
      "\n",
      "The most recent complete year of earthquakes is 2014. Below, we compare 2005-2014 to the prior decade, 1995-2004."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def quake_percentage_change(state):\n",
      "    by_year = pd.DataFrame(us_quakes[us_quakes[\"state\"] == state].set_index(\"time\")[\"id\"].resample(\"AS\", how=\"count\"))\n",
      "    by_year[\"start\"] = by_year.index\n",
      "    by_year[\"year\"] = by_year[\"start\"].apply(lambda x: x.year)\n",
      "    decade_05_14 = by_year[(by_year[\"year\"] >= 2005) & (by_year[\"year\"] <= 2014)]\n",
      "    total_05_14 = decade_05_14[\"id\"].sum()\n",
      "    decade_95_04 = by_year[(by_year[\"year\"] >= 1995) & (by_year[\"year\"] <= 2004)]\n",
      "    total_95_04 = decade_95_04[\"id\"].sum()\n",
      "    if total_95_04 != 0:\n",
      "        pct = round(100.0 * (total_05_14 - total_95_04) / total_95_04, 2)\n",
      "    else:\n",
      "        pct = None\n",
      "    return pct, total_05_14, total_95_04"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_counts[\"name\"] = state_counts.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_counts[\"percentage_change\"], state_counts[\"total_05-14\"], state_counts[\"total_95-04\"] =\\\n",
      "    zip(*state_counts[\"name\"].apply(lambda x: quake_percentage_change(x)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The overall percentage change in the United States decade-over-decade:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "round(100.0 * (state_counts[\"total_05-14\"].sum() - state_counts[\"total_95-04\"].sum()) / state_counts[\"total_95-04\"].sum(), 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "-6.56"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "States with at least 5 earthquakes from 1995-2004 (sorted by percentage change decade-over-decade):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_counts[state_counts[\"total_95-04\"] >= 5].sort(\"percentage_change\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>total_number_of_earthquakes</th>\n",
        "      <th>name</th>\n",
        "      <th>percentage_change</th>\n",
        "      <th>total_05-14</th>\n",
        "      <th>total_95-04</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Oklahoma</th>\n",
        "      <td>   899</td>\n",
        "      <td>     Oklahoma</td>\n",
        "      <td> 6042.86</td>\n",
        "      <td>  860</td>\n",
        "      <td>   14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arkansas</th>\n",
        "      <td>   147</td>\n",
        "      <td>     Arkansas</td>\n",
        "      <td>  677.78</td>\n",
        "      <td>   70</td>\n",
        "      <td>    9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kansas</th>\n",
        "      <td>    61</td>\n",
        "      <td>       Kansas</td>\n",
        "      <td>     650</td>\n",
        "      <td>   45</td>\n",
        "      <td>    6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
        "      <td>   130</td>\n",
        "      <td>        Texas</td>\n",
        "      <td>  252.38</td>\n",
        "      <td>   74</td>\n",
        "      <td>   21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hawaii</th>\n",
        "      <td>   640</td>\n",
        "      <td>       Hawaii</td>\n",
        "      <td>  233.33</td>\n",
        "      <td>  240</td>\n",
        "      <td>   72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Illinois</th>\n",
        "      <td>    61</td>\n",
        "      <td>     Illinois</td>\n",
        "      <td>     200</td>\n",
        "      <td>   15</td>\n",
        "      <td>    5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
        "      <td>   174</td>\n",
        "      <td>      Arizona</td>\n",
        "      <td>  195.45</td>\n",
        "      <td>   65</td>\n",
        "      <td>   22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Virginia</th>\n",
        "      <td>    28</td>\n",
        "      <td>     Virginia</td>\n",
        "      <td>     160</td>\n",
        "      <td>   13</td>\n",
        "      <td>    5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <td>   143</td>\n",
        "      <td>     Colorado</td>\n",
        "      <td>   97.14</td>\n",
        "      <td>   69</td>\n",
        "      <td>   35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Mexico</th>\n",
        "      <td>   165</td>\n",
        "      <td>   New Mexico</td>\n",
        "      <td>   46.15</td>\n",
        "      <td>   57</td>\n",
        "      <td>   39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nevada</th>\n",
        "      <td>  1975</td>\n",
        "      <td>       Nevada</td>\n",
        "      <td>   19.38</td>\n",
        "      <td>  536</td>\n",
        "      <td>  449</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>South Dakota</th>\n",
        "      <td>    29</td>\n",
        "      <td> South Dakota</td>\n",
        "      <td>   14.29</td>\n",
        "      <td>    8</td>\n",
        "      <td>    7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Washington</th>\n",
        "      <td>   973</td>\n",
        "      <td>   Washington</td>\n",
        "      <td>    8.28</td>\n",
        "      <td>  183</td>\n",
        "      <td>  169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Wyoming</th>\n",
        "      <td>   714</td>\n",
        "      <td>      Wyoming</td>\n",
        "      <td>    5.41</td>\n",
        "      <td>  156</td>\n",
        "      <td>  148</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maine</th>\n",
        "      <td>    36</td>\n",
        "      <td>        Maine</td>\n",
        "      <td>       0</td>\n",
        "      <td>    6</td>\n",
        "      <td>    6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nebraska</th>\n",
        "      <td>    29</td>\n",
        "      <td>     Nebraska</td>\n",
        "      <td>       0</td>\n",
        "      <td>    5</td>\n",
        "      <td>    5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Montana</th>\n",
        "      <td>   726</td>\n",
        "      <td>      Montana</td>\n",
        "      <td>    -2.6</td>\n",
        "      <td>  150</td>\n",
        "      <td>  154</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alaska</th>\n",
        "      <td> 12326</td>\n",
        "      <td>       Alaska</td>\n",
        "      <td>  -14.26</td>\n",
        "      <td> 3486</td>\n",
        "      <td> 4066</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Utah</th>\n",
        "      <td>   559</td>\n",
        "      <td>         Utah</td>\n",
        "      <td>  -17.61</td>\n",
        "      <td>  117</td>\n",
        "      <td>  142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Missouri</th>\n",
        "      <td>    92</td>\n",
        "      <td>     Missouri</td>\n",
        "      <td>     -20</td>\n",
        "      <td>   12</td>\n",
        "      <td>   15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alabama</th>\n",
        "      <td>    35</td>\n",
        "      <td>      Alabama</td>\n",
        "      <td>     -25</td>\n",
        "      <td>    9</td>\n",
        "      <td>   12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tennessee</th>\n",
        "      <td>    81</td>\n",
        "      <td>    Tennessee</td>\n",
        "      <td>  -26.32</td>\n",
        "      <td>   14</td>\n",
        "      <td>   19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oregon</th>\n",
        "      <td>   376</td>\n",
        "      <td>       Oregon</td>\n",
        "      <td>  -33.85</td>\n",
        "      <td>   43</td>\n",
        "      <td>   65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>California</th>\n",
        "      <td> 18108</td>\n",
        "      <td>   California</td>\n",
        "      <td>  -34.53</td>\n",
        "      <td> 2490</td>\n",
        "      <td> 3803</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Idaho</th>\n",
        "      <td>  1231</td>\n",
        "      <td>        Idaho</td>\n",
        "      <td>  -35.92</td>\n",
        "      <td>   91</td>\n",
        "      <td>  142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Indiana</th>\n",
        "      <td>    20</td>\n",
        "      <td>      Indiana</td>\n",
        "      <td>     -40</td>\n",
        "      <td>    3</td>\n",
        "      <td>    5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kentucky</th>\n",
        "      <td>    33</td>\n",
        "      <td>     Kentucky</td>\n",
        "      <td>     -40</td>\n",
        "      <td>    3</td>\n",
        "      <td>    5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
        "      <td>    35</td>\n",
        "      <td>     New York</td>\n",
        "      <td>  -64.29</td>\n",
        "      <td>    5</td>\n",
        "      <td>   14</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "              total_number_of_earthquakes          name percentage_change  \\\n",
        "Oklahoma                              899      Oklahoma           6042.86   \n",
        "Arkansas                              147      Arkansas            677.78   \n",
        "Kansas                                 61        Kansas               650   \n",
        "Texas                                 130         Texas            252.38   \n",
        "Hawaii                                640        Hawaii            233.33   \n",
        "Illinois                               61      Illinois               200   \n",
        "Arizona                               174       Arizona            195.45   \n",
        "Virginia                               28      Virginia               160   \n",
        "Colorado                              143      Colorado             97.14   \n",
        "New Mexico                            165    New Mexico             46.15   \n",
        "Nevada                               1975        Nevada             19.38   \n",
        "South Dakota                           29  South Dakota             14.29   \n",
        "Washington                            973    Washington              8.28   \n",
        "Wyoming                               714       Wyoming              5.41   \n",
        "Maine                                  36         Maine                 0   \n",
        "Nebraska                               29      Nebraska                 0   \n",
        "Montana                               726       Montana              -2.6   \n",
        "Alaska                              12326        Alaska            -14.26   \n",
        "Utah                                  559          Utah            -17.61   \n",
        "Missouri                               92      Missouri               -20   \n",
        "Alabama                                35       Alabama               -25   \n",
        "Tennessee                              81     Tennessee            -26.32   \n",
        "Oregon                                376        Oregon            -33.85   \n",
        "California                          18108    California            -34.53   \n",
        "Idaho                                1231         Idaho            -35.92   \n",
        "Indiana                                20       Indiana               -40   \n",
        "Kentucky                               33      Kentucky               -40   \n",
        "New York                               35      New York            -64.29   \n",
        "\n",
        "              total_05-14  total_95-04  \n",
        "Oklahoma              860           14  \n",
        "Arkansas               70            9  \n",
        "Kansas                 45            6  \n",
        "Texas                  74           21  \n",
        "Hawaii                240           72  \n",
        "Illinois               15            5  \n",
        "Arizona                65           22  \n",
        "Virginia               13            5  \n",
        "Colorado               69           35  \n",
        "New Mexico             57           39  \n",
        "Nevada                536          449  \n",
        "South Dakota            8            7  \n",
        "Washington            183          169  \n",
        "Wyoming               156          148  \n",
        "Maine                   6            6  \n",
        "Nebraska                5            5  \n",
        "Montana               150          154  \n",
        "Alaska               3486         4066  \n",
        "Utah                  117          142  \n",
        "Missouri               12           15  \n",
        "Alabama                 9           12  \n",
        "Tennessee              14           19  \n",
        "Oregon                 43           65  \n",
        "California           2490         3803  \n",
        "Idaho                  91          142  \n",
        "Indiana                 3            5  \n",
        "Kentucky                3            5  \n",
        "New York                5           14  "
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}