Austin_311_2014_2016.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Austin 311 Call Analysis from 2014 - 2017\n",
    "-----\n",
    "- The cleaned file csv file of Austin 311 calls, \"All_Austin_311.csv\" was loaded and used to do this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib.patches import Polygon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first analysis was to do the number of calls by Department and Complaint Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>owning_department</th>\n",
       "      <th>complaint_description</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78741</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Dog</td>\n",
       "      <td>ACLONAG</td>\n",
       "      <td>30.224549</td>\n",
       "      <td>-97.690675</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78757</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Dog</td>\n",
       "      <td>ACLONAG</td>\n",
       "      <td>30.350881</td>\n",
       "      <td>-97.747492</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78744</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Animal (not dog)</td>\n",
       "      <td>ACLOANIM</td>\n",
       "      <td>30.199263</td>\n",
       "      <td>-97.711366</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78727</td>\n",
       "      <td>Austin Code Department</td>\n",
       "      <td>Austin Code - Request Code Officer</td>\n",
       "      <td>CODECOMP</td>\n",
       "      <td>30.425112</td>\n",
       "      <td>-97.707188</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78723</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Animal - Proper Care</td>\n",
       "      <td>ACPROPER</td>\n",
       "      <td>30.311821</td>\n",
       "      <td>-97.669302</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   incident_zip       owning_department               complaint_description  \\\n",
       "0         78741  Animal Services Office                           Loose Dog   \n",
       "1         78757  Animal Services Office                           Loose Dog   \n",
       "2         78744  Animal Services Office              Loose Animal (not dog)   \n",
       "3         78727  Austin Code Department  Austin Code - Request Code Officer   \n",
       "4         78723  Animal Services Office                Animal - Proper Care   \n",
       "\n",
       "  complaint_type   latitude  longitude  year  month  \n",
       "0        ACLONAG  30.224549 -97.690675  2015      1  \n",
       "1        ACLONAG  30.350881 -97.747492  2016      5  \n",
       "2       ACLOANIM  30.199263 -97.711366  2014      1  \n",
       "3       CODECOMP  30.425112 -97.707188  2014     11  \n",
       "4       ACPROPER  30.311821 -97.669302  2014      7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the csv file that was downloaded from the City of Austin: \"https://data.austintexas.gov/\"\n",
    "Austin_311_df = pd.read_csv('data/All_Austin_311.csv',low_memory=False)\n",
    "\n",
    "Austin_311_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tom Berton analyzed the number of 311 calls over 3 years, from 2014-2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>owning_department</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austin Code Department</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        owning_department  year  month\n",
       "0  Animal Services Office  2015      1\n",
       "1  Animal Services Office  2016      5\n",
       "2  Animal Services Office  2014      1\n",
       "3  Austin Code Department  2014     11\n",
       "4  Animal Services Office  2014      7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the Type of Complaint, Complaint Description, and Date since they are not needed.\n",
    "datetime_austin311 = Austin_311_df.drop([\"complaint_description\",\n",
    "                                         \"complaint_type\", \"incident_zip\",\n",
    "                                        \"latitude\", \"longitude\"], axis=1)\n",
    "datetime_austin311.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Number of Complaints</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>8192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Number of Complaints\n",
       "Year Month                      \n",
       "2013 12                       44\n",
       "2014 1                      8192\n",
       "     2                      7466\n",
       "     3                      9253\n",
       "     4                     10751"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pivot the table to make 'Year' the index and get the counts for Department \n",
    "# and change the name of Department to Number of Complaints.\n",
    "\n",
    "datetime_austin311 = datetime_austin311.rename(columns={\"owning_department\":\"Number of Complaints\",\n",
    "                                                     \"year\":\"Year\", \"month\":\"Month\"})\n",
    "austin311_by_year = datetime_austin311.pivot_table(datetime_austin311, index=['Year','Month'], aggfunc='count')\n",
    "\n",
    "austin311_by_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Number of Complaints</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>8192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2015</th>\n",
       "      <th>1</th>\n",
       "      <td>8356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>8379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Number of Complaints\n",
       "Year Month                      \n",
       "2014 1                      8192\n",
       "     2                      7466\n",
       "     3                      9253\n",
       "     4                     10751\n",
       "     5                     10124\n",
       "     6                     11943\n",
       "     7                     11591\n",
       "     8                     11620\n",
       "     9                     11649\n",
       "     10                    10951\n",
       "     11                     8407\n",
       "     12                     8417\n",
       "2015 1                      8356\n",
       "     2                      7745\n",
       "     3                      9929\n",
       "     4                     11021\n",
       "     5                     13462\n",
       "     6                     14552\n",
       "     7                     12991\n",
       "     8                     11763\n",
       "     9                     10778\n",
       "     10                    11487\n",
       "     11                     9273\n",
       "     12                     8007\n",
       "2016 1                      8379\n",
       "     2                      8069\n",
       "     3                      9618\n",
       "     4                     10702\n",
       "     5                     11843\n",
       "     6                     10828\n",
       "     7                      9082\n",
       "     8                     11141\n",
       "     9                     11616\n",
       "     10                     9749\n",
       "     11                     8387\n",
       "     12                     7658"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the year 2013 since it has only one month, December and 2107 since it only has 8 months, Jan - Aug.\n",
    "\n",
    "austin311_by_year.drop(austin311_by_year.index[0],inplace=True)\n",
    "austin311_by_year.drop(austin311_by_year.index[36:],inplace=True)\n",
    "austin311_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>8192</td>\n",
       "      <td>8356</td>\n",
       "      <td>8379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7466</td>\n",
       "      <td>7745</td>\n",
       "      <td>8069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9253</td>\n",
       "      <td>9929</td>\n",
       "      <td>9618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>10751</td>\n",
       "      <td>11021</td>\n",
       "      <td>10702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10124</td>\n",
       "      <td>13462</td>\n",
       "      <td>11843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>11943</td>\n",
       "      <td>14552</td>\n",
       "      <td>10828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>11591</td>\n",
       "      <td>12991</td>\n",
       "      <td>9082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>11620</td>\n",
       "      <td>11763</td>\n",
       "      <td>11141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>11649</td>\n",
       "      <td>10778</td>\n",
       "      <td>11616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10951</td>\n",
       "      <td>11487</td>\n",
       "      <td>9749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>8407</td>\n",
       "      <td>9273</td>\n",
       "      <td>8387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>8417</td>\n",
       "      <td>8007</td>\n",
       "      <td>7658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Year  Month   2014   2015   2016\n",
       "0         1   8192   8356   8379\n",
       "1         2   7466   7745   8069\n",
       "2         3   9253   9929   9618\n",
       "3         4  10751  11021  10702\n",
       "4         5  10124  13462  11843\n",
       "5         6  11943  14552  10828\n",
       "6         7  11591  12991   9082\n",
       "7         8  11620  11763  11141\n",
       "8         9  11649  10778  11616\n",
       "9        10  10951  11487   9749\n",
       "10       11   8407   9273   8387\n",
       "11       12   8417   8007   7658"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pivot the df so 'Month' is the index \n",
    "\n",
    "austin311_by_month = austin311_by_year.pivot_table('Number of Complaints', ['Month'],'Year' )\n",
    "\n",
    "#Reset the index to and save to .cvs file.\n",
    "austin311_reset = austin311_by_month.reset_index()\n",
    "\n",
    "#Save the file as a .csv\n",
    "austin311_reset.to_csv('data/austin311_year_month.csv', encoding='utf-8', index=False)\n",
    "\n",
    "austin311_reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2014, 2015, 2016], dtype='int64', name='Year')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the number of calls by year with the months on the x-axis. Find out what if the year is the index or not. \n",
    "years = austin311_by_month.keys()\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118755550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vj9RoHkFE9iql6qSmjQMHDpytWbNmtnK8PXDgQJGaNWv6xXfM0SHrbcAEEXkf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\npZQX0ISslFJKeYH/AwBY3XRjCLbnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11873ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of calls by year.\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plot = austin311_by_month.plot(kind='line')\n",
    "plt.xticks(austin311_by_month.index)\n",
    "\n",
    "plt.xlabel(\"Months\", fontsize=14)\n",
    "plt.ylabel(\"Number of Calls\", fontsize=14)\n",
    "plt.title(\"Number of 311 Calls 2014-2016\", fontsize=16)\n",
    "plt.legend(bbox_to_anchor=(1.05,1),loc= 2, borderaxespad = 0.,title=\"Year\")\n",
    "\n",
    "fig1 = plt.gcf()\n",
    "plt.show()\n",
    "fig1.savefig('images/Number_311_calls.png', bbox_inches='tight', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}