chap02ex01soln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.1: Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>\n",
    "Allen Downey\n",
    "\n",
    "Read the female respondent file and display the variables names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chap01soln.py: All tests passed.\n"
     ]
    }
   ],
   "source": [
    "%run chap01soln"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>caseid</th>\n",
       "      <th>rscrinf</th>\n",
       "      <th>rdormres</th>\n",
       "      <th>rostscrn</th>\n",
       "      <th>rscreenhisp</th>\n",
       "      <th>rscreenrace</th>\n",
       "      <th>age_a</th>\n",
       "      <th>age_r</th>\n",
       "      <th>cmbirth</th>\n",
       "      <th>agescrn</th>\n",
       "      <th>...</th>\n",
       "      <th>pubassis_i</th>\n",
       "      <th>basewgt</th>\n",
       "      <th>adj_mod_basewgt</th>\n",
       "      <th>finalwgt</th>\n",
       "      <th>secu_r</th>\n",
       "      <th>sest</th>\n",
       "      <th>cmintvw</th>\n",
       "      <th>cmlstyr</th>\n",
       "      <th>screentime</th>\n",
       "      <th>intvlngth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2298</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>902</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3247.916977</td>\n",
       "      <td>5123.759559</td>\n",
       "      <td>5556.717241</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1234</td>\n",
       "      <td>1222</td>\n",
       "      <td>18:26:36</td>\n",
       "      <td>110.492667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5012</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>718</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2335.279149</td>\n",
       "      <td>2846.799490</td>\n",
       "      <td>4744.191350</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1233</td>\n",
       "      <td>1221</td>\n",
       "      <td>16:30:59</td>\n",
       "      <td>64.294000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11586</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>708</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2335.279149</td>\n",
       "      <td>2846.799490</td>\n",
       "      <td>4744.191350</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1234</td>\n",
       "      <td>1222</td>\n",
       "      <td>18:19:09</td>\n",
       "      <td>75.149167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6794</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1042</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3783.152221</td>\n",
       "      <td>5071.464231</td>\n",
       "      <td>5923.977368</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1234</td>\n",
       "      <td>1222</td>\n",
       "      <td>15:54:43</td>\n",
       "      <td>28.642833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>616</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>991</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5341.329968</td>\n",
       "      <td>6437.335772</td>\n",
       "      <td>7229.128072</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1233</td>\n",
       "      <td>1221</td>\n",
       "      <td>14:19:44</td>\n",
       "      <td>69.502667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3087 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   caseid  rscrinf  rdormres  rostscrn  rscreenhisp  rscreenrace  age_a  \\\n",
       "0    2298        1         5         5            1          5.0     27   \n",
       "1    5012        1         5         1            5          5.0     42   \n",
       "2   11586        1         5         1            5          5.0     43   \n",
       "3    6794        5         5         4            1          5.0     15   \n",
       "4     616        1         5         4            1          5.0     20   \n",
       "\n",
       "   age_r  cmbirth  agescrn     ...      pubassis_i      basewgt  \\\n",
       "0     27      902       27     ...               0  3247.916977   \n",
       "1     42      718       42     ...               0  2335.279149   \n",
       "2     43      708       43     ...               0  2335.279149   \n",
       "3     15     1042       15     ...               0  3783.152221   \n",
       "4     20      991       20     ...               0  5341.329968   \n",
       "\n",
       "   adj_mod_basewgt     finalwgt  secu_r  sest  cmintvw  cmlstyr  screentime  \\\n",
       "0      5123.759559  5556.717241       2    18     1234     1222    18:26:36   \n",
       "1      2846.799490  4744.191350       2    18     1233     1221    16:30:59   \n",
       "2      2846.799490  4744.191350       2    18     1234     1222    18:19:09   \n",
       "3      5071.464231  5923.977368       2    18     1234     1222    15:54:43   \n",
       "4      6437.335772  7229.128072       2    18     1233     1221    14:19:44   \n",
       "\n",
       "    intvlngth  \n",
       "0  110.492667  \n",
       "1   64.294000  \n",
       "2   75.149167  \n",
       "3   28.642833  \n",
       "4   69.502667  \n",
       "\n",
       "[5 rows x 3087 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resp = ReadFemResp()\n",
    "# Let's take a look at our data\n",
    "resp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'caseid', u'rscrinf', u'rdormres', u'rostscrn', u'rscreenhisp',\n",
       "       u'rscreenrace', u'age_a', u'age_r', u'cmbirth', u'agescrn',\n",
       "       ...\n",
       "       u'pubassis_i', u'basewgt', u'adj_mod_basewgt', u'finalwgt', u'secu_r',\n",
       "       u'sest', u'cmintvw', u'cmlstyr', u'screentime', u'intvlngth'],\n",
       "      dtype='object', length=3087)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write the command to look at the columns of the DataFrame \n",
    "resp.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a histogram of <tt>totincr</tt> the total income for the respondent's family.  To interpret the codes see the [codebook](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=MALE&section=R&subSec=7958&srtLabel=609776)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import thinkstats2\n",
    "hist_totincr = thinkstats2.Hist(resp.totincr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQJIaZghIUsMMAUlq2P8H5LP2UPwdKvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114851b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ef2810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import thinkplot\n",
    "thinkplot.Hist(hist_totincr, label='totincr')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a histogram of <tt>age_r</tt>, the respondent's age at the time of interview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hist_age_r = thinkstats2.Hist(resp.age_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a histogram of <tt>numfmhh</tt>, the number of people in the respondent's household."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZ9BLUuH+H1vlTetmkq4kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114851dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d8b150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Hist(hist_age_r, label='age_R')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explain what is shown in the histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ages of the respondents varies between 15 to 44. The number of respondents of each age rises gradually between ages 15 to 23. Between the ages 23 to 33 - the number stays about the same, and the variance increases from age 34 and higher. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a histogram of <tt>numfmhh</tt>, the number of people in the respondent's household."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118cb73d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120c40e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_numfmhh = thinkstats2.Hist(resp.numfmhh)\n",
    "thinkplot.Hist(hist_numfmhh, label='numfmhh')\n",
    "thinkplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explain what you see in the distribution. Remember, you can use the [codebook](http://www.icpsr.umich.edu/nsfg6/Controller?displayPage=labelDetails&fileCode=MALE&section=R&subSec=7958&srtLabel=609776) to interpret the codes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most respondents have between 1 to 3 family members their household (mening: households of 2-4 family members). The histogram has a right tail, meaning the number of respondents gradually becomes smaller the more members in a household. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a histogram of <tt>parity</tt>, the number children the respondent has borne.  How would you describe this distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hist_parity = thinkstats2.Hist(resp.parity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Hist.Largest to find the largest values of <tt>parity</tt>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114831a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c1ce050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Hist(hist_parity, label='parity')\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the values shown in the histogram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearly half the respondents have 0 babies. Most respondents have 0-3 babies. The histogram has a right tail, mening the number of respondents becomes smaller the more babies they have (3 and above babies)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use <tt>totincr</tt> to select the respondents with the highest income.  Compute the distribution of <tt>parity</tt> for just the high income respondents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1160, 3087)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a variable of respondents with highest income\n",
    "high_income = resp[resp.totincr == 14]\n",
    "# sanity check: compare number of rows with codebook\n",
    "high_income.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEPCAYAAACgFqixAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMDMzHAZmZobDwMzMcBiYmRnw/wHDCh9YvPmkogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114831dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c506050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "high_income_parity = thinkstats2.Hist(high_income.parity, label='parity')\n",
    "thinkplot.Hist(high_income_parity)\n",
    "thinkplot.Show(xlabel='Parity for high income families', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe what you see in the distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of parity for high income families seems very similar to the distribution of parity for all respondents. Nearly half of the respondents have 0 babies; There is a large number of families with 1-2 babies; There is a right tail, mening the number of respondents lowers as the number of babies grows. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the largest parities for high income respondents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_income.parity.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    515\n",
       "2    267\n",
       "1    229\n",
       "3    123\n",
       "4     19\n",
       "5      5\n",
       "8      1\n",
       "7      1\n",
       "Name: parity, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_income.parity.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a histogram of the other respondents parities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6483, 3087)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_income = resp[resp.totincr < 14]\n",
    "other_income.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEPCAYAAABsj5JaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntTbEhfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189eb4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114d02810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other_income_parity = thinkstats2.Hist(other_income.parity, label='parity')\n",
    "thinkplot.Hist(other_income_parity)\n",
    "thinkplot.Show(xlabel='Parity for other income families', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the maximum parity for other (lower) income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2715\n",
       "2     1336\n",
       "1     1290\n",
       "3      705\n",
       "4      290\n",
       "5       90\n",
       "6       29\n",
       "7       14\n",
       "8        7\n",
       "10       3\n",
       "9        2\n",
       "22       1\n",
       "16       1\n",
       "Name: parity, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_income.parity.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the mean <tt>parity</tt> for high income respondents and others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of parity in high income families: 1.07586206897\n",
      "Mean of parity in high income families: 1.24957581367\n"
     ]
    }
   ],
   "source": [
    "print 'Mean of parity in high income families: {}'.format(high_income.parity.mean())\n",
    "print 'Mean of parity in high income families: {}'.format(other_income.parity.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigate any other variables that look interesting."
   ]
  },
  {
   "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}