triestpa/classsexagg.ipynb Secret

## classsexagg.ipynb
{
 "cells": [
{
   "cell_type": "code",
   "execution_count": 5,
   "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></th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>body</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>0.965278</td>\n",
       "      <td>37.037594</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>109.412385</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.340782</td>\n",
       "      <td>41.029250</td>\n",
       "      <td>0.340782</td>\n",
       "      <td>0.279330</td>\n",
       "      <td>69.888385</td>\n",
       "      <td>162.828571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>0.886792</td>\n",
       "      <td>27.499191</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.650943</td>\n",
       "      <td>23.234827</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.146199</td>\n",
       "      <td>30.815401</td>\n",
       "      <td>0.327485</td>\n",
       "      <td>0.192982</td>\n",
       "      <td>19.904946</td>\n",
       "      <td>171.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>0.490741</td>\n",
       "      <td>22.185307</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.731481</td>\n",
       "      <td>15.324250</td>\n",
       "      <td>183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.152130</td>\n",
       "      <td>25.962273</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.255578</td>\n",
       "      <td>12.415462</td>\n",
       "      <td>151.854167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               survived        age     sibsp     parch        fare        body\n",
       "pclass sex                                                                    \n",
       "1      female  0.965278  37.037594  0.555556  0.472222  109.412385         NaN\n",
       "       male    0.340782  41.029250  0.340782  0.279330   69.888385  162.828571\n",
       "2      female  0.886792  27.499191  0.500000  0.650943   23.234827   52.000000\n",
       "       male    0.146199  30.815401  0.327485  0.192982   19.904946  171.233333\n",
       "3      female  0.490741  22.185307  0.791667  0.731481   15.324250  183.000000\n",
       "       male    0.152130  25.962273  0.470588  0.255578   12.415462  151.854167"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_sex_grouping = titanic_df.groupby(['pclass','sex']).mean()\n",
    "class_sex_grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1115ff400>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115ffc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_sex_grouping['survived'].plot.bar()"
   ]
  }
 ],
 "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 5,
	"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></th>\n",
	" <th>survived</th>\n",
	" <th>age</th>\n",
	" <th>sibsp</th>\n",
	" <th>parch</th>\n",
	" <th>fare</th>\n",
	" <th>body</th>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>pclass</th>\n",
	" <th>sex</th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th rowspan=\"2\" valign=\"top\">1</th>\n",
	" <th>female</th>\n",
	" <td>0.965278</td>\n",
	" <td>37.037594</td>\n",
	" <td>0.555556</td>\n",
	" <td>0.472222</td>\n",
	" <td>109.412385</td>\n",
	" <td>NaN</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>male</th>\n",
	" <td>0.340782</td>\n",
	" <td>41.029250</td>\n",
	" <td>0.340782</td>\n",
	" <td>0.279330</td>\n",
	" <td>69.888385</td>\n",
	" <td>162.828571</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th rowspan=\"2\" valign=\"top\">2</th>\n",
	" <th>female</th>\n",
	" <td>0.886792</td>\n",
	" <td>27.499191</td>\n",
	" <td>0.500000</td>\n",
	" <td>0.650943</td>\n",
	" <td>23.234827</td>\n",
	" <td>52.000000</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>male</th>\n",
	" <td>0.146199</td>\n",
	" <td>30.815401</td>\n",
	" <td>0.327485</td>\n",
	" <td>0.192982</td>\n",
	" <td>19.904946</td>\n",
	" <td>171.233333</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th rowspan=\"2\" valign=\"top\">3</th>\n",
	" <th>female</th>\n",
	" <td>0.490741</td>\n",
	" <td>22.185307</td>\n",
	" <td>0.791667</td>\n",
	" <td>0.731481</td>\n",
	" <td>15.324250</td>\n",
	" <td>183.000000</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>male</th>\n",
	" <td>0.152130</td>\n",
	" <td>25.962273</td>\n",
	" <td>0.470588</td>\n",
	" <td>0.255578</td>\n",
	" <td>12.415462</td>\n",
	" <td>151.854167</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" survived age sibsp parch fare body\n",
	"pclass sex \n",
	"1 female 0.965278 37.037594 0.555556 0.472222 109.412385 NaN\n",
	" male 0.340782 41.029250 0.340782 0.279330 69.888385 162.828571\n",
	"2 female 0.886792 27.499191 0.500000 0.650943 23.234827 52.000000\n",
	" male 0.146199 30.815401 0.327485 0.192982 19.904946 171.233333\n",
	"3 female 0.490741 22.185307 0.791667 0.731481 15.324250 183.000000\n",
	" male 0.152130 25.962273 0.470588 0.255578 12.415462 151.854167"
	]
	},
	"execution_count": 5,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"class_sex_grouping = titanic_df.groupby(['pclass','sex']).mean()\n",
	"class_sex_grouping"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": false,
	"scrolled": true
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.axes._subplots.AxesSubplot at 0x1115ff400>"
	]
	},
	"execution_count": 6,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
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	"text/plain": [
	"<matplotlib.figure.Figure at 0x1115ffc88>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"class_sex_grouping['survived'].plot.bar()"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
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