swkm/1変数データ_質的データの定性的把握.ipynb Secret

## 1変数データ_質的データの定性的把握.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib\n",
    "import matplotlib.pyplot\n",
    "import numpy\n",
    "import pandas\n",
    "import seaborn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pandas.read_csv('train.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observation:891, n:891, missing:0\n"
     ]
    }
   ],
   "source": [
    "# 行数\n",
    "observation = len(df)\n",
    "n = df['Pclass'].count()\n",
    "print('observation:{0}, n:{1}, missing:{2}'.format(observation, n, observation - n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"階級\",\"度数\",\"相対度数\",\"累積度数\",\"累積相対度数\"\n",
      "\"1\",\"216\",\"0.242\",\"216\",\"0.242\"\n",
      "\"2\",\"184\",\"0.207\",\"400\",\"0.449\"\n",
      "\"3\",\"491\",\"0.551\",\"891\",\"1.0\"\n"
     ]
    }
   ],
   "source": [
    "# 度数表\n",
    "frequency = df['Pclass'].value_counts()\n",
    "total = frequency.sum()\n",
    "print('\"階級\",\"度数\",\"相対度数\",\"累積度数\",\"累積相対度数\"')\n",
    "keys = [1, 2, 3]\n",
    "i = 0\n",
    "cumulative_v = 0\n",
    "for k in keys:\n",
    "    cumulative_v += frequency[k]\n",
    "    print('\"{0}\",\"{1}\",\"{2}\",\"{3}\",\"{4}\"'.format(\n",
    "            k, frequency[k], round(frequency[k]/total,3),\n",
    "            cumulative_v, round(cumulative_v/total,3)))\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115159710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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9KWDs2CoyGYOGJFVXVxS6BA2w6uoKamqqUr1GoYPGe4FJIYRdudflACGEdwP/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\n/w9j4Ck7oqSDOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11537fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.pyplot.title('Bar Plot')\n",
    "matplotlib.pyplot.annotate(\n",
    "    '(plot) n={0}\\n(not plot) missing={1}'.format(n,observation-n), xy=(0.95, 0.85), xycoords='axes fraction', fontsize=12,\n",
    "    horizontalalignment='right', verticalalignment='bottom')\n",
    "seaborn.barplot(keys, frequency[keys])\n",
    "matplotlib.pyplot.xlabel('Pclass')\n",
    "matplotlib.pyplot.ylabel('Frequency')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}