lec22-データと統計量.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pandas import Series,DataFrame\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>One</th>\n",
       "      <th>Two</th>\n",
       "      <th>Three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   One  Two  Three\n",
       "A  1.0  2.0    NaN\n",
       "B  NaN  3.0    4.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[1,2,np.nan],[np.nan,3,4]])\n",
    "dframe1 = DataFrame(arr,index=['A','B'],columns=['One','Two','Three'])\n",
    "dframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "One      1.0\n",
       "Two      5.0\n",
       "Three    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 行方向に計算するので列の合計となる\n",
    "dframe1.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3.0\n",
       "B    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 行の合計を求める\n",
    "dframe1.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "One      1.0\n",
       "Two      2.0\n",
       "Three    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe1.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "One      A\n",
       "Two      A\n",
       "Three    B\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最小値を持つindex\n",
    "dframe1.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>One</th>\n",
       "      <th>Two</th>\n",
       "      <th>Three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   One  Two  Three\n",
       "A  1.0  2.0    NaN\n",
       "B  NaN  3.0    4.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>One</th>\n",
       "      <th>Two</th>\n",
       "      <th>Three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   One  Two  Three\n",
       "A  1.0  2.0    NaN\n",
       "B  NaN  5.0    4.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 累積\n",
    "dframe1.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>One</th>\n",
       "      <th>Two</th>\n",
       "      <th>Three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       One       Two  Three\n",
       "count  1.0  2.000000    1.0\n",
       "mean   1.0  2.500000    4.0\n",
       "std    NaN  0.707107    NaN\n",
       "min    1.0  2.000000    4.0\n",
       "25%    1.0  2.250000    4.0\n",
       "50%    1.0  2.500000    4.0\n",
       "75%    1.0  2.750000    4.0\n",
       "max    1.0  3.000000    4.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrameの全体像\n",
    "dframe1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 株価のデータで共分散と相関をみる\n",
    "# Pandasはwebからデータを取得することも可能\n",
    "from pandas_datareader import DataReader\n",
    "import datetime\n",
    "# 米国Yahooのサービスを使って、株価を取得する\n",
    "# すべて石油関連会社\n",
    "# CVX シェブロン、XOM エクソンモービル、BP 英BP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>BP</th>\n",
       "      <th>CVX</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>35.024239</td>\n",
       "      <td>53.552139</td>\n",
       "      <td>49.951328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>35.737183</td>\n",
       "      <td>54.991985</td>\n",
       "      <td>50.654552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>35.990925</td>\n",
       "      <td>55.381500</td>\n",
       "      <td>50.852329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>36.178230</td>\n",
       "      <td>55.388470</td>\n",
       "      <td>51.291859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>36.166138</td>\n",
       "      <td>55.179783</td>\n",
       "      <td>51.130695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols            BP        CVX        XOM\n",
       "Date                                       \n",
       "2009-12-31  35.024239  53.552139  49.951328\n",
       "2010-01-04  35.737183  54.991985  50.654552\n",
       "2010-01-05  35.990925  55.381500  50.852329\n",
       "2010-01-06  36.178230  55.388470  51.291859\n",
       "2010-01-07  36.166138  55.179783  51.130695"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = DataReader(['CVX','XOM','BP'],'yahoo',\n",
    "                             start=datetime.datetime(2010,1,1),\n",
    "                             end=datetime.datetime(2013,1,1))['Adj Close']\n",
    "prices.head()\n",
    "                                            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>BP</th>\n",
       "      <th>CVX</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>1517600.0</td>\n",
       "      <td>4246600.0</td>\n",
       "      <td>18852300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>3956100.0</td>\n",
       "      <td>10173800.0</td>\n",
       "      <td>27809100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>4109600.0</td>\n",
       "      <td>10593700.0</td>\n",
       "      <td>30174700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>6227900.0</td>\n",
       "      <td>11014600.0</td>\n",
       "      <td>35044700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>4431300.0</td>\n",
       "      <td>9626900.0</td>\n",
       "      <td>27192100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols            BP         CVX         XOM\n",
       "Date                                         \n",
       "2009-12-31  1517600.0   4246600.0  18852300.0\n",
       "2010-01-04  3956100.0  10173800.0  27809100.0\n",
       "2010-01-05  4109600.0  10593700.0  30174700.0\n",
       "2010-01-06  6227900.0  11014600.0  35044700.0\n",
       "2010-01-07  4431300.0   9626900.0  27192100.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 出来高(何株売買されたか)のデータ\n",
    "volume = DataReader(['CVX','XOM','BP'],'yahoo',\n",
    "                             start=datetime.datetime(2010,1,1),\n",
    "                             end=datetime.datetime(2013,1,1))['Volume']\n",
    "volume.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 変化の割合\n",
    "rets = prices.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>BP</th>\n",
       "      <th>CVX</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.020356</td>\n",
       "      <td>0.026887</td>\n",
       "      <td>0.014078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.007083</td>\n",
       "      <td>0.003904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.005204</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.008643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>-0.000334</td>\n",
       "      <td>-0.003768</td>\n",
       "      <td>-0.003142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols           BP       CVX       XOM\n",
       "Date                                    \n",
       "2009-12-31       NaN       NaN       NaN\n",
       "2010-01-04  0.020356  0.026887  0.014078\n",
       "2010-01-05  0.007100  0.007083  0.003904\n",
       "2010-01-06  0.005204  0.000126  0.008643\n",
       "2010-01-07 -0.000334 -0.003768 -0.003142"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16b1b898>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 単純な株価の変化を描画\n",
    "%matplotlib inline\n",
    "prices.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113b55940>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(rets.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     w\n",
       "1     w\n",
       "2     x\n",
       "3     y\n",
       "4     z\n",
       "5     w\n",
       "6     w\n",
       "7     x\n",
       "8     x\n",
       "9     y\n",
       "10    a\n",
       "11    z\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = Series(['w','w','x', 'y', 'z' ,'w' ,'w' ,'x' ,'x' ,'y' ,'a' ,'z'])\n",
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['w', 'x', 'y', 'z', 'a'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "w    4\n",
       "x    3\n",
       "z    2\n",
       "y    2\n",
       "a    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}