1_basis_of_Python_2_short_ex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "data_dir = './Data/'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "####################\n",
    "# 練習１\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "temper_data=pd.read_csv(data_dir+\"temperature_data.csv\")\n",
    "\n",
    "#他の地方の分布も見てみよう\n",
    "#福岡、札幌、那覇それぞれのヒストグラムを作成\n",
    "temper_data.plot(y='Fukuoka',kind='hist')\n",
    "temper_data.plot(y='Sapporo',kind='hist')\n",
    "temper_data.plot(y='Naha',kind='hist')\n",
    "\n",
    "#bins と alpha を変えて３地点の分布を比較してみよう\n",
    "temper_data.plot.hist(bins=10, alpha=0.5)\n",
    "temper_data.plot.hist(bins=100, alpha=0.5)\n",
    "\n",
    "#身長体重データでの統計値も見てみよう\n",
    "HW_data=pd.read_csv(data_dir+\"height_weight.csv\", sep=\",\")\n",
    "HW_data.mean()#平均\n",
    "HW_data.var()#分散\n",
    "HW_data.std()#標準偏差\n",
    "temper_data.describe()\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Naha</th>\n",
       "      <th>Fukuoka</th>\n",
       "      <th>Sapporo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.131421</td>\n",
       "      <td>18.091803</td>\n",
       "      <td>9.295902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.955768</td>\n",
       "      <td>7.883285</td>\n",
       "      <td>9.775228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-6.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.225000</td>\n",
       "      <td>11.025000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>25.050000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>8.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.600000</td>\n",
       "      <td>24.650000</td>\n",
       "      <td>18.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.800000</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>27.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Naha     Fukuoka     Sapporo\n",
       "count  366.000000  366.000000  366.000000\n",
       "mean    24.131421   18.091803    9.295902\n",
       "std      4.955768    7.883285    9.775228\n",
       "min      9.200000   -2.000000   -6.800000\n",
       "25%     20.225000   11.025000    0.300000\n",
       "50%     25.050000   18.500000    8.650000\n",
       "75%     28.600000   24.650000   18.050000\n",
       "max     30.800000   31.400000   27.100000"
      ]
     },
     "metadata": {},
     "execution_count": 3
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "####################\n",
    "#練習２\n",
    "x = np.array([3,5]) #(3,5)というベクトルを作成\n",
    "y = np.array([6,1])\n",
    "print(x+y)\n",
    "print(x-y)\n",
    "\n",
    "x = np.array([3,5,2])\n",
    "y = np.array([6,1,2])\n",
    "print(x+y)\n",
    "print(x-y)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[9 6]\n",
      "[-3  4]\n",
      "[9 6 4]\n",
      "[-3  4  0]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "colab": {
   "name": "introduction_excercise.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}