loop-plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ループしながらプロットする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1件ずつ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal width (cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "petal length (cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "petal width (cm)\n"
     ]
    },
    {
     "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": [
    "for x in df.columns:\n",
    "    print(x)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    df[x].hist(ax=ax)\n",
    "    plt.show()\n",
    "    plt.close(fig)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一緒にプロット\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm)\n",
      "sepal width (cm)\n",
      "petal length (cm)\n",
      "petal width (cm)\n"
     ]
    },
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "for x in df.columns:\n",
    "    print(x)\n",
    "    df[x].hist(ax=ax, label=x)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n",
    "plt.close(fig)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## subplot で1枚に複数フラフを描く"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm)\n",
      "sepal width (cm)\n",
      "petal length (cm)\n",
      "petal width (cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "\n",
    "for i, x in enumerate(df.columns, start=1):\n",
    "    print(x)\n",
    "    ax = fig.add_subplot(2,2,i)\n",
    "    df[x].hist(ax=ax, label=x)\n",
    "    ax.legend()\n",
    "\n",
    "plt.show()\n",
    "plt.close(fig)\n"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}