{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(chap:pandas_plot)=\n", "# `Pandas`:図示" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " in English or the language of your choice.\n", "

" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## 説明" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hidden": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import japanize_matplotlib\n", "\n", "# 警告メッセージを非表示\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "プロット用のパッケージ`Matplotlib`を紹介したが,実は`Pandas`の`DataFrame`と`Series`にはメソッド`plot()`が備えられており,それを使えば基本的なプロットをより簡単なコードで実現できる。裏で動いているのは`Matplotlib`であり,より複雑な図を作成する場合は、`Matplotlib`のコードを直接書くことが必要になるだろうが,手っ取り早くプロットしたい場合には重宝する手法である。詳細は[参考サイト(英語)](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html)を参考にして欲しいが,ここでは基本的な使い方を紹介する。" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "次の`df0`を使って説明する。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " X Y Z\n", "0 10 5.0 3.0\n", "1 20 30.0 2.0\n", "2 30 15.0 5.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dic = {'X':[10, 20, 30],\n", " 'Y':[5.0, 30.0, 15.0],\n", " 'Z':[3.0, 2.0, 5.0]}\n", "df0 = pd.DataFrame(dic)\n", "df0" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## プロット方法" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "プロット方法は簡単で,メソッド`.plot()`を使うと全ての列がプロットされる。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0.plot()" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "* デフォルトでは横軸に行インデックスが使われ、`float`として扱われて表示されている。\n", "* 凡例は自動的に表示され、列ラベルが使われる。\n", "* 図の上に文字が表示されるが,表示したくない場合は最後に`;`を加えるか次の行に`pass`と書くと良いだろう。\n", "\n", "次にある列だけをプロットしたい場合を考えよう。その場合は,プロットしたい列を抽出しメソッド`plot()`を使う。選択には`[]`を使い,その中に列ラベルをリストとして指定する。まず1つの列を選択する場合を考えよう。例えば,`X`を選択するとしよう。この場合、2つの方法がある。\n", "\n", "* `X`を`Series`として抽出する場合\n", " ```\n", " df0['X']\n", " ```\n", "* `X`を`DataFrame`として抽出する場合\n", " ```\n", " df0[['X']]\n", " ```\n", " ここでは`X`をリスト`['X']`として使っている。\n", " \n", "列ラベルは文字列`X`で指定していることに留意しよう。\n", "\n", "まず`X`の`Series`として抽出しよう。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "0 10\n", "1 20\n", "2 30\n", "Name: X, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0['X']" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "次に`X`を`DataFrame`として抽出する。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " X\n", "0 10\n", "1 20\n", "2 30" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0[['X']]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "表示が少し異なることが確認できるが,メソッド`plot()`でも少しだけ違いがある。まず`X`を`Series`としてプロットしてみる。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0['X'].plot()\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "`X`を`DataFrame`としてプロットすると凡例も表示される。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0[['X']].plot()\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "この場合の凡例は列ラベルがそのまま使われている。\n", "\n", "次に`X`と`Z`をプロットしたいとしよう。2つの列を抽出するためには,以下のように列ラベルをリストとして書く。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " X Z\n", "0 10 3.0\n", "1 20 2.0\n", "2 30 5.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0[['X','Z']]" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "メソッド`plot()`を使いプロットする。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0[['X','Z']].plot()\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "もしくは`Series`を2行に分けても同じ図となるが,凡例は表示されない。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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1C7jt/FxSNbmUTBBq6eIqnb4+XnirgSe31rD/eBdzZqTxwy8t5fpls4jX15XJBKMAF1c42t7DM9vr+On2Wo539nJBQRaP3riQy+ZO14gSmbAU4DKmNRzrZNOWal6saqC7N8CVC2awvrSYFfmTnS5NxHEKcBmT3j3QSoWnmt++00SMgZuW51C2pphzpqc5XZrImKEAlzHDWst271E2eLxs+fgIaYlx3HtJIfdcUsiM9CSnyxMZcxTg4jh/wPK7fc1UVHp5u7GVqWmJPHTVXO64MJ+MZE0uJXImCnBxTHevn5d3N7Kpsprao50UTk3l+zcv5qblOZpcSmQYFOASda1dvfxsRx0/2VbDkXYfS2dnsOGO87hyYbYmlxI5CwpwiZrm1m6e3FrN82/W0+HzUzpnGveXFrOyKEtDAUVGQAEuEffJoTbKPdX8as9+AhauWzKT9WuKWTAr3enSRFxNAS4Rs6vuGBs2V/Pa+wdJio/hjgvz+dolheRmaXIpkXBQgEtYBQKW1z84RLnHS1VdC5kp8fz55edy16oCslITnC5PZFxRgEtY+PoCvLL3ABUeLx8faicnM5m/++ICbj0/l5QENTORSNB/loxKe08fL7xVz5Nba2hq7WZe9iR+tG4Z1y6ZqcmlRCJMAS4jcqS9h6e31fLT7bWc6O5jZVEW3795MaVzpmlEiUiUKMDlrNQd7WBjZTX/uqsRnz/AVQuzWV9azLLcTKdLE5lwFOAyLO80tlJe6eXVd5qIi4nhlhU53Le6iKJpmlxKxCkKcDkjay1bPzlCucfLtk+OMikxjrI1xdxzcQHTNbmUiOMU4PIZff4A/3dfMxUeL+8eOMH0SYn81dXz+PKFeUxK0uRSImOFAlw+1d3r56WqBjZtqaH+WCdF01L5wS2LuXF5DolxmlxKZKxRgAvHO308u72Op9+o5WiHj+V5mXz72vl8Yf4MYjS5lMiYpQCfwA4c7+KJLTW8sLOeTp+fy+YGJ5e6oFCTS4m4gQJ8AvqwuY2KSi+v7DkAwPVLZ1FWWsS8bE0uJeImCvAJwlrLztoWyj1eXv/gEMnxsdx5UT73ri4iJzPZ6fJEZASGDHBjzFrgVmCltTYvtOyrwLeA5tBmr1trH4lYlTJigYDlD+8fpMLjZXf9cbJSE/jmF+Zw58p8JmtyKRFXG84e+GHgAWBfv2UFwDestb+PRFEyej19fn71xwNUVHrxHu4gNyuZR25YyJdW5JKcoBElIuPBkAFurfUAgw9qFQC5xpiHgRbgm9bamkgUKGenrbuXn4cmlzp4oocFM9N5/PblXLMomzhNLiUyroy0D/w94E1r7WZjzKXAc8Cq021ojCkDygDy8vJG+HAylENt3fxkWy0/21FHW3cfq4qn8Njapaw+d6pGlIiMU8ZaO7wNjWm21mafYd0BIMcOcWclJSW2qqrq7KuUM6o5Epxc6uXdjfT5A1y9aCbrS4tYMjvT6dJEJEyMMbustSWDl49oD9wY8y3geWttgzGmBGgYKrwlvPY2HKfc4+V37zYTHxvD2hWzKVtdRMHUVKdLE5EoGWkXyk7gZWNMD+AD7gxfSXIm1lo8Hx2m3ONlR/Ux0pPieODSYu5eVci0SYlOlyciUTbsAO/ffWKtfR24ICIVyWf0+QP89p0myj3VvN90guz0JP762vncdkEeaYkayi8yUem/fwzr8vl5saqBTVuqaWzp4pzpaTy2dgk3LMshIU4jSkQmOgX4GNTS4eOZ7bU880YtLZ29rMifzHe+uJDL503X5FIi8ikF+BjS2NLJE1tq+MXOBrp6/Vwxfzr3lxZTUpDldGkiMgYpwMeA95tOUOHx8uu3mzDAjctzKFtTxJwZk5wuTUTGMAW4Q6y17Kg+RrnHi+ejw6QmxPLVVQV8bXUhMzM0uZSIDE0BHmX+gOUP7zWzwVPN3objTE1L4H/86Vz+7MJ8MlL0dWUiMnwK8Cjp6fPzb7v3s6mymuojHeRPSeHvb1zE2hWzSYrX5FIicvYU4BF2oruX53bU89S2Gg639bAoJ51//vJyrl40k1iNKBGRUVCAR8jBE908tbWG596sp72nj9XnTuVH65axqniKJpcSkbBQgIeZ93A7Gz3V/PKP++kLBLh2ySzWryliUU6G06WJyDijAA+T3fUtlG/28of3D5IQG8O683O5b3UReVNSnC5NRMYpBfgoWGvZ/OFhNni8vFVzjIzkeB687BzuWlXAlDRNLiUikaUAH4Fef4Bf7z1AhaeaDw+2MSsjib+5bgG3nZ9LqiaXEpEoUdqchY6ePn6xs4Ent9aw/3gXc2ak8cMvLeX6ZbOI19eViUiUKcCH4Wh7D8+8UctPd9RxvLOXCwqyePTGhVw2d7pGlIiIYxTgn6P+aCdPbK3mxaoGunsDXLlgButLi1mRP9np0kREFOCns29/KxWV1fz27QPExhhuWp5D2Zpizpme5nRpIiKfUoCHWGvZ7j3KBo+XLR8fIS0xjvtWF3HPJYXMSE9yujwRkc+Y8AHuD1h+t6+Zikovbze2MjUtkYeumssdF+aTkazJpURk7JqwAd7d6+fl3Y1sqqym9mgnhVNT+f7Ni7lpeY4mlxIRV5hwAd7a2cvP3qzjJ9tqONLuY+nsDDbccR5XLszW5FIi4ioTJsCbWrt4amsNz79ZT4fPT+mcadxfWszKoiwNBRQRVxr3Af7xwTYqKqv51Z79BCxct2Qm69cUs2BWutOliYiMyrgN8Kra4NeVvfb+IZLiY7jjwny+dkkhuVmaXEpExodxFeCBgOX1Dw5R7vFSVddCZko8f375udy1qoCs1ASnyxMRCatxEeC+vgCv7D1AhcfLx4fayclM5u++uIBbz88lJWFcPEURkc9wdbq19/Txwlv1PLm1hqbWbuZlT+JH65Zx7ZKZmlxKRMY9Vwb44bYenn6jhme313Giu4+VRVl8/+bFlM6ZphElIjJhDBngxpi1wK3ASmttXmjZUuBxIBE4DHzFWtsSyUIB6o52sLGympd2NdLrD3DVwmzWlxazLDcz0g8tIjLmDGcP/DDwALAPwAR3cV8AbrPW7jXGPAA8AjwYqSLfaWyl3OPl1X1NxMXEcMuKHO5bXUTRNE0uJSIT15ABbq31AP27JuYALdbavaHrTwAfcIYAN8aUAWUAeXl5Z12gP2B54PldHO/opWxNMfdcXMB0TS4lIjKiPvApQPPJK9ZanzHmjPdjrd0IbAQoKSmxZ/tgsTGGDXesIG9KCulJmlxKROSkkQT4QWD6ySvGmETAF7aKTmNRTkYk715ExJXOeqydtdYLpBljFoUW3Qm8GtaqRERkSCMdRng3sMkYEwCOAneFrSIRERmWYQe4tTa73+97gIsiUZCIiAyPTlcUEXEpBbiIiEspwEVEXEoBLiLiUgpwERGXUoCLiLiUAlxExKUU4CIiLqUAFxFxKQW4iIhLKcBFRFxKAS4i4lIKcBERl1KAi4i4lAJcRMSlFOAiIi6lABcRcSkFuIiISynARURcSgEuIuJSCnAREZdSgIuIuJQCXETEpRTgIiIupQAXEXEpBbiIiEspwEVEXCpuNDc2xjwDzAW6Q4v+t7X2lVFXJSIiQxpVgAO5wGXW2q5wFCMiMi71tENiWtjvdrQBnglsMMYUAW8DD1lrO0ddlYiIm1gLncfgWHXw0lIT+j30s6sFvt0McQlhfdjRBngV8Ki1tsEY8x3gb4C/6r+BMaYMKAPIy8sb5cOJiDgkEID25oHB3D+oe07029hAxmzIKoR510JWEQR6gfAGuLHWhueOjFkA/JO19vIzbVNSUmKrqqrC8ngiImHn74PWhkF70CfDuhb6+vUWx8RBZn4wpLOKgpfJod8z8yA+KWxlGWN2WWtLBi8f8R64MSYZeJjgHrgPuBrYPfISRUSioK8HWuoG7UGHwvp4HQT6Tm0bl3QqlM+5/FRYTy6EjFyIHW0nxuiM+NGttV3GmCPAW8aYVmA/sD5slYmIjFRPeyicawb1S9dAayPQr+chMT0YzNmLYcENob3pUFCnZUPM2B1tPaq3D2vtj4Efh6kWEZHh6zw2KKT79Uu3Hxy4bcrUYCjnrxrY1ZFVBClZYIwzz2GUnN3/FxE5E2uh/dCZR3Z0Hx+4/aRZwUA+98qBXR1ZhZCU4chTiDQFuIg4J+CHE/tPM7IjdOntOLWtiYXM3GAwL7plYFfH5AKIT3bsaThFAS4ikdXng+P1n92DPlYdPGjo953aNjbh1F5z4Zp+XR2FwZEdsfHOPY8xSAEuIqPn6wwOszvdyI7WBrCBU9vGpwZDefp8mHfNwCF46bMgJtaxp+E2CnARGZ7u1kF70DWnwrqtaeC2yZODoTz7fFiybmB3R+o01x40HGsU4CISZC10HPnsHvTJverOowO3T8sOhnLxn5zq9jgZ1MmTnXkOE4wCXGQiCQSCe8unHdlRA762fhub4MkqWYUw/4sDuzomF0RkciY5OwpwkfHG3wet9QOD+WRQt9RCX/epbWPiYXJ+MJTzLhrY1ZGZB3GJjj0NGZo7Avzp64JHsRPSICEVElL6/X7y0u96/BmWn/w9LlF9cOJuvd3BERyDuzqOVQcPGg44HTw5GMpTzoFzvzBwZEf6bMdPB5eRc8dfLu8iSM8BXzv4OqC3M9hXd/L6yWXDZWLO4g3gdG8CKae/jf4RJJx62k4/692xmuDY6QGng2cEA3nWclh088CzDSdla4dlnHJH4vzJt4feJuAPhvjJQB9waR/4+6fbtQ/cruNw8COmrzO0rn3gnsxQ4pKG8QYwxJtD/KA3h/hk/fONV9YG54kevAd9Mqw7Dg/cPnVaMJALLhnY1ZFVFDxoqHYy4bgjwIcjJhYSJwUv4dTn+2zQ937Om4Ovo98bQGh557FBnxY6hn7cT5lBIT+4+2jQm8NQ3Ucnb68TIqLDWmhrPvPIju7Wgdun5wQDee7VA+frmFwASemOPAUZu8ZPgEdKXALEZQUnvAmXQCA4r/Bw3wAGb9fbEdxza20cuK7/GW1DiU04yzeAz+k++vTYQvKYnrktYgL+4N9i8Kx3Jw8e9u/eM7HBg4NZRTC7ZNDIjvwJeTq4jJwC3AkxMafCj+nhu98+X+jTwem6iAa9AZzpzaGrsd/tQ+v697V+LtOvC2gEbwBn6n4K89dQjUifL3TQ8DRdHS11oW9bCYlNDHZvTC6EoktDXR2hvemMXH36kbBRgI8ncQnBSzhPorAWegd9Wugd/Alh8KeFQW8O3a1w4sDAYxD9h7INJSZ+iDeA/scMhuhi6v9GMfjTgq/j1Ongg884PNE48HTwhLRgKM9YCPOuO7UnnVUYnBVvIn4SkahTgMvnMyYUminAtPDdr7/vc44lfN7yfgeh25o+u65/yA6l/6eF3u7g9x32l5wVDOW8CyHr9oEjO1Kn6qChOE4BLs6IjYPYjPDO02xtcM/+M8cQztR91G95TPzAro7JhZCcGb7aRCJAAS7jhzHBg4DxyZA6xelqRCJOHXUiIi6lABcRcSkFuIiISynARURcSgEuIuJSCnAREZdSgIuIuJQCXETEpYy1w52oKAwPZsxhoG6EN58KHAljOeGius6O6jo7quvsjNe68q21n5nLIqoBPhrGmCprbYnTdQymus6O6jo7quvsTLS61IUiIuJSCnAREZdyU4BvdLqAM1BdZ0d1nR3VdXYmVF2u6QMXEZGB3LQHLiIi/SjARURcakwEuDHmVmPMW8aYXcaYH55m/TeMMTuNMXuMMX/Zb/llxpjtods+a4wJ67ffDqOuB40xO0I1/IsxJia0/LuhWjeHLmVRrmvzoMsFoeWOvV7GmHWDatpvjPlvoXWRfr3WGmNeNMbUn2G9U+1rqLqcal9D1eVU+zpjXQ63r1tDz3tLqL6UQesj176stY5egHzgQyADMMAvgFv6rb8Y2A4khC5bgRIgDagFckLb/QPw36NY10Lg90Bs6PpLwPWh358B5jjxeoW22X6a2zn6eg3aNhV4E0iN9OsVuv9SgidSNJ9mnSPtaxh1OdK+hqrLqfY1nLqcaF9AFlAFJIeuPwZ8I1rtayzsgV8FvGytbbXBZ1IB3Nhv/XXAT6y1PmutD3gKuIHgC/OGtXZ/aLvyQbeLaF3W2ncJ/kP5Q4vigK7Q73nAN40xntA769Ro1WWMiQMyQnsClcaYR40xsTj8eg3yEPAv1tqO0PVIvl5Yaz3W2jOdBedU+/rcuhxsX59bl4Pta6i/Y39Ra1/W2mPAJdbak3+b/n8niHD7GgsBPgXo/3XgTcD0Yawf6naRrgtrbbcxJtMY8zywx1r7h9CqncD/sdaWAq8B/xTFutIAD1AGXArMBO4dxu0iXRcAxpjJwPXAc/0WR/L1GopT7WtIDrWvoTjVvobFifYV+jslGWN+DCQTDOmTItq+xkKAH2Rg4dmhZUOtH+p2ka4LY8wigl0FP7bWfvfkcmvtQ9bad0JXXwIuiFZd1trj1tqvh34GgH8LPb7jr1fIeuB5a21fv5oj+XoNxan2NSSH2tfncrB9DVfU25cxZjbwS+B31tr7+31qgki3r0j0C51lH9JM4F1gUuj6swzsay4BKoF4IBbYHFqWBHwMzAxt9/eEt093qLqmEXw3zxh0OwM8enI5cAvwUhTrygYe5tQY/8eB/+L069Vvu0+A7Gi9XoMe+3R9zY60r2HU5Uj7GkZdjrSvoepyqn2FnvdrQO4Z1ke0fcXhMGttkzHme0ClMcYHbLHWvmyM2QzcZq2tMsa8QvCghB94wVpbBWCM+TrwG2NMD8E/3CPRqgtYCxQCvzLGnLzZ89bajcaYfcB/GmPagVbgvijWdZDgx9zdocffA2y01vY6+XpZa5uNMSXAcWttc7/b2Ui+XmfidPsaqi4cal/DqMuR9jVUXQ62ryuA+cCz/f5OrwN/QhTal87EFBFxqbHQBy4iIiOgABcRcSkFuIiISynARURcSgEuIuJSCnAREZdSgIuIuNT/B4IDCnttCXY/AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0['X'].plot()\n", "df0['Z'].plot()\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "デフォルトでは横軸に行インデックスが使われるが,横軸を指定したい場合は,次の引数を使う。\n", "* `x`:横軸に使う列ラベル\n", "* `y`:縦軸に使う列ラベル(複数の場合はリスト)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0.plot(x='X',y=['Y','Z'])\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "tags": [ "remove-cell" ] }, "source": [ "## 引数とメソッド" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "(sec:5-options)=\n", "## 引数とメソッド" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 基本的な引数" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "`plot()`には様々な引数があり図に「飾り付け」をすることができる。詳しくは[このリンク](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html)を参照することにして,ここでは基本的な引数だけを紹介する。\n", "\n", "* `style`:線のスタイル(複数ある場合はリストにして列の順番で指定する;`-`,`--`,`-.`,`:`)\n", "* `linewidth` or `lw`:線の幅\n", "* `color`:色(リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html))\n", " * `r`は赤\n", " * `k`は黒\n", " * `g`はグリーン\n", "* `marker`:観測値のマーカー(`o`,`.`,`>`,`^`などがある; [参照サイト](https://matplotlib.org/3.2.2/api/markers_api.html))\n", "* `markersize`:マーカーの大きさ\n", "* `fontsize`:横軸・縦軸の数字のフォントサイズの設定\n", "* `figsize`:図の大きさ\n", " * `figsize=(キャンバスの横幅、キャンバスの縦の長さ)`\n", "* `legend`:凡例の表示を指定\n", " * `DataFrame`の場合はデフォルトは`True`\n", " * `Series`の場合はデフォルトは`False`\n", " * 複数の図を表示する際は下で説明する「軸」のメソッドとして指定することもできる。\n", "* `label`:凡例の表現を指定(`Series`のみ有効)\n", "* `grid`:グリッド表示(ブール型;デフォルトは`False`)\n", " * 複数の図を表示する際は,この引数は使わずに下で説明する「軸」のメソッドとして指定する。\n", "* `ax`:プロットする「軸」を指定する。\n", " * 1つの「軸」に複数の図を表示する際に使う(後で使い方を説明する)。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# df0はDataFrame\n", "\n", "df0.plot(style=[':','--','-'],\n", " linewidth=2,\n", " color=['r','k','g'],\n", " marker='o',\n", " markersize=10,\n", " fontsize=15,\n", " figsize=(8, 4), # 8は横軸、4は縦軸のサイズ\n", " legend=False,\n", " grid=True,\n", " )\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true, "tags": [ "remove-cell" ] }, "source": [ "### タイトルとラベルのサイズの調整" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "(sec:5-titlelabels)=\n", "### タイトルとラベルのサイズの調整" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "タイトルのフォント・サイズの指定,横軸と縦軸のラベルとフォント・サイズの指定をおこなう場合は、`plot()`の引数ではなく下で説明する方法でおこなう。この方法を理解するために、`Pandas`(実は`Matplotlib`)が表示する図は{numref}`fig:5-figure_axes`で示している階層的な構造となることをイメージして欲しい。ここで重要なのは「キャンバス」と「軸」の違いである。\n", "\n", "```{figure} /images/figure_axes_pandas.png\n", "---\n", "scale: 40%\n", "name: fig:5-figure_axes\n", "---\n", "「キャンバス」と「軸」の関係\n", "```\n", "\n", "1. 「キャンバス」とは実際に表示される**領域**であり,実際には表示されない「透明のキャンバス」である。\n", " * `figure`や`fig`などの変数名や引数名があれば、「キャンバス」を指していると理解すれば良いだろう。\n", "1. 「軸」とは1つの図を表示する「キャンバス」上の**区域**である。\n", " * `ax`や`axes`などの変数名があれば、「軸」を表していると理解すれば良いだろう。\n", " * 「キャンバス」上に複数の図を表示する場合は複数の「軸」が必要となる。\n", " * 「軸」に図のタイトルや縦軸・横軸のラベルなどを追加することになる。\n", " \n", "「軸」の中にメソッド`.plot()`を使いグラフを表示することになる。" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "従って,概ね次のように理解して良いだろう。\n", "* 上で説明した「基本的な引数」は上の図のピンクのエリア内での変更となる。\n", " * `figsize`は「キャンバス」の大きさを指定する引数だが,メソッド`plot()`は自動で「キャンバス」を作成するため`plot()`内で変更できるようになっている。\n", "* タイトルや縦・横軸ラベルは「軸」のメソッドを使い変更する。\n", "\n", "では実際に手順を説明する。\n", "* `df0.plot()`は自動で「キャンバス」と「軸」を作成すると同時に「軸」を返す。それを変数(例えば,`ax`)に割り当てる。\n", "* `ax`のメソッドを使って以下を設定する。\n", " * タイトル:`.set_title()`\n", " * 横軸ラベル:`.set_xlabel()` \n", " * 縦軸ラベル:`.set_ylabel()`\n", "\n", "実際にそれらの引数を使ってプロットしてみよう。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df0.plot(\n", " grid=True,\n", " style=[':','--','-'],\n", " marker='o',\n", " fontsize=15,\n", " )\n", "ax.set_title('A Large Title', size= 30) # タイトルの設定\n", "ax.set_xlabel('Horizontal Axis', size=20) # 横軸ラベルの設定\n", "ax.set_ylabel('Vertical Axis', size=20) # 縦軸ラベルの設定\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 「軸」を指定してプロットする場合" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "上で「軸」のメソッドとしてタイトルなどを追加できることを説明したが,`plot()`の引数として「軸」を指定して図を追加することができる。次のコードを考えてみよう。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_ = df0[['X','Y']].plot() # 1行目\n", "df0[['Z']].plot(ax=ax_) # 2行目\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "1行目の右辺では「キャンバス」と「軸」が自動生成され,その内「軸」のみが返され変数`ax_`に割り当てられている。2行目の`plot()`の引数`ax`は「軸」を指定する引数であり,それに`ax_`を設定している。即ち,`Z`を「軸」`ax_`にプロットすることを指定している。このコードには2つの利点がある。\n", "1. `X`と`Y`と異なる「飾り付け」を`Z`に簡単に施すことができる。\n", "1. 2行目に`df0`ではなく別の`DataFrame`を使うことも可能となる。\n", "\n", "もちろん,タイトルや軸ラベルのメソッドをつけ加えることも可能である。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_ = df0[['X','Y']].plot(marker='o',markersize=10)\n", "df0[['Z']].plot(ax=ax_, marker='^',markersize=15)\n", "ax_.set_title('This is a title', size=20)\n", "ax_.set_xlabel('Horizontal axis', size=15)\n", "ax_.set_ylabel('Vertical axis', size=15)\n", "ax_.grid()\n", "ax_.legend(['Xの凡例','Yの凡例','Zの凡例'])\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "また上のコードの最後に2行を付け加えているので便利なので参考にして欲しい。\n", "* `ax_.grid()`:グリッド線を表示するメソッド。\n", " * 1つの「軸」に`plot`を複数回適用する場合,個々の`plot`の引数`grid=True`を使わず`ax_.grid()`を指定する。\n", "* `ax_.legend(['Xの凡例','Yの凡例','Zの凡例'])`\n", " * 凡例に列ラベルではなく他の表現を指定したい場合に使う。" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 図を並べる" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "図を縦に並べるには`subplots=True`を指定する。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0.plot(subplots=True)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "図を横に並べるには`layout=(1,3)`を付け加える。`layout`は図の配置を行列のように考えて指定し、`1`は行の数であり、`3`は列の数。\n", "```\n", "layout(行の数、列の数)\n", "```" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0.plot(subplots=True, layout=(1,3), figsize=(8,3))\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 2軸グラフ" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "左軸を`X`と`Y`に,右軸を`Z`に使うとしよう。その場合,引数`secondary_y`に`Z`を設定する。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df0.plot(secondary_y='Z')\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "別々の飾り付けをする場合は次のようにすると良いだろう。" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_ = df0[['X','Y']].plot()\n", "df0[['Z']].plot(marker='x', markersize=10, linestyle=':',\n", " secondary_y=True, ax=ax_)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "上の図との主な違いが3行目にある\n", "* `secondary_y=True`:`Z`を右軸に使うことを指定する。\n", "* `ax=ax_`:「軸」`ax_`に`Z`をプロットする。" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## 日本語" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "2つ方法を紹介するが、`japanize_matplotlib`を使う方法がより簡単であろう。" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### `japanize_matplotlib`" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "使い方は到って簡単で、`Pandas`と同様にインポートするだけである。" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hidden": true }, "outputs": [], "source": [ "import japanize_matplotlib" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df0.plot(grid=True\n", " ,style=[':','--','-']\n", " ,marker='o'\n", " ,fontsize=15\n", " )\n", "ax.set_title('縦横タイトル', size= 30)\n", "ax.set_xlabel('横軸', size=20)\n", "ax.set_ylabel('縦軸', size=20)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### フォントを指定する" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "2つの方法:\n", "1. フォントはインストールせず、PC内にあるフォントを指定する。\n", "1. フォントをインストールする方法" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "方法1の場合、以下で説明に使う変数`jfont`にフォントを指定する。\n", " * Macの場合、例えば`AppleGothic`\n", " * Windowsの場合、例えば`Yu Gothic`\n", " * この方法では一部の日本語が文字化けする場合がある。\n", "\n", "方法2の場合:\n", "* [このサイト](https://ipafont.ipa.go.jp/node193#jp)から次の内の1つをダウンロードする。\n", " * 2書体パック(IPAex明朝(Ver.xxx)、IPAexゴシック(Ver.xxx))\n", " * IPAex明朝 (Ver.xxx)\n", " * IPAexゴシック(Ver.xxx)\n", "* [このサイト](https://ipafont.ipa.go.jp/node72#jp)に従ってインストールする。\n", "* 次の両方もしくは1つがPCにインストールされる\n", " * IPAexMincho(IPAex明朝)\n", " * IPAexGothic(IPAexゴシック)\n", "\n", "上の例を使い、設定方法の例を示す。" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "jfont = 'IPAexGothic' # (1)\n", "\n", "ax = df0.plot(grid=True\n", " ,style=[':','--','-']\n", " ,marker='o'\n", " ,fontsize=15\n", " )\n", "ax.set_title('縦横タイトル', size= 30, fontname=jfont) # (2)\n", "ax.set_xlabel('横軸', size=20, fontname=jfont) # (3)\n", "ax.set_ylabel('縦軸', size=20, fontname=jfont) # (4)\n", "ax.legend(prop={'family':jfont, 'size':17}) # (5)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ ">* (1) 使用するフォントを`jfont`に割り当てる。\n", ">* (2) 引数`fontname`で`jfont`を指定する。タイトルのフォントが変更される。\n", ">* (3) 引数`fontname`で`jfont`を指定する。横軸名のフォントが変更される。\n", ">* (4) 引数`fontname`で`jfont`を指定する。縦軸名のフォントが変更される。\n", ">* (5) `legend`は他と設定方法が異なる。\n", " * `prop`はフォントのプロパティを設定する引数であり、辞書で指定する。\n", " * キー`family`に値`jfont`を指定する。凡例のフォントが変更される。\n", " * キー`size`に数値を設定してフォントの大きさが変更される。" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "この例では個別にフォントを設定したが、一括で全てのフォントを変更する方法もあるが説明は割愛する。" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## マクロ経済学の例" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 投資関数" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "実質利子率`r`によって投資がどのように変化するかを考えてみよう。まず投資関数を次のように仮定する。" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hidden": true }, "outputs": [], "source": [ "def investment(r):\n", " return 100/(1+r)**50" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "* `100`:実質利子率が`0`の場合の投資\n", "\n", "実質利子率は次の`array`で与えられるとする。" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r_arr = np.arange(0.01,0.11,0.01)\n", "r_arr" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "次に関数`investment`と`r_arr`を使い投資の`array`を作成しよう。" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "array([60.80388247, 37.15278821, 22.81070798, 14.07126153, 8.7203727 ,\n", " 5.42883618, 3.39477594, 2.13212286, 1.34485389, 0.85185513])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inv_arr = investment(r_arr)\n", "inv_arr" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "これらのデータを使い`DataFrame`を作成する。" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "hidden": true }, "outputs": [], "source": [ "df_inv = pd.DataFrame({'investment':inv_arr,\n", " 'interest_rate':r_arr})" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "最初の5行を表示する。" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
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investmentinterest_rate
060.8038820.01
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" ], "text/plain": [ " investment interest_rate\n", "0 60.803882 0.01\n", "1 37.152788 0.02\n", "2 22.810708 0.03\n", "3 14.071262 0.04\n", "4 8.720373 0.05" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_inv.head()" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "ではプロットしてみよう。" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_inv.plot('interest_rate','investment')\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 将来価値" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "`x`万円を実質年率`r`%の利息を得る金融商品に投資し,`t`年間の将来価値(期首の値)をリストで示す関数は以下で与えられた。" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hidden": true }, "outputs": [], "source": [ "def calculate_futre_value(x, r, t):\n", " \n", " value_list = [x] # 初期値が入ったリスト\n", " \n", " for year in range(1,t+1): # 1からtまでの期間\n", " x = x*(1+r) # 来期のxの値の計算\n", " value_list.append(x) # リストに追加\n", " \n", " return value_list # リストを返す" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "これを使い,\n", "* `x`=`100`\n", "* `t`=`30`\n", "\n", "の下で実質利子率が次のリストで与えられる値を取る場合の将来価値を図示する。" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hidden": true }, "outputs": [], "source": [ "r_list = [0.01, 0.03, 0.06] # 実質利子率のリスト\n", "dic = {} # 空の辞書\n", "\n", "for r in r_list:\n", " dic['r='+str(r)] = calculate_futre_value(100, r, 30) # 辞書に追加\n", "\n", "df_future = pd.DataFrame(dic) # DataFrameの作成" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "`dic['r='+str(r)]`の説明:\n", "* `str(r)`:`r_list`の要素のダミーである`r`は浮動小数点型なので関数`str()`を使って文字列型に変換する。\n", "* `'r='+str(r)`:文字列型の`r=`と文字列型の`str(r)`を`+`で結合する。\n", "* `dic['r='+str(r)]`:辞書`dic`にキー・値のペアを作成する。\n", " * キー:`'r='+str(r)`\n", " * 値:`calculate_futre_value(100, r, 30)`の返り値" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "最初の5行を表示する。" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_future.plot()\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## その他のプロット" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 種類" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "メソッド`.plot()`には様々な引数が用意されているが,その中に`kind`がある。次を文字列として指定すると様々な種類のプロットが可能となる。\n", "* `line`:ライン・プロット\n", " * デフォルトの値であり,上で説明した直線・曲線のプロットとなる。\n", " * `.plot()`は`.plot.line()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.line.html)\n", "* `bar`:縦向きの棒グラフ(カテゴリーなどの離散変数に使う)\n", " * `.plot(kind='bar')`は`.plot.bar()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.bar.html)\n", "* `barh`:横向き棒グラフ(カテゴリーなどの離散変数に使う)\n", " * `barh`の`h`はHorizontalのH\n", " * `.plot(kind='hbar')`は`.plot.hbar()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.barh.html)\n", "* `hist`:ヒストグラム(連続変数に使う)\n", " * `hist`はHISTogramのHIST\n", " * `.plot(kind='hist')`は`.plot.hist()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hist.html)\n", "* `box`:ボックスプロット\n", " * `.plot(kind='box')`は`.plot.box()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.box.html)\n", "* `kde`:カーネル密度推定プロット\n", " * `kde`はKernel Density EstimateのKDE\n", " * `.plot(kind='kde')`は`.plot.kde()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.kde.html)\n", "* `density`:`kde`と同じ\n", "* `area`:エリア・プロット\n", " * `.plot(kind='area')`は`.plot.area()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.area.html)\n", "* `pie`:パイチャート\n", " * `.plot(kind='pie')`は`.plot.pie()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.pie.html)\n", "* `scatter`:散布図(`DataFrame`のみ)\n", " * `.plot(kind='scatter')`は`.plot.scatter()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.scatter.html)\n", "* `hexbin`: : 六角形プロット(`DataFrame`のみ)\n", " * `.plot(kind='hexbin')`は`.plot.hexbin()`と同じでもある。\n", " * [プロットの例](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hexbin.html)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "上で説明した[ライン・プロットの引数](sec:5-options)は他のプロットと共通のものが多いが,それぞれ独自の引数もある。\n", "\n", "以下では散布図,ヒストグラム,カーネル密度推定プロット,棒グラフについて説明する。\n", "\n", "説明には次のコードで生成する`DataFrame`を使う。列`X`と`Y`には標準正規分布(平均`0`,標準偏差`1`)から生成した100個のランダム変数が含まれている。`Z`には正規分布(平均`2`,標準偏差`1`)から抽出した100個のランダム変数が格納されている。" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "hidden": true }, "outputs": [], "source": [ "df1 = pd.DataFrame({'X':np.random.normal(size=100),\n", " 'Y':np.random.normal(size=100),\n", " 'Z':np.random.normal(loc=2, size=100)})" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "`X`,`Y`は同じ標準正規分布から生成されているが,異なる値から構成されている。" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 散布図" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "散布図をプロットする場合は次の構文となる。\n", "```\n", "df1.plot(<横軸の列ラベル>, <縦軸の列ラベル>, kind='scatter')\n", "```\n", "列`X`と`Y`を使ってプロットしてみよう。" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df1.plot('X', 'Y', kind='scatter')\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**<基本的な引数>**\n", "\n", "様々な引数があり図に「飾り付け」をすることができる。詳しくは[このリンク](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.scatter.html)を参照することにして,ここでは基本的な引数だけを紹介する。\n", "* `title`:図のタイトル(文字列型で指定)\n", "* `color`:色(リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html))\n", " * `r`は赤\n", " * `k`は黒\n", " * `g`はグリーン\n", "* `marker`:観測値のマーカー(`o`,`.`,`>`,`^`などがある; [参照サイト](https://matplotlib.org/3.2.2/api/markers_api.html))\n", "* `s`:マーカーの大きさ(`markersize`ではない!)\n", "* `fontsize`:横軸・縦軸の数字のフォントサイズの設定\n", "* `figsize`:図の大きさ\n", " * `figsize=(キャンバスの横幅、キャンバスの縦の長さ)`\n", "* `legend`:凡例の表示を指定\n", " * `DataFrame`の場合はデフォルトは`True`\n", " * `Series`の場合はデフォルトは`False`\n", "* `label`:凡例の表現を指定(`Series`のみ有効)\n", "* `grid`:グリッド表示(ブール型;デフォルトは`False`)\n", "* `ax`:プロットする「軸」を指定する。" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# df1はDataFrame\n", "\n", "df1.plot('X', 'Y', kind='scatter',\n", " title='タイトルです',\n", " color='red',\n", " marker='^',\n", " s=100,\n", " fontsize=20,\n", " figsize=(8,4),\n", "# legend=True,\n", " label='Y',\n", " grid=True\n", " )\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "この図ではタイトルと横軸・縦軸ラベルの大きさが調整できていないが,上で説明した[タイトルとラベルのサイズの調整](sec:5-titlelabels)のコードと共通なので,そちらを参照しよう。" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "またライン・プロットと同じように引数`ax`を使うことにより,複数の散布図を重ねてプロットできる。次のコードは`X`と`Y`,そして`X`と`Z`の散布図を同じ「軸」に表示している。" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# df1はDataFrame\n", "\n", "ax_ = df1.plot('X', 'Y', kind='scatter',label='Yのデータ')\n", "df1.plot('X', 'Z', kind='scatter',\n", " color='red', marker='^', label='Zのデータ', ax=ax_)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### ヒストグラム" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "ヒストグラムは次の構文となる。\n", "```\n", "df1.plot(kind='hist')\n", "```\n", "この場合,`df1`にある全ての列(整数型もしくは浮動小数点型)がヒストグラムとして重ねて表示される。特定の列だけを使う場合は次のように列を選択して`plot()`を使おう。" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# df1['Y']はSeries\n", "\n", "df1['Y'].plot(kind='hist')\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**<基本的な引数>**\n", "\n", "様々な引数があり図に「飾り付け」をすることができる。詳しくは[このリンク](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hist.html)を参照することにして,ここでは基本的な引数だけを紹介する。\n", "* `title`:図のタイトル(文字列型で指定)\n", "* `bins`:柱の数\n", "* `color`:色(リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html))\n", " * `r`は赤\n", " * `k`は黒\n", " * `g`はグリーン\n", "* `edgecolor`又は`ec`:柱の境界線の色\n", "* `alpha`:透明度(`0`から`1.0`; デフォルトは`1`)\n", "* `density`:縦軸を相対度数にする(デフォルトは`False`)\n", " * 全ての柱の**面積**の合計が`1`になるように縦軸が調整される。1つの柱の高さが`1`よりも大きくなる場合もある。\n", "* `fontsize`:横軸・縦軸の数字のフォントサイズの設定\n", "* `figsize`:図の大きさ\n", " * `figsize=(キャンバスの横幅、キャンバスの縦の長さ)`\n", "* `legend`:凡例の表示を指定\n", " * `DataFrame`の場合はデフォルトは`True`\n", " * `Series`の場合はデフォルトは`False`\n", "* `label`:凡例の表現を指定(`Series`のみ有効)\n", "* `grid`:グリッド表示(ブール型;デフォルトは`False`)\n", "* `subplots`:複数の図をプロットする(詳細はライン・プロットを参照)\n", "* `ax`:プロットする「軸」を指定する。" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "引数を指定して`X`のヒストグラムをプロットしてみよう。" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# XはSeriesとして抽出されている\n", "\n", "df1['X'].plot(kind='hist',\n", " bins=20,\n", " title='タイトルです',\n", " color='red',\n", " ec='white',\n", " alpha=0.5,\n", " density=True,\n", " fontsize=20,\n", " figsize=(8,4),\n", " legend=True,\n", " label='Xの凡例',\n", " grid=True\n", " )\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "この図ではタイトルと横軸・縦軸ラベルの大きさが調整できていないが,上で説明した[タイトルとラベルのサイズの調整](sec:5-titlelabels)のコードと共通なのでそちらを参照しよう。" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "次に複数のデータを重ねてプロットする場合を考えよう。ここで役に立つ引数が`alpha`である。" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df1[['Y','Z']].plot(kind='hist',\n", " bins=30,\n", " color=['r','k'],\n", " edgecolor='k',\n", " alpha=0.4)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "濃い部分が重なっている部分となる。また柱を積み上げて表示するには`stacked=True`(デフォルトは`False`)を使う。" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df1[['Y','Z']].plot(kind='hist', \n", " bins=30,\n", " color=['r','k'],\n", " edgecolor='white',\n", " stacked=True)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "赤の上に黒が積み上げられている。" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true, "tags": [ "remove-cell" ] }, "source": [ "### カーネル密度推定プロット" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "(sec:5-kernel)=\n", "### カーネル密度推定プロット" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "ヒストグラムは縦軸に度数,横軸に階級を取ったグラフだが,関連する手法にカーネル密度推定と呼ばれるものがある。考え方は簡単で,上のようなヒストグラムのデータに基づき面積が1になるようにスムーズな分布を推定する手法である。ヒストグラムとカーネル密度関数を重ねてプロットすることもできる。\n", "\n", "次の構文となる。\n", "```\n", "df1.plot(kind='kde')\n", "```\n", "この場合,`df1`にある全ての列がヒストグラムとして重ねて表示される。特定の列だけを使う場合は列を選択して`plot()`を使う。" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df1.plot(kind='kde')\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**<基本的な引数>**\n", "\n", "様々な引数があり図に「飾り付け」をすることができる。詳しくは[このリンク](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.kde.html)を参照することにして,ここでは基本的な引数だけを紹介する。\n", "* `title`:図のタイトル(文字列型で指定)\n", "* `linestyle`又は`style`:線のスタイル(リストにして列の順番で指定する;`-`,`--`,`-.`,`:`)\n", "* `linewidth` or `lw`:線の幅\n", "* `color`:色(リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html))\n", " * `r`は赤\n", " * `k`は黒\n", " * `g`はグリーン\n", "* `alpha`:透明度(`0`から`1.0`; デフォルトは`1`)\n", "* `fontsize`:横軸・縦軸の数字のフォントサイズの設定\n", "* `figsize`:図の大きさ\n", " * `figsize=(キャンバスの横幅、キャンバスの縦の長さ)`\n", "* `legend`:凡例の表示を指定\n", " * `DataFrame`の場合はデフォルトは`True`\n", " * `Series`の場合はデフォルトは`False`\n", "* `label`:凡例の表現を指定(`Series`のみ有効)\n", "* `grid`:グリッド表示(ブール型;デフォルトは`False`)\n", "* `ax`:プロットする「軸」を指定する。" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "引数を指定して`X`をプロットしてみる。" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# XはSeriesとして抽出されている\n", "\n", "df1['X'].plot(kind='kde',\n", " title='タイトルです',\n", " linewidth=5,\n", " linestyle='-.',\n", " color='red',\n", " alpha=0.5,\n", " fontsize=20,\n", " figsize=(8,4),\n", " legend=True,\n", " label='Xの凡例',\n", " grid=True\n", " )\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "この図ではタイトルと横軸・縦軸ラベルの大きさが調整できていないが,上で説明した[タイトルとラベルのサイズの調整](sec:5-titlelabels)のコードと共通なのでそちらを参照しよう。" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "次にヒストグラムとカーネル密度推定プロットを重ねて図示してみる。ここで重要な点がヒストグラムに引数`density=True`を設定することである。これがないと縦軸の単位が異なり上手く表示できない。" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_ = df1['X'].plot(kind='hist',\n", " density=True,\n", " label='Xのヒストグラム',\n", " legend=True)\n", "df1['X'].plot(kind='kde',\n", " ax=ax_,\n", " label='XのKDE',\n", " legend=True)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### 縦線・横線" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "図に縦線や横線を追加したい場合がある。その場合は,[タイトルとラベルのサイズの調整](sec:5-titlelabels)にあるように「軸」に追加していく事になる。次のような書き方となる。\n", "\n", "* 縦線の場合\n", " ```\n", " ax_.axvline(<横軸の値>)\n", " ```\n", " ここで`axvline`の`ax`はAXis,`v`はVertical,`line`はLINEのことを表している。\n", "* 横線の場合\n", " ```\n", " ax_.axhline(<縦軸の値>)\n", " ```\n", " ここで`axhline`の`ax`はAXis,`h`はHorizontal,`line`はLINEのことを表している。\n", "\n", "ここで`ax_`は`.plot()`で返された「軸」のことである。\n", "\n", "`Y`のヒストグラムを使ってプロットしてみよう。" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_ = df1['Y'].plot(kind='hist', alpha=0.1)\n", "ax_.axvline(0)\n", "ax_.axhline(10)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**<基本的な引数>**\n", "\n", "様々な引数があり図に「飾り付け」をすることができる。詳しくは[このリンク](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axvline.html)と[このリンク](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html)を参照することにして,ここでは基本的な引数だけを紹介する。\n", "* `ymin`:`axvline`の縦軸における最小値(`0`~`1`の値; デフォルト`0`)\n", "* `ymax`:`axvline`の縦軸における最大値(`0`~`1`の値; デフォルト`1`)\n", "* `xmin`:`axhline`の横軸における最小値(`0`~`1`の値; デフォルト`0`)\n", "* `xmax`:`axhline`の横軸における最大値(`0`~`1`の値; デフォルト`1`)\n", "* `linestyle`:線のスタイル(リストにして列の順番で指定する;`-` `--` `-.` `:`)\n", "* `linewidth` or `lw`:線の幅\n", "* `color`:色(リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html))\n", " * `r`は赤\n", " * `k`は黒\n", " * `g`はグリーン\n", "* `alpha`:透明度(`0`から`1.0`; デフォルトは`1`)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "引数を指定してプロットしてみる。" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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l5v3A/eN9FhFLgEuBizOzn581BOz4WKja40pUnojYH7geuCYz79mZfdje6QER8QfAxcCpJQS+JuTjStSls8L/LjC0s4EPrvR7xXnAh4B7I945d3lVZt7VXklqi48r0XacCBwGLB+TE/dn5uWT2Yl35EpSQWzvSFJBDH1JKoihL0kFMfQlqSCGviQVxNCXpIIY+pJUkP8HUTmf8TRSya8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_ = df1['Y'].plot(kind='hist', alpha=0.1)\n", "ax_.axvline(0,\n", " ymin=0.3,\n", " ymax=0.95,\n", " linestyle=':',\n", " linewidth=5,\n", " color='g',\n", " alpha=0.8)\n", "ax_.axhline(10,\n", " xmin=0.05,\n", " xmax=0.7,\n", " linestyle='-.',\n", " linewidth=3,\n", " color='k',\n", " alpha=0.5)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "最後に上のヒストグラムとカーネル密度推定プロットに縦線を加えてみよう。" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_ = df1['X'].plot(kind='hist',\n", " density=True,\n", " label='Xのヒストグラム',\n", " legend=True)\n", "df1['X'].plot(kind='kde',\n", " ax=ax_,\n", " label='XのKDE',\n", " legend=True)\n", "ax_.axvline(0, color='red')\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true, "tags": [ "remove-cell" ] }, "source": [ "### 棒グラフ" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "(sec:5-bar)=\n", "### 棒グラフ" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "まず次のコードでデータを準備しよう。" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "hidden": true }, "outputs": [], "source": [ "df2 = pd.DataFrame({'country':['A','B','C'],\n", " 'gdp':[100,90,110],\n", " 'con':[50,60,55],\n", " 'inv':[15,10,20],\n", " 'gov':[10,5,30],\n", " 'netex':[25,15,5]})" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "3国のGDPとその構成要素からなる`DataFrame`である。\n", "* `country`:国\n", "* `gdp`:GDP\n", "* `con`:消費\n", "* `inv`:投資\n", "* `gov`:政府支出\n", "* `netex`:純輸出\n", "\n", "この`DataFrame`を使って棒グラフの作成方法を説明するが,次の構文となる。\n", "```\n", "df1.plot(<横軸の列ラベル>, <縦軸の列ラベル>, kind='bar')\n", "```\n", "まずA国の`gdp`の棒グラフを表示してみよう。" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEDCAYAAAAhsS8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAPOklEQVR4nO3df6zddX3H8edrtFJMWS3ldt2oeNHoEGuwcGcL27RjistYBmTG+IdQYLQjZpL4zyQuxozGH4NsUUGYpSrqWBojwUTHj0WXthDsWFFB0C0RJnIn1UtX6/hRLnrf++Oesku9tbf3nHvP7ec+H//0fL/3e77nfXLJk28/p+ecVBWSpHb8Wr8HkCT1lmGXpMYYdklqjGGXpMYYdklqjGGXpMYs6PcAACeeeGINDg72ewxJOqrcf//9T1bVwMH750TYBwcH2bVrV7/HkKSjSpLHJtvvUowkNcawS1JjDLskNWZOrLFP5vnnn2d4eJj9+/f3e5SeWbRoEStXrmThwoX9HkVSw+Zs2IeHhzn++OMZHBwkSb/H6VpVsWfPHoaHhznllFP6PY6khs3ZpZj9+/ezbNmyJqIOkIRly5Y19TcQSXPTnA070EzUD2jt+Uiam+bsUszBBq/6556e7wcfPa/rc6xYsYLdu3f3YBpJ6p2jJuySjn69vkCba3pxwdgLhv0QxsbGuPTSS3nkkUdYvHgxVcU111zDFVdcwZIlSzj77LNfOHbbtm1cd911jI2NMTw8zNDQEDfccINLL5L6Yk6vsffTXXfdxf79+7nnnnt473vfy+DgIBs2bOD666/nzjvv5Nxzz33R8d/85jf5zGc+w3333cfIyAi33XZbnyaXNN8Z9kNYsmQJTz/9NFXFvn372LdvH48//jhnnnkmAGvXrn3R8WvWrGHp0qUk4ZxzzuHhhx/ux9iSZNgPZfXq1Tz11FO8+c1v5tOf/jSbNm1icHCQnTt3AnD77be/aKnlwQcf5JlnngFgx44dvO51r+vL3JLkGvsh7N27l9HRURYsWEBVsXXrVrZs2cKGDRs45phjWLduHcuWLXvh+OXLl7NhwwYeffRRzjjjDC688MI+Ti9pPjtqwj7brzZv376dNWvWcM0117Bv3z5Wr17Nhg0buPfee184ZtOmTS/cXrFiBbfccsuszihJkzlqwj7bTj/9dG688UbOPfdcnnvuOS6//HJWrFjR77Ek6bAM+yGcdtpp7NixY0rHrlu3jnXr1s3sQJI0Rb54KkmNmdNhr6p+j9BTrT0fSXPTnA37okWL2LNnTzMxPPCxvYsWLer3KJIaN2fX2FeuXMnw8DAjIyP9HqVnDnzRhiTNpDkb9oULF/qFFJI0DXN2KUaSND2GXZIac9iwJ3l7ki8m+eGEfacn2Z5kZ5KvJFna2f+yJLcmuTfJvyV5wwzOLkmaxFSu2EeAdwMvAcj4J19tBa6sqrXAHcDVnWOvBbZV1dnABuDmXg8sSfrVDhv2qtpeVU9O2PUaYG9VPdDZ3gIc+CCXP+5sU1UPAj9L8qoezitJOozp/KuYZcALX/RZVaNJDpxnQVU9O+HYJ4DlwCMHnyTJRmAjwMknnzyNMabPr+eS1LLpvHj6Y8ZjDUCSY4HRzuazne0DVnSO/yVVtbmqhqpqaGBgYBpjSJImc8Rhr6pHgMVJVnV2XcT4OjvAV4FLAZK8Fji+qh7txaCSpKmZ7huULgFuSjIG7AHWd/Z/APhckvVAAZd1PaEk6YhMOexVtWLC7W8DZ01yzF7gT3symSRpWnyDkiQ1xrBLUmMMuyQ1xrBLUmMMuyQ1xrBLUmMMuyQ1xrBLUmMMuyQ1xrBLUmMMuyQ1xrBLUmMMuyQ1xrBLUmOm+3nsUl/4tYbS4XnFLkmNMeyS1BjDLkmNMeyS1BjDLkmNMeyS1BjDLkmNMeyS1BjDLkmNMeyS1BjDLkmNMeyS1BjDLkmNMeyS1BjDLkmN6erz2JO8H7gAeB74EXAZ8ErgE8CxwAhwcVXt7W5MSdJUTfuKPcnrgfOBs6rqd4Fh4ApgK3BlVa0F7gCu7sWgkqSp6WYp5kngOf7/qv8Y4GfA3qp6oLNvC+BXwkjSLJr2UkxVPZHkeuCGJN8H9gIPAbsnHDOaZNLHSLIR2Ahw8sknT3cMSdJBulmK+QPgTVX151X1EeBhxpdilk845lhgdLL7V9XmqhqqqqGBgYHpjiFJOkg3SzGnMv4C6QEvYfxvAIuTrOrsu4jxdXZJ0izp5l/FfB5Ym+QhYB/wLHA58DLgpiRjwB5gfbdDSpKmrps19qc5dLTPmu55JUnd8Q1KktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktSYBd3cOcnLgU8CxwM/B94H/AL4BHAsMAJcXFV7u5xTkjRFXYUd2Ay8r6oeTHICcAywA3hnVT2Q5N3A1cB7unwcSdIUTXspJskKYBFwWZIdwIeA3wL2VtUDncO2AOd1PaUkacq6WWM/GVgNfK6q3gT8CLgW2H3ggKoapfu/FUiSjkA3Yf8p8N2q+lZn+0vAQmD5gQOSHAuMTnbnJBuT7Eqya2RkpIsxJEkTdRP27wOLkpza2X4LcD+wOMmqzr6LgDsmu3NVba6qoaoaGhgY6GIMSdJE014mqaqxJJcANyZZAPwEuAz4R+CmJGPAHmB9LwaVJE1NV+vfVfUg8AcH7f42cFY355UkTZ9vUJKkxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWqMYZekxhh2SWpMT8Ke5ANJtnVun55ke5KdSb6SZGkvHkOSNDVdhz3JEHBK53aArcCVVbUWuAO4utvHkCRNXVdhT3Ic8DHgqs6u1wB7q+qBzvYW4LxuHkOSdGS6vWK/FvhYVf2ks70M2H3gh1U1CiyY7I5JNibZlWTXyMhIl2NIkg6YdtiTvA1YWlVfmrD7x8DyCcccC4xOdv+q2lxVQ1U1NDAwMN0xJEkHmfRqeor+BBhI8uXO9irgg8DiJKuq6iHgIsbX2SVJs2TaYa+q90zcTrKtqi5O8gbgpiRjwB5gfXcjSpKORDdX7C9SVes6f34bOKtX55UkHRnfoCRJjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5JjTHsktQYwy5Jjekq7EnekeQbSe5O8sUkL01yepLtSXYm+UqSpb0aVpJ0eNMOe5ITgL8Czqmq3wceAzYAW4Erq2otcAdwdS8GlSRNzbTDXlX/A/xeVT3b2bUA2A/sraoHOvu2AOd1N6Ik6Uh0tRRTVfuTLEryceA44CFg94SfjzIefEnSLOl2jX0lcBtwZ1VdwXjUl0/4+bHA6CHuuzHJriS7RkZGuhlDkjRBN2vsi4CbgY1VdQdAVT0CLE6yqnPYRYyvs/+SqtpcVUNVNTQwMDDdMSRJB+lmmeQtwGuBLyQ5sO9fgUuAm5KMAXuA9d0MKEk6MtMOe1V9FTjpED8+a7rnlSR1xzcoSVJjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNcawS1JjDLskNWZGwp7kHUnuS3J/kr+biceQJE2u52FP8gpgE/BWYAhYmeTPev04kqTJzcQV+x8Bt1bVvqoq4FPABTPwOJKkScxE2JcBuydsPwEsn4HHkSRNYsEMnPPHwCkTtld09r1Iko3Axs7mU0n+cwZmmStOBJ6crQfL387WI80L/u6Obq3//l4x2c6Mr5b0TpLfBL4GrK2q/03yBeDLVXVrTx/oKJJkV1UN9XsOHTl/d0e3+fr76/kVe1U9keTDwI4ko8Dd8znqkjTbZmIphqq6BbhlJs4tSfrVfIPS7Njc7wE0bf7ujm7z8vfX8zV2SVJ/ecU+C5K8qt8zSJo/DPsMSbIgyduTfA3Y2u95JM0fhr3HkpyS5CPALuBvgPdU1e/0eSypeUmOS3LlQfvel2RRv2bqF8PeQ0nuAq4FvgGcCTxYVd/r71Q6EklWJbktyYc7/5P+XpJHk7y137PpsD4xyb7HgY/N8hx9Z9h7aytwPOOfjfNGwFemjz7XAzcBPwRuZfzd0WuBq/s5lKbktKp6Udyr6p+A1/Rpnr4x7D1UVZ+tqrcBHwfWA29KclWSl/d5NE3dgqq6var+AVhSVXdX1U+A0X4PpsM61O9o3nVu3j3h2VBVD1TVFcCpwE+B2/o7kY7AxDg8NuH2L2Z7EB2x/0pywcQdnY8Mf7Q/4/SP/45dmiDJc8AeIMAJE24vrap59yLc0STJCcCXgOeA/wBeDfw6cH5V7e3nbLPNsEtqSpIzgFcCj1XVv/d7nn4w7JLUGNfYJakxhl2SGmPYpWlIsiTJ6/s9hzSZGfk8dmkeuBAYBL7T5zmkX+IVu5qW5P1J7ktyf5KNSV6V5F+SbEvytSS/3TluW5JTO7ffkuTmzu2bk3ykc5/vJnlrkuOAq4BLkmztHPdQkg8m+VCS7yQ5qbP/XUmu7cuT17xl2NWsJH8IrAPOAtYw/t/7Z4FNVbUOeD9w8xRO9dKqOhf4C+Avq+pZ4KPAzVX1zs4xJwE7q+qvgU8Cl3T2vwv4VA+ejjRlhl0tWw3cVVW/qKqfdz4m4NVVdTdAVd0HvCJJDnOeOzp/PgEsOcQxVVV3dW5/Hji/c9U+VlXf7+5pSEfGsKtl3wLelmQBQJLLgB8keWNn+0zgv2v8zRz7gN/o3O+CKZy7gJdM2H7howiq6hng64x/oNi8/Go29ZcvnqpZVfX1JGuAnUnGGP+0xouBTyZZCIx1tgH+HrguyW7GP0t/8WFOfz/wwSQnVdX6SX7+KeAe4O09eCrSEfGdp9IMSHIRcEpV+XG/mnVesUs9luRSxl88Pb/Po2ie8opdkhrji6eS1BjDLkmNMeyS1BjDLkmNMeyS1BjDLkmN+T8fxpXqwoOR4wAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df2.plot('country', 'gdp', kind='bar')\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "複数の棒(データ)を並べたい場合もあるだろう。その場合は`<縦軸の列ラベル>`にリストとして指定すれば表示できる。" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df2.plot('country', ['gdp','con'], kind='bar')\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**<基本的な引数>**\n", "\n", "詳しい引数についての説明は[このリンク](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.bar.html)を参照することにして,ここでは基本的な引数だけを紹介する。\n", "* `color`:色(リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html))\n", " * `r`又は`red`:赤\n", " * `k`又は`black`:黒\n", " * `g`又は`green`:グリーン\n", "* `stacked`:(ブール型;デフォルトは`False`)\n", " * 複数データを使う場合に棒を積み上げるかどうかを指定\n", "* `fontsize`:横軸・縦軸の数字のフォントサイズの設定\n", "* `figsize`:図の大きさ\n", " * `figsize=(キャンバスの横幅、キャンバスの縦の長さ)`\n", "* `legend`:凡例の表示を指定\n", " * `DataFrame`の場合はデフォルトは`True`\n", "* `label`:凡例の表現を指定\n", "* `grid`:グリッド表示(ブール型;デフォルトは`False`)\n", "* `rot`(rotationの略):横軸の変数の表示の角度(デフォルトは`90`)\n", "* `subplots`:複数の図をプロットする(詳細はライン・プロットを参照)\n", "* `ax`:プロットする「軸」を指定する。\n", "\n", "これらの引数を使いプロットしてみよう。" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df2.plot('country', ['con','inv','gov','netex'], kind='bar',\n", " color=['red','black','green','orange'],\n", " stacked=True,\n", " fontsize=20,\n", " figsize=(8,4),\n", "# legend=False \n", " label=['消費','投資','政府支出','純輸出'],\n", " grid=True,\n", " rot=0\n", " )\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "次に,ライン・プロットを追加する例を考えてみよう。`df2`には列`gdp`があり,それを表すライン・プロットを重ねることにしよう。" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_ = df2.plot('country', ['con','inv','gov','netex'],\n", " kind='bar',\n", " stacked=True,\n", " fontsize=15,\n", " label=['消費','投資','政府支出','純輸出'],\n", " rot=0)\n", "\n", "df2['gdp'].plot(color='black', # ライン・プロット (Series)\n", " marker='o',\n", " legend=True,\n", " label='国内総生産',\n", " ax=ax_) # Seriesに有効\n", "\n", "ax_.set_title('3カ国のGDPと構成要素', size=20)\n", "ax_.set_xlabel('国', size=15)\n", "ax_.set_ylabel('単位:億米ドル', size=15)\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "棒の高さとGDPのマーカーの高さは同じであることがわかる。このコードでは次の点に注意する必要がある。`gdp`を`Series`として抽出し,メソッド`plot()`を使っていることである。これにより,凡例の表記を自由に変更できることになる。" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 4 }