{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# equals\n", "## DataFrameが等しいことを確認する\n", "\n", "２つのDataFrameを比較して正しいことを確認する機会があった時のメモ" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NAを含むDataFrameを作成" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
abcd
014NaN1
144NaN4
224NaN3
\n", "
" ], "text/plain": [ " a b c d\n", "0 1 4 NaN 1\n", "1 4 4 NaN 4\n", "2 2 4 NaN 3" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(0)\n", "df = pd.DataFrame(np.random.random_integers(1, 4, size=(3, 4)), columns=list(\"abcde\"))\n", "df[\"c\"] = np.nan\n", "other = df.copy()\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 各要素が等しいか, DataFrame同士が等しいかを確認" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
abcd
0TrueTrueFalseTrue
1TrueTrueFalseTrue
2TrueTrueFalseTrue
\n", "
" ], "text/plain": [ " a b c d\n", "0 True True False True\n", "1 True True False True\n", "2 True True False True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df == other" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(False, True)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.nan == np.nan, np.nan != np.nan" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.equals(other)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NA同士は等しくない(SQLにおけるNULL)が、DataFrameとしては等しい" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 等しくない場合、どこが等しくないかを確認する\n", "\n", "- NAを特定の文字列にし、要素の比較をしたときに等しくなるようにする\n", "- DataFrame同士が等しくないようにするため、otherを変更する" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
abcd
04100NA String1
144NA String4
244NA String3
\n", "
" ], "text/plain": [ " a b c d\n", "0 4 100 NA String 1\n", "1 4 4 NA String 4\n", "2 4 4 NA String 3" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.fillna(\"NA String\")\n", "other = other.fillna(\"NA String\")\n", "other[\"a\"] = 4\n", "other.iloc[0, 1] = 100\n", "other" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
abcd
0FalseFalseTrueTrue
1TrueTrueTrueTrue
2FalseTrueTrueTrue
\n", "
" ], "text/plain": [ " a b c d\n", "0 False False True True\n", "1 True True True True\n", "2 False True True True" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# == の method version\n", "eq = df.eq(other)\n", "eq" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.equals(other)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- NAであった要素は等しくなっている\n", "- 変更をしたため、DataFrameとしては等しくない" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 等しくないColumnとIndexの特定およびどれくらい等しいか" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "要素比較結果のDataFrameに対してallをColumnとIndex方向の両方に適用して特定する" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
0
0False
1True
2False
\n", "
" ], "text/plain": [ " 0\n", "0 False\n", "1 True\n", "2 False" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(eq.all(axis=1))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
abcd
0FalseFalseTrueTrue
\n", "
" ], "text/plain": [ " a b c d\n", "0 False False True True" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(eq.all()).T" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
abcd
01.0000002.00000033
10.3333330.66666711
\n", "
" ], "text/plain": [ " a b c d\n", "0 1.000000 2.000000 3 3\n", "1 0.333333 0.666667 1 1" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat(\n", " [\n", " pd.DataFrame(eq.sum()).T,\n", " pd.DataFrame(eq.sum()).T / len(df)\n", " ]\n", ", ignore_index=True)\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n", " a b c d\n", "0 33.33% 66.67% 100.00% 100.00%\n" ] } ], "source": [ "print(pd.options.display.float_format)\n", "with pd.option_context(\"display.float_format\", \"{:.2f}%\".format):\n", " print(pd.DataFrame(eq.sum()).T / len(df) * 100)" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python [default]", "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": "3.5.2" }, "toc": { "toc_cell": false, "toc_number_sections": true, "toc_threshold": 6, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }