{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 十分钟上手 Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`pandas` 是一个 `Python Data Analysis Library`。\n", "\n", "安装请参考官网的教程，如果安装了 `Anaconda`，则不需要安装 `pandas` 库。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 产生 Pandas 对象" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`pandas` 中有三种基本结构：\n", "\n", "- `Series`\n", " - 1D labeled homogeneously-typed array\n", "- `DataFrame`\n", " - General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns\n", "- `Panel`\n", " - General 3D labeled, also size-mutable array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Series" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "一维 `Series` 可以用一维列表初始化：" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1\n", "1 3\n", "2 5\n", "3 NaN\n", "4 6\n", "5 8\n", "dtype: float64\n" ] } ], "source": [ "s = pd.Series([1,3,5,np.nan,6,8])\n", "\n", "print s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "默认情况下，`Series` 的下标都是数字（可以使用额外参数指定），类型是统一的。\n", "\n", "### DataFrame\n", "\n", "`DataFrame` 则是个二维结构，这里首先构造一组时间序列，作为我们第一维的下标：" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n", " '2013-01-05', '2013-01-06'],\n", " dtype='datetime64[ns]', freq='D')\n" ] } ], "source": [ "dates = pd.date_range('20130101', periods=6)\n", "\n", "print dates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "然后创建一个 `DataFrame` 结构：" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-01-0.605936-0.861658-1.0019241.528584
2013-01-02-0.1654080.3883381.1871871.819818
2013-01-030.065255-1.608074-1.282331-0.286067
2013-01-041.2893050.497115-0.2253510.040239
2013-01-050.0382320.875057-0.0925260.934432
2013-01-06-2.163453-0.0102791.6998861.291653
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432\n", "2013-01-06 -2.163453 -0.010279 1.699886 1.291653" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "默认情况下，如果不指定 `index` 参数和 `columns`，那么他们的值将用从 `0` 开始的数字替代。\n", "\n", "除了向 `DataFrame` 中传入二维数组，我们也可以使用字典传入数据：" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCDEF
012013-01-0213testfoo
112013-01-0213trainfoo
212013-01-0213testfoo
312013-01-0213trainfoo
\n", "
" ], "text/plain": [ " A B C D E F\n", "0 1 2013-01-02 1 3 test foo\n", "1 1 2013-01-02 1 3 train foo\n", "2 1 2013-01-02 1 3 test foo\n", "3 1 2013-01-02 1 3 train foo" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = pd.DataFrame({'A' : 1.,\n", " 'B' : pd.Timestamp('20130102'),\n", " 'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n", " 'D' : np.array([3] * 4,dtype='int32'),\n", " 'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n", " 'F' : 'foo' })\n", "\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "字典的每个 `key` 代表一列，其 `value` 可以是各种能够转化为 `Series` 的对象。\n", "\n", "与 `Series` 要求所有的类型都一致不同，`DataFrame` 值要求每一列数据的格式相同：" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A float64\n", "B datetime64[ns]\n", "C float32\n", "D int32\n", "E category\n", "F object\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 查看数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 头尾数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`head` 和 `tail` 方法可以分别查看最前面几行和最后面几行的数据（默认为 5）：" ] }, { "cell_type": "code", "execution_count": 7, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCD
2013-01-01-0.605936-0.861658-1.0019241.528584
2013-01-02-0.1654080.3883381.1871871.819818
2013-01-030.065255-1.608074-1.282331-0.286067
2013-01-041.2893050.497115-0.2253510.040239
2013-01-050.0382320.875057-0.0925260.934432
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "最后 3 行：" ] }, { "cell_type": "code", "execution_count": 8, "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
2013-01-041.2893050.497115-0.2253510.040239
2013-01-050.0382320.875057-0.0925260.934432
2013-01-06-2.163453-0.0102791.6998861.291653
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432\n", "2013-01-06 -2.163453 -0.010279 1.699886 1.291653" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 下标，列标，数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下标使用 `index` 属性查看：" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n", " '2013-01-05', '2013-01-06'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "列标使用 `columns` 属性查看：" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'A', u'B', u'C', u'D'], dtype='object')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "数据值使用 `values` 查看：" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.60593585, -0.86165752, -1.00192387, 1.52858443],\n", " [-0.16540784, 0.38833783, 1.18718697, 1.81981793],\n", " [ 0.06525454, -1.60807414, -1.2823306 , -0.28606716],\n", " [ 1.28930486, 0.49711531, -0.22535143, 0.04023897],\n", " [ 0.03823179, 0.87505664, -0.0925258 , 0.93443212],\n", " [-2.16345271, -0.01027865, 1.69988608, 1.29165337]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 统计数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "查看简单的统计数据：" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
count6.0000006.0000006.0000006.000000
mean-0.257001-0.1199170.0474900.888110
std1.1266570.9387051.1826290.841529
min-2.163453-1.608074-1.282331-0.286067
25%-0.495804-0.648813-0.8077810.263787
50%-0.0635880.189030-0.1589391.113043
75%0.0584990.4699210.8672591.469352
max1.2893050.8750571.6998861.819818
\n", "
" ], "text/plain": [ " A B C D\n", "count 6.000000 6.000000 6.000000 6.000000\n", "mean -0.257001 -0.119917 0.047490 0.888110\n", "std 1.126657 0.938705 1.182629 0.841529\n", "min -2.163453 -1.608074 -1.282331 -0.286067\n", "25% -0.495804 -0.648813 -0.807781 0.263787\n", "50% -0.063588 0.189030 -0.158939 1.113043\n", "75% 0.058499 0.469921 0.867259 1.469352\n", "max 1.289305 0.875057 1.699886 1.819818" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 转置" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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2013-01-01 00:00:002013-01-02 00:00:002013-01-03 00:00:002013-01-04 00:00:002013-01-05 00:00:002013-01-06 00:00:00
A-0.605936-0.1654080.0652551.2893050.038232-2.163453
B-0.8616580.388338-1.6080740.4971150.875057-0.010279
C-1.0019241.187187-1.282331-0.225351-0.0925261.699886
D1.5285841.819818-0.2860670.0402390.9344321.291653
\n", "
" ], "text/plain": [ " 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06\n", "A -0.605936 -0.165408 0.065255 1.289305 0.038232 -2.163453\n", "B -0.861658 0.388338 -1.608074 0.497115 0.875057 -0.010279\n", "C -1.001924 1.187187 -1.282331 -0.225351 -0.092526 1.699886\n", "D 1.528584 1.819818 -0.286067 0.040239 0.934432 1.291653" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 排序" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`sort_index(axis=0, ascending=True)` 方法按照下标大小进行排序，`axis=0` 表示按第 0 维进行排序。" ] }, { "cell_type": "code", "execution_count": 14, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCD
2013-01-06-2.163453-0.0102791.6998861.291653
2013-01-050.0382320.875057-0.0925260.934432
2013-01-041.2893050.497115-0.2253510.040239
2013-01-030.065255-1.608074-1.282331-0.286067
2013-01-02-0.1654080.3883381.1871871.819818
2013-01-01-0.605936-0.861658-1.0019241.528584
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-06 -2.163453 -0.010279 1.699886 1.291653\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818\n", "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_index(ascending=False)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DCBA
2013-01-011.528584-1.001924-0.861658-0.605936
2013-01-021.8198181.1871870.388338-0.165408
2013-01-03-0.286067-1.282331-1.6080740.065255
2013-01-040.040239-0.2253510.4971151.289305
2013-01-050.934432-0.0925260.8750570.038232
2013-01-061.2916531.699886-0.010279-2.163453
\n", "
" ], "text/plain": [ " D C B A\n", "2013-01-01 1.528584 -1.001924 -0.861658 -0.605936\n", "2013-01-02 1.819818 1.187187 0.388338 -0.165408\n", "2013-01-03 -0.286067 -1.282331 -1.608074 0.065255\n", "2013-01-04 0.040239 -0.225351 0.497115 1.289305\n", "2013-01-05 0.934432 -0.092526 0.875057 0.038232\n", "2013-01-06 1.291653 1.699886 -0.010279 -2.163453" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_index(axis=1, ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`sort_values(by, axis=0, ascending=True)` 方法按照 `by` 的值的大小进行排序，例如按照 `B` 列的大小：" ] }, { "cell_type": "code", "execution_count": 16, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCD
2013-01-030.065255-1.608074-1.282331-0.286067
2013-01-01-0.605936-0.861658-1.0019241.528584
2013-01-06-2.163453-0.0102791.6998861.291653
2013-01-02-0.1654080.3883381.1871871.819818
2013-01-041.2893050.497115-0.2253510.040239
2013-01-050.0382320.875057-0.0925260.934432
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067\n", "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584\n", "2013-01-06 -2.163453 -0.010279 1.699886 1.291653\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values(by=\"B\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 索引" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "虽然 `DataFrame` 支持 `Python/Numpy` 的索引语法，但是推荐使用 `.at, .iat, .loc, .iloc 和 .ix` 方法进行索引。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 读取数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "选择单列数据：" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2013-01-01 -0.605936\n", "2013-01-02 -0.165408\n", "2013-01-03 0.065255\n", "2013-01-04 1.289305\n", "2013-01-05 0.038232\n", "2013-01-06 -2.163453\n", "Freq: D, Name: A, dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"A\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "也可以用 `df.A`：" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2013-01-01 -0.605936\n", "2013-01-02 -0.165408\n", "2013-01-03 0.065255\n", "2013-01-04 1.289305\n", "2013-01-05 0.038232\n", "2013-01-06 -2.163453\n", "Freq: D, Name: A, dtype: float64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.A" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用切片读取多行：" ] }, { "cell_type": "code", "execution_count": 19, "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
2013-01-01-0.605936-0.861658-1.0019241.528584
2013-01-02-0.1654080.3883381.1871871.819818
2013-01-030.065255-1.608074-1.282331-0.286067
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`index` 名字也可以进行切片：" ] }, { "cell_type": "code", "execution_count": 20, "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
2013-01-01-0.605936-0.861658-1.0019241.528584
2013-01-02-0.1654080.3883381.1871871.819818
2013-01-030.065255-1.608074-1.282331-0.286067
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"20130101\":\"20130103\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 使用 `label` 索引" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`loc` 可以方便的使用 `label` 进行索引：" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A -0.605936\n", "B -0.861658\n", "C -1.001924\n", "D 1.528584\n", "Name: 2013-01-01 00:00:00, dtype: float64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[dates[0]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "多列数据：" ] }, { "cell_type": "code", "execution_count": 22, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AB
2013-01-01-0.605936-0.861658
2013-01-02-0.1654080.388338
2013-01-030.065255-1.608074
2013-01-041.2893050.497115
2013-01-050.0382320.875057
2013-01-06-2.163453-0.010279
\n", "
" ], "text/plain": [ " A B\n", "2013-01-01 -0.605936 -0.861658\n", "2013-01-02 -0.165408 0.388338\n", "2013-01-03 0.065255 -1.608074\n", "2013-01-04 1.289305 0.497115\n", "2013-01-05 0.038232 0.875057\n", "2013-01-06 -2.163453 -0.010279" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:,['A','B']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "选择多行多列：" ] }, { "cell_type": "code", "execution_count": 23, "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", "
AB
2013-01-02-0.1654080.388338
2013-01-030.065255-1.608074
2013-01-041.2893050.497115
\n", "
" ], "text/plain": [ " A B\n", "2013-01-02 -0.165408 0.388338\n", "2013-01-03 0.065255 -1.608074\n", "2013-01-04 1.289305 0.497115" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['20130102':'20130104',['A','B']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "数据降维：" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A -0.165408\n", "B 0.388338\n", "Name: 2013-01-02 00:00:00, dtype: float64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['20130102',['A','B']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "得到标量值：" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-0.86165751902832299" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[dates[0],'B']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "不过得到标量值可以用 `at`，速度更快：" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 329 µs per loop\n", "100 loops, best of 3: 31.1 µs per loop\n", "-0.861657519028\n" ] } ], "source": [ "%timeit -n100 df.loc[dates[0],'B']\n", "%timeit -n100 df.at[dates[0],'B']\n", "\n", "print df.at[dates[0],'B']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 使用位置索引" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`iloc` 使用位置进行索引：" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A 1.289305\n", "B 0.497115\n", "C -0.225351\n", "D 0.040239\n", "Name: 2013-01-04 00:00:00, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "连续切片：" ] }, { "cell_type": "code", "execution_count": 28, "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", "
AB
2013-01-041.2893050.497115
2013-01-050.0382320.875057
\n", "
" ], "text/plain": [ " A B\n", "2013-01-04 1.289305 0.497115\n", "2013-01-05 0.038232 0.875057" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[3:5,0:2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "索引不连续的部分：" ] }, { "cell_type": "code", "execution_count": 29, "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", "
AC
2013-01-02-0.1654081.187187
2013-01-030.065255-1.282331
2013-01-050.038232-0.092526
\n", "
" ], "text/plain": [ " A C\n", "2013-01-02 -0.165408 1.187187\n", "2013-01-03 0.065255 -1.282331\n", "2013-01-05 0.038232 -0.092526" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[[1,2,4],[0,2]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "索引整行：" ] }, { "cell_type": "code", "execution_count": 30, "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", "
ABCD
2013-01-02-0.1654080.3883381.1871871.819818
2013-01-030.065255-1.608074-1.282331-0.286067
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[1:3,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "整列：" ] }, { "cell_type": "code", "execution_count": 31, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
BC
2013-01-01-0.861658-1.001924
2013-01-020.3883381.187187
2013-01-03-1.608074-1.282331
2013-01-040.497115-0.225351
2013-01-050.875057-0.092526
2013-01-06-0.0102791.699886
\n", "
" ], "text/plain": [ " B C\n", "2013-01-01 -0.861658 -1.001924\n", "2013-01-02 0.388338 1.187187\n", "2013-01-03 -1.608074 -1.282331\n", "2013-01-04 0.497115 -0.225351\n", "2013-01-05 0.875057 -0.092526\n", "2013-01-06 -0.010279 1.699886" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:, 1:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "标量值：" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.3883378290420279" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[1,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "当然，使用 `iat` 索引标量值更快：" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 236 µs per loop\n", "100 loops, best of 3: 14.5 µs per loop\n" ] }, { "data": { "text/plain": [ "0.3883378290420279" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%timeit -n100 df.iloc[1,1]\n", "%timeit -n100 df.iat[1,1]\n", "\n", "df.iat[1,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 布尔型索引" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "所有 `A` 列大于 0 的行：" ] }, { "cell_type": "code", "execution_count": 34, "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
2013-01-030.065255-1.608074-1.282331-0.286067
2013-01-041.2893050.497115-0.2253510.040239
2013-01-050.0382320.875057-0.0925260.934432
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.A > 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "只留下所有大于 0 的数值：" ] }, { "cell_type": "code", "execution_count": 35, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCD
2013-01-01NaNNaNNaN1.528584
2013-01-02NaN0.3883381.1871871.819818
2013-01-030.065255NaNNaNNaN
2013-01-041.2893050.497115NaN0.040239
2013-01-050.0382320.875057NaN0.934432
2013-01-06NaNNaN1.6998861.291653
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-01 NaN NaN NaN 1.528584\n", "2013-01-02 NaN 0.388338 1.187187 1.819818\n", "2013-01-03 0.065255 NaN NaN NaN\n", "2013-01-04 1.289305 0.497115 NaN 0.040239\n", "2013-01-05 0.038232 0.875057 NaN 0.934432\n", "2013-01-06 NaN NaN 1.699886 1.291653" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df > 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用 `isin` 方法做 `filter` 过滤：" ] }, { "cell_type": "code", "execution_count": 36, "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", " \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", "
ABCDE
2013-01-01-0.605936-0.861658-1.0019241.528584one
2013-01-02-0.1654080.3883381.1871871.819818one
2013-01-030.065255-1.608074-1.282331-0.286067two
2013-01-041.2893050.497115-0.2253510.040239three
2013-01-050.0382320.875057-0.0925260.934432four
2013-01-06-2.163453-0.0102791.6998861.291653three
\n", "
" ], "text/plain": [ " A B C D E\n", "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584 one\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818 one\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067 two\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239 three\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432 four\n", "2013-01-06 -2.163453 -0.010279 1.699886 1.291653 three" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "df2['E'] = ['one', 'one','two','three','four','three']\n", "\n", "df2" ] }, { "cell_type": "code", "execution_count": 37, "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", "
ABCDE
2013-01-030.065255-1.608074-1.282331-0.286067two
2013-01-050.0382320.875057-0.0925260.934432four
\n", "
" ], "text/plain": [ " A B C D E\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067 two\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432 four" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2[df2['E'].isin(['two','four'])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 设定数据的值" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2013-01-02 1\n", "2013-01-03 2\n", "2013-01-04 3\n", "2013-01-05 4\n", "2013-01-06 5\n", "2013-01-07 6\n", "Freq: D, dtype: int64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))\n", "\n", "s1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "像字典一样，直接指定 `F` 列的值为 `s1`，此时以 `df` 已有的 `index` 为标准将二者进行合并，`s1` 中没有的 `index` 项设为 `NaN`，多余的项舍去：" ] }, { "cell_type": "code", "execution_count": 39, "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", " \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", "
ABCDF
2013-01-01-0.605936-0.861658-1.0019241.528584NaN
2013-01-02-0.1654080.3883381.1871871.8198181
2013-01-030.065255-1.608074-1.282331-0.2860672
2013-01-041.2893050.497115-0.2253510.0402393
2013-01-050.0382320.875057-0.0925260.9344324
2013-01-06-2.163453-0.0102791.6998861.2916535
\n", "
" ], "text/plain": [ " A B C D F\n", "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584 NaN\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818 1\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067 2\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239 3\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432 4\n", "2013-01-06 -2.163453 -0.010279 1.699886 1.291653 5" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['F'] = s1\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "或者使用 `at` 或 `iat` 修改单个值：" ] }, { "cell_type": "code", "execution_count": 40, "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", " \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", "
ABCDF
2013-01-010.000000-0.861658-1.0019241.528584NaN
2013-01-02-0.1654080.3883381.1871871.8198181
2013-01-030.065255-1.608074-1.282331-0.2860672
2013-01-041.2893050.497115-0.2253510.0402393
2013-01-050.0382320.875057-0.0925260.9344324
2013-01-06-2.163453-0.0102791.6998861.2916535
\n", "
" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 -0.861658 -1.001924 1.528584 NaN\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818 1\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067 2\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239 3\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432 4\n", "2013-01-06 -2.163453 -0.010279 1.699886 1.291653 5" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.at[dates[0],'A'] = 0\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 41, "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", " \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", "
ABCDF
2013-01-010.0000000.000000-1.0019241.528584NaN
2013-01-02-0.1654080.3883381.1871871.8198181
2013-01-030.065255-1.608074-1.282331-0.2860672
2013-01-041.2893050.497115-0.2253510.0402393
2013-01-050.0382320.875057-0.0925260.9344324
2013-01-06-2.163453-0.0102791.6998861.2916535
\n", "
" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 0.000000 -1.001924 1.528584 NaN\n", "2013-01-02 -0.165408 0.388338 1.187187 1.819818 1\n", "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067 2\n", "2013-01-04 1.289305 0.497115 -0.225351 0.040239 3\n", "2013-01-05 0.038232 0.875057 -0.092526 0.934432 4\n", "2013-01-06 -2.163453 -0.010279 1.699886 1.291653 5" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iat[0, 1] = 0\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "设定一整列：" ] }, { "cell_type": "code", "execution_count": 42, "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", " \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", "
ABCDF
2013-01-010.0000000.000000-1.0019245NaN
2013-01-02-0.1654080.3883381.18718751
2013-01-030.065255-1.608074-1.28233152
2013-01-041.2893050.497115-0.22535153
2013-01-050.0382320.875057-0.09252654
2013-01-06-2.163453-0.0102791.69988655
\n", "
" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 0.000000 -1.001924 5 NaN\n", "2013-01-02 -0.165408 0.388338 1.187187 5 1\n", "2013-01-03 0.065255 -1.608074 -1.282331 5 2\n", "2013-01-04 1.289305 0.497115 -0.225351 5 3\n", "2013-01-05 0.038232 0.875057 -0.092526 5 4\n", "2013-01-06 -2.163453 -0.010279 1.699886 5 5" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:,'D'] = np.array([5] * len(df))\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "设定满足条件的数值：" ] }, { "cell_type": "code", "execution_count": 43, "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", " \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", "
ABCDF
2013-01-010.0000000.000000-1.001924-5NaN
2013-01-02-0.165408-0.388338-1.187187-5-1
2013-01-03-0.065255-1.608074-1.282331-5-2
2013-01-04-1.289305-0.497115-0.225351-5-3
2013-01-05-0.038232-0.875057-0.092526-5-4
2013-01-06-2.163453-0.010279-1.699886-5-5
\n", "
" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 0.000000 -1.001924 -5 NaN\n", "2013-01-02 -0.165408 -0.388338 -1.187187 -5 -1\n", "2013-01-03 -0.065255 -1.608074 -1.282331 -5 -2\n", "2013-01-04 -1.289305 -0.497115 -0.225351 -5 -3\n", "2013-01-05 -0.038232 -0.875057 -0.092526 -5 -4\n", "2013-01-06 -2.163453 -0.010279 -1.699886 -5 -5" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "\n", "df2[df2 > 0] = -df2\n", "\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 缺失数据" ] }, { "cell_type": "code", "execution_count": 44, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCDFE
2013-01-010.0000000.000000-1.0019245NaN1
2013-01-02-0.1654080.3883381.187187511
2013-01-030.065255-1.608074-1.28233152NaN
2013-01-041.2893050.497115-0.22535153NaN
\n", "
" ], "text/plain": [ " A B C D F E\n", "2013-01-01 0.000000 0.000000 -1.001924 5 NaN 1\n", "2013-01-02 -0.165408 0.388338 1.187187 5 1 1\n", "2013-01-03 0.065255 -1.608074 -1.282331 5 2 NaN\n", "2013-01-04 1.289305 0.497115 -0.225351 5 3 NaN" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])\n", "df1.loc[dates[0]:dates[1],'E'] = 1\n", "\n", "df1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "丢弃所有缺失数据的行得到的新数据：" ] }, { "cell_type": "code", "execution_count": 45, "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", "
ABCDFE
2013-01-02-0.1654080.3883381.187187511
\n", "
" ], "text/plain": [ " A B C D F E\n", "2013-01-02 -0.165408 0.388338 1.187187 5 1 1" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.dropna(how='any')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "填充缺失数据：" ] }, { "cell_type": "code", "execution_count": 46, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCDFE
2013-01-010.0000000.000000-1.001924551
2013-01-02-0.1654080.3883381.187187511
2013-01-030.065255-1.608074-1.282331525
2013-01-041.2893050.497115-0.225351535
\n", "
" ], "text/plain": [ " A B C D F E\n", "2013-01-01 0.000000 0.000000 -1.001924 5 5 1\n", "2013-01-02 -0.165408 0.388338 1.187187 5 1 1\n", "2013-01-03 0.065255 -1.608074 -1.282331 5 2 5\n", "2013-01-04 1.289305 0.497115 -0.225351 5 3 5" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.fillna(value=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "检查缺失数据的位置：" ] }, { "cell_type": "code", "execution_count": 47, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCDFE
2013-01-01FalseFalseFalseFalseTrueFalse
2013-01-02FalseFalseFalseFalseFalseFalse
2013-01-03FalseFalseFalseFalseFalseTrue
2013-01-04FalseFalseFalseFalseFalseTrue
\n", "
" ], "text/plain": [ " A B C D F E\n", "2013-01-01 False False False False True False\n", "2013-01-02 False False False False False False\n", "2013-01-03 False False False False False True\n", "2013-01-04 False False False False False True" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.isnull(df1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 计算操作" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 统计信息" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "每一列的均值：" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A -0.156012\n", "B 0.023693\n", "C 0.047490\n", "D 5.000000\n", "F 3.000000\n", "dtype: float64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "每一行的均值：" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2013-01-01 0.999519\n", "2013-01-02 1.482023\n", "2013-01-03 0.834970\n", "2013-01-04 1.912214\n", "2013-01-05 1.964153\n", "2013-01-06 1.905231\n", "Freq: D, dtype: float64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mean(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "多个对象之间的操作，如果维度不对，`pandas` 会自动调用 `broadcasting` 机制：" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2013-01-01 NaN\n", "2013-01-02 NaN\n", "2013-01-03 1\n", "2013-01-04 3\n", "2013-01-05 5\n", "2013-01-06 NaN\n", "Freq: D, dtype: float64\n" ] } ], "source": [ "s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)\n", "\n", "print s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "相减 `df - s`：" ] }, { "cell_type": "code", "execution_count": 51, "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", " \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", "
ABCDF
2013-01-01NaNNaNNaNNaNNaN
2013-01-02NaNNaNNaNNaNNaN
2013-01-03-0.934745-2.608074-2.28233141
2013-01-04-1.710695-2.502885-3.22535120
2013-01-05-4.961768-4.124943-5.0925260-1
2013-01-06NaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " A B C D F\n", "2013-01-01 NaN NaN NaN NaN NaN\n", "2013-01-02 NaN NaN NaN NaN NaN\n", "2013-01-03 -0.934745 -2.608074 -2.282331 4 1\n", "2013-01-04 -1.710695 -2.502885 -3.225351 2 0\n", "2013-01-05 -4.961768 -4.124943 -5.092526 0 -1\n", "2013-01-06 NaN NaN NaN NaN NaN" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sub(s, axis='index')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### apply 操作" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "与 `R` 中的 `apply` 操作类似，接收一个函数，默认是对将函数作用到每一列上：" ] }, { "cell_type": "code", "execution_count": 52, "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", " \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", "
ABCDF
2013-01-010.0000000.000000-1.0019245NaN
2013-01-02-0.1654080.3883380.185263101
2013-01-03-0.100153-1.219736-1.097067153
2013-01-041.189152-0.722621-1.322419206
2013-01-051.2273830.152436-1.4149452510
2013-01-06-0.9360690.1421570.2849413015
\n", "
" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 0.000000 -1.001924 5 NaN\n", "2013-01-02 -0.165408 0.388338 0.185263 10 1\n", "2013-01-03 -0.100153 -1.219736 -1.097067 15 3\n", "2013-01-04 1.189152 -0.722621 -1.322419 20 6\n", "2013-01-05 1.227383 0.152436 -1.414945 25 10\n", "2013-01-06 -0.936069 0.142157 0.284941 30 15" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.apply(np.cumsum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "求每列最大最小值之差：" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A 3.452758\n", "B 2.483131\n", "C 2.982217\n", "D 0.000000\n", "F 4.000000\n", "dtype: float64" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.apply(lambda x: x.max() - x.min())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 直方图" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 2\n", "1 5\n", "2 6\n", "3 6\n", "4 6\n", "5 3\n", "6 5\n", "7 0\n", "8 4\n", "9 4\n", "dtype: int64\n" ] } ], "source": [ "s = pd.Series(np.random.randint(0, 7, size=10))\n", "print s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "直方图信息：" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6 3\n", "5 2\n", "4 2\n", "3 1\n", "2 1\n", "0 1\n", "dtype: int64\n" ] } ], "source": [ "print s.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "绘制直方图信息：" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h = s.hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 字符串方法" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "当 `Series` 或者 `DataFrame` 的某一列是字符串时，我们可以用 `.str` 对这个字符串数组进行字符串的基本操作： " ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 a\n", "1 b\n", "2 c\n", "3 aaba\n", "4 baca\n", "5 NaN\n", "6 caba\n", "7 dog\n", "8 cat\n", "dtype: object\n" ] } ], "source": [ "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n", "\n", "print s.str.lower()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 合并" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 连接" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3\n", "0 -2.346373 0.105651 -0.048027 0.010637\n", "1 -0.682198 0.943043 0.147312 -0.657871\n", "2 0.515766 -0.768286 0.361570 1.146278\n", "3 -0.607277 -0.003086 -1.499001 1.165728\n", "4 -1.226279 -0.177246 -1.379631 -0.639261\n", "5 0.807364 -1.855060 0.325968 1.898831\n", "6 0.438539 -0.728131 -0.009924 0.398360\n", "7 1.497457 -1.506314 -1.557624 0.869043\n", "8 0.945985 -0.519435 -0.510359 -1.077751\n", "9 1.597679 -0.285955 -1.060736 0.608629" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.random.randn(10, 4))\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以使用 `pd.concat` 函数将多个 `pandas` 对象进行连接：" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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0123
0-2.3463730.105651-0.0480270.010637
1-0.6821980.9430430.147312-0.657871
4-1.226279-0.177246-1.379631-0.639261
71.497457-1.506314-1.5576240.869043
80.945985-0.519435-0.510359-1.077751
91.597679-0.285955-1.0607360.608629
\n", "
" ], "text/plain": [ " 0 1 2 3\n", "0 -2.346373 0.105651 -0.048027 0.010637\n", "1 -0.682198 0.943043 0.147312 -0.657871\n", "4 -1.226279 -0.177246 -1.379631 -0.639261\n", "7 1.497457 -1.506314 -1.557624 0.869043\n", "8 0.945985 -0.519435 -0.510359 -1.077751\n", "9 1.597679 -0.285955 -1.060736 0.608629" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pieces = [df[:2], df[4:5], df[7:]]\n", "\n", "pd.concat(pieces)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 数据库中的 Join" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`merge` 可以实现数据库中的 `join` 操作：" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " key lval\n", "0 foo 1\n", "1 foo 2\n", " key rval\n", "0 foo 4\n", "1 foo 5\n" ] } ], "source": [ "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n", "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})\n", "\n", "print left\n", "print right" ] }, { "cell_type": "code", "execution_count": 61, "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", " \n", " \n", "
keylvalrval
0foo14
1foo15
2foo24
3foo25
\n", "
" ], "text/plain": [ " key lval rval\n", "0 foo 1 4\n", "1 foo 1 5\n", "2 foo 2 4\n", "3 foo 2 5" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.merge(left, right, on='key')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### append" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "向 `DataFrame` 中添加行：" ] }, { "cell_type": "code", "execution_count": 62, "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", " \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", " \n", "
ABCD
01.587778-0.1102970.6022451.212597
1-0.5511090.337387-0.2209190.363332
21.207373-0.1283940.619937-0.612694
3-0.978282-1.0381700.048995-0.788973
40.843893-1.0790210.0922120.485422
5-0.0565941.8312061.910864-1.331739
6-0.487106-1.4953670.8534400.410854
71.830852-0.0148930.2540250.197422
\n", "
" ], "text/plain": [ " A B C D\n", "0 1.587778 -0.110297 0.602245 1.212597\n", "1 -0.551109 0.337387 -0.220919 0.363332\n", "2 1.207373 -0.128394 0.619937 -0.612694\n", "3 -0.978282 -1.038170 0.048995 -0.788973\n", "4 0.843893 -1.079021 0.092212 0.485422\n", "5 -0.056594 1.831206 1.910864 -1.331739\n", "6 -0.487106 -1.495367 0.853440 0.410854\n", "7 1.830852 -0.014893 0.254025 0.197422" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "将第三行的值添加到最后：" ] }, { "cell_type": "code", "execution_count": 63, "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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCD
01.587778-0.1102970.6022451.212597
1-0.5511090.337387-0.2209190.363332
21.207373-0.1283940.619937-0.612694
3-0.978282-1.0381700.048995-0.788973
40.843893-1.0790210.0922120.485422
5-0.0565941.8312061.910864-1.331739
6-0.487106-1.4953670.8534400.410854
71.830852-0.0148930.2540250.197422
8-0.978282-1.0381700.048995-0.788973
\n", "
" ], "text/plain": [ " A B C D\n", "0 1.587778 -0.110297 0.602245 1.212597\n", "1 -0.551109 0.337387 -0.220919 0.363332\n", "2 1.207373 -0.128394 0.619937 -0.612694\n", "3 -0.978282 -1.038170 0.048995 -0.788973\n", "4 0.843893 -1.079021 0.092212 0.485422\n", "5 -0.056594 1.831206 1.910864 -1.331739\n", "6 -0.487106 -1.495367 0.853440 0.410854\n", "7 1.830852 -0.014893 0.254025 0.197422\n", "8 -0.978282 -1.038170 0.048995 -0.788973" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = df.iloc[3]\n", "\n", "df.append(s, ignore_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grouping" ] }, { "cell_type": "code", "execution_count": 64, "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", " \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", " \n", "
ABCD
0fooone0.7730620.206503
1barone1.414609-0.346719
2footwo0.9641740.706623
3barthree0.182239-1.516509
4footwo-0.0962550.494177
5bartwo-0.759471-0.389213
6fooone-0.257519-1.411693
7foothree-0.1093680.241862
\n", "
" ], "text/plain": [ " A B C D\n", "0 foo one 0.773062 0.206503\n", "1 bar one 1.414609 -0.346719\n", "2 foo two 0.964174 0.706623\n", "3 bar three 0.182239 -1.516509\n", "4 foo two -0.096255 0.494177\n", "5 bar two -0.759471 -0.389213\n", "6 foo one -0.257519 -1.411693\n", "7 foo three -0.109368 0.241862" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n", " 'foo', 'bar', 'foo', 'foo'],\n", " 'B' : ['one', 'one', 'two', 'three',\n", " 'two', 'two', 'one', 'three'],\n", " 'C' : np.random.randn(8),\n", " 'D' : np.random.randn(8)})\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "按照 `A` 的值进行分类：" ] }, { "cell_type": "code", "execution_count": 65, "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", "
CD
A
bar0.837377-2.252441
foo1.2740940.237472
\n", "
" ], "text/plain": [ " C D\n", "A \n", "bar 0.837377 -2.252441\n", "foo 1.274094 0.237472" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('A').sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "按照 `A, B` 的值进行分类：" ] }, { "cell_type": "code", "execution_count": 66, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CD
AB
barone1.414609-0.346719
three0.182239-1.516509
two-0.759471-0.389213
fooone0.515543-1.205191
three-0.1093680.241862
two0.8679191.200800
\n", "
" ], "text/plain": [ " C D\n", "A B \n", "bar one 1.414609 -0.346719\n", " three 0.182239 -1.516509\n", " two -0.759471 -0.389213\n", "foo one 0.515543 -1.205191\n", " three -0.109368 0.241862\n", " two 0.867919 1.200800" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(['A', 'B']).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 改变形状" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stack" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "产生一个多 `index` 的 `DataFrame`：" ] }, { "cell_type": "code", "execution_count": 67, "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", " \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", "
AB
firstsecond
barone-0.1091740.958551
two-0.254743-0.975924
bazone-0.132039-0.119009
two0.587063-0.819037
fooone-0.7541230.430747
two-0.4265440.389822
quxone-0.382501-0.562910
two-0.5292870.826337
\n", "
" ], "text/plain": [ " A B\n", "first second \n", "bar one -0.109174 0.958551\n", " two -0.254743 -0.975924\n", "baz one -0.132039 -0.119009\n", " two 0.587063 -0.819037\n", "foo one -0.754123 0.430747\n", " two -0.426544 0.389822\n", "qux one -0.382501 -0.562910\n", " two -0.529287 0.826337" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n", " 'foo', 'foo', 'qux', 'qux'],\n", " ['one', 'two', 'one', 'two',\n", " 'one', 'two', 'one', 'two']]))\n", "\n", "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\n", "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`stack` 方法将 `columns` 变成一个新的 `index` 部分：" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "first second \n", "bar one A -0.109174\n", " B 0.958551\n", " two A -0.254743\n", " B -0.975924\n", "baz one A -0.132039\n", " B -0.119009\n", " two A 0.587063\n", " B -0.819037\n", "dtype: float64" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df[:4]\n", "\n", "stacked = df2.stack()\n", "\n", "stacked" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以使用 `unstack()` 将最后一级 `index` 放回 `column`：" ] }, { "cell_type": "code", "execution_count": 69, "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", " \n", " \n", " \n", " \n", " \n", " \n", "
AB
firstsecond
barone-0.1091740.958551
two-0.254743-0.975924
bazone-0.132039-0.119009
two0.587063-0.819037
\n", "
" ], "text/plain": [ " A B\n", "first second \n", "bar one -0.109174 0.958551\n", " two -0.254743 -0.975924\n", "baz one -0.132039 -0.119009\n", " two 0.587063 -0.819037" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stacked.unstack()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "也可以指定其他的级别：" ] }, { "cell_type": "code", "execution_count": 70, "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", " \n", " \n", " \n", " \n", " \n", " \n", "
secondonetwo
first
barA-0.109174-0.254743
B0.958551-0.975924
bazA-0.1320390.587063
B-0.119009-0.819037
\n", "
" ], "text/plain": [ "second one two\n", "first \n", "bar A -0.109174 -0.254743\n", " B 0.958551 -0.975924\n", "baz A -0.132039 0.587063\n", " B -0.119009 -0.819037" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stacked.unstack(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 时间序列" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "金融分析中常用到时间序列数据：" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-03-06 1.096788\n", "2012-03-07 0.029678\n", "2012-03-08 0.511461\n", "2012-03-09 -0.332369\n", "2012-03-10 1.720321\n", "Freq: D, dtype: float64" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')\n", "ts = pd.Series(np.random.randn(len(rng)), rng)\n", "\n", "ts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "标准时间表示：" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-03-06 00:00:00+00:00 1.096788\n", "2012-03-07 00:00:00+00:00 0.029678\n", "2012-03-08 00:00:00+00:00 0.511461\n", "2012-03-09 00:00:00+00:00 -0.332369\n", "2012-03-10 00:00:00+00:00 1.720321\n", "Freq: D, dtype: float64" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts_utc = ts.tz_localize('UTC')\n", "\n", "ts_utc" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "改变时区表示：" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "2012-03-05 19:00:00-05:00 1.096788\n", "2012-03-06 19:00:00-05:00 0.029678\n", "2012-03-07 19:00:00-05:00 0.511461\n", "2012-03-08 19:00:00-05:00 -0.332369\n", "2012-03-09 19:00:00-05:00 1.720321\n", "Freq: D, dtype: float64" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts_utc.tz_convert('US/Eastern')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categoricals" ] }, { "cell_type": "code", "execution_count": 74, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idraw_grade
01a
12b
23b
34a
45a
56e
\n", "
" ], "text/plain": [ " id raw_grade\n", "0 1 a\n", "1 2 b\n", "2 3 b\n", "3 4 a\n", "4 5 a\n", "5 6 e" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({\"id\":[1,2,3,4,5,6], \"raw_grade\":['a', 'b', 'b', 'a', 'a', 'e']})\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以将 `grade` 变成类别：" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 a\n", "1 b\n", "2 b\n", "3 a\n", "4 a\n", "5 e\n", "Name: grade, dtype: category\n", "Categories (3, object): [a, b, e]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")\n", "\n", "df[\"grade\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "将类别的表示转化为有意义的字符：" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 very good\n", "1 good\n", "2 good\n", "3 very good\n", "4 very good\n", "5 very bad\n", "Name: grade, dtype: category\n", "Categories (3, object): [very good, good, very bad]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]\n", "\n", "df[\"grade\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "添加缺失的类别：" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 very good\n", "1 good\n", "2 good\n", "3 very good\n", "4 very good\n", "5 very bad\n", "Name: grade, dtype: category\n", "Categories (5, object): [very bad, bad, medium, good, very good]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"grade\"] = df[\"grade\"].cat.set_categories([\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"])\n", "df[\"grade\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用 `grade` 分组：" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "grade\n", "very bad 1\n", "bad 0\n", "medium 0\n", "good 2\n", "very good 3\n", "dtype: int64" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(\"grade\").size()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 绘图" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用 `ggplot` 风格：" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.style.use('ggplot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Series` 绘图：" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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FBQVs2rSJ+fPns3nzZgoKCvryNLGjsgwc0j0TCmrCSajRx6P/vAI+XwcH9sKI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\nQogwFnY3WXtCKUXcM6+GuhpCCBHWIrM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n", "\n", "p = ts.cumsum().plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`DataFrame` 按照 `columns` 绘图：" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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44+Pjyc3NBcBut5OVlUV9fT0bN25k3rx5AMyfP58NG47O1e90Quo6smSvep17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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\n", " columns=['A', 'B', 'C', 'D'])\n", "\n", "df.cumsum().plot()\n", "p = plt.legend(loc=\"best\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 文件读写" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写入文件：" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.to_csv('foo.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从文件中读取：" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0ABCD
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12000-01-02-1.0308940.660518-0.214002-0.422014
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42000-01-05-0.1656841.2973030.8172330.174767
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" ], "text/plain": [ " Unnamed: 0 A B C D\n", "0 2000-01-01 -1.011554 1.200283 -0.310949 -1.060734\n", "1 2000-01-02 -1.030894 0.660518 -0.214002 -0.422014\n", "2 2000-01-03 -0.488692 1.709209 -0.602208 1.115456\n", "3 2000-01-04 -0.440243 0.826692 0.321648 -0.351698\n", "4 2000-01-05 -0.165684 1.297303 0.817233 0.174767" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv('foo.csv').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### hdf5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写入文件：" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.to_hdf(\"foo.h5\", \"df\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "读取文件：" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2000-01-01-1.0115541.200283-0.310949-1.060734
2000-01-02-1.0308940.660518-0.214002-0.422014
2000-01-03-0.4886921.709209-0.6022081.115456
2000-01-04-0.4402430.8266920.321648-0.351698
2000-01-05-0.1656841.2973030.8172330.174767
\n", "
" ], "text/plain": [ " A B C D\n", "2000-01-01 -1.011554 1.200283 -0.310949 -1.060734\n", "2000-01-02 -1.030894 0.660518 -0.214002 -0.422014\n", "2000-01-03 -0.488692 1.709209 -0.602208 1.115456\n", "2000-01-04 -0.440243 0.826692 0.321648 -0.351698\n", "2000-01-05 -0.165684 1.297303 0.817233 0.174767" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_hdf('foo.h5','df').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### excel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写入文件：" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.to_excel('foo.xlsx', sheet_name='Sheet1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "读取文件：" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2000-01-01-1.0115541.200283-0.310949-1.060734
2000-01-02-1.0308940.660518-0.214002-0.422014
2000-01-03-0.4886921.709209-0.6022081.115456
2000-01-04-0.4402430.8266920.321648-0.351698
2000-01-05-0.1656841.2973030.8172330.174767
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" ], "text/plain": [ " A B C D\n", "2000-01-01 -1.011554 1.200283 -0.310949 -1.060734\n", "2000-01-02 -1.030894 0.660518 -0.214002 -0.422014\n", "2000-01-03 -0.488692 1.709209 -0.602208 1.115456\n", "2000-01-04 -0.440243 0.826692 0.321648 -0.351698\n", "2000-01-05 -0.165684 1.297303 0.817233 0.174767" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "清理生成的临时文件：" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import glob\n", "import os\n", "\n", "for f in glob.glob(\"foo*\"):\n", " os.remove(f)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }