{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#coding:utf8\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "css = open('style-table.css').read() + open('style-notebook.css').read()\n", "HTML(''.format(css))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这个一篇针对pandas新手的简短入门，想要了解更多复杂的内容，参阅[*Cookbook*](http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通常，我们首先要导入以下几个库：" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 创建对象" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过传递一个list来创建**Series**，pandas会默认创建整型索引：" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 1\n", "1 3\n", "2 5\n", "3 NaN\n", "4 6\n", "5 8\n", "dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series([1,3,5,np.nan,6,8])\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过传递一个numpy array，日期索引以及列标签来创建一个**DataFrame**：" ] }, { "cell_type": "code", "execution_count": 5, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates = pd.date_range('20130101', periods=6)\n", "dates" ] }, { "cell_type": "code", "execution_count": 6, "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
2013-01-01-0.797290-0.251394-0.479293-1.258262
2013-01-022.5703230.2693880.5601231.017688
2013-01-030.5127821.3122470.6514441.158869
2013-01-04-0.687212-1.906938-1.8951921.460285
2013-01-050.4900810.204613-0.581628-0.232023
2013-01-060.8895280.803676-0.9866170.256299
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.797290 -0.251394 -0.479293 -1.258262\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869\n", "2013-01-04 -0.687212 -1.906938 -1.895192 1.460285\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023\n", "2013-01-06 0.889528 0.803676 -0.986617 0.256299" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过传递一个能够被转换为类似series的dict对象来创建一个**DataFrame**:" ] }, { "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", " \n", " \n", " \n", "
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": 7, "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", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看到各列的数据类型为：" ] }, { "cell_type": "code", "execution_count": 8, "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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 查看数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "查看frame中头部和尾部的几行：" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-01-0.797290-0.251394-0.479293-1.258262
2013-01-022.5703230.2693880.5601231.017688
2013-01-030.5127821.3122470.6514441.158869
2013-01-04-0.687212-1.906938-1.8951921.460285
2013-01-050.4900810.204613-0.581628-0.232023
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.797290 -0.251394 -0.479293 -1.258262\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869\n", "2013-01-04 -0.687212 -1.906938 -1.895192 1.460285\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-04-0.687212-1.906938-1.8951921.460285
2013-01-050.4900810.204613-0.581628-0.232023
2013-01-060.8895280.803676-0.9866170.256299
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-04 -0.687212 -1.906938 -1.895192 1.460285\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023\n", "2013-01-06 0.889528 0.803676 -0.986617 0.256299" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "显示索引、列名以及底层的numpy数据" ] }, { "cell_type": "code", "execution_count": 11, "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": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'A', u'B', u'C', u'D'], dtype='object')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.79728972, -0.25139407, -0.47929318, -1.25826194],\n", " [ 2.57032272, 0.26938806, 0.56012338, 1.01768776],\n", " [ 0.51278197, 1.31224685, 0.65144361, 1.15886932],\n", " [-0.68721203, -1.90693793, -1.89519175, 1.46028525],\n", " [ 0.49008072, 0.20461343, -0.58162821, -0.23202324],\n", " [ 0.88952831, 0.80367591, -0.98661684, 0.25629939]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "describe()能对数据做一个快速统计汇总" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
count6.0000006.0000006.0000006.000000
mean0.4963690.071932-0.4551940.400476
std1.2272401.1091340.9620901.024847
min-0.797290-1.906938-1.895192-1.258262
25%-0.392889-0.137392-0.885370-0.109943
50%0.5014310.237001-0.5304610.636994
75%0.7953420.6701040.3002691.123574
max2.5703231.3122470.6514441.460285
\n", "
" ], "text/plain": [ " A B C D\n", "count 6.000000 6.000000 6.000000 6.000000\n", "mean 0.496369 0.071932 -0.455194 0.400476\n", "std 1.227240 1.109134 0.962090 1.024847\n", "min -0.797290 -1.906938 -1.895192 -1.258262\n", "25% -0.392889 -0.137392 -0.885370 -0.109943\n", "50% 0.501431 0.237001 -0.530461 0.636994\n", "75% 0.795342 0.670104 0.300269 1.123574\n", "max 2.570323 1.312247 0.651444 1.460285" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对数据做转置：" ] }, { "cell_type": "code", "execution_count": 15, "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-0.7972902.5703230.512782-0.6872120.4900810.889528
B-0.2513940.2693881.312247-1.9069380.2046130.803676
C-0.4792930.5601230.651444-1.895192-0.581628-0.986617
D-1.2582621.0176881.1588691.460285-0.2320230.256299
\n", "
" ], "text/plain": [ " 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06\n", "A -0.797290 2.570323 0.512782 -0.687212 0.490081 0.889528\n", "B -0.251394 0.269388 1.312247 -1.906938 0.204613 0.803676\n", "C -0.479293 0.560123 0.651444 -1.895192 -0.581628 -0.986617\n", "D -1.258262 1.017688 1.158869 1.460285 -0.232023 0.256299" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "按轴进行排序：" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DCBA
2013-01-01-1.258262-0.479293-0.251394-0.797290
2013-01-021.0176880.5601230.2693882.570323
2013-01-031.1588690.6514441.3122470.512782
2013-01-041.460285-1.895192-1.906938-0.687212
2013-01-05-0.232023-0.5816280.2046130.490081
2013-01-060.256299-0.9866170.8036760.889528
\n", "
" ], "text/plain": [ " D C B A\n", "2013-01-01 -1.258262 -0.479293 -0.251394 -0.797290\n", "2013-01-02 1.017688 0.560123 0.269388 2.570323\n", "2013-01-03 1.158869 0.651444 1.312247 0.512782\n", "2013-01-04 1.460285 -1.895192 -1.906938 -0.687212\n", "2013-01-05 -0.232023 -0.581628 0.204613 0.490081\n", "2013-01-06 0.256299 -0.986617 0.803676 0.889528" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_index(axis=1, ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "按值进行排序 :" ] }, { "cell_type": "code", "execution_count": 17, "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
2013-01-04-0.687212-1.906938-1.8951921.460285
2013-01-01-0.797290-0.251394-0.479293-1.258262
2013-01-050.4900810.204613-0.581628-0.232023
2013-01-022.5703230.2693880.5601231.017688
2013-01-060.8895280.803676-0.9866170.256299
2013-01-030.5127821.3122470.6514441.158869
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-04 -0.687212 -1.906938 -1.895192 1.460285\n", "2013-01-01 -0.797290 -0.251394 -0.479293 -1.258262\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688\n", "2013-01-06 0.889528 0.803676 -0.986617 0.256299\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values(by='B')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 数据选择" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "注意：虽然标准的Python/Numpy的表达式能完成选择与赋值等功能，但我们仍推荐使用优化过的pandas数据访问方法：.at，.iat，.loc，.iloc和.ix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 选取" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "选择某一列数据，它会返回一个**Series**，等同于**df.A**：" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2013-01-01 -0.797290\n", "2013-01-02 2.570323\n", "2013-01-03 0.512782\n", "2013-01-04 -0.687212\n", "2013-01-05 0.490081\n", "2013-01-06 0.889528\n", "Freq: D, Name: A, dtype: float64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['A']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过使用**[ ]**进行切片选取：" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-01-0.797290-0.251394-0.479293-1.258262
2013-01-022.5703230.2693880.5601231.017688
2013-01-030.5127821.3122470.6514441.158869
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-01 -0.797290 -0.251394 -0.479293 -1.258262\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0:3]" ] }, { "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
2013-01-022.5703230.2693880.5601231.017688
2013-01-030.5127821.3122470.6514441.158869
2013-01-04-0.687212-1.906938-1.8951921.460285
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" ], "text/plain": [ " A B C D\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869\n", "2013-01-04 -0.687212 -1.906938 -1.895192 1.460285" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['20130102':'20130104']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 通过标签选取" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过标签进行交叉选取：" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A -0.797290\n", "B -0.251394\n", "C -0.479293\n", "D -1.258262\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": [ "使用标签对多个轴进行选取" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AB
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2013-01-022.5703230.269388
2013-01-030.5127821.312247
2013-01-04-0.687212-1.906938
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2013-01-060.8895280.803676
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" ], "text/plain": [ " A B\n", "2013-01-01 -0.797290 -0.251394\n", "2013-01-02 2.570323 0.269388\n", "2013-01-03 0.512782 1.312247\n", "2013-01-04 -0.687212 -1.906938\n", "2013-01-05 0.490081 0.204613\n", "2013-01-06 0.889528 0.803676" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:,['A','B']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "进行标签切片，包含两个端点" ] }, { "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
2013-01-022.5703230.269388
2013-01-030.5127821.312247
2013-01-04-0.687212-1.906938
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" ], "text/plain": [ " A B\n", "2013-01-02 2.570323 0.269388\n", "2013-01-03 0.512782 1.312247\n", "2013-01-04 -0.687212 -1.906938" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['20130102':'20130104',['A','B']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于返回的对象进行降维处理" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A 2.570323\n", "B 0.269388\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": [ "获取一个标量" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-0.79728972181227264" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[dates[0],'A']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "快速获取标量（与上面的方法等价）" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-0.79728972181227264" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.at[dates[0],'A']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 通过位置选取" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过传递整型的位置进行选取" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A -0.687212\n", "B -1.906938\n", "C -1.895192\n", "D 1.460285\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": [ "通过整型的位置切片进行选取，与python/numpy形式相同" ] }, { "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
2013-01-04-0.687212-1.906938
2013-01-050.4900810.204613
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" ], "text/plain": [ " A B\n", "2013-01-04 -0.687212 -1.906938\n", "2013-01-05 0.490081 0.204613" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[3:5,0:2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "只对行进行切片" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-022.5703230.2693880.5601231.017688
2013-01-030.5127821.3122470.6514441.158869
\n", "
" ], "text/plain": [ " A B C D\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[1:3,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "只对列进行切片" ] }, { "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
BC
2013-01-01-0.251394-0.479293
2013-01-020.2693880.560123
2013-01-031.3122470.651444
2013-01-04-1.906938-1.895192
2013-01-050.204613-0.581628
2013-01-060.803676-0.986617
\n", "
" ], "text/plain": [ " B C\n", "2013-01-01 -0.251394 -0.479293\n", "2013-01-02 0.269388 0.560123\n", "2013-01-03 1.312247 0.651444\n", "2013-01-04 -1.906938 -1.895192\n", "2013-01-05 0.204613 -0.581628\n", "2013-01-06 0.803676 -0.986617" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:,1:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "只获取某个值" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.26938806116429348" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[1,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "快速获取某个值（与上面的方法等价）" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.26938806116429348" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iat[1,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 布尔索引" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "用某列的值来选取数据" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2013-01-022.5703230.2693880.5601231.017688
2013-01-030.5127821.3122470.6514441.158869
2013-01-050.4900810.204613-0.581628-0.232023
2013-01-060.8895280.803676-0.9866170.256299
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" ], "text/plain": [ " A B C D\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023\n", "2013-01-06 0.889528 0.803676 -0.986617 0.256299" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.A > 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "用**where**操作来选取数据" ] }, { "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ABCD
2013-01-01NaNNaNNaNNaN
2013-01-022.5703230.2693880.5601231.017688
2013-01-030.5127821.3122470.6514441.158869
2013-01-04NaNNaNNaN1.460285
2013-01-050.4900810.204613NaNNaN
2013-01-060.8895280.803676NaN0.256299
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" ], "text/plain": [ " A B C D\n", "2013-01-01 NaN NaN NaN NaN\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869\n", "2013-01-04 NaN NaN NaN 1.460285\n", "2013-01-05 0.490081 0.204613 NaN NaN\n", "2013-01-06 0.889528 0.803676 NaN 0.256299" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df > 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "用**isin()**方法来过滤数据" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df2 = df.copy()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCDE
2013-01-01-0.797290-0.251394-0.479293-1.258262one
2013-01-022.5703230.2693880.5601231.017688one
2013-01-030.5127821.3122470.6514441.158869two
2013-01-04-0.687212-1.906938-1.8951921.460285three
2013-01-050.4900810.204613-0.581628-0.232023four
2013-01-060.8895280.803676-0.9866170.256299three
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" ], "text/plain": [ " A B C D E\n", "2013-01-01 -0.797290 -0.251394 -0.479293 -1.258262 one\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688 one\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869 two\n", "2013-01-04 -0.687212 -1.906938 -1.895192 1.460285 three\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023 four\n", "2013-01-06 0.889528 0.803676 -0.986617 0.256299 three" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\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
2013-01-030.5127821.3122470.6514441.158869two
2013-01-050.4900810.204613-0.581628-0.232023four
\n", "
" ], "text/plain": [ " A B C D E\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869 two\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023 four" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2[df2['E'].isin(['two', 'four'])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 赋值" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "赋值一个新的列，通过索引来自动对齐数据" ] }, { "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", "s1" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCDF
2013-01-01-0.797290-0.251394-0.479293-1.258262NaN
2013-01-022.5703230.2693880.5601231.0176881
2013-01-030.5127821.3122470.6514441.1588692
2013-01-04-0.687212-1.906938-1.8951921.4602853
2013-01-050.4900810.204613-0.581628-0.2320234
2013-01-060.8895280.803676-0.9866170.2562995
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" ], "text/plain": [ " A B C D F\n", "2013-01-01 -0.797290 -0.251394 -0.479293 -1.258262 NaN\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688 1\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869 2\n", "2013-01-04 -0.687212 -1.906938 -1.895192 1.460285 3\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023 4\n", "2013-01-06 0.889528 0.803676 -0.986617 0.256299 5" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['F'] = s1\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过标签赋值" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCDF
2013-01-010.000000-0.251394-0.479293-1.258262NaN
2013-01-022.5703230.2693880.5601231.0176881
2013-01-030.5127821.3122470.6514441.1588692
2013-01-04-0.687212-1.906938-1.8951921.4602853
2013-01-050.4900810.204613-0.581628-0.2320234
2013-01-060.8895280.803676-0.9866170.2562995
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ABCDF
2013-01-010.0000000.000000-0.479293-1.258262NaN
2013-01-022.5703230.2693880.5601231.0176881
2013-01-030.5127821.3122470.6514441.1588692
2013-01-04-0.687212-1.906938-1.8951921.4602853
2013-01-050.4900810.204613-0.581628-0.2320234
2013-01-060.8895280.803676-0.9866170.2562995
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" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 0.000000 -0.479293 -1.258262 NaN\n", "2013-01-02 2.570323 0.269388 0.560123 1.017688 1\n", "2013-01-03 0.512782 1.312247 0.651444 1.158869 2\n", "2013-01-04 -0.687212 -1.906938 -1.895192 1.460285 3\n", "2013-01-05 0.490081 0.204613 -0.581628 -0.232023 4\n", "2013-01-06 0.889528 0.803676 -0.986617 0.256299 5" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iat[0,1] = 0\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过传递numpy array赋值" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 0.000000 -0.479293 5 NaN\n", "2013-01-02 2.570323 0.269388 0.560123 5 1\n", "2013-01-03 0.512782 1.312247 0.651444 5 2\n", "2013-01-04 -0.687212 -1.906938 -1.895192 5 3\n", "2013-01-05 0.490081 0.204613 -0.581628 5 4\n", "2013-01-06 0.889528 0.803676 -0.986617 5 5" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:,'D'] = np.array([5] * len(df))\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过**where**操作来赋值" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 0.000000 -0.479293 -5 NaN\n", "2013-01-02 -2.570323 -0.269388 -0.560123 -5 -1\n", "2013-01-03 -0.512782 -1.312247 -0.651444 -5 -2\n", "2013-01-04 -0.687212 -1.906938 -1.895192 -5 -3\n", "2013-01-05 -0.490081 -0.204613 -0.581628 -5 -4\n", "2013-01-06 -0.889528 -0.803676 -0.986617 -5 -5" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df.copy()\n", "df2[df2 > 0] = -df2\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 缺失值处理" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在pandas中，用**np.nan**来代表缺失值，这些值默认不会参与运算。\n", "\n", "reindex()允许你修改、增加、删除指定轴上的索引，并返回一个数据副本。" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCDFE
2013-01-010.0000000.000000-0.4792935NaN1
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" ], "text/plain": [ " A B C D F E\n", "2013-01-01 0.000000 0.000000 -0.479293 5 NaN 1\n", "2013-01-02 2.570323 0.269388 0.560123 5 1 1\n", "2013-01-03 0.512782 1.312247 0.651444 5 2 NaN\n", "2013-01-04 -0.687212 -1.906938 -1.895192 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", "df1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "剔除所有包含缺失值的行数据" ] }, { "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
2013-01-022.5703230.2693880.560123511
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" ], "text/plain": [ " A B C D F E\n", "2013-01-02 2.570323 0.269388 0.560123 5 1 1" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.dropna(how='any')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "填充缺失值" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCDFE
2013-01-010.0000000.000000-0.479293551
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" ], "text/plain": [ " A B C D F E\n", "2013-01-01 0.000000 0.000000 -0.479293 5 5 1\n", "2013-01-02 2.570323 0.269388 0.560123 5 1 1\n", "2013-01-03 0.512782 1.312247 0.651444 5 2 5\n", "2013-01-04 -0.687212 -1.906938 -1.895192 5 3 5" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.fillna(value=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "获取值是否为**nan**的布尔标记" ] }, { "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
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2013-01-03FalseFalseFalseFalseFalseTrue
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" ], "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": [ "# 运算" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 统计" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运算过程中，通常不包含缺失值。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "进行描述性统计" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A 0.629250\n", "B 0.113831\n", "C -0.455194\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": [ "对其他轴进行同样的运算" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "2013-01-01 1.130177\n", "2013-01-02 1.879967\n", "2013-01-03 1.895294\n", "2013-01-04 0.702132\n", "2013-01-05 1.822613\n", "2013-01-06 2.141317\n", "Freq: D, dtype: float64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mean(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于拥有不同维度的对象进行运算时需要对齐。除此之外，pandas会自动沿着指定维度计算。" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "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" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)\n", "s" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCDF
2013-01-01NaNNaNNaNNaNNaN
2013-01-02NaNNaNNaNNaNNaN
2013-01-03-0.4872180.312247-0.34855641
2013-01-04-3.687212-4.906938-4.89519220
2013-01-05-4.509919-4.795387-5.5816280-1
2013-01-06NaNNaNNaNNaNNaN
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" ], "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 -0.487218 0.312247 -0.348556 4 1\n", "2013-01-04 -3.687212 -4.906938 -4.895192 2 0\n", "2013-01-05 -4.509919 -4.795387 -5.581628 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 函数作用" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过apply()对函数作用" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCDF
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" ], "text/plain": [ " A B C D F\n", "2013-01-01 0.000000 0.000000 -0.479293 5 NaN\n", "2013-01-02 2.570323 0.269388 0.080830 10 1\n", "2013-01-03 3.083105 1.581635 0.732274 15 3\n", "2013-01-04 2.395893 -0.325303 -1.162918 20 6\n", "2013-01-05 2.885973 -0.120690 -1.744546 25 10\n", "2013-01-06 3.775502 0.682986 -2.731163 30 15" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.apply(np.cumsum)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A 3.257535\n", "B 3.219185\n", "C 2.546635\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": [ "## 频数统计" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 5\n", "1 6\n", "2 3\n", "3 5\n", "4 3\n", "5 3\n", "6 4\n", "7 4\n", "8 0\n", "9 0\n", "dtype: int32" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series(np.random.randint(0, 7, size=10))\n", "s" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3 3\n", "5 2\n", "4 2\n", "0 2\n", "6 1\n", "dtype: int64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 字符串方法" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于Series对象，在其str属性中有着一系列的字符串处理方法。就如同下段代码一样，能很方便的对array中各个元素进行运算。值得注意的是，在str属性中的模式匹配默认使用正则表达式。" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "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" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n", "s.str.lower()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 合并" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Concat 连接" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pandas中提供了大量的方法能够轻松对Series，DataFrame和Panel对象进行不同满足逻辑关系的合并操作" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过**concat()**来连接pandas对象" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3\n", "0 -0.358328 -0.917267 -0.713872 -0.991957\n", "1 -1.002017 0.952365 -0.279115 -1.172856\n", "2 1.227910 -0.156482 3.245353 0.067354\n", "3 1.237094 0.309711 -0.863719 -1.871049\n", "4 -0.401347 0.226986 -0.672437 -0.166693\n", "5 1.108645 0.272753 -1.281637 0.726997\n", "6 0.244444 -2.211821 1.943479 -1.098533\n", "7 -0.620943 0.710649 -0.180102 -1.144573\n", "8 0.140132 0.783428 -0.542937 0.177039\n", "9 -1.221415 -0.059672 -2.288082 1.727063" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.random.randn(10,4))\n", "df" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[ 0 1 2 3\n", " 0 -0.358328 -0.917267 -0.713872 -0.991957\n", " 1 -1.002017 0.952365 -0.279115 -1.172856\n", " 2 1.227910 -0.156482 3.245353 0.067354,\n", " 0 1 2 3\n", " 3 1.237094 0.309711 -0.863719 -1.871049\n", " 4 -0.401347 0.226986 -0.672437 -0.166693\n", " 5 1.108645 0.272753 -1.281637 0.726997\n", " 6 0.244444 -2.211821 1.943479 -1.098533,\n", " 0 1 2 3\n", " 7 -0.620943 0.710649 -0.180102 -1.144573\n", " 8 0.140132 0.783428 -0.542937 0.177039\n", " 9 -1.221415 -0.059672 -2.288082 1.727063]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#break it into pieces\n", "pieces = [df[:3], df[3:7], df[7:]]\n", "pieces" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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0123
0-0.358328-0.917267-0.713872-0.991957
1-1.0020170.952365-0.279115-1.172856
21.227910-0.1564823.2453530.067354
31.2370940.309711-0.863719-1.871049
4-0.4013470.226986-0.672437-0.166693
51.1086450.272753-1.2816370.726997
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7-0.6209430.710649-0.180102-1.144573
80.1401320.783428-0.5429370.177039
9-1.221415-0.059672-2.2880821.727063
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" ], "text/plain": [ " 0 1 2 3\n", "0 -0.358328 -0.917267 -0.713872 -0.991957\n", "1 -1.002017 0.952365 -0.279115 -1.172856\n", "2 1.227910 -0.156482 3.245353 0.067354\n", "3 1.237094 0.309711 -0.863719 -1.871049\n", "4 -0.401347 0.226986 -0.672437 -0.166693\n", "5 1.108645 0.272753 -1.281637 0.726997\n", "6 0.244444 -2.211821 1.943479 -1.098533\n", "7 -0.620943 0.710649 -0.180102 -1.144573\n", "8 0.140132 0.783428 -0.542937 0.177039\n", "9 -1.221415 -0.059672 -2.288082 1.727063" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat(pieces)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Join 合并" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "类似于SQL中的合并(merge)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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keylval
0foo1
1foo2
\n", "
" ], "text/plain": [ " key lval\n", "0 foo 1\n", "1 foo 2" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "left = pd.DataFrame({'key':['foo', 'foo'], 'lval':[1,2]})\n", "left" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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keylval
0foo4
1foo5
\n", "
" ], "text/plain": [ " key lval\n", "0 foo 4\n", "1 foo 5" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "right = pd.DataFrame({'key':['foo', 'foo'], 'lval':[4,5]})\n", "right" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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keylval_xlval_y
0foo14
1foo15
2foo24
3foo25
\n", "
" ], "text/plain": [ " key lval_x lval_y\n", "0 foo 1 4\n", "1 foo 1 5\n", "2 foo 2 4\n", "3 foo 2 5" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.merge(left, right, on='key')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Append 添加" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "将若干行添加到dataFrame后面" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
0-0.547356-0.885905-1.7917421.908368
10.9612590.8259071.0748540.093868
20.782033-0.9140231.5595720.616944
30.353374-0.484523-1.7308232.503638
40.495378-0.6647381.192463-0.900465
50.446744-0.456149-0.0881711.450870
60.422372-2.207023-1.007185-0.304415
72.1830430.069862-0.3765671.403847
\n", "
" ], "text/plain": [ " A B C D\n", "0 -0.547356 -0.885905 -1.791742 1.908368\n", "1 0.961259 0.825907 1.074854 0.093868\n", "2 0.782033 -0.914023 1.559572 0.616944\n", "3 0.353374 -0.484523 -1.730823 2.503638\n", "4 0.495378 -0.664738 1.192463 -0.900465\n", "5 0.446744 -0.456149 -0.088171 1.450870\n", "6 0.422372 -2.207023 -1.007185 -0.304415\n", "7 2.183043 0.069862 -0.376567 1.403847" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])\n", "df" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A 0.353374\n", "B -0.484523\n", "C -1.730823\n", "D 2.503638\n", "Name: 3, dtype: float64" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = df.iloc[3]\n", "s" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
0-0.547356-0.885905-1.7917421.908368
10.9612590.8259071.0748540.093868
20.782033-0.9140231.5595720.616944
30.353374-0.484523-1.7308232.503638
40.495378-0.6647381.192463-0.900465
50.446744-0.456149-0.0881711.450870
60.422372-2.207023-1.007185-0.304415
72.1830430.069862-0.3765671.403847
80.353374-0.484523-1.7308232.503638
\n", "
" ], "text/plain": [ " A B C D\n", "0 -0.547356 -0.885905 -1.791742 1.908368\n", "1 0.961259 0.825907 1.074854 0.093868\n", "2 0.782033 -0.914023 1.559572 0.616944\n", "3 0.353374 -0.484523 -1.730823 2.503638\n", "4 0.495378 -0.664738 1.192463 -0.900465\n", "5 0.446744 -0.456149 -0.088171 1.450870\n", "6 0.422372 -2.207023 -1.007185 -0.304415\n", "7 2.183043 0.069862 -0.376567 1.403847\n", "8 0.353374 -0.484523 -1.730823 2.503638" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.append(s, ignore_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 分组" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于“group by”操作，我们通常是指以下一个或几个步骤：\n", "* **划分** 按照某些标准将数据分为不同的组\n", "* **应用** 对每组数据分别执行一个函数\n", "* **组合** 将结果组合到一个数据结构" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
0fooone1.5118320.323650
1barone1.1351992.046447
2footwo0.048247-0.250839
3barthree0.232861-0.827749
4footwo-1.502759-0.869657
5bartwo1.6047470.072619
6fooone-0.5536330.388030
7barthree-2.5807020.546721
\n", "
" ], "text/plain": [ " A B C D\n", "0 foo one 1.511832 0.323650\n", "1 bar one 1.135199 2.046447\n", "2 foo two 0.048247 -0.250839\n", "3 bar three 0.232861 -0.827749\n", "4 foo two -1.502759 -0.869657\n", "5 bar two 1.604747 0.072619\n", "6 foo one -0.553633 0.388030\n", "7 bar three -2.580702 0.546721" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', \n", " 'foo', 'bar', 'foo', 'bar'],\n", " 'B' : ['one', 'one', 'two', 'three', \n", " 'two', 'two', 'one', 'three'],\n", " 'C' : np.random.randn(8),\n", " 'D' : np.random.randn(8)})\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "分组并对每个分组应用sum函数" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CD
A
bar0.3921041.838039
foo-0.496312-0.408816
\n", "
" ], "text/plain": [ " C D\n", "A \n", "bar 0.392104 1.838039\n", "foo -0.496312 -0.408816" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('A').sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "按多个列分组形成层级索引，然后应用函数" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CD
AB
barone1.1351992.046447
three-2.347841-0.281028
two1.6047470.072619
fooone0.9581990.711681
two-1.454511-1.120496
\n", "
" ], "text/plain": [ " C D\n", "A B \n", "bar one 1.135199 2.046447\n", " three -2.347841 -0.281028\n", " two 1.604747 0.072619\n", "foo one 0.958199 0.711681\n", " two -1.454511 -1.120496" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(['A','B']).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 变形" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 堆叠" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n", " 'foo', 'foo', 'qux', 'qux'],\n", " ['one', 'two', 'one', 'two',\n", " 'one', 'two', 'one', 'two']]))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [], "source": [ "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AB
firstsecond
barone-0.305811-0.539703
two0.2145110.514641
bazone-0.208686-0.686508
two-0.5657800.945584
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" ], "text/plain": [ " A B\n", "first second \n", "bar one -0.305811 -0.539703\n", " two 0.214511 0.514641\n", "baz one -0.208686 -0.686508\n", " two -0.565780 0.945584" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = df[:4]\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**stack()**方法对DataFrame的列“压缩”一个层级" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "first second \n", "bar one A -0.305811\n", " B -0.539703\n", " two A 0.214511\n", " B 0.514641\n", "baz one A -0.208686\n", " B -0.686508\n", " two A -0.565780\n", " B 0.945584\n", "dtype: float64" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stacked = df2.stack()\n", "stacked" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于一个“堆叠过的”DataFrame或者Series（拥有MultiIndex作为索引），**stack()**的逆操作是**unstack()**，默认反堆叠到上一个层级" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AB
firstsecond
barone-0.305811-0.539703
two0.2145110.514641
bazone-0.208686-0.686508
two-0.5657800.945584
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secondonetwo
first
barA-0.3058110.214511
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bazA-0.208686-0.565780
B-0.6865080.945584
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firstbarbaz
second
oneA-0.305811-0.208686
B-0.539703-0.686508
twoA0.214511-0.565780
B0.5146410.945584
\n", "
" ], "text/plain": [ "first bar baz\n", "second \n", "one A -0.305811 -0.208686\n", " B -0.539703 -0.686508\n", "two A 0.214511 -0.565780\n", " B 0.514641 0.945584" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stacked.unstack(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数据透视表" ] }, { "cell_type": "code", "execution_count": 77, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \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
0oneAfoo-0.5601280.607515
1oneBfoo0.749692-0.470557
2twoCfoo1.2396021.378625
3threeAbar0.706071-0.614495
4oneBbar1.304376-1.128074
5oneCbar-0.901451-0.220037
6twoAfoo-0.685465-0.174906
7threeBfoo-0.2248950.156997
8oneCfoo-1.7221160.761828
9oneAbar-0.3376291.093555
10twoBbar-1.3433530.797256
11threeCbar0.573971-0.930710
\n", "
" ], "text/plain": [ " A B C D E\n", "0 one A foo -0.560128 0.607515\n", "1 one B foo 0.749692 -0.470557\n", "2 two C foo 1.239602 1.378625\n", "3 three A bar 0.706071 -0.614495\n", "4 one B bar 1.304376 -1.128074\n", "5 one C bar -0.901451 -0.220037\n", "6 two A foo -0.685465 -0.174906\n", "7 three B foo -0.224895 0.156997\n", "8 one C foo -1.722116 0.761828\n", "9 one A bar -0.337629 1.093555\n", "10 two B bar -1.343353 0.797256\n", "11 three C bar 0.573971 -0.930710" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n", " 'B' : ['A', 'B', 'C'] * 4,\n", " 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n", " 'D' : np.random.randn(12),\n", " 'E' : np.random.randn(12)})\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " 我们可以轻松地从这个数据得到透视表" ] }, { "cell_type": "code", "execution_count": 78, "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", "
Cbarfoo
AB
oneA-0.337629-0.560128
B1.3043760.749692
C-0.901451-1.722116
threeA0.706071NaN
BNaN-0.224895
C0.573971NaN
twoANaN-0.685465
B-1.343353NaN
CNaN1.239602
\n", "
" ], "text/plain": [ "C bar foo\n", "A B \n", "one A -0.337629 -0.560128\n", " B 1.304376 0.749692\n", " C -0.901451 -1.722116\n", "three A 0.706071 NaN\n", " B NaN -0.224895\n", " C 0.573971 NaN\n", "two A NaN -0.685465\n", " B -1.343353 NaN\n", " C NaN 1.239602" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 时间序列" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pandas在对频率转换进行重新采样时拥有着简单，强大而且高效的功能（例如把按秒采样的数据转换为按5分钟采样的数据）。这在金融领域很常见，但又不限于此。" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',\n", " '2012-01-01 00:00:02', '2012-01-01 00:00:03',\n", " '2012-01-01 00:00:04', '2012-01-01 00:00:05',\n", " '2012-01-01 00:00:06', '2012-01-01 00:00:07',\n", " '2012-01-01 00:00:08', '2012-01-01 00:00:09',\n", " '2012-01-01 00:00:10', '2012-01-01 00:00:11',\n", " '2012-01-01 00:00:12', '2012-01-01 00:00:13',\n", " '2012-01-01 00:00:14', '2012-01-01 00:00:15',\n", " '2012-01-01 00:00:16', '2012-01-01 00:00:17',\n", " '2012-01-01 00:00:18', '2012-01-01 00:00:19',\n", " '2012-01-01 00:00:20', '2012-01-01 00:00:21',\n", " '2012-01-01 00:00:22', '2012-01-01 00:00:23',\n", " '2012-01-01 00:00:24', '2012-01-01 00:00:25',\n", " '2012-01-01 00:00:26', '2012-01-01 00:00:27',\n", " '2012-01-01 00:00:28', '2012-01-01 00:00:29',\n", " '2012-01-01 00:00:30', '2012-01-01 00:00:31',\n", " '2012-01-01 00:00:32', '2012-01-01 00:00:33',\n", " '2012-01-01 00:00:34', '2012-01-01 00:00:35',\n", " '2012-01-01 00:00:36', '2012-01-01 00:00:37',\n", " '2012-01-01 00:00:38', '2012-01-01 00:00:39',\n", " '2012-01-01 00:00:40', '2012-01-01 00:00:41',\n", " '2012-01-01 00:00:42', '2012-01-01 00:00:43',\n", " '2012-01-01 00:00:44', '2012-01-01 00:00:45',\n", " '2012-01-01 00:00:46', '2012-01-01 00:00:47',\n", " '2012-01-01 00:00:48', '2012-01-01 00:00:49',\n", " '2012-01-01 00:00:50', '2012-01-01 00:00:51',\n", " '2012-01-01 00:00:52', '2012-01-01 00:00:53',\n", " '2012-01-01 00:00:54', '2012-01-01 00:00:55',\n", " '2012-01-01 00:00:56', '2012-01-01 00:00:57',\n", " '2012-01-01 00:00:58', '2012-01-01 00:00:59',\n", " '2012-01-01 00:01:00', '2012-01-01 00:01:01',\n", " '2012-01-01 00:01:02', '2012-01-01 00:01:03',\n", " '2012-01-01 00:01:04', '2012-01-01 00:01:05',\n", " '2012-01-01 00:01:06', '2012-01-01 00:01:07',\n", " '2012-01-01 00:01:08', '2012-01-01 00:01:09',\n", " '2012-01-01 00:01:10', '2012-01-01 00:01:11',\n", " '2012-01-01 00:01:12', '2012-01-01 00:01:13',\n", " '2012-01-01 00:01:14', '2012-01-01 00:01:15',\n", " '2012-01-01 00:01:16', '2012-01-01 00:01:17',\n", " '2012-01-01 00:01:18', '2012-01-01 00:01:19',\n", " '2012-01-01 00:01:20', '2012-01-01 00:01:21',\n", " '2012-01-01 00:01:22', '2012-01-01 00:01:23',\n", " '2012-01-01 00:01:24', '2012-01-01 00:01:25',\n", " '2012-01-01 00:01:26', '2012-01-01 00:01:27',\n", " '2012-01-01 00:01:28', '2012-01-01 00:01:29',\n", " '2012-01-01 00:01:30', '2012-01-01 00:01:31',\n", " '2012-01-01 00:01:32', '2012-01-01 00:01:33',\n", " '2012-01-01 00:01:34', '2012-01-01 00:01:35',\n", " '2012-01-01 00:01:36', '2012-01-01 00:01:37',\n", " '2012-01-01 00:01:38', '2012-01-01 00:01:39'],\n", " dtype='datetime64[ns]', freq='S')" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n", "rng" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-01-01 00:00:00 292\n", "2012-01-01 00:00:01 28\n", "2012-01-01 00:00:02 195\n", "2012-01-01 00:00:03 340\n", "2012-01-01 00:00:04 256\n", "2012-01-01 00:00:05 169\n", "2012-01-01 00:00:06 362\n", "2012-01-01 00:00:07 197\n", "2012-01-01 00:00:08 164\n", "2012-01-01 00:00:09 437\n", "2012-01-01 00:00:10 467\n", "2012-01-01 00:00:11 16\n", "2012-01-01 00:00:12 2\n", "2012-01-01 00:00:13 36\n", "2012-01-01 00:00:14 127\n", "2012-01-01 00:00:15 171\n", "2012-01-01 00:00:16 208\n", "2012-01-01 00:00:17 216\n", "2012-01-01 00:00:18 487\n", "2012-01-01 00:00:19 377\n", "2012-01-01 00:00:20 13\n", "2012-01-01 00:00:21 444\n", "2012-01-01 00:00:22 180\n", "2012-01-01 00:00:23 262\n", "2012-01-01 00:00:24 422\n", "2012-01-01 00:00:25 297\n", "2012-01-01 00:00:26 234\n", "2012-01-01 00:00:27 252\n", "2012-01-01 00:00:28 31\n", "2012-01-01 00:00:29 303\n", " ... \n", "2012-01-01 00:01:10 483\n", "2012-01-01 00:01:11 410\n", "2012-01-01 00:01:12 254\n", "2012-01-01 00:01:13 357\n", "2012-01-01 00:01:14 468\n", "2012-01-01 00:01:15 243\n", "2012-01-01 00:01:16 458\n", "2012-01-01 00:01:17 446\n", "2012-01-01 00:01:18 310\n", "2012-01-01 00:01:19 276\n", "2012-01-01 00:01:20 223\n", "2012-01-01 00:01:21 231\n", "2012-01-01 00:01:22 340\n", "2012-01-01 00:01:23 355\n", "2012-01-01 00:01:24 227\n", "2012-01-01 00:01:25 172\n", "2012-01-01 00:01:26 111\n", "2012-01-01 00:01:27 33\n", "2012-01-01 00:01:28 138\n", "2012-01-01 00:01:29 150\n", "2012-01-01 00:01:30 313\n", "2012-01-01 00:01:31 278\n", "2012-01-01 00:01:32 126\n", "2012-01-01 00:01:33 119\n", "2012-01-01 00:01:34 409\n", "2012-01-01 00:01:35 5\n", "2012-01-01 00:01:36 307\n", "2012-01-01 00:01:37 397\n", "2012-01-01 00:01:38 142\n", "2012-01-01 00:01:39 236\n", "Freq: S, dtype: int32" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts = pd.Series(np.random.randint(0,500,len(rng)), index=rng)\n", "ts" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-01-01 25778\n", "Freq: 5T, dtype: int32" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts.resample('5Min', how='sum')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "时区表示" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',\n", " '2012-03-10'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rng = pd.date_range('3/6/2012', periods=5, freq='D')\n", "rng" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-03-06 1.883053\n", "2012-03-07 1.657763\n", "2012-03-08 0.676596\n", "2012-03-09 0.477890\n", "2012-03-10 -0.566615\n", "Freq: D, dtype: float64" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n", "ts" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-03-06 00:00:00+00:00 1.883053\n", "2012-03-07 00:00:00+00:00 1.657763\n", "2012-03-08 00:00:00+00:00 0.676596\n", "2012-03-09 00:00:00+00:00 0.477890\n", "2012-03-10 00:00:00+00:00 -0.566615\n", "Freq: D, dtype: float64" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts_utc = ts.tz_localize('UTC')\n", "ts_utc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "时区转换" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-03-05 19:00:00-05:00 1.883053\n", "2012-03-06 19:00:00-05:00 1.657763\n", "2012-03-07 19:00:00-05:00 0.676596\n", "2012-03-08 19:00:00-05:00 0.477890\n", "2012-03-09 19:00:00-05:00 -0.566615\n", "Freq: D, dtype: float64" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts_utc.tz_convert('US/Eastern')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "时间跨度转换" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2012-01-31', '2012-02-29', '2012-03-31', '2012-04-30',\n", " '2012-05-31'],\n", " dtype='datetime64[ns]', freq='M')" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rng = pd.date_range('1/1/2012', periods=5, freq='M')\n", "rng" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-01-31 0.569720\n", "2012-02-29 2.055821\n", "2012-03-31 -0.850667\n", "2012-04-30 1.895128\n", "2012-05-31 2.204973\n", "Freq: M, dtype: float64" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n", "ts" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-01 0.569720\n", "2012-02 2.055821\n", "2012-03 -0.850667\n", "2012-04 1.895128\n", "2012-05 2.204973\n", "Freq: M, dtype: float64" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ps = ts.to_period()\n", "ps" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2012-01-01 0.569720\n", "2012-02-01 2.055821\n", "2012-03-01 -0.850667\n", "2012-04-01 1.895128\n", "2012-05-01 2.204973\n", "Freq: MS, dtype: float64" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ps.to_timestamp()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "日期与时间戳之间的转换使得可以使用一些方便的算术函数。例如，我们把以11月为年底的季度数据转换为当前季度末月底为始的数据" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PeriodIndex(['1990Q1', '1990Q2', '1990Q3', '1990Q4', '1991Q1', '1991Q2',\n", " '1991Q3', '1991Q4', '1992Q1', '1992Q2', '1992Q3', '1992Q4',\n", " '1993Q1', '1993Q2', '1993Q3', '1993Q4', '1994Q1', '1994Q2',\n", " '1994Q3', '1994Q4', '1995Q1', '1995Q2', '1995Q3', '1995Q4',\n", " '1996Q1', '1996Q2', '1996Q3', '1996Q4', '1997Q1', '1997Q2',\n", " '1997Q3', '1997Q4', '1998Q1', '1998Q2', '1998Q3', '1998Q4',\n", " '1999Q1', '1999Q2', '1999Q3', '1999Q4', '2000Q1', '2000Q2',\n", " '2000Q3', '2000Q4'],\n", " dtype='int64', freq='Q-NOV')" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')\n", "prng" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1990Q1 1.219483\n", "1990Q2 0.295619\n", "1990Q3 2.177954\n", "1990Q4 0.437791\n", "1991Q1 -1.150226\n", "1991Q2 -0.155793\n", "1991Q3 -0.899163\n", "1991Q4 1.108673\n", "1992Q1 -0.189505\n", "1992Q2 -0.919934\n", "1992Q3 -2.593031\n", "1992Q4 0.184326\n", "1993Q1 0.822122\n", "1993Q2 -2.534715\n", "1993Q3 -0.482651\n", "1993Q4 0.234846\n", "1994Q1 -1.038624\n", "1994Q2 -1.985862\n", "1994Q3 1.445492\n", "1994Q4 1.325495\n", "1995Q1 -0.823989\n", "1995Q2 -0.633812\n", "1995Q3 -0.572880\n", "1995Q4 0.426740\n", "1996Q1 0.411217\n", "1996Q2 -0.676600\n", "1996Q3 -1.350879\n", "1996Q4 -1.557402\n", "1997Q1 -0.302270\n", "1997Q2 1.077077\n", "1997Q3 -0.187689\n", "1997Q4 -0.102634\n", "1998Q1 0.038094\n", "1998Q2 -1.321694\n", "1998Q3 -2.049545\n", "1998Q4 0.205804\n", "1999Q1 0.627608\n", "1999Q2 2.099334\n", "1999Q3 1.490896\n", "1999Q4 -0.652193\n", "2000Q1 0.408912\n", "2000Q2 -1.426349\n", "2000Q3 0.038938\n", "2000Q4 -1.118917\n", "Freq: Q-NOV, dtype: float64" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts = pd.Series(np.random.randn(len(prng)), index = prng)\n", "ts" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1990-02-01 09:00 1.219483\n", "1990-05-01 09:00 0.295619\n", "1990-08-01 09:00 2.177954\n", "1990-11-01 09:00 0.437791\n", "1991-02-01 09:00 -1.150226\n", "1991-05-01 09:00 -0.155793\n", "1991-08-01 09:00 -0.899163\n", "1991-11-01 09:00 1.108673\n", "1992-02-01 09:00 -0.189505\n", "1992-05-01 09:00 -0.919934\n", "1992-08-01 09:00 -2.593031\n", "1992-11-01 09:00 0.184326\n", "1993-02-01 09:00 0.822122\n", "1993-05-01 09:00 -2.534715\n", "1993-08-01 09:00 -0.482651\n", "1993-11-01 09:00 0.234846\n", "1994-02-01 09:00 -1.038624\n", "1994-05-01 09:00 -1.985862\n", "1994-08-01 09:00 1.445492\n", "1994-11-01 09:00 1.325495\n", "1995-02-01 09:00 -0.823989\n", "1995-05-01 09:00 -0.633812\n", "1995-08-01 09:00 -0.572880\n", "1995-11-01 09:00 0.426740\n", "1996-02-01 09:00 0.411217\n", "1996-05-01 09:00 -0.676600\n", "1996-08-01 09:00 -1.350879\n", "1996-11-01 09:00 -1.557402\n", "1997-02-01 09:00 -0.302270\n", "1997-05-01 09:00 1.077077\n", "1997-08-01 09:00 -0.187689\n", "1997-11-01 09:00 -0.102634\n", "1998-02-01 09:00 0.038094\n", "1998-05-01 09:00 -1.321694\n", "1998-08-01 09:00 -2.049545\n", "1998-11-01 09:00 0.205804\n", "1999-02-01 09:00 0.627608\n", "1999-05-01 09:00 2.099334\n", "1999-08-01 09:00 1.490896\n", "1999-11-01 09:00 -0.652193\n", "2000-02-01 09:00 0.408912\n", "2000-05-01 09:00 -1.426349\n", "2000-08-01 09:00 0.038938\n", "2000-11-01 09:00 -1.118917\n", "Freq: H, dtype: float64" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ts.index = (prng.asfreq('M', 'end') ) .asfreq('H', 'start') +9\n", "ts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 分类" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从版本0.15开始，pandas在**DataFrame**中开始包括分类数据。" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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idraw_grade
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" ], "text/plain": [ " id raw_grade\n", "0 1 a\n", "1 2 b\n", "2 3 b\n", "3 4 a\n", "4 5 e\n", "5 6 e" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({\"id\":[1,2,3,4,5,6], \"raw_grade\":['a', 'b', 'b', 'a', 'e', 'e']})\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "把raw_grade转换为分类类型" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 a\n", "1 b\n", "2 b\n", "3 a\n", "4 e\n", "5 e\n", "Name: grade, dtype: category\n", "Categories (3, object): [a, b, e]" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")\n", "df[\"grade\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "重命名类别名为更有意义的名称" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对分类重新排序，并添加缺失的分类" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 very good\n", "1 good\n", "2 good\n", "3 very good\n", "4 very bad\n", "5 very bad\n", "Name: grade, dtype: category\n", "Categories (5, object): [very bad, bad, medium, good, very good]" ] }, "execution_count": 96, "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": [ "排序是按照分类的顺序进行的，而不是字典序" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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idraw_gradegrade
45every bad
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01avery good
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" ], "text/plain": [ " id raw_grade grade\n", "4 5 e very bad\n", "5 6 e very bad\n", "1 2 b good\n", "2 3 b good\n", "0 1 a very good\n", "3 4 a very good" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values(by=\"grade\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "按分类分组时，也会显示空的分类" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "grade\n", "very bad 2\n", "bad 0\n", "medium 0\n", "good 2\n", "very good 2\n", "dtype: int64" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(\"grade\").size()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 绘图" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" }, { "data": { 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887YphTv2HdAsVneIJODElifC4sV6fkBsYd+61esqSgT7MMjPb9v3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\n", "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", "ts = ts.cumsum()\n", "ts.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于DataFrame类型，**plot()**能很方便地画出所有列及其标签" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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fYtasgWuusfX1taEFIrR34sjJEbPX7t3i1zhyRH735GT5Xb/5punPV1SIZq8o\nLdfwjyHHhWBPyEtgR+aOzjvBjh3yD2oq1buiAm65BQYNEo2tLkOGiDMFOtxZWpdjIdibw9mhpkov\nNzf2lZfzWFIS9zksw3ftgoTcKrb9Vnus4S4upFVVUWKxUGg280VWFh/278/Abh7u2CH4+Ulzle+/\nJ8yqkfqsXYtHaiplQUFSb7+kBJorvHXllfDqqx0yJFVVcQLyJ0zAvZHGJBplkyaxbeRIVhUWUmEV\naFrS2o1hYUzw9WWanx+57S2boLUhXLpUNITMTHt/WWj+92mOl16C6dPlfj77bKnjFBkpgj40FN54\nQ+75xjhyBGJiZGxGY/3qrV1MtxDsh/IPEfFK4+n1Yz8Yy7B363eq7xBUFaxp+Dgk89TDsQ3a+efX\n/kdecUVtTX7AgI4dowPVqkqu2VyrRG9X4GiKmeDjQ6rVtFJosdhitMeNV1HCK0nZUUewW00x/01I\nwO/vv1lXXMzkBhpWn5BoGvFVV3Hytm3suuUW2LYNz23bKO/RQ95fswZOOqlhYVFRAaedJs87qCtT\nVU0NRkWx1bhvCkVRGObtTYybm23VpSWpnR8UxKJBgwhydiavPWN77TWYNw+uugree08qo4aFiba+\ncqW0j2xv/Pn69dLoBqTch7bCOP98uOwy0dyjo8Us1lAk0Ecf2f8P0PhKvovoFoI9IS+B9JL0RrMY\nCysLcTY0LMj2ZO+h1NQO73ZysrSlg/rLPkcOHQLH/puaDS4pCT7/3L7922/bbn9tAVkmE35GY4tu\nws7i7+HDWTpkiO210WAg0sFZPHDjRgqLVSrCSlF9zBTtqq2J+xiNGBSFldbImLkREa0LGTye0SJo\nLr8cBRh8773w+ut4rl5Nmbe3RFBZyzfQUDJXQgL8/rs8twrP7SUlfN1I4lejqKpt4iiZPx+vVpoT\nfJycbLkLBRYLka6utsJyAc7OLdPYX3wR3nqr/vZ16+Rx1ix7XgiITXvgQMnkLiiQ8bfV5HPkCPTu\nLc8dlSSTCQYPllLb2dkQFwcvvFD/83v2iMZ/9dWi8c+dCytWtG0snUC3EOyWGrlAMkvrJ2dUmGU5\npKKSXVb/4h389mAuXXxp20++cSOMHw8bNsjSqjEOHbKXBoiKki5GIBqFtg1gypS2j6UF7Cors7WF\n6ypO8vWtV57AsY55QkUFfmNLIKSKQQYf8tOcqFup1qgoZJpMvBYbywOtbTRyPBMfLyaF008Xm+6o\nUZCUhOflnoIhAAAgAElEQVTEiZT5+Ejjbc2+WzeRq7KytvJh1SRvS0zkkr17qayuJrcltufCQtGC\nH30UFIXSN97AS3MkthBfo5Hi6mqSKys5UF7O1SEhNmUjxNmZZ44ebb4S6H33weuv19+uKKIsNVRy\nIypKJseCAjj55Lbdb3l58htotfy1pjfPPCOPgwaJfV+jTo0lAA4elMzyjz6CSZOkwfz117d+LJ1E\ntxDs+RUSy5tanFrvvc92fsaQ4CFMjZ7KoysfbfDzOWXtSJpITJSiXf361e5qVJdDh2Q/kH/kwYOw\nfDm8845si44WDaJulmkHszAjg0mO5WG7CZqzbPGgQbIhvAL8TYzr74y3d/0AgpN79GCyry9zIyMJ\n7cTQ0G6J5p8ID7eZ7TyGD2djSQnK+eezqX9/EToHD9o+8nxyMmcvXw7XXkvNTTfZnbBgqyF/94oV\nBGnablVV47ZfbYU5bx4Ape7ueJWWtip8z8dopMhiYeSWLbyZnl6rhn6kqyu5ZnPT/Xc1od/Q/XLk\niDR41wTqyy/b34uLk++emSkRMq3NiH36aTG1jB9vX1nPmQO//WYv/TFwIGj3WL9+9X8XVZVVlaaQ\naFVao6LkPYfchK6iWwn2vIr6WsONP92I0WDkiSlPsC1zm217bnkuypOt73NZj0OHJIrF1xcCAmTm\nbYjDh2U/VZWbceVKabRw113Spf6HH9o/ljqsyM/n9sREfsnL48usLJRVq1iUk8MlmumoG6FlYQ7w\n8ODy6l5ETynn3mfFyevuLo2jHPlm0CD+HNZJfpPjCaMRzjkHPwf/zpqhQ0X7cxDsy7OyWNajB1d8\n9RVOl1xCRXY2mM2oFRXstQqeA1YhXjZ0KL1WrhRfh1W4/pqfzxGrM9C8fj19v/mGvwcPZsHMmSLY\nKyrEqXvHHWJObAZvJyeSKittvpbp/v72r2SdaEqb0ti1rlyOJqTSUrGd79kjK5s+feDee+FS64r8\nwAF46CER7NussiAlBVavbna8Nh57TEwmcXH2bQEBMG2aRDzFxIifQ1FE6bvjDnFmO1JQAC4udn+J\nJtjXrRPnd0BAy8fTSXQrwZ5bLhpzqamUV/8Rj38Ptx4su3wZ8aHx7MnZg7labHdf7vrS9nntc42R\nWpzKqqRVDb956JAsqRQFzjvPfsE4UlAAy5bZwxjDwuyC/OGH5R/bmuzDFjJ9507eSEtj+q5dXKaZ\nfqBVPUGPFXtHj8ZZUQhydsa/1ANLeDlHDGX09/DgxRdFVmzcCFrPCSdFaVMD6hOSH38kKi6OT6zC\nxvzgg3KNOTgIo63PP7eaJ+YlJ/P75Mk8d+gQkVbB84fVPLbOxYVkd3dSgoJsJS7O3LmT83bvlvfN\nZg4GBjJxwQLmzJnDP4MGiWAfOlRCehu6B+rwc14ecw8eJKWqisWDBhHt4COZYS0f0WRzjosvFiFa\nXi5+A5D7LzRUtnl5SQXUF16Q30JVRXt2cbEL9rg40Y5bY47RxqnZ1x0ZMgRmz7a/jo2VZuV1Nfb0\n9Nq9FMaMkSgaEDkBXZqLAN1EsBdUFjAw7GVCnlzI17u/5rt933HXiru49edbKawsJNgzGC8XL6J8\notifK7Ws5yyfY/t8SnGKzRbfEPNWz+Pkj0/mUP4h2zZVVXk7LY395eV8qxUCiooSDQAkG23DBrmg\nfv1VtmkXQ3i47TgWtZoateNbne0pK0PTd5ygVpJPd0jkOXhQ5jQNL6MR05QpBLq44JnrQVlgGRuL\niznJ15fLLhM/2EknSQCCTsNolTRzvL1FeDks6asLC7nbWhH0tdhY5iUnc8ZDD/FzTg6vPfIIr7/z\nDpEVFXg7OXH6Sy8BcDAiApYts9W6dzMYYMMGEhxS6e/x9eXuW24hWEtGM5lkddoMjqutyDqmNG9r\nVnK21YG6sbiYb7KzbV2/UFXYvFlWJUOG2FcmWkDCk082fXI/P9Gix42DCRNk1dNUaKLGL7/Iftdd\nBzNm1H+/f3/R6Gt9Ge/6GntaWm3B7ukJt94KH35o3+a4IugCuoVgLyrKI6H3UIpzq7nl00t4b6s4\nJN/aLB5zgyLDHB42nK0ZtSNXZvSfganahP8L/jRGsUlm3NgFsWzP3E62yUSu2cwtiYkMf+YZLszO\n5qfcXOjbV0wsDz8sdrdx4+SflZcHN99sn+2nTaPivrsAGPjmQK5f2vFOk0KHcLEYd3csqsqGESO4\nLjTUliTUlSxcKL25G8Il04MinwqSq6roab3pBw/ulnkc3YoJvr5M9vWVUEF/f8mGtAqsPJOJSbt2\nsdholAYrgG9pKVnp6YTl5XH7b7+Rsngx9zs4QQ+ecw58+SUHysrwcXIi02SCv/8mzaEB+O3WPqwD\nRowQLfOuu2qH9jbCCG9vWytGrwZi3wOcncmzCvaxW7dy0d69xG3cKG9qdvWbbhJtXEu00vwBjzbs\nS7OhrY779pVCXsHB9izSppg+XR4/+EB8Yi3B379+uGNdwa7hqO1D0z67TqbTBbuiKGcqirJfUZQE\nRVHub2gfzwOFWIxGEiIjudI4grXJUnVtRv8ZFN5vd8AMDx3O9sztVJgrcDY4Y37UzPeXfA9ApaXx\nsKf9uft595x3AThv0cWErFvHPmvcaaVVE741MVHClvz8RGItXChxqomJYgd0tGs7OzN9wBZcHoHE\n/ET+ONKIXb4JEvMSm2xSoYWLPRkdTWVNDQUWC0M9PfmgizUBDW2VPWOGuBgcr+HUw0541MiqQouU\ncMzZam/tpRMVV4OBu6KiRCD6+cmPvGgRH2ZksKx3b0JeeYVZEycyyMODaDc3yr29yfT3J6S8HO65\nB4qLediatNTDZOLg+efDli1suuMOTl+1ikyTif3FxTx5ySW2c0Z5ejJbVTkrPFz+Sffc0yKNHeCP\nYcM4PHZsg4ll/kYj+WYzljrhUOuKiuwrkR49ZPWrhTSGhkqYYXOKi7bs04Szv3/nOSyHDBEbouNv\n0phg1wgIgIkT7U3su4BOFeyKohiAN4AzgEHApYqi1JNMpvwaFFXl98E9mRUwCYCfL/uZz2Z9hq+b\nPQJkQOAAfkz4kV3Zu4j1j8VoqG2SqLLUtumll6SzKmkVhckJnBUtyQRHy+QCmFLHY59vsTB0yxYO\nnHqqbIiPh2uvldodTz1Vz3ufW5GH2Xr65KJkZn49E1N1y9Kc00vS6fdGP3499Guj++SYTFwTGsp9\nUVGkVlVhANyayQo8VpjN9lDrpUvlp3LsaLdqFTzo16dWrZlRo0TBCguD998/tuM9nvA3GkWwa8LL\n05PHrSaKCOvv6WU0cmTcOGoMBsrc3fGZP1/s45mZsHkzRaNH89WoUaw2m9n++uvcf/753Pzdd5hU\nlQFTpwIw03o9K4rChyefzCjrKoDQUNFQWxgMEOPuLpp3nX9qgDVJ6WAdE8kER/u9k5NcEI89JuGf\nCQlSGbU5NOek5nAOCGiZxg5g/f4txtdXwioXLrRvO3iwYRu9hqJIf+Mvv2x8n06mszX2MUCiqqpH\nVVU1A18B9YxbJkMgI01VlMeOwK9M1uvT+07Hy6V23YrBwYM5VHCI6Z9PZ0ovu8Mk825xEN356521\n9n9r01tiW59XStTCbzE/aubUAfYL50FNOiEe/F1lZazUtICQEDj1VPheVgR1Lzgt9l7j+/3fk1SY\nBMAfh/9gfer6Bn+QwwWHbVm2DcXla+RYs0s1Ye7UDcwvAIsXi/9qzx7xX40ZI8qSlidiNosGf+/Q\nUN7TwkMR82VCQotX+v9aApydWVdcTE5kpAi7pUuptNqwQus0hdDq9SjXXy+24H37IDISH09Ppvn5\nsaO0lBUzZjDJZOKUOg7RRQMHkn3SSfUHoChw++1NJ+vV5b336k0EQc7OZJtMbCwp4dSmHCva5P/b\nb2Kra0njC3//2glGsbFSCqA5oqPhf/9rfr+63HablB3R2Lq1drKiI9deK+Uepk6Vm6GuFpOQ0PyK\npAPobMEeATi2tU+1bqs9iJpoelf6ke7lxUCCWHX1qgYPFuMXw8brN5JXkcfoiNG27SFeITx7yrN8\nsuMTfk74maLKIpQnFeatmYezJn9zcjAlJfNHj/PxKjlM6VlnMdKaErzAobFAhqYVBwfb41p9fetp\n7FmlEqUQHxJv26ZF90z7dBqTP5zc4HfYnW2/AA8X1F/y3p6YyNHKSptg17igbnu9LkJz+u/bJ/eV\ng7mW4mIR6qGhtZP5HImIaDihUkeItfpxdpWVSTz1p59itIYSOdf5Uf2MRgZ6eIig8PISe5j1H+Kk\nKJhUlfsPH2ZivP0aPSU/n5Xx8RgNBoIai66Kj7eHIzaHlqBQx6zY292dw5WV/F1UxDkBATzlsKRT\nN2+2l0NwFOSXXtpyoedoIz/9dHuAQ1MUFrbNez9sGGzfLs9VtXZOS13+9z8xJ2nmqZtvrt18x/E4\nnUi3cJ5+MeMUlnwcRoGLC065eUyJbjx8aXTEaDbdsInLh1xea/u5/c+lzFzGOV+ew8F88bK//5/3\n+a+X9VgJCaRbnRtGxQN6hvGfkBCei4nh1ogIRnl7M8rbm1eiolBff90umUaPrheEXW4up9wsHn6j\nwcj2m7ZzZuyZ5JXnoaoq3i7emGvM5JXXXx4eLTzKf0f+lyenPklCXgJf7f7KFrppqjbxcUYqv+Tl\nsSI/39Zy7anoaJ5sqbOnE6mpga++spsXvb1BS0Dt319Mj0lJTfulgoN1wd4ULgYD/wkIkAbiBgNr\nhgwhw9OTlAsvrLfv3tGj+Vkr7aAl8zTw40d5ecGRI+y76iqWVFRwcnOhub16tVywJyZKVEqdZZin\nwUCwszNfZWczzseHR2fP5uAdd+BqMlHeuzfbdzmxYQMS8qiZVs46q2XnrMu0aRLL3lTWbU2NRLdo\nJqfWEB0tWktenvy5uTXfPEeboLy97dnp+fl2wd7Jtdw7O24uDXDMF4+0bqtF9aef4rE7mNJPsnhY\nVXmouhoPg4G0qioiGygdOyq8fqH9Pv72UrnLEpcxM24m14+4HnWPKzUnKxhWriRj4kQA3n/1NSq8\n3fA0GHjAWip1YaQL03+8i7LYh0m+4QZsXUM3bqTcXM7CjW/g4uTCjSNvJLU4lQifCD6f9TmBHoHE\n+scS4B5AXkUe61PXU2IqYUTYCFYeWcmFgy5k/tr5/HLwF/6a/RdpJWlE+kQyrfc03tvyHl/uFqF+\n/sDz+Xj3t5TUhNtKovpZBfujXSzUVVWu09RUUXgiIkSIK4oED2m5LR99JFFedVqu1iIsTAIZUlJq\n2+V17NjqsIwdy+T//AeA4AayOGtl7GrJMg7C8cGePXkuOZlBHh4QEkLckSNSSqA5WivYzzhDosm0\nC2XXLhg6lKiNG0muqsK/wBvWrqUPUKMoHHJ2ZvRosPhX4rwoAVNurgjltuZn+PuL49fVVaLY6kan\ngHjs3d3bVsfJYBAfxo4dcgM4LlOb4tprJfdFi2k/80zYtEmep6XZs1tbyKpVq1i1alXLhtyqI7ee\nTUCsoii9FEVxAS4Bltbba/Zs+k98EM+rZvPstdfyZ0EB64uLiVq/vsXd3N2MbuTfl8/8U+fz2KrH\n6OPXB5YsQbnqKgynTgMvLzZ5eXFhdjYXrNyA727R6mvUGl7f8DoLty0kNe13pvp41mrUvDNrJ57P\nenL7L7dz0083AXDrsltJLkpmXOQ4Yv3FjBPkEUR2WTZ7c/ZyxdAruGzwZaw8shKA5QeXs/qoZMcV\nVhbi7+7PmIgxOBnsztBlicvYWpAO1XZnU1f3AD3lFPEZRUVJ9nlCgtw/J59s32f0aEk+2rxZckk2\nbGhasMfFiemxPSXNjzVffQUPPnjszudjNHK4spIUh4bnLs3ZnjUTw5ln2jY9YVUIbLWFnJxaZuqI\nihLB05L41IQEsTd7e9u1UWvJXVdrM44Du+xixuzszIRt28QSE1aBWVXlHm9v0p1W52l9w76tNpth\nNOLjYedO8QVYFcRm+d//xAmVkiLRBppQd3W115pqBVOnTuWJJ56w/TVFpwp2VVWrgduAFcAe4CtV\nVet9o8V+I8n5owelTnLRlVdVsdUqXHe2IjbOz93PZsbp7ddbakIA144Zw+mPPcbdt9zCbdOm8f2K\n17l/3lRAMljnLp/LxnSJsQ00WGp192moDryzwZkvz6/t8Y7yjWJb3lGuXzYXo8HIlOgprE1Zi6qq\nmGvsle4KKgvo4dYDRVFs5pyXTnuJV/55hZ1FOVAoTi7Djju7tJRtVZXcnx98IPd4WppdsD/7bG2z\nIdgrmG7e3LRCYzCInb6bBPi0iEsvhfnzj935fJyceCIpiZ7r1+NuMLA3PLz5TEZ3d5kxHRpluBgM\nqFOntr4SqJubmCzuu0/q0TQVSpiYKBfF7NkSBbJ5s1Q6BL6eMYO8c8/l8GEox53sgDi+GDAAf6NR\n5hdvsbMvzc2tFxbZarTom61b7UmGjsyaJUvOttKnj4Q8JiS0ruF4RIR85rrr5PWSJVKmQJsEO4lO\nt7GrqrpcVdX+qqr2VVW1wdtj5lBvonsqMHcos/5cR/rhw2yw2qBGtLIN1oBAKao0OHiwLdj6Q2dn\nfouLY8aWLUzy86Nv/Cm8WvobW9K3cMV3VwCimfu6+pKUs50cs5nVR1eTXJRMVlkWN464kWWXLcPH\n1Qdl1Sr2EUBMD3slSN81a3D1iuYL97NQTlrCk1OfwuDZh90RN2N42pXCSllGf7PnGxbtWUQPNxHY\nWrbsnLFzKKwsZF3uEQZ6+XNe1gJqCrc3Gefe2Tz9tDxq/Qyqq+2C3WCoX3hvxQoxde7Z03BRPkcm\nTpQgBrNZFMidO1s2pjJTWfM7dTCaXuHmZo/46Wx8HFZqzorCgH797M6MpujIFZ6/vzgBzz1XbOCO\npiDH6zIhQeJYx4+XmuSaJjpzJoHFxfiXlDBg0ZMYqeaxs7cyIzCQLJMJNayCniPEJj5rzx7Wtdfm\nPH26aNWbNklxrrqae3vb6fXuLYI9N7d1tWD8/UUr+v13mShnzoSxY1tUtqE9dAvnqaLAp5/CS1f5\nE5ZhYU1lJd86ZHuVWD3oldXVtvToxvB186XmsRomufYFX1/yHQoafXPbbSiKQr8AyZY563O7PbLU\nVMrg4MFsPrqSx5KSmPLRVOYun0t2WTYxfjGc1fcs+vhLll5SxHWEe0tZgSKLheLqatKce4HRA9Xg\nwl8VzizKzQefgRAwnr05e/Fx9eGNTVJPwttVHF1aUpWzkzODggeBsx8X95vGdxcvxsfVxzYhHGtM\nJvjpJ7u5ZOZMEb7vvtt0c6jAQAl7bE6we3vLal+bNJoqAqhxKP8QXs813rats0hLE7nl4SG5NFrV\niZ9/7ryotWkOzs0uC3P1r5PJrV0Mqioz+4oVoiXv3y/RO337Sny39s+89VbbR09f9wTlLr68+4k7\n8XFOWMqd4PMNJM+0lyXOaG+ru1tuqa0F79/fvuPVJSZG/ulLl7augqvjqlurBjl4cMvCM9tBtxDs\nII7n0aPBpcCJ74xGrg0NZfdoCWncWVZGRXU1nmvWMKoFM6+iKPDcc3DaaWw75RR8nZx4tU8fnK3O\nJmcnZ1ZcsYKc8hxuH3M7M+Pkov35sp+Z2vc8OYizL54uPiSUFRLmJbG2Qd52P7C7WyD7ysrosVay\nZI/WuOOBmbsiwthcUsKe8nKm+/kR0lvMQX39+9pi2z2cRftadvkyll++HIBnTn6GmUNmE+oq74V5\nhZFR6tBkoAksNRbe2tRAw4I2Mnas+Inee0+S7t5/XxSgykq5fxtDi8hsSSjyiBH2HgtXX127/HVd\nMksziV0gvoxjrbWnpspq2t/fnl2bkdG5K+kxPj7UWAtbFXdQl6RW41gL/quvRMNUVXunoM2bRQse\nMEDMNpGRYi6aM0fMHqecUutwNR4yKR88CNWlDmInT2zrmSZTi8q9tBjHMMy4OJmFV65s+/EcezW0\nxkTq6ODWtKLwcLFlduKKvNsIdhBNryrNBVVRuDA4mEGenswMDGTitm14rFlDDXCkpR1TliyBRx4h\nsbyci4KDuaNOCIa/u2gkI8NG8sJpL7D4osX4uvny4ZhZssNJ33HY2JMfelxGbJCElO3vJQlQsW6u\nZJpMrHAoMv5Fdjb39IplUg9/Xk9LY3NJCQ9FR1Pi0Y8hYWMI8gzCoBjIviebEWFiozu9z+mcEXsG\nAIp7ON8VVdkaVvTx78Njfz7WZKkEjb05e7l12a0dpuF7e0sNtMBAmWwDAiRM+L77mhbsWq5JU85T\njaFDwVrTiqSkppP0wl62Z7AeKjjU+I4dyOTJ0hYzLU1kluO9PHMmPPKIPO+se1NRFKb4+tYrsHXM\n0DI53dykXZy7uzgOBw+W7YWFIsi1+vuOYYRnny2C9N13obSU70JvJmvaFfb3D8iK9eO4OEa9P5Iz\nzWGkVlThEWLmm5VVrCwo4NO6TpzWovkFiotlnEZj+5rgODbbaEtjmPPOszuiPD1lPJ0Y8titBHu/\nfpBtHkFkTg4TLRY47TQy6jhPe7u7s6O0FKWpsJ9Fi6C0FMuQIcw5eLDB/qCaYO8f2J9Y/1hmDRCB\n7lh+9B9vKS/g7h1DgdlMssmaAejqRrbZzN6y2trjHZGRthjhTJOJMd7eRLl58PXlKwnyCGJU+CiC\nPBtONFpg7UzvYXV0zeg/g8X7FtsiaxqjRq3h2TVSjeuFvxto4dUGSkvrt389/XR4/vmmnZ7XXivV\nS1viq9MqIGuPDTWpqcvAoIF8vP3j5nfsANaskVV3aqoIds06ctZZ9ppVIN+1vX2VG2PZ0KHsGD26\n+R07g3/+kUScigoRQkajhDVqrF0r1Rm1RB1Hk5FWV+nGGylVPbm28i2c5j1lf//JQVRNnsxVoaGM\njHYlIMeHxFwTPL+Ty9nAbYmJXNVWU8r69VIdUhPs2gQVEtKyC7MpLBaZydvS6Oaqq2q/Dglpf9/W\nJuhWgl1RoMD7ZA7Mvgandevg99+Zs2FDrX0yTSY+sBYNatS5uGwZzJ9PclUVZlXFowFppAn2Pn59\n6r13VR0j8eqiUg45rBODnZ3JMZspsFh4uGdPPrRe3H7OzvgajXw7aBDDvLxwNhjwNhopqa4mzCuM\nCVETGv3uWiW8OKuT7MaRN3LnuDvZlbWr0c8APPbnY3y952uCPYN5bu1z7dLaU1JEicjNbVsjqJCQ\nWqbVJtEqH0+2Jujm5oocaYy/r/2bxyY/RnJxcuM7dTCurnZTjGO+Wl2aMiO1Bw8nJ3y7KuR13Lja\n9VDqeo212WzWLPu2558HwOwTgNVCyf33i3Lft69Yb5YsgXffNOBiFbLR0VCR4kJqhQkiKjAbatoX\n5jt2rJQYqCvYW2IfbI62hnJVV9eP7w0K6tTqj91KsAMUlRjwKC+DGTP4x286l95xB/1n+kOiF+6V\nzgz38uJPqwmkuLE420OHIC7OVui/pAE7pY+rD/eedC+BHvUl2McDBvBFv2gAnGpMLM7M41BlJWf6\n+7N+xAhbHYwii4WJvr7MDgtDdSgudH5QENtGSRKVlmzy4KQHeXjSw/XOpZFaVcVvQ4dKUSUrQ0OG\n8t3+75osLjZvjbQ3u2bYNUyNnsrGtI2N7tsco0bJqiknx24v7yxOOkmCDDQfuZdXw3klVZYqjAYj\n4yPHE+wZ3L42iC1E8//V1NhNMVqenLYKt3alA8Shunu3BIacsBw6VN/h98gj0L8/P/1kVdjvuw8y\nMvg4YTyTpJYfR49KuSVFgZEjRb7deKP9EIMHw5IPXNhiKAAfuU/L0sTuPvjelvmY6pJW4S/pzYpi\nT9hqqhpjZ9PQSsHX1951JiWlw2163U6w5+TAS0i5y+sLxLTwVuEd+N0ZRf87Igl2dmaPtWB/juZJ\n37JFrhhNqy4sZEOCH3vyxD4d7xDbazLJjVhUpPDCaS80Wtv80vBo8sfGc8mGkazdUs3BigriPT0Z\n6+NDsIsL2WYzRRZLsxqVj9HI0aoqvskvt0XDNERaVZWt0YLG4ODBbEjbwFN/PdXgZxxXLHePv5ue\nvj1JK66X2NsijhwRf1NWluSKdHa/bEURf1RkpCiHiYkiN0wmUbYeekj2yynPIcgjCEVRCPIMIqus\n85avGprfMD9fzLORkfDJJzIRaTH6AweKxe+DD8QhuH69/JnNjR/3uKZ3b7Gnp6dLFtpFF9liYq3J\nsUJoaC1T1f79TfecGD0ayLcK8qOhUGVgx1ETLA1jz9kHuG5PyzoRffuttFGYOxdmXe+HaZ01GSgv\nT9rVvfturf27MJJY0AR7SYloC4215Gwj3U6wV1TAa9zBHgayl0HseXEZp/An+WWhbDwQi6dDqV5b\nSOSKFaIW/PWXvC4s5IIb/Vi0upJHevXiUgfTSlKSrCq/+KL5sSxd5Mfnb7uDl4XnP61g1wrRpoM1\njb26ulnB7u3kxDvp6dyYkIC5kSQMVVVJraqq5ygbGTaSO8fdyZHCIyTmJWKpsVCj1tjaAzr2iA3y\nDGpVJE1dfvvNvlo8lvXGXnm9gnXrZKXs5CTmj4AACWpKT5dG5ZpfQuugVbfZSkeTkCACZ/t2u2D3\n9JSJaOpU6a0cFQUXXigRPZmZ9uY/Dm1KT0zCwiSb0ur5rhuleOAAPP64PM/PlxVPUxVuQ0LgynNF\nsE8K8cYl1w0GF8NaWUkvzMng/ffFhNMUDzwgiXOvvw75+ONSWSKROVu2yAzsIAPS00WJHjmyCydi\nX1+J+dfqULXXWVyHbinY04hkMHtYsQIGnWu3gTtjsWnhiwcN4kutmpT2aLVZqQUFFNKDUp8Ketep\nNaPdeG+/3fSsbTJZTQOlTuBpocS7grSNItgDnZ15Oz2dPLO5WcFebLGw2dpaa591pVFZx4T0S34+\n5TU1eNc5lqIonN33bL7Y9QX93ujHGxvfwOkpJwa+NVC+dlk2BsXAmmvWABDqFcrhgsP16tK3hCNH\npKcAHLtV65b0LXg862Hzu91wQ+33IyJge6Jo7CA5CtcPv54NqRvoLFRVIvvGjpXXr71W2zzr6iql\nh8hNPT4AACAASURBVDWbu9EoJt3ffpPXycfOBdAtePttefT0lOTKyx1q8wUESIRUY5U+NSpK5QLo\n1x8GJMrFN9XLn4sre8FuH258sYTzz5dzLLUWJHEMOU1MtPtnXF1FsANYho2UuFoH8ybYKh6wdWuH\ny9OWc/So9HnQNMwOznzrdoL9tdck1M5kskYH9e0r07AVn7f78FffUcwIDCTdZCK5shKys6np4UdB\nYi41TzyJUlZGKV7kuVTQ292d/Hy58X79VRz511xj1QabMNfaKhCWGsHPDNFl+JbJBTLMywsVSU5q\nrrH01aGhTPf3Z6KvL4kVFewoLcV9zRoKrKpC1D//cPauXY32MQ3ztof6afXmteqVueW5nBR1EhN7\nSu2KGf1n8L9t/yP4pWCWH1ze5LjqcuSIaKTr1sHnn7fqoy2i0lJZb0zaiqOgQnwmzz0Hb75pj0gB\nWPRzTq1IorjAOA7kdVyj4LKy2iVRnJ1FI9f6rUyb1vwxBg+WeH8/P4n//zd1iFqwACZNEoVs4UJR\nkC+5BL75Rt5vLlkN7L91Ty8XzjeGc9XiCfy5UuHhiUEwoBjek9yV8nL4+2/Z9/rr7SXRX3nFfqyq\nKkguktjUXNeGa1ukOVgrO6rSqKqq3HjgAI+0sPuUzb6usWZNxwzESrcT7NdeK3XtbbO8Vvi/ooKM\ngEHkfXGA/T974aQonOXvz9LcXMjJYXfNQN58KpcPn9RUJoUc50r6uLnx0UcSrnf77TLT+/nJfKE1\nR2+IzEyxDaYctnrCf4igJlNMJXFWA3QvNzcMzWQGzgwK4uehQymyWLhgzx4SrFr7vvJyyqqrSbU6\neA9qKmId4gLj+PWKX3nn7HcAWHLREowGI+ZqM38c/gN3o10bifGTJIriqmLO+vwsWy2alqAJ9vHj\nW168rjX8lfQXZ31+Frctu822TWs08u4Wu/3zlluk5MDIkfL6N7cbcDPaV13BnsG1TFDt5eqrZTIB\n0darqyUZacYMMbs0ZUbQ0EK5b7xRokBuvrnDhtftKSmRHATHigeBgXDBBRL1WDeBtSH++1/InzCB\nmYGB3H2XwuvPyM0f7OIindyBEeNk9tVW2ZoJRXut+WTi4sDTx4nrphzkwSMOXloHHAX70qUiYhYs\naF9PXsNff/F+RgbzkpNZX1doN8SSJVL+YM4c0TRXr277yRsaT4cerTNxc6Ny0EiGs832z7w6NJSX\nk5NRd+9mffFAhrKTk1jH00MX0X9oNeVOZrxNrvz0k+yvOcV69BAtQ2uO1BDJybLEjoyEkBsmwIcx\npKbYhfjC/v15r6n8+jp8Z03s0JKaUquq2ObQ/dy/EY3doBg4vc/ptiqSM+JmEOAeQG55Lk+tfqqe\nvfmSwfZOTwl5TcxcddAEe2dRXCXJGG9uetM2rtTiVLxcvGwrEEfCw+Gff1SqzUaujXrett3XzbfN\nIZ0Wi9hWFcVudtq4ET77TJ5ruW/Dh8s+f/5ZbxXfIJpg/+9/pbiiq6sInLrRbH/9Ze8vURfN7//m\nm5ILcDxQXi7hsWFhtfMQtKjAvn1bXlbFz9kZRVHw8LCHiQc62HDufU6M+VrqiBYCvmoVvPOOaP1D\nh4oDFaAkuA8ffeVWq35ZZqZYAtLSJJn2llvEGgIiXx0MA+1i/LZt7Ghu2RYRIWFo//d/UgWypqbR\nhKXDFRVkVLXOvHr8CHYg5uxBXDd+n61423A3b7JLSlEyMtjCSM7lRwawn2X7+3Dx3EqULDfOnq7w\n55+1e1E7OYmjsLFJ8uuvJdlOU6KdyuUCS0+3z+rXhIUx1Zq1oqrNJ5H1cXfnkuBgvrRekR9kZDDX\nwdPWWHSOxuiI0Vw3/DoMioGssixmfj0TFycXNt2wqdZ+X57/JdcNl0py+3NbluRRWip/HRHq2xiO\nTt3EPJlhv9z9JbPjZzca6RIWmwM1TiTvs5tierj1aFKwv/22JD42RHq6XcPbvVts6cXFMokfPGgX\nGo11PWuM004TQREdLX6C3FzJWg0Ksp8vPV1WAHfdVf/z+fmi8b79tqxW/+//Wnf+ruLoUXEiGwy1\nM5KffFIe+/VrmcbeGI51cm42buGuuyQgx2yW1fawYbIK791bftsdO+AKa4Krpie9IwtdVFUmoLff\ntveifu45MR1p9bjaakKracBZN2zz5pYfQFHsTQ7qkFpZSZ8NGwj/5x+2OiiCzXFcCXb8/Ij0LrI5\nPwI8nahwNlKjKGzG3nxjn6k3luAKfMrcbTa5GQ6dVnNzxS66b199gfzXX/b2plpHsbffFtu/Vg6j\nLp9/3rJktOFeXpTV1DDR15ffCgrYWlrK67GxfNpUPJgVH1cfPjj3AwBxIKZtwFRtoqdv/fTmd855\nh3vG38O+nJbVfD5yRIRSZ9ab+jPpT24fczun9T6NpMIknlvzHDuzdnLRoItsbQY1XJ9xZfXR1ewt\n2EJPlxEcPWofmCbYt6Rvoaiy/pL3s8/s7fvqUtexOWKEJE5OmyaJlGVlcvM//3zDn28MLy+4+GJ5\nHhEhYclaxcpHH5Xja8JuwQLpQ+GINt5bbpHHoUObP+e8eWJ+aGkP587gl18kHwEkHPT33+U31u6F\niy+uVR6+XfRzd+fpp+W3W71a7t+4OKkm+uGH9UPFX3tNHLeaBn/99fKYl2cX7D4+cg0MGwavvtq0\nz60p6tbzmezri2drs1wbEeyOxdFGbtnCohY6BY4vwe7tTbhPCQcOWKMQVAX3KhPlrq7sYwBJRomg\nKaIH5pAKLp3sziuviMf8zTcl8eTccyVnwcfH3qu6pkZ+1+pquUisuUW2ZJRzz5X42DFj7LXyHdG6\ngjVno9P6WU6yXvkfxcVxe2QkV7RSVX7r7Ld4YdoLPHfqc7WadWgYDUaGhw1nf17LNPbdu+2p/Z3F\n2uS1PDzpYU6NOZUl+5cwb808nj3lWWL8Ykguqi1xTdUmW0PwwT3G2epOgQj2wwWHGfX+KC5bclmr\nxrBmjZgz9+yxb5s7V7TKoiIR7L6+7cs8j4uTaIvPPpMw73nzJETZsYey48SzYYP0PgZxNF5+uT2E\nMDtbfE0vvGCP+qislEnikUdE42xJ2G5nUFkpv6dWZaBXL7mfHEsynXFGvVpgreaioCBe6tMHo6Lw\nS1kOJpNMlJddZq9N1FDpluBgqVfm4iL398KFsn3zZlkhaZnPGkFBbRfsmvB93FokaV5MDAZFIb81\nsZQNCfa8PNJfeaVWGPS2Fi4rjjvB7lRWwqxZog1cdx14mao46+FfqPhzI7Mu3cLAYRbwMZHvXc5Q\nHw/uvFOWas7OctP+8IO9Fs/o0SLUMjJkqZyWBosX221tdZ2I0dENTqq2kKnmHOLjfHz4T0AAp1gr\nSk1pS80JpDrlvRPu5YGJDzS6T1xgXItNMd99J5nh6SXpLD1Qv8FVezFXmymsLCTQI5AYvxhWHlnJ\nHePu4MFJDxLhHUG5udzWCFzzGfx66FfWpa7jzMFj2eiQTBvqFWpzCh8trN++TYsaKymR2HLHOOsf\nfpCSHdr/9ccfRUD4+NgFe0vKnjeFr69d4Nx2mwj3qCiZ9F94QTTNBx4QwZOTY6/J8/LLcm3dcIN9\nFbl9u9jk779fzDPFxWLzf+IJeyZsW1p4toeDB+WeGTBAlKLGejp3FF8PGsR0f3/WFRdzgcOMfPPN\n9rIXdYW0hoeHCHZHWfjLL2KGrVtbLSJCVhuWmhpuOnAAUysaf9xjnXWfiIkhadw4Jvj6EuLiwo3N\nNUdxxM9P4qsd62osWkTqjh1M9/Zm7+jRvNOvHx9mZLSoT8NxJ9gpKWHOHCgoUImIAM+KCgxnSGZp\n1dgcSm7eDz+s47eCfAY0kz45bBi8+KL8s0FumNJSWQrv2lXf8RMS0nDcqybsmyuxHO7qytIhQxjj\n48OC2NhaBcc6mn4B/UjMS2T5weVNliQAEYb9+sFL615ixlczmty3LWSXZRPkEYSTwUk6WwFTo6cC\n2Orjf7nrS/488idzfpkDQIWlgt8P/86kgf04etRuq3ZxciHII4hLB19aL2Z/1y4RoIMHw913izN4\n8WJ5z2SS/8+IEXZHn6ZN+vqK0Cwr65iM29RUGW+PHqJZa4lf994rGjyIUHf0+0yaJL4fHx+7YHdc\nWXz+ee1uapWV4qxtSQBGRzJ8uDietYSsgQM7/5yOIcXB4f/f3nmHR1WlDfx30nsnnRCEJIQeBKSD\niqwVUBRFcUXws4Cuq+KuinWXtfcCioIIVmyAgqKoKFWpIRJMKEmAJKSR3pO53x9n7mQC6ZlkUs7v\neebJzK3vzZ1573ve8xYDX3whH2xTpkhXS0MRx15e5/TZrtM9NGyY9NH/mJPHsvR0ps4rb3KUjLON\nDR9GywY/vZycEEJwtLSUL7OzKW3qQS65RA4dly6ViunAAYiP52hwMBGFhUS7unJHcDDVQEZFBXc1\nFNJHZ1Ts27Yx4P/GoGGDl0sFrqWl/FZcxGO9epEckUGFh1RiJ8rLiW7E/NILUOk+1fffl0M2V9ea\n6qTmBAbWXZAtPV1OtJ79BaqLhATISrHj7raIKTTDzcGN0qpSLvvoMhwXOzb4lD+ZnYtfQCWBbtIl\nVFje9EmahsgtzaWgvICTBSdNjUnODzqfjTduNCl2kLH6d393Nxetuogo3yhemvISF4TImetwv0Dc\n3GpHmGQ+mMnHMz6mj08f4jLj2LFDuj42b5bVUfv2lTXkXT3LuTFRUFFdwbZt8p56eMi5BE2rsc51\ni72oqFZnuRZztitn8WLZowFqJuRr/Z9ya4qL6bKAnJy87jppzA0YICdozfHzk778jIymfffqYuZM\nGSjQmBGoR+3Y2MgHzD//KScm6+g1b3G8zKJjDiVVMWOGfD98uPzfNoSnZ83/Tbfs9bh5czw85Ouv\nbGkobDpU0uQs4hPl5fQ56x/xkVHR72pqad6pU2XUxvr18gmTkED1kSPERkYSkV4TeBDp7ExiaSlv\nm9dtqIPOp9gBsUtWlpu44UHOGN0ZN/r7U+VQhbeP/IYOc3Ors1zv2Yd78snaaeANxS0HBtZtsael\nSYu3KZPW/fo1LenFUrg7uGMjbMgvz2fp7qX0fq12TOPk12/n1E0+PBs332TZ3/DlDaYHgaZpLPxh\nIQdOH2i2wu/zeh+u/uxq4rPiZYcopIV+WcRl2JmVhtAbmYCMnonyjSLKV47xPR09CQ6uXSpXp7dX\nb04VnGL2bOmnvv9+6S4LCQEC9zP4QmnV7E/fXyubFODiVRfz9eGv5TmMZTv0SWRL4+4uO7eBHPUt\nXiwt+TfewHR+neBgaShUVkqr+LrrpE84Kkr66ufNk66ZSy6R2y1fLr+XLZ0j+fxzGVJdl4tRZ+FC\naeyUlsqRQlSUnGy8446WnbMlfGJUlL81c4ji5CT71b7yirzG7Oz6o7/8/CAp3zi6fT6OfScb7oVw\nsKiIpampnCgrI+wsxX5jQACPhIXxXHNSkYcOrVWo6LaLL+aXIUM4PyFBDtlSUwlxdOTPs8qF10Xn\nUuxn3ZFhW19HM/qr+7m6EujogKN3NbP8/dk7fHijIYRQkz6vz57X568D+aNMT5d+Wb1rfWamVOgD\nBjRNsUPTYqMtxfnB5xPuFU52STYHMw6SnJdsWldcDD/lvgvA5uTvTRmgG49sJLM4k6W7l3Iw4yAv\n7XyJmHdi8Hi26Q7dx35+jNyyXH5O+pn7Nt1n6kVbF7o1D5CUl0SoRyhXR19NsHswQoj6AgbwdfHl\nr5ScWtbqeecZfeh3DiN+kPSBbD+5nYSEGn9wtaGan5N+Nvnz/fzkfTxypOFGIpZi0SLpe9cbkph/\nTZ2d5WTgm2/KEkh66GV4uHQ1TZ4sFdWmTbUtz5b0Pc/LkyOUiRMb7iS3ebO06DMy5E/QGt36bjCm\nsM4w9081AX2kp5eTPtu9Wm7mS7cZkcum0zUPjkOnG3ZhPpaUxPwjR8itqiKwDn/QHcHBHGhODKV5\nreyMDP40Fm0KXbJExnE+9BABBgO79Iy6Buhcir2OcbK9mZYMcHDgYHExjzSjw8nUqTJs7G5jQmRQ\nUP3bBgbKH/8nn8gerSAn4C68sMZPWx8pKTXW4FnNnNqUANcAerj0IKs4Cw9HqZh1y9x89JFRlEFu\nWS43D5YhGr+n/s78jfOZvHoy1w+4vtnnXby1ZoxcUF5QZ1imzuCAmvi+UwWnGOg/kPO8zyP1fqnN\ng4NluOrZ9dp9nX3Zti+7VqbnNddAv0HSb5BvK3fYcXIniYk1ij2tUJr/Bk3+qMPCZIhieyl2nZEj\na/eu0AkJkYW0li6VbiWoSajSmwAJAbNmySQhaLgXbX3oSWlRUXWH8erobuKYmLbNdWgqYssWtuTm\n8n8JCfxk1sWsLkaNkt+fswfvmqZx7Z9/4vTbbyxLSyO9vJzYm2NJ9DqDXbVUiydLG04Kcjb63Nxs\nbevMQPe1tye/uemsq1fL8sevvkqREPwZHi7dCgAVFfinp7MrOprpjZQg6FyKHeRspxl2Zv9Qvezt\ngGbMgPn7ywmtwECZ9NCQj1W3ikaNktbu1q2yjeKVV5rmdeslNlYqd6g/+9DSvHX5Wzw16SlCPUJ5\nb997PL9DlkHOLpFmjLlirzRUkpKfwswBM7ki4gq+SfjGtO38EfPZeGM9weFnYdAMVBvkl9nT0ZM3\nLpP+hhD3+iuL6f50gJsH33xOCGdAgPyfnd2y0s/FjxPZOQweDJMvL0bcdCXphekMGWN2YXtuZ2vK\ndv5KrDIpdn3UcrpIbhcWJic84+JapiBbSkAAfF9HSR9vb/ld0ssqgIwX79nzXMPD2VlOsrZkElVX\n7JGRchRQn59djyzKy7NcXHpLMJi1tnv2xAneS09ncmwsxQ0oz08+qd2+VWdDTg5fGs35OxITCTZr\ng9VHSP1xxK5h/3iu8YdcVM/5XWxsMGha0ydQQVrm994L+flkeHsT6O0t/W+ffw7HjxPwxhsc6dmT\nS4YObfAwnU+xL1xY897Tk7lBQVxtHMKsiIri+AUXNMkFUxeNWWv6YT095Zd8wgQZR9yzZ/2K/a+/\n5H7mWa7NLTy0ZUtNVmRjbN8u3UJCwIKR8+njFcX8EfNZcWCFaZuQl0M4nnucxESwr+jBm0P+IMgt\niAOnDxDiHkK0XzQbjmzgtpjbKF1UyoReE7i0r/xFN9Sa7vuj32P7H1tT6KKdjR13j7ybCb0mEN2j\nfldMiEeN0h/kP+ic9bp/+uywYD+bvsQX/EH//nD7Mz+jRWzg56SfySjOINRDTk6PzHsRJ4M3eUFf\nmtxsyXnJuDm4kVkib4STk6xPk5ratmUVmopuQJiPzP386q8c6e1NrdT5pnL8uHRdjR0rH2x1BQYY\nDNL9qDNvXvPPYynMf9d6V7TRHh581UAAuhDnhrCWVFdz1Z9/YicE/6pj+PxCaATXpvflsG/NjP3a\ntefmqeRUVnKxlxf/q+dLI4TAx97e9ABoMsHBfPj99+R6eOCth3D16QN79uBvHKGMbKR/a+dT7CD9\nJn/+CadOsahXL74yhrB429vX6kDUVlRU1O6Wo9fKOFuxl5XJyS+QccpPPSWt9mPHzq1jXR/LlklX\nz5IlTdt+3DiZmKGfd88eTP7tJyfUNOzYmrJVdr5xLOaWy6MJcAvgTOkZgt2DmRg+kfSidILcg0wF\nuPQf1Zx1c+o99/FcGcjv/6KM43tusgw3+nXOr/i7+te7H0DC3Qn8Nuc35g07V3OMHy9reqSlSau6\nvFyGC/7fhKlUh/7Km6l/59AZWQEwOS+ZjKIMhgQM4aNrPiK6jzsnNs3AK/KQKVolOS+ZmMAY05wC\n1LjHGisx2x7oselNrbPi5ycnNh9/vHnn0S12PZa+LjdkYqKUw2CQFn1L+jhbkleNvqmUsjJW9uvH\nVF/fJift6BwtLSXU0ZFDI0bwcFgYC4KD2R4TQ+ro0Thhw0V9XJlqCKHYqYKsigoqKmTI6tmWf05V\nFe9ERbGwgX+Kr50d2S0o+r7dOKNuo39pjbGx/sb2Xj0aqSrbORX7G29Is9QSsWktoKysJlQyIEBO\ngvn6npvevXev9F3qzVseeUT+MMLCGvZpmqO715555tzjl5fXfpjk58vEiyeflL0FFi6Uk2zbvg+E\njW/gfvwW/nnBP4nwHEB6fg4paSUgqnG1d8XFXpo1PVx7mNwmAa61a67+ftvvpjj0utB914P8B7H6\n6tV1Kun6iPSNZHyv8TjY1v2FDQ6WLrPBg2W6+EMPAdVy288TV7MnbQ/jwsaRkp9Cdkk2/q7+3Djo\nRpmmnx1FpWcC41aMY/m+5TWKvaxGsY8de27SirXQDdOmPmTs7aXl/d//yu9EfWhazfrKSjk521g1\niz/+kNFE1pgwrYt7Q0MZ7+nJgaIighwciHBxIams4eiVs/klL4/xnp5EurjgZW/Pm5GRjPH0JNjR\nkdJJE3C1tcXHS1DpUI3/jh2m74X5HJpB00gvLye4EQUb5uTEpjNnmpRUpPO/lBTeLivjbb2DOpgm\nN/yNTQsai/jrnIrditxyixyOTpkikzNOn5YWlp5JaE5enoxiuP12OcmlFyaKjKzb71cXgYHy4eHj\nI+dUzLnzztrD9cOHZay23sFm+nSp5GfOFPzN+26O7AnjhcmvcOTr61nzbQ6pBWkEuMjIE70rk42w\nIcBNKnS9DLBOL89eFJQX8MCmByitLK217vKPLmfNoTWsu2Ede2/fy+zBs5t2gU3EvPnHF1/ISWvz\n8g4bjmzgovCLSM5LJq8sDy8n6c8YORKunxyFV58Etp/czt3f3U1GcQbRPaKJz4o3Xcf//V9NdUdr\nc+ON0ufdHPRou/oaR2Rnyxh0d3eppN98U4b56lE378kyRCY/e1aWzNTds6fuBt7WpJeTEwZgoKsr\nQQ4Oza58+F1ODjf4NzyCvOAC4L9ypDtmjPyfZWdDhcFAbFERmRUVeNjZ4dxIg+vksjL+dfw4O5sa\nzw6mYl8+5kMoW1vYsgV/Y5Uzl0bOqxR7M1m5Uir2iy6qnRno5yf9nFVVNaFh+fk1McrmHqKIiKYr\n9sxM2UvztdfODfnLyqrt0klIqG2B6QWaHn5YuoHeflsmo1Dqy+mCHLLKUwnzkhrz0QmPmkr+6h2L\nIn1rzyT6OPuQXZLNy7te5vfUmi5Ge9L28N3R7xjTcwyX9r0Ue1vL+zP03+Hx41LZODvLBBXtCY07\nzpcB1aNCR5GSn0J+eT6ejjXB4cueieR05RE8HD0oqyojrTCN3l7yoTV/43yLy9pahg+XserNQY8p\nry9v5ZNP5F/dK/DTT/Khr7dBnDdPKvBnn5Vx7WvWSMMgKakmLLOjoKu0IAcHghwcSGuqX9NIQmlp\no8mLfn4QkeoPFYLNv1Vz001Ssa/OyGDonj0kl5UR1oQh3hsREfSwtzcp638cOcKU2NgG97EVgmm+\nvkw2t9gBJk7Ew9cXbdKkRs+rFLuFsLOTk1729jVRNhs31l31MSKiab0xi4rkwyIsTPpCz84u1Eui\n6kWyMjJq+0mFkMp/8WKZ+wAyse1fd/uRnpuDc+AJenrJScapUVP5ZIb89dvb2rP66tXnuF3MFfbe\ntL1kl2TzW8pvjHhXmnQrp6+s15XSWnr3lqOR3r2l1Wk+kRjhI2e9R4WOIrUglbTCNDydav7xHo4e\n2NvYU1BeQIh7CMdyjxHoFoifix+xp2PZcXJHm8jcntx6qxzB1ddjYMcOaZTobNggFbc5//2vdBfO\nmFGTKfvtt03rgtSePNW7N78NHYoQgiAHBzIqKprs6vi9oIAqTaN3E1Jm7W0FnHbiYHExPoHVpGYY\nTHHvo/fvb1KQxsXe3jwUFsYRY+ruvsJCfszNNXVQy6msJM1sxFFhMHCwqIiHwsLwbsWEj1LsFkT3\nu2/eLP/+8kv9ir0ui33y5Nqx2idPSqVuYyMnxU+erIlbBjnsDgiQ0w3r1kmLwtw1A/KzjY30H+u1\nqi8Z6wsu2RQ5/kU/37qdrLMHz8ZGnPv10Gu9/5ryKz1e6MHElROxETaNTo62Fm/vmnK3v/9e07cc\n4Jroa+jp0RNvZ29igmJYvn95LYsd5EPJ0daRALcACsoL8HTyZOONG9l/ej9jV4w9x7XUGXn4YRn7\nXmp2KZomi4+tXXuuP/3sshm6IXjHHbJ+kj7a72iKvZeTE+ONoUNOtra42tqSU88EZVl1NZ5bt1Jt\nVPxb8/KY4eeHXRNKeA4aBBTbMWrfPg6MPM6LkXtYYPbDre+cZxPq6Mip8nLKDQZ8jco6zhjmNjk2\nlsjfa0a/kw4cIKG0lP6tLFqkFLsFufZa+VdvBJKWVneYYt++0mK/6y6ZCq9PgP70U43CWrRIJq/o\nPypHR5lI8o0MLyc9Xbok9Iy6tWuldX62YjfnySflA8DP1RfXHjnQ43CDYYh18e5V7/LT33/im8Rv\nTMvuGXkPGQvrbpbRFkRH1zxEQc4FnLhPmvB6q8DCitohSj/M/oFNszfh5yL/QZ6OnrUeRgczDrax\n1G1PWJj8Lrm41HxPUlKkW6esrHYo5y231ChuHUdH6Ur8+9/lZ13Rt1dz85YS5ODAhxkZHCgspLS6\nmvXZ2dxsrJi2LT+fgupqk1V8qKSkyUpz9WqYM0qGG+Z5FXPGU1pVb0ZEcGtgIHObmK0V6ODAV9nZ\nOP32G6fKywlycDCV9E0sKaHYLPs1vrgYByHwqKejWlNRit2CzJol44HNy/f+5z/nbtezp4xweftt\nWbxq7NiadXqs7OrV8gFhPsdTUSGbgFRVycCgm26Sk4Mgf8BZWQ2HyPXpIzNtfZ19KXaLg+ivG0z1\nrwshhKlY2D9GykqMesx4R6Bak//AOUPn1FoeExTDxPCJxATK2UIPR49aTbJHLR/VbjK2B1Onyr97\n90oD4f77a/zpAwbURGqdja2tnDgcPFhGHl14oWUqXrYlzjY23HfsGDF79+KydSsfZ2TwYUYGcUVF\npoiZFOPfQ8XFTU5gtLeH92MiWREVxUH7mq5dEc7OrOjXj0ebWFhoqJsb/Yw+/b9KShjl4cEdr78d\n1QAAIABJREFUiYlMiY2lwsyFdLi4mPzqavLHjWvScRtCKXYL06NH7c91tQazsamdfRoXV+O+2bOn\ndqEic7+6vs++fbIc7bx5NbVt9u+X+zal+45utQJE+DY/h153cwz0l2P5UaEdRynqVrhePuFs/nPh\nf0i9PxVbG1tc7F14acpLfH39123uSmovzOcRMzNlBNFVV8k8CiGkD/311xsO7bS1lZnSkyefm+3b\nEdk0ZAhbzDIx9fT+2YcPm9wlF8bGUq1pHC4poX8zi+5P8vLiZu+ayavIZubKeNjZcdDYvcffwYHD\nJSVkVlbyY24uVUbFnldZyWJjarpTIxEvTUEpdgujh7X26lXTS7Euzk5G05t/LFsmHw6HD8uJzs8+\nq9lGDzt76SXp5hk4UIZcrlolQyuFaLg6pY6zvTO/3PIL1Y9XmxKQmoM+MRnsHszJ+04yLqz1Foal\nWHblMk78s/6Keg62DrWKjt0/+n4uOe8SCsoLTLVjOjP29jVGwjPPSJeM3rYP5MR+a7sadTR87e0Z\nY9ZxRJ/gdLKxIaeqintCQqjSNA4XF+Nua9vsScnezs58MDgK/jWYr0rGt6iPgr3Rp+9tZ8eUs6Jd\nZvn74719O1vy8vha74zeSpRibyMcHWsiUerihx9ke7/bb5efL7pIulb02j56ww/zcqwrV0oLbM0a\nGUdvZyetK721WnPijSeFT6pzcrQpuNrLoayfi1+HcsMAuDu609OzeVXWXB1c8XD0OKf3amfl4ovl\n5Of778sIoobmXboK9jY2XGscLn+Vnc2C4GAKqqvJrqxkqJsbwQ4O7CooaLa1riME/M3Hhw1ftc6a\ndrGx4bWICCZ4etLTOGy62TiRllZRwUAL+b2UYm8DPDwar+t9ySVyqPv22zJc8aefpF997NiaicGz\nw1jt7Goscr3Rts7y5e3X3V4P89ITmboC4V7htUoad3b+9jeZR9HSPp6dkZVmYT99nJ05UVbG/sJC\n+jo742Fnx5HSUoJakV780EMNlzduDA9bW5Pi/jUmxqTYLzAbbViqJIpS7G1AUpK0rJuCEDLKQ38v\nhLTGoaaFmzl6ZMPZE/Jz57ZvAauChwoI9wpvvxO2MV1NsQshM5ybWzW2M+Nqa8ua/v0JdHBgRo8e\nlBgMxBYXM9bTE09bW1LKyvBuRbSJ3vQcoLiimLKq5qUqZ44dy+tmlQazjP5/H3t7Jhjjom0tVLuh\nVYpdCPG8EOKwEOKAEOJLIYSH2bqHhRBHjOuntF7UzoOPT91KuanoXfPqusd65T9r/2DdHVtxgR2Q\ncM+updhBJsjpiUbdhev8/UkfM4YwJyf2nX8+70RGYmsMH/w8KwuvVih28360fd/oy41f3tjg9kuX\n1m6k7Whjg4NZ/PxdwcH80/hjfycykp16fQcL0LpgSfgBeEjTNIMQ4lngYeBhIUR/YCYQDYQCm4UQ\nEVpzKuF0Yy68sOG64Fu3yua7CssR7hXOgdMHrC2GRenTp+Ut87oCMe7uxBgtrKLqagzIzM6Woldw\nXbp7KaeLTnOy4OQ526SlyQCIJ5+E+fNrmsTUxX1mJYP7WTimtFUWu6ZpmzXNFEqwC6nEAaYCn2qa\nVqVpWjJwBBjZmnN1Jxqr/jhu3Lk1phWtI9wrnOT85Ea3+zPzT04VnGp7gRQW5Vnj5FRLSujquLvL\n6DO9vlBJZQlTVk/hxS3LTEXbXnlF1mXSG6jExzdccbOtsKSPfS6gt9kJAcwfZ6nGZQpFhyTcK5wf\njv3Agg0LGtxu0NJB3PDFDe0klcJSTPDyYtewYfyvKfHA9eDgIEs0eFXIkMT4rHh+PP4jaw9u5pU3\nyqisrjQVBnzlFfn3kUfgvvtqHyc/H/75zxaL0SQaVexCiB+FEAfNXnHGv1eZbbMIqNQ07ZM2lVah\naCN6eckShkv2nNvRZMzyMSz6aZHpc1eId++OXODhQUAj9dObQtWZUDbeuJGnL3oagOzCPDKnXMqw\nt0eYCvLZ2ckqnXBuVdbt25sXwVZd3Xyrv1Efu6ZplzS0XggxB7gcME97SAXMg4lDjcvq5Em9mwQw\nadIkJjWhLKVCYUlc7F346JqPuG39bQxeOpjHJz6OnY0duaW57Dy1k/SidG6NuRUAR7sO0pFD0e7c\neSd87pnPqnc92b3uAT5bM5BZX8+A8Er+zLSBFFlSOi5O9kLWM8lBKuj33qtxoxoMUF8tsieekLkI\nf/whc12+/x4efHALu3dvISSkcVesaM18phDiUuAlYIKmaTlmy/sDHwEXIF0wPwJ1Tp4KIdScqqLD\n4Pe8HzmlOVwYfiG/JP9Sa51AMDRwKJWGSuLuirOShAprsvGHUq7Y6YLzikN4lPfn229h5Ld2aFX2\nUO3I5UePcXCXL6dOycJ80+cexWbeRKqfT+W332DiRFkW+csvZTHADz+UtXk0TS7LzJSTrldeKSOa\nDAYZSGFe5vvhh+Hpp2U+iaZpdcZHttbH/gbgBvwohNgnhFgCoGlaPLAGiEf63ecr7a3oDDw87mEi\nfCLOUeoAGho3DLyBzOJmdiNXdGpc/udCYk4iAFfsNLaQdA5i9GjZlERbup+In2LxFX256fFNXCr7\nvstCbP+IwOCaxq59RezaJZd/+aX8e/QovPuujKSxsZF9ihcsgKziLNw9pLtvwZePktX35VryNCWU\nurVRMRGapvXSNG2Y8TXfbN0zmqb11TQtWtO0H1pzHoWivXhgzAO8e9W75yy/Z+Q9AEzoNYEzpWeU\nn72bUFJZQmlVKVFvRvHBgQ/kwnXL8XXxJjxc1uMhcxCHfoskx3EvN311E3Pnys3M81BGX32Af/+7\nJhlRD2fOzq5dFtnBqQr/F/35PWcTXl6w9ND/KIz5Xy2ZmtAjpONmnoaHhyOE6FKv8CaW+VRYF73X\n6/ob1nPsH8f4dta3pkJngwMG4+noSXxWvDVFVLQTx3NranDPWTeH87zPIyRzLpGRNaWRQRZfe37y\n8wCM+UGwcv8HaJqGnY0dHPg7BO8BYNs26XbRE1DNXSzbt0PIQNlpJym5iuk3yGbrBsczuPSV+wcF\n1W5gXx+tTVBqM1JSUprV2bsz0JRWWgrrE+IuTaiYoBhCPUI5z/s8KqsrOXjnQVzsXbhz+J28t+89\nXr30VStLqmhrDpw+QC/PXqTky5K61YZqEhNl8T0HB5ldOmeO3PbBsQ/yr82y4/ycdXNMvQFImQi9\nf2b0aJmVfuzMMRw8fABvDoXPx95zIPuXzcfFBZJykwHoMyqelYFTcSzpTblLEiWzR3BgusZPP8me\nD43RYS12hcJa2NrYknZ/Wq3Klfa29gwKGATA1f2u5vuj31tLPEU7cTDjIMv2LuPBMQ8SP79mhObi\nIqu3CiGjZMxdI7MGzjK9n7d+HlWGKq4YMhq7qB/od+8DiKcEfd/oy9dRPvzyezaMWIrhwkcYMEAW\nDvSa/hQAw6+SWdDl5TXG7aDBBry8ZB/kxlCKXaGogyD3oHrXxQTFkF2STWpBvRG8ii7AkLeHsPXE\nVq4feD3RPaKZFzOPBSMaTmBbOGYhIHsDLxy9kL4+ffn2/WjGRw3k/b9qT4Je+F0PyBiIZlNJYXkh\nQoBr8AkifCL47NCnADj7ZcHThXg6epJakEpAgIycaQyl2BWKZmIjbBgUMIhDWYesLYqiDenj3Yfr\n+l9n6jj23tT3eHDsgw3uMyxoGNoTGu9NfY8XprzAkXtk8+t+fjUlhc2tf3Y8iGtZhCnqxsnOieHB\nw02rS6uLSU50o1qrJuzVMPKc9/PdBYI1h9Y0KIdS7ApFC+jv15/DWYetLYaiDdHQ+N9F/2t8wyZw\n/YCaNla9vXvXtKc8PYRg92BOF50GZBN2fd3n133O65e+Tq9eskwwwOytsvrf9V+YtcWqA6XYW8mk\nSZPw8fGhshXFhRSdj/49+qvImC5MUUURGUUZplITrWVi+EQ+uuYjABxtHclcmEni3YlUnhrCqEH+\nptyIwvJCrh9wPU9f9DTX9r+Wey6QYbb2ts1r56cUeytISUlh27Zt2NjYsH79emuLo2hHontE81dO\nK9rpKDo0+9P3M9B/IA62ra8to+NoK0tR6OHPEb4R2NnJBuwZxRlUGaoory5nTM8xPDz+4Vr77pi7\ng2uirzF9Xj6l4U4+SrG3glWrVjF69GjmzJnDypUrrS2Ooh0J9wonJS/F2mIo2ohTBacs3iEs0C2w\nzuU9PXqyJ20PK/avwKAZ6gyLPj/4fB6f8Lg8zqps/LJmNHiuVtWKsQT11Yox1kGwgkRNJyIigoUL\nFzJixAhGjRpFamoqPYwNdeuiM1yTommUV5Xj/ow7pYtKsbVpXYNjRcfjlZ2vkJKfYvFchcziTPxd\n/WstKygvIPDFQEqrSgHQnqhbR1Qbqvkg9gPmDTOmttJ2tWKsit4jtLWvlrBt2zZOnDjBzJkzGTZs\nGH379uXjjz+27AUqOiyOdo74OPuQUZxhbVEUbUB6UXq9FnZrOFupA3g4ehDmGQbAiX+eqHdfWxtb\n5sbMJTZWFhBriE6t2DXNMq+WsGrVKqZMmYK3tzcAs2bN4oMPPrDg1Sk6OuXV5Vz64aVUVFdYWxSF\nhfk28VsuCLmg3c7Xx0f2MGwof0InOrrx7NMOW1KgI1NWVsaaNWswGAwEBckbUVFRQV5eHnFxcQwa\nNMjKEirag7yyPPLK8rjz2zt56/K3cLZ3trZICgtg0Awk5iSa6gO1B/Y2MurFzqZxlWxvL3vZHmog\njaJTW+zW4uuvv8bOzo7Dhw8TGxtLbGwshw8fZvz48cpq74a8f+B9Pjz4obXFUFgATdNIK0zD1cG1\n2SGGrWFa1DTmDp3b+IZGBgxoeL1S7C1g1apVzJ07l5CQEPz9/U2vBQsW8PHHH2NoRSd0RefBx9nH\n9P6bxG/UxHgXYOmepfR8pSe+zr7tet5bY25l+bTlTd6+McWuomLaka54Td2ZvLI8HGwdWPvXWm76\n6iZ2zN3B6J6jrS2WohXMWzePFQdWcH7Q+ey5fY+1xamXrVthwoQuGhWjUFgTLycvXOxduCLiCgJc\nA9hxcoe1RVI0gTOlZ1i+bzmni06fY2gVVRYBsh5QR2b8+IbXq8lThaKVeDp58tC4h2o1ZVB0XL5J\n+IbbvrkNgK9mfsWa+DVMOW8KR88cNRXXivCNsKaIrUYpdoXCAvT06MmvKb9aWwxFEzB/AB89c5RP\n//yUtMI0fkv5DYBtt25jWNAwa4lnEZRiVygsQE/PnpzMPwlAZXUlQogmha4p2pcqQxV70/ey5PIl\n5JXlmRLMjp05ZtpmUMCgTh+62rEdSQpFJyHUI5RTBTJrJOadGGZ9OauRPRTtiV5i+YXtL7DhyAYG\n+g8k2D3YVAc9tTCVB8c8yNrr1+Lh6GFNUS2CMikUCgsQ4BpARnEGd3xzB4eyDqn+tlZk3V/riPCN\noH+P/lRWV1JQXkD/Jf354rovTHXPz/M+j/Lqcnad2kVPj56cLDjJ7MGzGRww2MrSWwZlsSsUFkAv\nBLZs3zIAU+0PRfsz/bPpXPrhpexO3Y3DYgd+PP4jANd+fi3FlcXEBMYQ7B5M/x79ySrJItI3kg+v\n/pCB/gOtLLnlUIpdobAQn8z4BICnJj3FxiMbKa0stbJE3Y/7vr8PgJMFJ7lo1UUAvLDjBdP65fuX\ns+SKJQghCHKT5UBc7F24afBNHT7EsTl0nStRKKzM5PMm8/zk53lg9AMAHMs91sgecjJv8/HNbS1a\nt+HDuA9ZMXUFILsgje05ln3p+2pto0e8CCGI9I1kTM8x7S5nW6N87C0kPDyczMxM7OzssLe3Z8yY\nMbz99tuEhIRYWzSFlfBz8TM1Ox7bcyy5pbmN7vNr8q9csvoSDI/X3WBB0XROF52m2lDNnKFziAmK\nYWjgUOIy4hj89mBO3ncSPxc/nOycau2TcHeClaRtW5TF3kKEEGzYsIGCggLS09Px9/fnnnvusbZY\nig6Ct7M3eWV5jW4384uZAOSU5rS1SF2e3am7OT/4fIQQDA0cCsBA/4GsvX4tIe4h5yj1roxS7K1A\nT0d2cHDg2muvJT5eNTdWSLycvJqk2M+UngFQWasWYF3COi7re1mtZUIIpvWb1u1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=['A', 'B', 'C', 'D'])\n", "df = df.cumsum()\n", "plt.figure(); df.plot(); plt.legend(loc='best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 获取数据的I/O" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CSV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写入一个csv文件" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.to_csv('foo.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从一个csv文件读入" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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02000-01-01-0.8717150.889797-0.452757-0.065875
12000-01-021.0307190.982527-0.6323240.453500
22000-01-030.6341781.389564-0.497967-0.269316
32000-01-041.0181941.176338-0.0246160.013515
42000-01-05-0.1379561.3851630.930470-0.698086
52000-01-060.6325413.7578071.6805431.157284
62000-01-07-1.0378925.6672312.3644002.170764
72000-01-08-1.3751104.5806632.2510723.029680
82000-01-09-1.6196622.6364653.0449002.165869
92000-01-10-1.4544071.7283912.3888011.033930
102000-01-11-1.2787442.6366282.462172-1.182419
112000-01-12-0.7491592.3736190.369816-1.098857
122000-01-13-2.8614632.486702-1.175913-0.933334
132000-01-14-3.0165262.839986-1.220834-0.309324
142000-01-15-5.8124312.166957-3.024841-1.995853
152000-01-16-4.1305021.058692-3.025683-3.285472
162000-01-17-4.2638272.202905-4.321207-2.821168
172000-01-18-4.4197132.277186-4.174866-2.442312
182000-01-19-5.3944373.308024-4.956483-1.923371
192000-01-20-5.5184341.931813-4.448700-1.844738
202000-01-21-5.1760092.196225-4.239499-1.049110
212000-01-22-4.2509082.425685-3.327828-0.440797
222000-01-23-4.9459752.891497-3.385393-0.999570
232000-01-24-4.7163412.962450-3.716062-2.122873
242000-01-25-4.1761202.519948-4.070270-1.461157
252000-01-26-4.4484764.094472-4.534445-2.317750
262000-01-27-4.6285803.880345-4.375836-3.585863
272000-01-28-4.4362214.510596-5.566178-3.192768
282000-01-29-4.5382045.071906-5.719714-3.775576
292000-01-30-3.9613435.942862-5.084055-2.717804
..................
9702002-08-28-22.141846-21.854787-9.483933-14.804906
9712002-08-29-22.185204-21.512387-9.119370-13.923381
9722002-08-30-22.527292-24.563961-10.784790-12.652051
9732002-08-31-21.959430-24.551227-11.284436-13.109942
9742002-09-01-21.625881-23.555066-11.086579-13.345760
9752002-09-02-20.256898-23.482735-11.263625-11.804981
9762002-09-03-20.869041-22.453604-12.320104-12.070209
9772002-09-04-21.374708-23.486186-9.588075-10.431600
9782002-09-05-22.691639-24.170321-8.605713-9.875439
9792002-09-06-22.813804-22.178215-8.280569-10.305069
9802002-09-07-23.016447-21.328598-9.360692-9.642332
9812002-09-08-22.677555-21.222574-10.244785-8.622022
9822002-09-09-23.511102-22.179863-10.482032-7.145644
9832002-09-10-21.250224-23.011550-10.611333-7.890854
9842002-09-11-20.204129-22.284909-10.698640-7.722051
9852002-09-12-21.606134-22.570158-10.936326-7.168786
9862002-09-13-21.336733-24.603869-10.134803-8.312152
9872002-09-14-22.253331-25.016824-10.627166-7.513619
9882002-09-15-20.523770-23.781222-12.410802-8.281121
9892002-09-16-21.869984-24.817785-12.844630-7.699183
9902002-09-17-21.912426-25.057542-11.951892-8.486812
9912002-09-18-21.722546-25.881762-11.320068-8.623145
9922002-09-19-20.474366-24.137588-12.151461-9.625507
9932002-09-20-22.934832-25.287777-11.061323-11.695006
9942002-09-21-24.508572-23.934989-10.424987-13.079831
9952002-09-22-26.130471-24.324060-11.265862-11.324558
9962002-09-23-25.928044-24.035055-10.924533-11.071177
9972002-09-24-26.898126-24.828889-10.825060-9.877738
9982002-09-25-27.718151-24.975646-10.535565-9.166899
9992002-09-26-28.606014-24.223275-10.279136-10.275953
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1000 rows × 5 columns

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" ], "text/plain": [ " Unnamed: 0 A B C D\n", "0 2000-01-01 -0.871715 0.889797 -0.452757 -0.065875\n", "1 2000-01-02 1.030719 0.982527 -0.632324 0.453500\n", "2 2000-01-03 0.634178 1.389564 -0.497967 -0.269316\n", "3 2000-01-04 1.018194 1.176338 -0.024616 0.013515\n", "4 2000-01-05 -0.137956 1.385163 0.930470 -0.698086\n", "5 2000-01-06 0.632541 3.757807 1.680543 1.157284\n", "6 2000-01-07 -1.037892 5.667231 2.364400 2.170764\n", "7 2000-01-08 -1.375110 4.580663 2.251072 3.029680\n", "8 2000-01-09 -1.619662 2.636465 3.044900 2.165869\n", "9 2000-01-10 -1.454407 1.728391 2.388801 1.033930\n", "10 2000-01-11 -1.278744 2.636628 2.462172 -1.182419\n", "11 2000-01-12 -0.749159 2.373619 0.369816 -1.098857\n", "12 2000-01-13 -2.861463 2.486702 -1.175913 -0.933334\n", "13 2000-01-14 -3.016526 2.839986 -1.220834 -0.309324\n", "14 2000-01-15 -5.812431 2.166957 -3.024841 -1.995853\n", "15 2000-01-16 -4.130502 1.058692 -3.025683 -3.285472\n", "16 2000-01-17 -4.263827 2.202905 -4.321207 -2.821168\n", "17 2000-01-18 -4.419713 2.277186 -4.174866 -2.442312\n", "18 2000-01-19 -5.394437 3.308024 -4.956483 -1.923371\n", "19 2000-01-20 -5.518434 1.931813 -4.448700 -1.844738\n", "20 2000-01-21 -5.176009 2.196225 -4.239499 -1.049110\n", "21 2000-01-22 -4.250908 2.425685 -3.327828 -0.440797\n", "22 2000-01-23 -4.945975 2.891497 -3.385393 -0.999570\n", "23 2000-01-24 -4.716341 2.962450 -3.716062 -2.122873\n", "24 2000-01-25 -4.176120 2.519948 -4.070270 -1.461157\n", "25 2000-01-26 -4.448476 4.094472 -4.534445 -2.317750\n", "26 2000-01-27 -4.628580 3.880345 -4.375836 -3.585863\n", "27 2000-01-28 -4.436221 4.510596 -5.566178 -3.192768\n", "28 2000-01-29 -4.538204 5.071906 -5.719714 -3.775576\n", "29 2000-01-30 -3.961343 5.942862 -5.084055 -2.717804\n", ".. ... ... ... ... ...\n", "970 2002-08-28 -22.141846 -21.854787 -9.483933 -14.804906\n", "971 2002-08-29 -22.185204 -21.512387 -9.119370 -13.923381\n", "972 2002-08-30 -22.527292 -24.563961 -10.784790 -12.652051\n", "973 2002-08-31 -21.959430 -24.551227 -11.284436 -13.109942\n", "974 2002-09-01 -21.625881 -23.555066 -11.086579 -13.345760\n", "975 2002-09-02 -20.256898 -23.482735 -11.263625 -11.804981\n", "976 2002-09-03 -20.869041 -22.453604 -12.320104 -12.070209\n", "977 2002-09-04 -21.374708 -23.486186 -9.588075 -10.431600\n", "978 2002-09-05 -22.691639 -24.170321 -8.605713 -9.875439\n", "979 2002-09-06 -22.813804 -22.178215 -8.280569 -10.305069\n", "980 2002-09-07 -23.016447 -21.328598 -9.360692 -9.642332\n", "981 2002-09-08 -22.677555 -21.222574 -10.244785 -8.622022\n", "982 2002-09-09 -23.511102 -22.179863 -10.482032 -7.145644\n", "983 2002-09-10 -21.250224 -23.011550 -10.611333 -7.890854\n", "984 2002-09-11 -20.204129 -22.284909 -10.698640 -7.722051\n", "985 2002-09-12 -21.606134 -22.570158 -10.936326 -7.168786\n", "986 2002-09-13 -21.336733 -24.603869 -10.134803 -8.312152\n", "987 2002-09-14 -22.253331 -25.016824 -10.627166 -7.513619\n", "988 2002-09-15 -20.523770 -23.781222 -12.410802 -8.281121\n", "989 2002-09-16 -21.869984 -24.817785 -12.844630 -7.699183\n", "990 2002-09-17 -21.912426 -25.057542 -11.951892 -8.486812\n", "991 2002-09-18 -21.722546 -25.881762 -11.320068 -8.623145\n", "992 2002-09-19 -20.474366 -24.137588 -12.151461 -9.625507\n", "993 2002-09-20 -22.934832 -25.287777 -11.061323 -11.695006\n", "994 2002-09-21 -24.508572 -23.934989 -10.424987 -13.079831\n", "995 2002-09-22 -26.130471 -24.324060 -11.265862 -11.324558\n", "996 2002-09-23 -25.928044 -24.035055 -10.924533 -11.071177\n", "997 2002-09-24 -26.898126 -24.828889 -10.825060 -9.877738\n", "998 2002-09-25 -27.718151 -24.975646 -10.535565 -9.166899\n", "999 2002-09-26 -28.606014 -24.223275 -10.279136 -10.275953\n", "\n", "[1000 rows x 5 columns]" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv('foo.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HDF5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "HDFStores的读写" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写入一个HDF5 Store" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.to_hdf('foo.h5', 'df')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从一个HDF5 Store读入" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2000-01-01-0.8717150.889797-0.452757-0.065875
2000-01-021.0307190.982527-0.6323240.453500
2000-01-030.6341781.389564-0.497967-0.269316
2000-01-041.0181941.176338-0.0246160.013515
2000-01-05-0.1379561.3851630.930470-0.698086
2000-01-060.6325413.7578071.6805431.157284
2000-01-07-1.0378925.6672312.3644002.170764
2000-01-08-1.3751104.5806632.2510723.029680
2000-01-09-1.6196622.6364653.0449002.165869
2000-01-10-1.4544071.7283912.3888011.033930
2000-01-11-1.2787442.6366282.462172-1.182419
2000-01-12-0.7491592.3736190.369816-1.098857
2000-01-13-2.8614632.486702-1.175913-0.933334
2000-01-14-3.0165262.839986-1.220834-0.309324
2000-01-15-5.8124312.166957-3.024841-1.995853
2000-01-16-4.1305021.058692-3.025683-3.285472
2000-01-17-4.2638272.202905-4.321207-2.821168
2000-01-18-4.4197132.277186-4.174866-2.442312
2000-01-19-5.3944373.308024-4.956483-1.923371
2000-01-20-5.5184341.931813-4.448700-1.844738
2000-01-21-5.1760092.196225-4.239499-1.049110
2000-01-22-4.2509082.425685-3.327828-0.440797
2000-01-23-4.9459752.891497-3.385393-0.999570
2000-01-24-4.7163412.962450-3.716062-2.122873
2000-01-25-4.1761202.519948-4.070270-1.461157
2000-01-26-4.4484764.094472-4.534445-2.317750
2000-01-27-4.6285803.880345-4.375836-3.585863
2000-01-28-4.4362214.510596-5.566178-3.192768
2000-01-29-4.5382045.071906-5.719714-3.775576
2000-01-30-3.9613435.942862-5.084055-2.717804
...............
2002-08-28-22.141846-21.854787-9.483933-14.804906
2002-08-29-22.185204-21.512387-9.119370-13.923381
2002-08-30-22.527292-24.563961-10.784790-12.652051
2002-08-31-21.959430-24.551227-11.284436-13.109942
2002-09-01-21.625881-23.555066-11.086579-13.345760
2002-09-02-20.256898-23.482735-11.263625-11.804981
2002-09-03-20.869041-22.453604-12.320104-12.070209
2002-09-04-21.374708-23.486186-9.588075-10.431600
2002-09-05-22.691639-24.170321-8.605713-9.875439
2002-09-06-22.813804-22.178215-8.280569-10.305069
2002-09-07-23.016447-21.328598-9.360692-9.642332
2002-09-08-22.677555-21.222574-10.244785-8.622022
2002-09-09-23.511102-22.179863-10.482032-7.145644
2002-09-10-21.250224-23.011550-10.611333-7.890854
2002-09-11-20.204129-22.284909-10.698640-7.722051
2002-09-12-21.606134-22.570158-10.936326-7.168786
2002-09-13-21.336733-24.603869-10.134803-8.312152
2002-09-14-22.253331-25.016824-10.627166-7.513619
2002-09-15-20.523770-23.781222-12.410802-8.281121
2002-09-16-21.869984-24.817785-12.844630-7.699183
2002-09-17-21.912426-25.057542-11.951892-8.486812
2002-09-18-21.722546-25.881762-11.320068-8.623145
2002-09-19-20.474366-24.137588-12.151461-9.625507
2002-09-20-22.934832-25.287777-11.061323-11.695006
2002-09-21-24.508572-23.934989-10.424987-13.079831
2002-09-22-26.130471-24.324060-11.265862-11.324558
2002-09-23-25.928044-24.035055-10.924533-11.071177
2002-09-24-26.898126-24.828889-10.825060-9.877738
2002-09-25-27.718151-24.975646-10.535565-9.166899
2002-09-26-28.606014-24.223275-10.279136-10.275953
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1000 rows × 4 columns

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" ], "text/plain": [ " A B C D\n", "2000-01-01 -0.871715 0.889797 -0.452757 -0.065875\n", "2000-01-02 1.030719 0.982527 -0.632324 0.453500\n", "2000-01-03 0.634178 1.389564 -0.497967 -0.269316\n", "2000-01-04 1.018194 1.176338 -0.024616 0.013515\n", "2000-01-05 -0.137956 1.385163 0.930470 -0.698086\n", "2000-01-06 0.632541 3.757807 1.680543 1.157284\n", "2000-01-07 -1.037892 5.667231 2.364400 2.170764\n", "2000-01-08 -1.375110 4.580663 2.251072 3.029680\n", "2000-01-09 -1.619662 2.636465 3.044900 2.165869\n", "2000-01-10 -1.454407 1.728391 2.388801 1.033930\n", "2000-01-11 -1.278744 2.636628 2.462172 -1.182419\n", "2000-01-12 -0.749159 2.373619 0.369816 -1.098857\n", "2000-01-13 -2.861463 2.486702 -1.175913 -0.933334\n", "2000-01-14 -3.016526 2.839986 -1.220834 -0.309324\n", "2000-01-15 -5.812431 2.166957 -3.024841 -1.995853\n", "2000-01-16 -4.130502 1.058692 -3.025683 -3.285472\n", "2000-01-17 -4.263827 2.202905 -4.321207 -2.821168\n", "2000-01-18 -4.419713 2.277186 -4.174866 -2.442312\n", "2000-01-19 -5.394437 3.308024 -4.956483 -1.923371\n", "2000-01-20 -5.518434 1.931813 -4.448700 -1.844738\n", "2000-01-21 -5.176009 2.196225 -4.239499 -1.049110\n", "2000-01-22 -4.250908 2.425685 -3.327828 -0.440797\n", "2000-01-23 -4.945975 2.891497 -3.385393 -0.999570\n", "2000-01-24 -4.716341 2.962450 -3.716062 -2.122873\n", "2000-01-25 -4.176120 2.519948 -4.070270 -1.461157\n", "2000-01-26 -4.448476 4.094472 -4.534445 -2.317750\n", "2000-01-27 -4.628580 3.880345 -4.375836 -3.585863\n", "2000-01-28 -4.436221 4.510596 -5.566178 -3.192768\n", "2000-01-29 -4.538204 5.071906 -5.719714 -3.775576\n", "2000-01-30 -3.961343 5.942862 -5.084055 -2.717804\n", "... ... ... ... ...\n", "2002-08-28 -22.141846 -21.854787 -9.483933 -14.804906\n", "2002-08-29 -22.185204 -21.512387 -9.119370 -13.923381\n", "2002-08-30 -22.527292 -24.563961 -10.784790 -12.652051\n", "2002-08-31 -21.959430 -24.551227 -11.284436 -13.109942\n", "2002-09-01 -21.625881 -23.555066 -11.086579 -13.345760\n", "2002-09-02 -20.256898 -23.482735 -11.263625 -11.804981\n", "2002-09-03 -20.869041 -22.453604 -12.320104 -12.070209\n", "2002-09-04 -21.374708 -23.486186 -9.588075 -10.431600\n", "2002-09-05 -22.691639 -24.170321 -8.605713 -9.875439\n", "2002-09-06 -22.813804 -22.178215 -8.280569 -10.305069\n", "2002-09-07 -23.016447 -21.328598 -9.360692 -9.642332\n", "2002-09-08 -22.677555 -21.222574 -10.244785 -8.622022\n", "2002-09-09 -23.511102 -22.179863 -10.482032 -7.145644\n", "2002-09-10 -21.250224 -23.011550 -10.611333 -7.890854\n", "2002-09-11 -20.204129 -22.284909 -10.698640 -7.722051\n", "2002-09-12 -21.606134 -22.570158 -10.936326 -7.168786\n", "2002-09-13 -21.336733 -24.603869 -10.134803 -8.312152\n", "2002-09-14 -22.253331 -25.016824 -10.627166 -7.513619\n", "2002-09-15 -20.523770 -23.781222 -12.410802 -8.281121\n", "2002-09-16 -21.869984 -24.817785 -12.844630 -7.699183\n", "2002-09-17 -21.912426 -25.057542 -11.951892 -8.486812\n", "2002-09-18 -21.722546 -25.881762 -11.320068 -8.623145\n", "2002-09-19 -20.474366 -24.137588 -12.151461 -9.625507\n", "2002-09-20 -22.934832 -25.287777 -11.061323 -11.695006\n", "2002-09-21 -24.508572 -23.934989 -10.424987 -13.079831\n", "2002-09-22 -26.130471 -24.324060 -11.265862 -11.324558\n", "2002-09-23 -25.928044 -24.035055 -10.924533 -11.071177\n", "2002-09-24 -26.898126 -24.828889 -10.825060 -9.877738\n", "2002-09-25 -27.718151 -24.975646 -10.535565 -9.166899\n", "2002-09-26 -28.606014 -24.223275 -10.279136 -10.275953\n", "\n", "[1000 rows x 4 columns]" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_hdf('foo.h5', 'df')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Excel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MS Excel的读写" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写入一个Excel文件" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.to_excel('foo.xlsx', sheet_name='Sheet1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从一个excel文件读入" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ABCD
2000-01-01-0.8717150.889797-0.452757-0.065875
2000-01-021.0307190.982527-0.6323240.453500
2000-01-030.6341781.389564-0.497967-0.269316
2000-01-041.0181941.176338-0.0246160.013515
2000-01-05-0.1379561.3851630.930470-0.698086
2000-01-060.6325413.7578071.6805431.157284
2000-01-07-1.0378925.6672312.3644002.170764
2000-01-08-1.3751104.5806632.2510723.029680
2000-01-09-1.6196622.6364653.0449002.165869
2000-01-10-1.4544071.7283912.3888011.033930
2000-01-11-1.2787442.6366282.462172-1.182419
2000-01-12-0.7491592.3736190.369816-1.098857
2000-01-13-2.8614632.486702-1.175913-0.933334
2000-01-14-3.0165262.839986-1.220834-0.309324
2000-01-15-5.8124312.166957-3.024841-1.995853
2000-01-16-4.1305021.058692-3.025683-3.285472
2000-01-17-4.2638272.202905-4.321207-2.821168
2000-01-18-4.4197132.277186-4.174866-2.442312
2000-01-19-5.3944373.308024-4.956483-1.923371
2000-01-20-5.5184341.931813-4.448700-1.844738
2000-01-21-5.1760092.196225-4.239499-1.049110
2000-01-22-4.2509082.425685-3.327828-0.440797
2000-01-23-4.9459752.891497-3.385393-0.999570
2000-01-24-4.7163412.962450-3.716062-2.122873
2000-01-25-4.1761202.519948-4.070270-1.461157
2000-01-26-4.4484764.094472-4.534445-2.317750
2000-01-27-4.6285803.880345-4.375836-3.585863
2000-01-28-4.4362214.510596-5.566178-3.192768
2000-01-29-4.5382045.071906-5.719714-3.775576
2000-01-30-3.9613435.942862-5.084055-2.717804
...............
2002-08-28-22.141846-21.854787-9.483933-14.804906
2002-08-29-22.185204-21.512387-9.119370-13.923381
2002-08-30-22.527292-24.563961-10.784790-12.652051
2002-08-31-21.959430-24.551227-11.284436-13.109942
2002-09-01-21.625881-23.555066-11.086579-13.345760
2002-09-02-20.256898-23.482735-11.263625-11.804981
2002-09-03-20.869041-22.453604-12.320104-12.070209
2002-09-04-21.374708-23.486186-9.588075-10.431600
2002-09-05-22.691639-24.170321-8.605713-9.875439
2002-09-06-22.813804-22.178215-8.280569-10.305069
2002-09-07-23.016447-21.328598-9.360692-9.642332
2002-09-08-22.677555-21.222574-10.244785-8.622022
2002-09-09-23.511102-22.179863-10.482032-7.145644
2002-09-10-21.250224-23.011550-10.611333-7.890854
2002-09-11-20.204129-22.284909-10.698640-7.722051
2002-09-12-21.606134-22.570158-10.936326-7.168786
2002-09-13-21.336733-24.603869-10.134803-8.312152
2002-09-14-22.253331-25.016824-10.627166-7.513619
2002-09-15-20.523770-23.781222-12.410802-8.281121
2002-09-16-21.869984-24.817785-12.844630-7.699183
2002-09-17-21.912426-25.057542-11.951892-8.486812
2002-09-18-21.722546-25.881762-11.320068-8.623145
2002-09-19-20.474366-24.137588-12.151461-9.625507
2002-09-20-22.934832-25.287777-11.061323-11.695006
2002-09-21-24.508572-23.934989-10.424987-13.079831
2002-09-22-26.130471-24.324060-11.265862-11.324558
2002-09-23-25.928044-24.035055-10.924533-11.071177
2002-09-24-26.898126-24.828889-10.825060-9.877738
2002-09-25-27.718151-24.975646-10.535565-9.166899
2002-09-26-28.606014-24.223275-10.279136-10.275953
\n", "

1000 rows × 4 columns

\n", "
" ], "text/plain": [ " A B C D\n", "2000-01-01 -0.871715 0.889797 -0.452757 -0.065875\n", "2000-01-02 1.030719 0.982527 -0.632324 0.453500\n", "2000-01-03 0.634178 1.389564 -0.497967 -0.269316\n", "2000-01-04 1.018194 1.176338 -0.024616 0.013515\n", "2000-01-05 -0.137956 1.385163 0.930470 -0.698086\n", "2000-01-06 0.632541 3.757807 1.680543 1.157284\n", "2000-01-07 -1.037892 5.667231 2.364400 2.170764\n", "2000-01-08 -1.375110 4.580663 2.251072 3.029680\n", "2000-01-09 -1.619662 2.636465 3.044900 2.165869\n", "2000-01-10 -1.454407 1.728391 2.388801 1.033930\n", "2000-01-11 -1.278744 2.636628 2.462172 -1.182419\n", "2000-01-12 -0.749159 2.373619 0.369816 -1.098857\n", "2000-01-13 -2.861463 2.486702 -1.175913 -0.933334\n", "2000-01-14 -3.016526 2.839986 -1.220834 -0.309324\n", "2000-01-15 -5.812431 2.166957 -3.024841 -1.995853\n", "2000-01-16 -4.130502 1.058692 -3.025683 -3.285472\n", "2000-01-17 -4.263827 2.202905 -4.321207 -2.821168\n", "2000-01-18 -4.419713 2.277186 -4.174866 -2.442312\n", "2000-01-19 -5.394437 3.308024 -4.956483 -1.923371\n", "2000-01-20 -5.518434 1.931813 -4.448700 -1.844738\n", "2000-01-21 -5.176009 2.196225 -4.239499 -1.049110\n", "2000-01-22 -4.250908 2.425685 -3.327828 -0.440797\n", "2000-01-23 -4.945975 2.891497 -3.385393 -0.999570\n", "2000-01-24 -4.716341 2.962450 -3.716062 -2.122873\n", "2000-01-25 -4.176120 2.519948 -4.070270 -1.461157\n", "2000-01-26 -4.448476 4.094472 -4.534445 -2.317750\n", "2000-01-27 -4.628580 3.880345 -4.375836 -3.585863\n", "2000-01-28 -4.436221 4.510596 -5.566178 -3.192768\n", "2000-01-29 -4.538204 5.071906 -5.719714 -3.775576\n", "2000-01-30 -3.961343 5.942862 -5.084055 -2.717804\n", "... ... ... ... ...\n", "2002-08-28 -22.141846 -21.854787 -9.483933 -14.804906\n", "2002-08-29 -22.185204 -21.512387 -9.119370 -13.923381\n", "2002-08-30 -22.527292 -24.563961 -10.784790 -12.652051\n", "2002-08-31 -21.959430 -24.551227 -11.284436 -13.109942\n", "2002-09-01 -21.625881 -23.555066 -11.086579 -13.345760\n", "2002-09-02 -20.256898 -23.482735 -11.263625 -11.804981\n", "2002-09-03 -20.869041 -22.453604 -12.320104 -12.070209\n", "2002-09-04 -21.374708 -23.486186 -9.588075 -10.431600\n", "2002-09-05 -22.691639 -24.170321 -8.605713 -9.875439\n", "2002-09-06 -22.813804 -22.178215 -8.280569 -10.305069\n", "2002-09-07 -23.016447 -21.328598 -9.360692 -9.642332\n", "2002-09-08 -22.677555 -21.222574 -10.244785 -8.622022\n", "2002-09-09 -23.511102 -22.179863 -10.482032 -7.145644\n", "2002-09-10 -21.250224 -23.011550 -10.611333 -7.890854\n", "2002-09-11 -20.204129 -22.284909 -10.698640 -7.722051\n", "2002-09-12 -21.606134 -22.570158 -10.936326 -7.168786\n", "2002-09-13 -21.336733 -24.603869 -10.134803 -8.312152\n", "2002-09-14 -22.253331 -25.016824 -10.627166 -7.513619\n", "2002-09-15 -20.523770 -23.781222 -12.410802 -8.281121\n", "2002-09-16 -21.869984 -24.817785 -12.844630 -7.699183\n", "2002-09-17 -21.912426 -25.057542 -11.951892 -8.486812\n", "2002-09-18 -21.722546 -25.881762 -11.320068 -8.623145\n", "2002-09-19 -20.474366 -24.137588 -12.151461 -9.625507\n", "2002-09-20 -22.934832 -25.287777 -11.061323 -11.695006\n", "2002-09-21 -24.508572 -23.934989 -10.424987 -13.079831\n", "2002-09-22 -26.130471 -24.324060 -11.265862 -11.324558\n", "2002-09-23 -25.928044 -24.035055 -10.924533 -11.071177\n", "2002-09-24 -26.898126 -24.828889 -10.825060 -9.877738\n", "2002-09-25 -27.718151 -24.975646 -10.535565 -9.166899\n", "2002-09-26 -28.606014 -24.223275 -10.279136 -10.275953\n", "\n", "[1000 rows x 4 columns]" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])" ] } ], "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }