十分钟搞定pandas
2016-06-06 19:15
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转载: http://pandas.pydata.org/pandas-docs/stable/10min.html
翻译: shizhuolin@hotmail.com
本文是对pandas官方网站上《10
Minutes to pandas》的一个简单的翻译,原文在这里。这篇文章是对pandas的一个简单的介绍,详细的介绍请参考:Cookbook 。习惯上,我们会按下面格式引入所需要的包:
可以通过 Data Structure Intro Setion 来查看有关该节内容的详细信息。
1、可以通过传递一个list对象来创建一个Series,pandas会默认创建整型索引:
array,时间索引以及列标签来创建一个DataFrame:
5、如果你使用的是IPython,使用Tab自动补全功能会自动识别所有的属性以及自定义的列,下图中是所有能够被自动识别的属性的一个子集:
详情请参阅:Basics Section
1、 查看frame中头部和尾部的行:
虽然标准的Python/Numpy的选择和设置表达式都能够直接派上用场,但是作为工程使用的代码,我们推荐使用经过优化的pandas数据访问方式:.at, .iat, .loc, .iloc 和 .ix
详情请参阅Indexing
and Selecing Data 和 MultiIndex
/ Advanced Indexing。
获取
1、 选择一个单独的列,这将会返回一个Series,等同于df.A:
2、 通过[]进行选择,这将会对行进行切片
1、 使用标签来获取一个交叉的区域
5、 获取一个标量
通过位置选择
1、 通过传递数值进行位置选择(选择的是行)
1、 使用一个单独列的值来选择数据:
3、 使用isin()方法来过滤
1、
设置一个新的列
5、 通过where操作来设置新的值
在 pandas 中,使用 np.nan 来代替缺失值,这些值将默认不会包含在计算中,详情请参阅:Missing Data Section。
1、 reindex()方法可以对指定轴上的索引进行改变/增加/删除操作,这将返回原始数据的一个拷贝
详情请参与
Basic Section On Binary Ops
统计(相关操作通常情况下不包括缺失值)
1、 执行描述性统计
1、 对数据应用函数
具体请参照:Histogrammingand Discretization
Series对象在其str属性中配备了一组字符串处理方法,可以很容易的应用到数组中的每个元素,如下段代码所示。更多详情请参考:Vectorized String Methods.
Pandas提供了大量的方法能够轻松的对Series,DataFrame和Panel对象进行各种符合各种逻辑关系的合并操作。具体请参阅:Mergingsection
Concat
joining
(Splitting)按照一些规则将数据分为不同的组;
(Applying)对于每组数据分别执行一个函数;
(Combining)将结果组合到一个数据结构中;
详情请参阅HierarchicalIndexing 和 Reshaping。
stack() method “compresses” a level in the DataFrame’scolumns.
as theindex), the inverse operation ofstack()
isunstack(),
which by default unstacks the last level:
Pandas在对频率转换进行重新采样时拥有简单、强大且高效的功能(如将按秒采样的数据转换为按5分钟为单位进行采样的数据)。这种操作在金融领域非常常见。具体参考:TimeSeries section。
将原始的grade转换为Categorical数据类型:
具体文档参看:Plotting docs
对于DataFrame来说,plot是一种将所有列及其标签进行绘制的简便方法:
翻译: shizhuolin@hotmail.com
本文是对pandas官方网站上《10
Minutes to pandas》的一个简单的翻译,原文在这里。这篇文章是对pandas的一个简单的介绍,详细的介绍请参考:Cookbook 。习惯上,我们会按下面格式引入所需要的包:
In [1]: import pandas as pd In [2]: import numpy as np In [3]: import matplotlib.pyplot as plt
一、创建对象
可以通过 Data Structure Intro Setion 来查看有关该节内容的详细信息。1、可以通过传递一个list对象来创建一个Series,pandas会默认创建整型索引:
In [4]: s = pd.Series([1,3,5,np.nan,6,8]) In [5]: s Out[5]: 0 1 1 3 2 5 3 NaN 4 6 5 8 dtype: float642、通过传递一个numpy
array,时间索引以及列标签来创建一个DataFrame:
In [6]: dates = pd.date_range('20130101',periods=6) In [7]: dates Out[7]: <class 'pandas.tseries.index.DatetimeIndex'> [2013-01-01, ..., 2013-01-06] Length: 6, Freq: D, Timezone: None In [8]: df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD')) In [9]: df Out[9]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.5249883、通过传递一个能够被转换成类似序列结构的字典对象来创建一个DataFrame:
In [10]: df2 = pd.DataFrame({ 'A' : 1., ....: 'B' : pd.Timestamp('20130102'), ....: 'C' : pd.Series(1,index=list(range(4)),dtype='float32'), ....: 'D' : np.array([3] * 4,dtype='int32'), ....: 'E' : pd.Categorical(["test","train","test","train"]), ....: 'F' : 'foo' }) ....: In [11]: df2 Out[11]: A B C D E F 0 1 2013-01-02 1 3 test foo 1 1 2013-01-02 1 3 train foo 2 1 2013-01-02 1 3 test foo 3 1 2013-01-02 1 3 train foo4、查看不同列的数据类型:
In [12]: df2.dtypes Out[12]: A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object
5、如果你使用的是IPython,使用Tab自动补全功能会自动识别所有的属性以及自定义的列,下图中是所有能够被自动识别的属性的一个子集:
In [13]: df2.<TAB> df2.A df2.boxplot df2.abs df2.C df2.add df2.clip df2.add_prefix df2.clip_lower df2.add_suffix df2.clip_upper df2.align df2.columns df2.all df2.combine df2.any df2.combineAdd df2.append df2.combine_first df2.apply df2.combineMult df2.applymap df2.compound df2.as_blocks df2.consolidate df2.asfreq df2.convert_objects df2.as_matrix df2.copy df2.astype df2.corr df2.at df2.corrwith df2.at_time df2.count df2.axes df2.cov df2.B df2.cummax df2.between_time df2.cummin df2.bfill df2.cumprod df2.blocks df2.cumsum df2.bool df2.D
二、查看数据
详情请参阅:Basics Section1、 查看frame中头部和尾部的行:
In [14]: df.head() Out[14]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 In [15]: df.tail(3) Out[15]: A B C D 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.5249882、 显示索引、列和底层的numpy数据:
In [16]: df.index Out[16]: <class 'pandas.tseries.index.DatetimeIndex'> [2013-01-01, ..., 2013-01-06] Length: 6, Freq: D, Timezone: None In [17]: df.columns Out[17]: Index([u'A', u'B', u'C', u'D'], dtype='object') In [18]: df.values Out[18]: array([[ 0.4691, -0.2829, -1.5091, -1.1356], [ 1.2121, -0.1732, 0.1192, -1.0442], [-0.8618, -2.1046, -0.4949, 1.0718], [ 0.7216, -0.7068, -1.0396, 0.2719], [-0.425 , 0.567 , 0.2762, -1.0874], [-0.6737, 0.1136, -1.4784, 0.525 ]])3、 describe()函数对于数据的快速统计汇总:
In [19]: df.describe() Out[19]: A B C D count 6.000000 6.000000 6.000000 6.000000 mean 0.073711 -0.431125 -0.687758 -0.233103 std 0.843157 0.922818 0.779887 0.973118 min -0.861849 -2.104569 -1.509059 -1.135632 25% -0.611510 -0.600794 -1.368714 -1.076610 50% 0.022070 -0.228039 -0.767252 -0.386188 75% 0.658444 0.041933 -0.034326 0.461706 max 1.212112 0.567020 0.276232 1.0718044、 对数据的转置:
In [20]: df.T Out[20]: 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06 A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690 B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648 C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427 D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.5249885、 按轴进行排序
In [21]: df.sort_index(axis=1, ascending=False) Out[21]: D C B A 2013-01-01 -1.135632 -1.509059 -0.282863 0.469112 2013-01-02 -1.044236 0.119209 -0.173215 1.212112 2013-01-03 1.071804 -0.494929 -2.104569 -0.861849 2013-01-04 0.271860 -1.039575 -0.706771 0.721555 2013-01-05 -1.087401 0.276232 0.567020 -0.424972 2013-01-06 0.524988 -1.478427 0.113648 -0.6736906、 按值进行排序
In [22]: df.sort_values(by='B') Out[22]: A B C D 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 2013-01-05 -0.424972 0.567020 0.276232 -1.087401
三、选择
虽然标准的Python/Numpy的选择和设置表达式都能够直接派上用场,但是作为工程使用的代码,我们推荐使用经过优化的pandas数据访问方式:.at, .iat, .loc, .iloc 和 .ix详情请参阅Indexing
and Selecing Data 和 MultiIndex
/ Advanced Indexing。
获取
1、 选择一个单独的列,这将会返回一个Series,等同于df.A:
In [23]: df['A'] Out[23]: 2013-01-01 0.469112 2013-01-02 1.212112 2013-01-03 -0.861849 2013-01-04 0.721555 2013-01-05 -0.424972 2013-01-06 -0.673690 Freq: D, Name: A, dtype: float64
2、 通过[]进行选择,这将会对行进行切片
In [24]: df[0:3] Out[24]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 In [25]: df['20130102':'20130104'] Out[25]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.27186通过标签获取
1、 使用标签来获取一个交叉的区域
In [26]: df.loc[dates[0]] Out[26]: A 0.469112 B -0.282863 C -1.509059 D -1.135632 Name: 2013-01-01 00:00:00, dtype: float642、通过标签来在多个轴上进行选择
In [27]: df.loc[:,['A','B']] Out[27]: A B 2013-01-01 0.469112 -0.282863 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 2013-01-06 -0.673690 0.1136483、 标签切片
In [28]: df.loc['20130102':'20130104',['A','B']] Out[28]: A B 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.7067714、 对于返回的对象进行维度缩减
In [29]: df.loc['20130102',['A','B']] Out[29]: A 1.212112 B -0.173215 Name: 2013-01-02 00:00:00, dtype: float64
5、 获取一个标量
In [30]: df.loc[dates[0],'A'] Out[30]: 0.469112299907186286、快速访问一个标量(与上一个方法等价)
In [31]: df.at[dates[0],'A'] Out[31]: 0.46911229990718628
通过位置选择
1、 通过传递数值进行位置选择(选择的是行)
In [32]: df.iloc[3] Out[32]: A 0.721555 B -0.706771 C -1.039575 D 0.271860 Name: 2013-01-04 00:00:00, dtype: float642、通过数值进行切片,与numpy/python中的情况类似
In [33]: df.iloc[3:5,0:2] Out[33]: A B 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.5670203、通过指定一个位置的列表,与numpy/python中的情况类似
In [34]: df.iloc[[1,2,4],[0,2]] Out[34]: A C 2013-01-02 1.212112 0.119209 2013-01-03 -0.861849 -0.494929 2013-01-05 -0.424972 0.2762324、对行进行切片
In [35]: df.iloc[1:3,:] Out[35]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.0718045、对列进行切片
In [36]: df.iloc[:,1:3] Out[36]: B C 2013-01-01 -0.282863 -1.509059 2013-01-02 -0.173215 0.119209 2013-01-03 -2.104569 -0.494929 2013-01-04 -0.706771 -1.039575 2013-01-05 0.567020 0.276232 2013-01-06 0.113648 -1.4784276、获取特定的值
In [37]: df.iloc[1,1] Out[37]: -0.17321464905330858
In [38]: df.iat[1,1] Out[38]: -0.17321464905330858布尔索引
1、 使用一个单独列的值来选择数据:
In [39]: df[df.A > 0] Out[39]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-04 0.721555 -0.706771 -1.039575 0.2718602、使用where操作来选择数据:
In [40]: df[df > 0] Out[40]: A B C D 2013-01-01 0.469112 NaN NaN NaN 2013-01-02 1.212112 NaN 0.119209 NaN 2013-01-03 NaN NaN NaN 1.071804 2013-01-04 0.721555 NaN NaN 0.271860 2013-01-05 NaN 0.567020 0.276232 NaN 2013-01-06 NaN 0.113648 NaN 0.524988
3、 使用isin()方法来过滤
In [41]: df2 = df.copy() In [42]: df2['E'] = ['one', 'one','two','three','four','three'] In [43]: df2 Out[43]: A B C D E 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three In [44]: df2[df2['E'].isin(['two','four'])] Out[44]: A B C D E 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four设置
1、
设置一个新的列
In [45]: s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6)) In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64 In [47]: df['F'] = s12、通过标签设置新的值
In [48]: df.at[dates[0],'A'] = 03、通过位置设置新的值
In [49]: df.iat[0,1] = 04、通过一个numpy数组设置一组新值
In [50]: df.loc[:,'D'] = np.array([5] * len(df))上述操作结果如下:
In [51]: df Out[51]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 2013-01-05 -0.424972 0.567020 0.276232 5 4.0 2013-01-06 -0.673690 0.113648 -1.478427 5 5.0
5、 通过where操作来设置新的值
In [52]: df2 = df.copy() In [53]: df2[df2 > 0] = -df2 In [54]: df2 Out[54]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 -5 NaN 2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0 2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0 2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0 2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0 2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0
四、缺失值处理
在 pandas 中,使用 np.nan 来代替缺失值,这些值将默认不会包含在计算中,详情请参阅:Missing Data Section。1、 reindex()方法可以对指定轴上的索引进行改变/增加/删除操作,这将返回原始数据的一个拷贝
In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E']) In [56]: df1.loc[dates[0]:dates[1],'E'] = 1 In [57]: df1 Out[57]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.0 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN2、 去掉包含缺失值的行
In [58]: df1.dropna(how='any') Out[58]: A B C D F E 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.03、 对缺失值进行填充
In [59]: df1.fillna(value=5) Out[59]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.04、 对数据进行布尔填充
In [60]: pd.isnull(df1) Out[60]: A B C D F E 2013-01-01 False False False False True False 2013-01-02 False False False False False False 2013-01-03 False False False False False True 2013-01-04 False False False False False True
五、相关操作
详情请参与Basic Section On Binary Ops
统计(相关操作通常情况下不包括缺失值)
1、 执行描述性统计
In [61]: df.mean() Out[61]: A -0.004474 B -0.383981 C -0.687758 D 5.000000 F 3.000000 dtype: float642、 在其他轴上进行相同的操作
In [62]: df.mean(1) Out[62]: 2013-01-01 0.872735 2013-01-02 1.431621 2013-01-03 0.707731 2013-01-04 1.395042 2013-01-05 1.883656 2013-01-06 1.592306 Freq: D, dtype: float643、 对于拥有不同维度,需要对齐的对象进行操作。Pandas会自动的沿着指定的维度进行广播
In [63]: s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2) In [64]: s Out[64]: 2013-01-01 NaN 2013-01-02 NaN 2013-01-03 1.0 2013-01-04 3.0 2013-01-05 5.0 2013-01-06 NaN Freq: D, dtype: float64 In [65]: df.sub(s, axis='index') Out[65]: A B C D F 2013-01-01 NaN NaN NaN NaN NaN 2013-01-02 NaN NaN NaN NaN NaN 2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0 2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0 2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0 2013-01-06 NaN NaN NaN NaN NaNApply
1、 对数据应用函数
In [66]: df.apply(np.cumsum) Out[66]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 2013-01-02 1.212112 -0.173215 -1.389850 10 1.0 2013-01-03 0.350263 -2.277784 -1.884779 15 3.0 2013-01-04 1.071818 -2.984555 -2.924354 20 6.0 2013-01-05 0.646846 -2.417535 -2.648122 25 10.0 2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0 In [67]: df.apply(lambda x: x.max() - x.min()) Out[67]: A 2.073961 B 2.671590 C 1.785291 D 0.000000 F 4.000000 dtype: float64直方图
具体请参照:Histogrammingand Discretization
In [68]: s = pd.Series(np.random.randint(0, 7, size=10)) In [69]: s Out[69]: 0 4 1 2 2 1 3 2 4 6 5 4 6 4 7 6 8 4 9 4 dtype: int64 In [70]: s.value_counts() Out[70]: 4 5 6 2 2 2 1 1 dtype: int64字符串方法
Series对象在其str属性中配备了一组字符串处理方法,可以很容易的应用到数组中的每个元素,如下段代码所示。更多详情请参考:Vectorized String Methods.
In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat']) In [72]: s.str.lower() Out[72]: 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object
六、合并
Pandas提供了大量的方法能够轻松的对Series,DataFrame和Panel对象进行各种符合各种逻辑关系的合并操作。具体请参阅:MergingsectionConcat
In [73]: df = pd.DataFrame(np.random.randn(10, 4)) In [74]: df Out[74]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495 # break it into pieces In [75]: pieces = [df[:3], df[3:7], df[7:]] In [76]: pd.concat(pieces) Out[76]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495Join 类似于SQL类型的合并,具体请参阅:Databasestyle
joining
In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]}) In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]}) In [79]: left Out[79]: key lval 0 foo 1 1 foo 2 In [80]: right Out[80]: key rval 0 foo 4 1 foo 5 In [81]: pd.merge(left, right, on='key') Out[81]: key lval rval 0 foo 1 4 1 foo 1 5 2 foo 2 4 3 foo 2 5Append 将一行连接到一个DataFrame上,具体请参阅Appending:
In [82]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D']) In [83]: df Out[83]: A B C D 0 1.346061 1.511763 1.627081 -0.990582 1 -0.441652 1.211526 0.268520 0.024580 2 -1.577585 0.396823 -0.105381 -0.532532 3 1.453749 1.208843 -0.080952 -0.264610 4 -0.727965 -0.589346 0.339969 -0.693205 5 -0.339355 0.593616 0.884345 1.591431 6 0.141809 0.220390 0.435589 0.192451 7 -0.096701 0.803351 1.715071 -0.708758 In [84]: s = df.iloc[3] In [85]: df.append(s, ignore_index=True) Out[85]: A B C D 0 1.346061 1.511763 1.627081 -0.990582 1 -0.441652 1.211526 0.268520 0.024580 2 -1.577585 0.396823 -0.105381 -0.532532 3 1.453749 1.208843 -0.080952 -0.264610 4 -0.727965 -0.589346 0.339969 -0.693205 5 -0.339355 0.593616 0.884345 1.591431 6 0.141809 0.220390 0.435589 0.192451 7 -0.096701 0.803351 1.715071 -0.708758 8 1.453749 1.208843 -0.080952 -0.264610
七、分组
(Splitting)按照一些规则将数据分为不同的组;(Applying)对于每组数据分别执行一个函数;
(Combining)将结果组合到一个数据结构中;
In [86]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ....: 'foo', 'bar', 'foo', 'foo'], ....: 'B' : ['one', 'one', 'two', 'three', ....: 'two', 'two', 'one', 'three'], ....: 'C' : np.random.randn(8), ....: 'D' : np.random.randn(8)}) In [87]: df Out[87]: A B C D 0 foo one -1.202872 -0.055224 1 bar one -1.814470 2.395985 2 foo two 1.018601 1.552825 3 bar three -0.595447 0.166599 4 foo two 1.395433 0.047609 5 bar two -0.392670 -0.136473 6 foo one 0.007207 -0.561757 7 foo three 1.928123 -1.6230331、 分组并对每个分组执行sum函数:
In [88]: df.groupby('A').sum() Out[88]: C D A bar -2.802588 2.42611 foo 3.146492 -0.639582、 通过多个列进行分组形成一个层次索引,然后执行函数
In [89]: df.groupby(['A','B']).sum() Out[89]: C D A B bar one -1.814470 2.395985 three -0.595447 0.166599 two -0.392670 -0.136473 foo one -1.195665 -0.616981 three 1.928123 -1.623033 two 2.414034 1.600434
八、Reshaping
详情请参阅HierarchicalIndexing 和 Reshaping。Stack
In [90]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', ....: 'foo', 'foo', 'qux', 'qux'], ....: ['one', 'two', 'one', 'two', ....: 'one', 'two', 'one', 'two']])) ....: In [91]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second']) In [92]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B']) In [93]: df2 = df[:4] In [94]: df2 Out[94]: A B first second bar one 0.029399 -0.542108 two 0.282696 -0.087302 baz one -1.575170 1.771208 two 0.816482 1.100230The
stack() method “compresses” a level in the DataFrame’scolumns.
In [95]: stacked = df2.stack() In [96]: stacked Out[96]: first second bar one A 0.029399 B -0.542108 two A 0.282696 B -0.087302 baz one A -1.575170 B 1.771208 two A 0.816482 B 1.100230 dtype: float64With a “stacked” DataFrame or Series (having aMultiIndex
as theindex), the inverse operation ofstack()
isunstack(),
which by default unstacks the last level:
In [97]: stacked.unstack() Out[97]: A B first second bar one 0.029399 -0.542108 two 0.282696 -0.087302 baz one -1.575170 1.771208 two 0.816482 1.100230 In [98]: stacked.unstack(1) Out[98]: second one two first bar A 0.029399 0.282696 B -0.542108 -0.087302 baz A -1.575170 0.816482 B 1.771208 1.100230 In [99]: stacked.unstack(0) Out[99]: first bar baz second one A 0.029399 -1.575170 B -0.542108 1.771208 two A 0.282696 0.816482 B -0.087302 1.100230数据透视表,详情请参阅:PivotTables.
In [100]: df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3, .....: 'B' : ['A', 'B', 'C'] * 4, .....: 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2, .....: 'D' : np.random.randn(12), .....: 'E' : np.random.randn(12)}) .....: In [101]: df Out[101]: A B C D E 0 one A foo 1.418757 -0.179666 1 one B foo -1.879024 1.291836 2 two C foo 0.536826 -0.009614 3 three A bar 1.006160 0.392149 4 one B bar -0.029716 0.264599 5 one C bar -1.146178 -0.057409 6 two A foo 0.100900 -1.425638 7 three B foo -1.035018 1.024098 8 one C foo 0.314665 -0.106062 9 one A bar -0.773723 1.824375 10 two B bar -1.170653 0.595974 11 three C bar 0.648740 1.167115可以从这个数据中轻松的生成数据透视表:
In [102]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C']) Out[102]: C bar foo A B one A -0.773723 1.418757 B -0.029716 -1.879024 C -1.146178 0.314665 three A 1.006160 NaN B NaN -1.035018 C 0.648740 NaN two A NaN 0.100900 B -1.170653 NaN C NaN 0.536826
九、时间序列
Pandas在对频率转换进行重新采样时拥有简单、强大且高效的功能(如将按秒采样的数据转换为按5分钟为单位进行采样的数据)。这种操作在金融领域非常常见。具体参考:TimeSeries section。In [103]: rng = pd.date_range('1/1/2012', periods=100, freq='S') In [104]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng) In [105]: ts.resample('5Min').sum() Out[105]: 2012-01-01 25083 Freq: 5T, dtype: int641、 时区表示:
In [106]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D') In [107]: ts = pd.Series(np.random.randn(len(rng)), rng) In [108]: ts Out[108]: 2012-03-06 0.464000 2012-03-07 0.227371 2012-03-08 -0.496922 2012-03-09 0.306389 2012-03-10 -2.290613 Freq: D, dtype: float64 In [109]: ts_utc = ts.tz_localize('UTC') In [110]: ts_utc Out[110]: 2012-03-06 00:00:00+00:00 0.464000 2012-03-07 00:00:00+00:00 0.227371 2012-03-08 00:00:00+00:00 -0.496922 2012-03-09 00:00:00+00:00 0.306389 2012-03-10 00:00:00+00:00 -2.290613 Freq: D, dtype: float642、 时区转换:
In [111]: ts_utc.tz_convert('US/Eastern') Out[111]: 2012-03-05 19:00:00-05:00 0.464000 2012-03-06 19:00:00-05:00 0.227371 2012-03-07 19:00:00-05:00 -0.496922 2012-03-08 19:00:00-05:00 0.306389 2012-03-09 19:00:00-05:00 -2.290613 Freq: D, dtype: float643、 时间跨度转换:
In [112]: rng = pd.date_range('1/1/2012', periods=5, freq='M') In [113]: ts = pd.Series(np.random.randn(len(rng)), index=rng) In [114]: ts Out[114]: 2012-01-31 -1.134623 2012-02-29 -1.561819 2012-03-31 -0.260838 2012-04-30 0.281957 2012-05-31 1.523962 Freq: M, dtype: float64 In [115]: ps = ts.to_period() In [116]: ps Out[116]: 2012-01 -1.134623 2012-02 -1.561819 2012-03 -0.260838 2012-04 0.281957 2012-05 1.523962 Freq: M, dtype: float64 In [117]: ps.to_timestamp() Out[117]: 2012-01-01 -1.134623 2012-02-01 -1.561819 2012-03-01 -0.260838 2012-04-01 0.281957 2012-05-01 1.523962 Freq: MS, dtype: float644、 时期和时间戳之间的转换使得可以使用一些方便的算术函数。
In [118]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV') In [119]: ts = pd.Series(np.random.randn(len(prng)), prng) In [120]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9 In [121]: ts.head() Out[121]: 1990-03-01 09:00 -0.902937 1990-06-01 09:00 0.068159 1990-09-01 09:00 -0.057873 1990-12-01 09:00 -0.368204 1991-03-01 09:00 -1.144073 Freq: H, dtype: float64
十、Categorical
从0.15版本开始,pandas可以在DataFrame中支持Categorical类型的数据,详细介绍参看:categoricalintroduction和APIdocumentation。In [122]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})1、
将原始的grade转换为Categorical数据类型:
In [123]: df["grade"] = df["raw_grade"].astype("category") In [124]: df["grade"] Out[124]: 0 a 1 b 2 b 3 a 4 a 5 e Name: grade, dtype: category Categories (3, object): [a, b, e]2、 将Categorical类型数据重命名为更有意义的名称:
In [125]: df["grade"].cat.categories = ["very good", "good", "very bad"]3、 对类别进行重新排序,增加缺失的类别:
In [126]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) In [127]: df["grade"] Out[127]: 0 very good 1 good 2 good 3 very good 4 very good 5 very bad Name: grade, dtype: category Categories (5, object): [very bad, bad, medium, good, very good]4、 排序是按照Categorical的顺序进行的而不是按照字典顺序进行:
In [128]: df.sort_values(by="grade") Out[128]: id raw_grade grade 5 6 e very bad 1 2 b good 2 3 b good 0 1 a very good 3 4 a very good 4 5 a very good5、 对Categorical列进行排序时存在空的类别:
In [129]: df.groupby("grade").size() Out[129]: grade very bad 1 bad 0 medium 0 good 2 very good 3 dtype: int64十一、画图
具体文档参看:Plotting docs
In [130]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000)) In [131]: ts = ts.cumsum() In [132]: ts.plot() Out[132]: <matplotlib.axes._subplots.AxesSubplot at 0x10efd5a90>
对于DataFrame来说,plot是一种将所有列及其标签进行绘制的简便方法:
In [133]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, .....: columns=['A', 'B', 'C', 'D']) In [134]: df = df.cumsum() In [135]: plt.figure(); df.plot(); plt.legend(loc='best') Out[135]: <matplotlib.legend.Legend at 0x112854d90>
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