搜索到
2
篇与
Pandas
的结果
-
十分钟搞定 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：秘籍 。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 数据结构入门 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 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float642、通过传递一个 numpyarray，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]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') 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 1.0 2013-01-02 1.0 3 test foo 1 1.0 2013-01-02 1.0 3 train foo 2 1.0 2013-01-02 1.0 3 test foo 3 1.0 2013-01-02 1.0 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: object5、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：基础。1、 查看DataFrame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]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') 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。详情请参阅索引和选取数据 和 多重索引/高级索引。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: float642、 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 0.271860通过标签选择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: float645、 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快速访问标量（等同于前一个方法）：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 0.5249883、 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 5.05、 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：缺失的数据。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：n [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 基本的二进制操作统计（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 NaNApply1、 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：直方图和离散化。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：字符串向量化方法。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：合并。ConcatIn [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：数据库风格的连接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 5另一个例子：In [82]: left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]}) In [83]: right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]}) In [84]: left Out[84]: key lval 0 foo 1 1 bar 2 In [85]: right Out[85]: key rval 0 foo 4 1 bar 5 In [86]: pd.merge(left, right, on='key') Out[86]: key lval rval 0 foo 1 4 1 bar 2 5Append将一行连接到一个DataFrame上，具体请参阅附加：In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D']) In [88]: df Out[88]: 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 [89]: s = df.iloc[3] In [90]: df.append(s, ignore_index=True) Out[90]: 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、 分组对于”group by”操作，我们通常是指以下一个或多个操作步骤：（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：Grouping sectionIn [91]: 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 [92]: df Out[92]: 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 [93]: df.groupby('A').sum() Out[93]: 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 [94]: df.groupby(['A','B']).sum() Out[94]: 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、 改变形状详情请参阅 层次索引 和 改变形状。StackIn [95]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', ....: 'foo', 'foo', 'qux', 'qux'], ....: ['one', 'two', 'one', 'two', ....: 'one', 'two', 'one', 'two']])) ....: In [96]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second']) In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B']) In [98]: df2 = df[:4] In [99]: df2 Out[99]: A B first second bar one 0.029399 -0.542108 two 0.282696 -0.087302 baz one -1.575170 1.771208 two 0.816482 1.100230In [100]: stacked = df2.stack() In [101]: stacked Out[101]: 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: float64In [102]: stacked.unstack() Out[102]: 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 [103]: stacked.unstack(1) Out[103]: 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 [104]: stacked.unstack(0) Out[104]: 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：数据透视表.In [105]: 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 [106]: df Out[106]: 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 [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C']) Out[107]: 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：时间序列。In [108]: rng = pd.date_range('1/1/2012', periods=100, freq='S') In [109]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng) In [110]: ts.resample('5Min').sum() Out[110]: 2012-01-01 25083 Freq: 5T, dtype: int641、 时区表示：In [111]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D') In [112]: ts = pd.Series(np.random.randn(len(rng)), rng) In [113]: ts Out[113]: 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 [114]: ts_utc = ts.tz_localize('UTC') In [115]: ts_utc Out[115]: 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 [116]: ts_utc.tz_convert('US/Eastern') Out[116]: 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 [117]: rng = pd.date_range('1/1/2012', periods=5, freq='M') In [118]: ts = pd.Series(np.random.randn(len(rng)), index=rng) In [119]: ts Out[119]: 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 [120]: ps = ts.to_period() In [121]: ps Out[121]: 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 [122]: ps.to_timestamp() Out[122]: 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 [123]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV') In [124]: ts = pd.Series(np.random.randn(len(prng)), prng) In [125]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9 In [126]: ts.head() Out[126]: 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：Categorical 简介和API documentation。In [127]: 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 [128]: df["grade"] = df["raw_grade"].astype("category") In [129]: df["grade"] Out[129]: 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 [130]: 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 [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) In [132]: df["grade"] Out[132]: 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 [133]: df.sort_values(by="grade") Out[133]: 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 [134]: df.groupby("grade").size() Out[134]: 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：绘图文档。In [135]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000)) In [136]: ts = ts.cumsum() In [137]: ts.plot() Out[137]: <matplotlib.axes._subplots.AxesSubplot at 0x7ff2ab2af550>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 [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, .....: columns=['A', 'B', 'C', 'D']) .....: In [139]: df = df.cumsum() In [140]: plt.figure(); df.plot(); plt.legend(loc='best') Out[140]: <matplotlib.legend.Legend at 0x7ff29c8163d0>十二、 导入和保存数据CSV参考：写入 CSV 文件。1、 写入 csv 文件：In [141]: df.to_csv('foo.csv')2、 从 csv 文件中读取：In [142]: pd.read_csv('foo.csv') Out[142]: Unnamed: 0 A B C D 0 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 4 2000-01-05 0.578117 0.511371 0.103552 -2.428202 5 2000-01-06 0.478344 0.449933 -0.741620 -1.962409 6 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753 .. ... ... ... ... ... 993 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940 994 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107 995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368 [1000 rows x 5 columns]HDF5参考：HDF5 存储1、 写入 HDF5 存储：In [143]: df.to_hdf('foo.h5','df')2、 从 HDF5 存储中读取：In [144]: pd.read_hdf('foo.h5','df') Out[144]: A B C D 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 2000-01-05 0.578117 0.511371 0.103552 -2.428202 2000-01-06 0.478344 0.449933 -0.741620 -1.962409 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753 ... ... ... ... ... 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368 [1000 rows x 4 columns]Excel参考：MS Excel1、 写入excel文件：In [145]: df.to_excel('foo.xlsx', sheet_name='Sheet1')2、 从excel文件中读取：In [146]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']) Out[146]: A B C D 2000-01-01 0.266457 -0.399641 -0.219582 1.186860 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953 2000-01-03 -1.734933 0.530468 2.060811 -0.515536 2000-01-04 -1.555121 1.452620 0.239859 -1.156896 2000-01-05 0.578117 0.511371 0.103552 -2.428202 2000-01-06 0.478344 0.449933 -0.741620 -1.962409 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753 ... ... ... ... ... 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368 [1000 rows x 4 columns]十三、陷阱如果你尝试某个操作并且看到如下异常：>>> if pd.Series([False, True, False]): print("I was true") Traceback ... ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().解释及处理方式请见比较。同时请见陷阱。 -
Pandas中文手册 如果你想学习Pandas，建议先看两个网站。（1）官网：Python Data Analysis Library（2）十分钟入门Pandas：10 Minutes to pandas关键缩写和包导入在这个速查手册中，我们使用如下缩写：df：任意的Pandas DataFrame对象s：任意的Pandas Series对象同时我们需要做如下的引入：import pandas as pdimport numpy as np导入数据pd.read_csv(filename)：从CSV文件导入数据pd.read_table(filename)：从限定分隔符的文本文件导入数据pd.read_excel(filename)：从Excel文件导入数据pd.read_sql(query, connection_object)：从SQL表/库导入数据pd.read_json(json_string)：从JSON格式的字符串导入数据pd.read_html(url)：解析URL、字符串或者HTML文件，抽取其中的tables表格pd.read_clipboard()：从你的粘贴板获取内容，并传给read_table()pd.DataFrame(dict)：从字典对象导入数据，Key是列名，Value是数据导出数据df.to_csv(filename)：导出数据到CSV文件df.to_excel(filename)：导出数据到Excel文件df.to_sql(table_name, connection_object)：导出数据到SQL表df.to_json(filename)：以Json格式导出数据到文本文件创建测试对象pd.DataFrame(np.random.rand(20,5))：创建20行5列的随机数组成的DataFrame对象pd.Series(my_list)：从可迭代对象my_list创建一个Series对象df.index = pd.date_range('1900/1/30', periods=df.shape[0])：增加一个日期索引查看、检查数据df.head(n)：查看DataFrame对象的前n行df.tail(n)：查看DataFrame对象的最后n行df.shape()：查看行数和列数http://df.info()：查看索引、数据类型和内存信息df.describe()：查看数值型列的汇总统计s.value_counts(dropna=False)：查看Series对象的唯一值和计数df.apply(pd.Series.value_counts)：查看DataFrame对象中每一列的唯一值和计数数据选取df[col]：根据列名，并以Series的形式返回列df[[col1, col2]]：以DataFrame形式返回多列s.iloc[0]：按位置选取数据s.loc['index_one']：按索引选取数据df.iloc[0,:]：返回第一行df.iloc[0,0]：返回第一列的第一个元素数据清理df.columns = ['a','b','c']：重命名列名pd.isnull()：检查DataFrame对象中的空值，并返回一个Boolean数组pd.notnull()：检查DataFrame对象中的非空值，并返回一个Boolean数组df.dropna()：删除所有包含空值的行df.dropna(axis=1)：删除所有包含空值的列df.dropna(axis=1,thresh=n)：删除所有小于n个非空值的行df.fillna(x)：用x替换DataFrame对象中所有的空值s.astype(float)：将Series中的数据类型更改为float类型s.replace(1,'one')：用‘one’代替所有等于1的值s.replace([1,3],['one','three'])：用'one'代替1，用'three'代替3df.rename(columns=lambda x: x + 1)：批量更改列名df.rename(columns={'old_name': 'new_ name'})：选择性更改列名df.set_index('column_one')：更改索引列df.rename(index=lambda x: x + 1)：批量重命名索引数据处理：Filter 、Sort 和 GroupBydf[df[col] > 0.5]：选择col列的值大于0.5的行df.sort_values(col1)：按照列col1排序数据，默认升序排列df.sort_values(col2, ascending=False)：按照列col1降序排列数据df.sort_values([col1,col2], ascending=[True,False])：先按列col1升序排列，后按col2降序排列数据df.groupby(col)：返回一个按列col进行分组的Groupby对象df.groupby([col1,col2])：返回一个按多列进行分组的Groupby对象df.groupby(col1)[col2]：返回按列col1进行分组后，列col2的均值df.pivot_table(index=col1, values=[col2,col3], aggfunc=max)：创建一个按列col1进行分组，并计算col2和col3的最大值的数据透视表df.groupby(col1).agg(np.mean)：返回按列col1分组的所有列的均值data.apply(np.mean)：对DataFrame中的每一列应用函数np.meandata.apply(np.max,axis=1)：对DataFrame中的每一行应用函数np.max数据合并df1.append(df2)：将df2中的行添加到df1的尾部df.concat([df1, df2],axis=1)：将df2中的列添加到df1的尾部df1.join(df2,on=col1,how='inner')：对df1的列和df2的列执行SQL形式的join数据统计df.describe()：查看数据值列的汇总统计df.mean()：返回所有列的均值df.corr()：返回列与列之间的相关系数df.count()：返回每一列中的非空值的个数df.max()：返回每一列的最大值df.min()：返回每一列的最小值df.median()：返回每一列的中位数df.std()：返回每一列的标准差
