译10分钟学会Pandas
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十分钟学会Pandas
这是关于Pandas的简短介绍主要面向新用户。你可以参考Cookbook了解更复杂的使用方法
习惯上，我们这样导入：
In [1]: import pandas as pd In [2]: import numpy as np In [3]: import matplotlib.pyplot as plt
创建对象
请参阅数据结构简介部分
通过传递一个列表的值创建一个Series，让Pandas创建一个默认的整数索引：
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: float64
通过传递的numpy数组创建一个DataFrame，并使用DataFrame索引和标记列：
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 0.524988
通过传递可转换成类似序列结构的字典序列来创建DataFrame。
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
如果你使用的是IPython，可以使用Tab自动补全列名称（以及公共属性）。以下是将要完成的属性的一个子集：
In [13]: df2.<TAB>
df2.A df2.bool
df2.abs df2.boxplot
df2.add df2.C
df2.add_prefix df2.clip
df2.add_suffix df2.clip_lower
df2.align df2.clip_upper
df2.all df2.columns
df2.any df2.combine
df2.append df2.combine_first
df2.apply df2.compound
df2.applymap df2.consolidate
df2.as_blocks df2.convert_objects
df2.asfreq df2.copy
df2.as_matrix df2.corr
df2.astype df2.corrwith
df2.at df2.count
df2.at_time df2.cov
df2.axes df2.cummax
df2.B df2.cummin
df2.between_time df2.cumprod
df2.bfill df2.cumsum
df2.blocks df2.D
像你见到的那样，A、B、C、D都是使用Tab自动补全的。E也是如此；为了简洁其它的属性被截断了。
查看数据
请参阅基础部分
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 0.524988
显示索引、行和底层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([\'A\', \'B\', \'C\', \'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 ]])
显示您的数据的快速统计摘要
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
数据转置
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 0.524988
按轴排序
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 -0.673690
按值排序
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
选择
读取
选择一个单独的列，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
使用[]选择，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
详情参阅标签选择
使用标签获取交叉区域
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: float64
通过标签选择多轴
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 0.113648
显示标签切片，包含端点
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 -0.706771
降低返回对象维度
In [29]: df.loc[\'20130102\',[\'A\',\'B\']] Out[29]: A 1.212112 B -0.173215 Name: 2013-01-02 00:00:00, dtype: float64
获取标量值
In [30]: df.loc[dates[0],\'A\'] Out[30]: 0.46911229990718628
快速访问标量（同上一方法等价）
In [31]: df.at[dates[0],\'A\'] Out[31]: 0.46911229990718628
按位置选择
详情参阅按位置选择
通过传递整数选择位置
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: float64
通过整数切片，类似于numpy/python
In [33]: df.iloc[3:5,0:2] Out[33]: A B 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020
通过列表指定位置，类似于numpy/python样式
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 0.276232
对行切片
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 1.071804
对列切片
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 -1.478427
获取指定值
In [37]: df.iloc[1,1]
Out[37]: -0.17321464905330858
快速访问标量（同上一方法等价）
In [38]: df.iat[1,1]
Out[38]: -0.17321464905330858
布尔索引
使用单列值选择数据。
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 0.271860
从满足布尔条件的DataFrame中选择值。
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
使用isin()方法进行过滤
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
设置一个新列会自动使索引对齐数据
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\'] = s1
按标签切片
In [48]: df.at[dates[0],\'A\'] = 0
按位置设置值
In [49]: df.iat[0,1] = 0
通过numpy数组设置
In [50]: df.loc[:,\'D\'] = np.array([5] * len(df))
设置结果如下
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
where操作赋值
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
缺失数据
Pandas主要使用np.nan来表示缺失数据。默认情况下不包括在计算中。请参阅缺失数据部分
重建索引允许修改/添加/删除指定轴的索引，并返回数据副本。
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 NaN
删除所有缺少数据的行。
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 1.0
填写缺失的数据行
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 5.0
获取值为nan的布尔值
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
运算
请参阅二进制运算的基础部分
统计
运算一般排除丢失的数据。
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: float64
在其他轴上执行相同的运算
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: float64
运算具有不同维度和需要对齐的对象。此外，Pandas会沿着指定维度运算。
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 NaN
应用
将函数应用于数据
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
详情请请参阅直方图和离散化
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在字符串中设置了一组字符串处理方法，可以方便地对数组中每个元素进行操作，如下面代码片段所示。请注意，字符串中的模式匹配默认使用正则表达式。（在某些情况下总是使用它们）。详情请参阅矢量字符串方法。
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
合并
连接
在连接/合并类型操作的情况下，Pandas提供了一些具有用于索引和关系代数的各种函数合并Series、DataFrame和Panel对象的方法
请参阅合并部分
使用concat()把Pandas对象连接：
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 -0.862495
连接
SQL风格合并。请参阅数据库风格连接
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
给出另一个例子：
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 5
追加
添加行到DataFrame。请参阅追加
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
分组
"分组"我们指的是涉及一个或多个以下步骤的过程
- Splitting：根据一些标准将数据分组
- Applying：将功能独立应用于每个组
- Combining：将结果合并成数据结构
请参阅分组部分
In [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 -1.623033
分组，然后将sum()函数应用于生成的组。
In [93]: df.groupby(\'A\').sum() Out[93]: C D A bar -2.802588 2.42611 foo 3.146492 -0.63958
按多列分组形成层次索引，然后应用该函数。
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
重塑
In [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 1.100230
堆
stack()方法将"压缩"DataFrame列中的一个级别。
In [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: float64
使用"stacked"DataFrame或Series（有一个MultilIndex作为索引）stack()的反向操作是unstack()，它默认情况下解除最后一个级别。
In [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
请参阅数据透视表部分。
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
我们可以轻松地从这些数据中快速生成数据透视表：
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
时间序列
Pandas具有简单、强大和高效的功能，用于在变频期间执行重采样操作。这在金融应用中非常常见，但是不限于此。请参阅时间序列部分
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: int64
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