转自:简书51reboot
Environment
- pandas 0.21.0
- python 3.6
- jupyter notebook
开始
习惯上，我们导入如下：
import pandas as pd import numpy as np import matplotlib.pyplot as plt
对象创建
具体参阅数据结构介绍
通过传递一个值列表来创建一个 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
通过传递具有日期时间索引和标签列的 numpy 数组来创建一个 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 0.524988
通过传递一个可以转换为一系列对象的字典来创建一个 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 foo
有特定的 dtypes
In [12]: df2.dtypes Out[12]: A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object
如果您使用 IPython，按下 TAB 将提示补全。以下是将要完成的属性的子集：
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.D
如您所见，列 A，B，C 和 D 自动完成。 E 也在那里;为了简洁，其余的属性被省略。
查看数据
具体参阅基本部分（http://pandas.pydata.org/pandas-docs/stable/basics.html#basics）
查看数据集中的最开始和最末尾的行
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，列和底层 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 ]])
描述显示您的数据的快速统计结果（ std 是标准偏差）
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 1.071804
转置数据
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
请参阅索引文档索引和选择数据（http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing）和多索引/高级索引（http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced）
直接选择
选择一个产生 Series 的列，相当于 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
选择通过 [] ，哪些切片的行。
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
按标签选择
请参阅按标签选择（http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-label）
使用标签获取整行数据
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
通过标签选择多列
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，包括两个端点
In [29]: df.loc['20130102',['A','B']] Out[29]: A 1.212112 B -0.173215 Name: 2013-01-02 00:00:00, dtype: float64
减少返回的对象的维度
In [29]: df.loc['20130102',['A','B']] Out[29]: A 1.212112 B -0.173215 Name: 2013-01-02 00:00:00, dtype: float64
获得标量值
In [30]: df.loc[dates[0],'A'] Out[30]: 0.46911229990718628 快速访问标量（等同于之前的方法） In [31]: df.at[dates[0],'A'] Out[31]: 0.46911229990718628
按位置选择
请参阅按位置选择（http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer）
通过传入整数的位置进行选择
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
整数位置的位置列表，类似于 numpy / python 风格
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
用于明确地切割行
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
用于明确地切分列
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
为了明确地获取一个值
In [37]: df.iloc[1,1]
Out[37]: -0.17321464905330858
为了快速访问标量（等同于之前的方法）
In [38]: df.iat[1,1]
Out[38]: -0.17321464905330858
布尔索引
使用单个列的值来选择数据。
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 0.271860
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
设置新列自动按索引排列数据
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
通过标签设置值
In [48]: df.at[dates[0],'A'] = 0
按位置设置值
In [49]: df.iat[0,1] = 0
通过分配一个 numpy 数组进行设置
In [50]: df.loc[:,'D'] = np.array([5] * len(df))
事先设置操作的结果
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
一个 where 操作与设置。
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
熊猫主要使用值 np.nan 来表示缺失的数据。这是默认情况下不包括在计算中。查看缺失数据（http://pandas.pydata.org/pandas-docs/stable/missing_data.html#missing-data）
Reindexing 允许您更改/添加/删除指定轴上的索引。这将返回数据的副本。 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
删除任何缺少数据的行。
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
填写缺少的数据
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.isna(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
操作
请参阅 Basic section on Binary Ops（http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-binop）
统计
一般操作不包括丢失的数据。
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
相同的操作在另一个轴上
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
使用具有不同维度和需要对齐的对象进行操作。另外，大熊猫会沿指定的尺寸自动变化。
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（apply）
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
直方图化（Histogramming）
请参阅 Histogramming and Discretization（http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-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 在 str 属性中配备了一组字符串处理方法，使得在数组的每个元素上操作都变得很容易，如下面的代码片段所示。请注意，str中的模式匹配通常默认使用正则表达式（在某些情况下始终使用它们）。在矢量化字符串方法（http://pandas.pydata.org/pandas-docs/stable/text.html#text-string-methods）中查看更多。
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
Concat
在连接/合并类型操作的情况下，熊猫提供了各种功能，可以方便地将 Series，DataFrame 和 Panel 对象与索引和关系代数功能的各种设置逻辑组合在一起。
请参阅合并部分（http://pandas.pydata.org/pandas-docs/stable/merging.html#merging）
连接 pandas 对象和 concat()：
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
Join
SQL 风格合并。请参阅数据库样式的 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 5
另一个可以给出的例子是：
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
Append
将行附加到数据框。见 Appending
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
分类
通过 “group by”，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 根据一些标准将数据分组
- Combining 将结果组合成一个数据结构
请参阅分组部分（http://pandas.pydata.org/pandas-docs/stable/groupby.html#groupby）
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，然后将函数总和应用于结果组。
In [93]: df.groupby('A').sum() Out[93]: C D A bar -2.802588 2.42611 foo 3.146492 -0.63958
按多列分组会形成一个分层索引，然后我们应用这个函数。
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
请参阅分层索引（http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced-hierarchical）和重塑（http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-stacking）的章节。
堆（Stack）
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
stack（）方法“压缩” DataFrame 列中的级别。
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
对于“堆叠的” DataFrame 或 Series（以MultiIndex为索引），stack（）的逆操作是 unstack（），默认情况下，它将卸载最后一层：
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（http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-pivot）
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
我们可以很容易地从这些数据生成数据透视表：
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
熊猫具有用于在频率转换期间执行重采样操作（例如，其次将数据转换为5分钟数据）的简单，强大且高效的功能。这在金融应用中非常普遍，但不限于此。请参阅时间系列（http://pandas.pydata.org/pandas-docs/stable/timeseries.html#timeseries）
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
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: float64
转换到另一个时区
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: float64
在时间跨度表示之间进行转换
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: float64
周期和时间戳之间的转换可以使用一些方便的算术功能。在下面的例子中，我们将季度结束时间从11月份转换为季末结束时的上午9点：
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
分类
Pandas可以在 DataFrame 中包含分类数据。有关完整文档，请参阅分类介绍（http://pandas.pydata.org/pandas-docs/stable/categorical.html#categorical）和 API 文档（http://pandas.pydata.org/pandas-docs/stable/api.html#api-categorical）
In [127]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
将原始等级转换为分类数据类型。
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
]
将类别重命名为更有意义的名称（指定到 Series.cat.categories 就是！）
In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"]
重新排列类别并同时添加缺少的类别（Series .cat 下的方法默认返回一个新的系列）。
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]
排序是按类别排序的，而不是词汇顺序。
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 good
按分类列分组也显示空白类别。
In [134]: df.groupby("grade").size() Out[134]: grade very bad 1 bad 0 medium 0 good 2 very good 3 dtype: int64
绘制（Plotting）
绘制文档（http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization）
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 0x1122ad630>
在 DataFrame 上，plot（）方便绘制所有带标签的列：
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 0x115033cf8>
数据输入/输出
CSV
写入一个CSV文件（http://pandas.pydata.org/pandas-docs/stable/io.html#io-store-in-csv）
In [141]: df.to_csv('foo.csv')
从 csv 文件读取（http://pandas.pydata.org/pandas-docs/stable/io.html#io-read-csv-table）
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
读写 HDFStore：http://pandas.pydata.org/pandas-docs/stable/io.html#io-hdf5
写入 HDF5
In [143]: df.to_hdf('foo.h5','df')
读取 HDF5
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
阅读和写入 MS Excel：http://pandas.pydata.org/pandas-docs/stable/io.html#io-excel
写入一个 excel 文件
In [145]: df.to_excel('foo.xlsx', sheet_name='Sheet1')
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]
陷阱
如果你正在尝试一个操作，你会看到一个异常：
>>> 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().
请参阅比较（http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-compare）以获取解释和做什么。
请参阅 Gotchas：http://pandas.pydata.org/pandas-docs/stable/gotchas.html#gotchas。
作者：51reboot
链接：https://www.jianshu.com/p/66bcd5582253
来源：简书
简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。
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