轻松入门学习 pandas

朴灿烈づ我的快乐病毒、 2022-05-28 04:11 170阅读 0赞

官方 10 分钟入门文档（ [http://pandas.pydata.org/pandas-docs/stable/10min.html][http_pandas.pydata.org_pandas-docs_stable_10min.html]）这是 pandas 的简短介绍，主要面向新用户。你可以看到更复杂的文档Cookbook（ [http://pandas.pydata.org/pandas-docs/stable/cookbook.html\#cookbook][http_pandas.pydata.org_pandas-docs_stable_cookbook.html_cookbook]）\[toc\]

## 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][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][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][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][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 [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

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][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][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][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][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][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][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][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][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][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][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][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 ##

熊猫可以在 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][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][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][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][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][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][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][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][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][http_pandas.pydata.org_pandas-docs_stable_gotchas.html_gotchas]。

作者：51reboot  
链接：https://www.jianshu.com/p/66bcd5582253  
來源：简书  
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[http_pandas.pydata.org_pandas-docs_stable_10min.html]: https://link.jianshu.com?t=http://pandas.pydata.org/pandas-docs/stable/10min.html
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