10分钟上手python pandas


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

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
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多索引/高级索引

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
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
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
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

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

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
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

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

请参阅分层索引重塑的章节。

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

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

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API文档

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

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

In [141]: df.to_csv('foo.csv')

从csv文件读取

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

读写HDFStore
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
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().

请参阅比较以获取解释和做什么。

请参阅Gotchas

posted on 2017-11-10 23:31  炮二平五  阅读(300)  评论(0编辑  收藏  举报

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