原文出处: pandas.pydata.org   译文出处:石卓林

这是关于pandas的简短介绍,主要面向新用户。可以参阅Cookbook了解更复杂的使用方法。

链接:http://python.jobbole.com/84416/

习惯上,我们做以下导入

Python
 
1
2
3
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: import matplotlib.pyplot as plt

创建对象

使用传递的值列表序列创建序列, 让pandas创建默认整数索引

Python
 
1
2
3
4
5
6
7
8
9
10
In [4]: s = pd.Series([1,3,5,np.nan,6,8])
In [5]: s
Out[5]:
0     1
1     3
2     5
3   NaN
4     6
5     8
dtype: float64

使用传递的numpy数组创建数据帧,并使用日期索引和标记列.

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
In [6]: dates = pd.date_range('20130101',periods=6)
In [7]: dates
Out[7]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01, ..., 2013-01-06]
Length: 6, Freq: D, Timezone: None
 
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

使用传递的可转换序列的字典对象创建数据帧.

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
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 2013-01-02  1  3   test  foo
1  1 2013-01-02  1  3  train  foo
2  1 2013-01-02  1  3   test  foo
3  1 2013-01-02  1  3  train  foo

所有明确类型

Python
 
1
2
3
4
5
6
7
8
9
In [12]: df2.dtypes
Out[12]:
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

如果你这个正在使用IPython,标签补全列名(以及公共属性)将自动启用。这里是将要完成的属性的子集:

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
In [13]: df2.<TAB>
df2.A                  df2.boxplot
df2.abs                df2.C
df2.add                df2.clip
df2.add_prefix         df2.clip_lower
df2.add_suffix         df2.clip_upper
df2.align              df2.columns
df2.all                df2.combine
df2.any                df2.combineAdd
df2.append             df2.combine_first
df2.apply              df2.combineMult
df2.applymap           df2.compound
df2.as_blocks          df2.consolidate
df2.asfreq             df2.convert_objects
df2.as_matrix          df2.copy
df2.astype             df2.corr
df2.at                 df2.corrwith
df2.at_time            df2.count
df2.axes               df2.cov
df2.B                  df2.cummax
df2.between_time       df2.cummin
df2.bfill              df2.cumprod
df2.blocks             df2.cumsum
df2.bool               df2.D

如你所见, 列 ABC, 和 D 也是自动完成标签. E 也是可用的; 为了简便起见,后面的属性显示被截断.

查看数据

参阅基础部分

查看帧顶部和底部行

 

Python

 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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

 

Python

 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
In [16]: df.index
Out[16]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01, ..., 2013-01-06]
Length: 6, Freq: D, Timezone: None
 
In [17]: df.columns
Out[17]: Index([u'A', u'B', u'C', u'D'], dtype='object')
 
In [18]: df.values
Out[18]:
array([[ 0.4691, -0.2829, -1.5091, -1.1356],
       [ 1.2121, -0.1732,  0.1192, -1.0442],
       [-0.8618, -2.1046, -0.4949,  1.0718],
       [ 0.7216, -0.7068, -1.0396,  0.2719],
       [-0.425 ,  0.567 ,  0.2762, -1.0874],
       [-0.6737,  0.1136, -1.4784,  0.525 ]])

描述显示数据快速统计摘要

Python
 
1
2
3
4
5
6
7
8
9
10
11
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

Python
1
2
3
4
5
6
7
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

Python
 
1
2
3
4
5
6
7
8
9
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

Python
 
1
2
3
4
5
6
7
8
9
In [22]: df.sort(columns='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

选择器

注释: 标准Python / Numpy表达式可以完成这些互动工作, 但在生产代码中, 我们推荐使用优化的pandas数据访问方法, .at, .iat, .loc, .iloc 和 .ix.

参阅索引文档 索引和选择数据 and 多索引/高级索引

读取

选择单列, 这会产生一个序列, 等价df.A

Python
 
1
2
3
4
5
6
7
8
9
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[]选择行片断

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
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

使用标签选择

更多信息请参阅按标签选择

使用标签获取横截面

Python
 
1
2
3
4
5
6
7
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

使用标签选择多轴

Python
 
1
2
3
4
5
6
7
8
9
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, 包含两个端点

Python
 
1
2
3
4
5
6
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

Python
 
1
2
3
4
5
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

Python
 
1
2
In [30]: df.loc[dates[0],'A']
Out[30]: 0.46911229990718628

快速访问并获取标量数据 (等价上面的方法)

Python
 
1
2
In [31]: df.at[dates[0],'A']
Out[31]: 0.46911229990718628

按位置选择

更多信息请参阅按位置参阅

传递整数选择位置

Python
 
1
2
3
4
5
6
7
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

使用整数片断,效果类似numpy/python

Python
 
1
2
3
4
5
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

使用整数偏移定位列表,效果类似 numpy/python 样式

Python
 
1
2
3
4
5
6
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

显式行切片

Python
 
1
2
3
4
5
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

显式列切片

Python
 
1
2
3
4
5
6
7
8
9
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

显式获取一个值

Python
 
1
2
In [37]: df.iloc[1,1]
Out[37]: -0.17321464905330861

快速访问一个标量(等同上个方法)

Python
 
1
2
In [38]: df.iat[1,1]
Out[38]: -0.17321464905330861

布尔索引

使用单个列的值选择数据.

Python
 
1
2
3
4
5
6
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

where 操作.

Python
 
1
2
3
4
5
6
7
8
9
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() 筛选:

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
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

赋值

赋值一个新列,通过索引自动对齐数据

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
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

按标签赋值

Python
 
1
In [48]: df.at[dates[0],'A'] = 0

按位置赋值

Python
 
1
In [49]: df.iat[0,1] = 0

通过numpy数组分配赋值

Python
 
1
In [50]: df.loc[:,'D'] = np.array([5] * len(df))

之前的操作结果

Python
 
1
2
3
4
5
6
7
8
9
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
2013-01-03 -0.861849 -2.104569 -0.494929  5   2
2013-01-04  0.721555 -0.706771 -1.039575  5   3
2013-01-05 -0.424972  0.567020  0.276232  5   4
2013-01-06 -0.673690  0.113648 -1.478427  5   5

where 操作赋值.

Python
 
1
2
3
4
5
6
7
8
9
10
11
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
2013-01-03 -0.861849 -2.104569 -0.494929 -5  -2
2013-01-04 -0.721555 -0.706771 -1.039575 -5  -3
2013-01-05 -0.424972 -0.567020 -0.276232 -5  -4
2013-01-06 -0.673690 -0.113648 -1.478427 -5  -5

丢失的数据

pandas主要使用np.nan替换丢失的数据. 默认情况下它并不包含在计算中. 请参阅 Missing Data section

重建索引允许更改/添加/删除指定轴索引,并返回数据副本.

Python
 
1
2
3
4
5
6
7
8
9
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
2013-01-02  1.212112 -0.173215  0.119209  5   1   1
2013-01-03 -0.861849 -2.104569 -0.494929  5   2 NaN
2013-01-04  0.721555 -0.706771 -1.039575  5   3 NaN

删除任何有丢失数据的行.

Python
 
1
2
3
4
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  1

填充丢失数据

Python
 
1
2
3
4
5
6
7
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  1
2013-01-02  1.212112 -0.173215  0.119209  5  1  1
2013-01-03 -0.861849 -2.104569 -0.494929  5  2  5
2013-01-04  0.721555 -0.706771 -1.039575  5  3  5

获取值是否nan的布尔标记

Python
 
1
2
3
4
5
6
7
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

参阅二元运算基础

统计

计算时一般不包括丢失的数据

执行描述性统计

Python
 
1
2
3
4
5
6
7
8
In [61]: df.mean()
Out[61]:
A   -0.004474
B   -0.383981
C   -0.687758
D    5.000000
F    3.000000
dtype: float64

在其他轴做相同的运算

Python
 
1
2
3
4
5
6
7
8
9
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

用于运算的对象有不同的维度并需要对齐.除此之外,pandas会自动沿着指定维度计算.

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
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
2013-01-04     3
2013-01-05     5
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   1
2013-01-04 -2.278445 -3.706771 -4.039575   2   0
2013-01-05 -5.424972 -4.432980 -4.723768   0  -1
2013-01-06       NaN       NaN       NaN NaN NaN

Apply

在数据上使用函数

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
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
2013-01-03  0.350263 -2.277784 -1.884779  15   3
2013-01-04  1.071818 -2.984555 -2.924354  20   6
2013-01-05  0.646846 -2.417535 -2.648122  25  10
2013-01-06 -0.026844 -2.303886 -4.126549  30  15
 
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

请参阅 直方图和离散化

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
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: int32
 
In [70]: s.value_counts()
Out[70]:
4    5
6    2
2    2
1    1
dtype: int64

字符串方法

序列可以使用一些字符串处理方法很轻易操作数据组中的每个元素,比如以下代码片断。 注意字符匹配方法默认情况下通常使用正则表达式(并且大多数时候都如此). 更多信息请参阅字符串向量方法.

 

Python

 
1
2
3
4
5
6
7
8
9
10
11
12
13
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

pandas提供各种工具以简便合并序列,数据桢,和组合对象, 在连接/合并类型操作中使用多种类型索引和相关数学函数.

请参阅合并部分

把pandas对象连接到一起

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
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样式合并. 请参阅 数据库style联接

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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

添加

添加行到数据增. 参阅 添加

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
In [82]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
In [83]: df
Out[83]:
          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 [84]: s = df.iloc[3]
In [85]: df.append(s, ignore_index=True)
Out[85]:
          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

对于“group by”指的是以下一个或多个处理

  • 将数据按某些标准分割为不同的组
  • 在每个独立组上应用函数
  • 组合结果为一个数据结构

请参阅 分组部分

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
In [86]: 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 [87]: df
Out[87]:
     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

分组然后应用函数统计总和存放到结果组

Python
 
1
2
3
4
5
6
In [88]: df.groupby('A').sum()
Out[88]:
            C        D
A                    
bar -2.802588  2.42611
foo  3.146492 -0.63958

按多列分组为层次索引,然后应用函数

Python
 
1
2
3
4
5
6
7
8
9
10
In [89]: df.groupby(['A','B']).sum()
Out[89]:
                  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

请参阅章节 分层索引 和 重塑.

堆叠

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
In [90]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
   ....:                      'foo', 'foo', 'qux', 'qux'],
   ....:                     ['one', 'two', 'one', 'two',
   ....:                      'one', 'two', 'one', 'two']]))
   ....:
In [91]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
In [92]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
In [93]: df2 = df[:4]
In [94]: df2
Out[94]:
                     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

堆叠 函数 “压缩” 数据桢的列一个级别.

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
In [95]: stacked = df2.stack()
In [96]: stacked
Out[96]:
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

被“堆叠”数据桢或序列(有多个索引作为索引), 其堆叠的反向操作是未堆栈, 上面的数据默认反堆叠到上一级别:

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
In [97]: stacked.unstack()
Out[97]:
                     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 [98]: stacked.unstack(1)
Out[98]:
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 [99]: stacked.unstack(0)
Out[99]:
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

查看数据透视表.

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
In [100]: 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 [101]: df
Out[101]:
        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

我们可以从此数据非常容易的产生数据透视表:

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
In [102]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
Out[102]:
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有易用,强大且高效的函数用于高频数据重采样转换操作(例如,转换秒数据到5分钟数据), 这是很普遍的情况,但并不局限于金融应用, 请参阅时间序列章节

Python
 
1
2
3
4
5
6
In [103]: rng = pd.date_range('1/1/2012', periods=100, freq='S')
In [104]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [105]: ts.resample('5Min', how='sum')
Out[105]:
2012-01-01    25083
Freq: 5T, dtype: int32

时区表示

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In [106]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
In [107]: ts = pd.Series(np.random.randn(len(rng)), rng)
In [108]: ts
Out[108]:
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 [109]: ts_utc = ts.tz_localize('UTC')
In [110]: ts_utc
Out[110]:
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

转换到其它时区

Python
 
1
2
3
4
5
6
7
8
In [111]: ts_utc.tz_convert('US/Eastern')
Out[111]:
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

转换不同的时间跨度

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
In [112]: rng = pd.date_range('1/1/2012', periods=5, freq='M')
In [113]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [114]: ts
Out[114]:
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 [115]: ps = ts.to_period()
In [116]: ps
Out[116]:
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 [117]: ps.to_timestamp()
Out[117]:
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

转换时段并且使用一些运算函数, 下例中, 我们转换年报11月到季度结束每日上午9点数据

Python
 
1
2
3
4
5
6
7
8
9
10
11
In [118]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
In [119]: ts = pd.Series(np.random.randn(len(prng)), prng)
In [120]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
In [121]: ts.head()
Out[121]:
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

自版本0.15起, pandas可以在数据桢中包含分类. 完整的文档, 请查看分类介绍 and the API文档.

Python
 
1
In [122]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

转换原始类别为分类数据类型.

 
 
 
 

Python

 
1
2
3
4
5
6
7
8
9
10
11
In [123]: df["grade"] = df["raw_grade"].astype("category")
In [124]: df["grade"]
Out[124]:
0    a
1    b
2    b
3    a
4    a
5    e
Name: grade, dtype: category
Categories (3, object): [a, b, e]

重命令分类为更有意义的名称 (分配到Series.cat.categories对应位置!)

Python
 
1
In [125]: df["grade"].cat.categories = ["very good", "good", "very bad"]

重排顺分类,同时添加缺少的分类(序列 .cat方法下返回新默认序列)

Python
 
1
2
3
4
5
6
7
8
9
10
11
In [126]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
In [127]: df["grade"]
Out[127]:
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]

排列分类中的顺序,不是按词汇排列.

Python
 
1
2
3
4
5
6
7
8
9
In [128]: df.sort("grade")
Out[128]:
   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

类别列分组,并且也显示空类别.

Python
 
1
2
3
4
5
6
7
8
9
In [129]: df.groupby("grade").size()
Out[129]:
grade
very bad      1
bad         NaN
medium      NaN
good          2
very good     3
dtype: float64

绘图

绘图文档.

Python
 
1
2
3
4
In [130]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
In [131]: ts = ts.cumsum()
In [132]: ts.plot()
Out[132]: <matplotlib.axes._subplots.AxesSubplot at 0xb02091ac>

在数据桢中,可以很方便的绘制带标签列:

Python
 
1
2
3
4
5
6
In [133]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
   .....:                   columns=['A', 'B', 'C', 'D'])
   .....:
In [134]: df = df.cumsum()
In [135]: plt.figure(); df.plot(); plt.legend(loc='best')
Out[135]: <matplotlib.legend.Legend at 0xb01c9cac>

获取数据输入/输出

CSV

写入csv文件

Python
 
1
In [136]: df.to_csv('foo.csv')

读取csv文件

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In [137]: pd.read_csv('foo.csv')
Out[137]:
     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HDF存储

写入HDF5存储

Python
 
1
In [138]: df.to_hdf('foo.h5','df')

读取HDF5存储

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In [139]: pd.read_hdf('foo.h5','df')
Out[139]:
                    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

读写MS Excel

写入excel文件

Python
 
1
In [140]: df.to_excel('foo.xlsx', sheet_name='Sheet1')

读取excel文件

Python
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
In [141]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
Out[141]:
                    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

如果尝试这样操作可能会看到像这样的异常:

Python
 
1
2
3
4
5
>>> 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().

查看对照获取解释和怎么做的帮助

也可以查看陷阱.

3、pandas的更多相关文章

  1. Python openpyxl、pandas操作Excel方法简介与具体实例

    本篇重点讲解windows系统下 Python3.5中第三方excel操作库-openpyxl: 其实Python第三方库有很多可以操作Excel,如:xlrd,xlwt,xlwings甚至注明的数据 ...

  2. numpy、scipy、pandas

    以下分别是numpy.Scipy.pandas的简介.虽然这些包提供的一些结构比python自身的“更高级.更高效”,更高级是因为它们能完成更高级的任务,但是,学习的时候尽量不要和python割裂开认 ...

  3. Anaconda 安装和使用Numpy、Scipy、pandas、Scikit-learn

    Anaconda 安装和使用 https://www.cnblogs.com/liruihuan/p/9101613.html 最近看了些关于数据分析的书,想系统的整理下相关知识,算是学习笔记吧,也希 ...

  4. 22、pandas表格、文件和数据库模块

    pandas官方文档:https://pandas.pydata.org/pandas-docs/stable/?v=20190307135750 pandas基于Numpy,可以看成是处理文本或者表 ...

  5. Python之(matplotlib、numpy、pandas)数据分析

    一.Matplotlib 是一个 Python 的 2D绘图库,它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形. 它主要用来回事图形,用来展现一些数据,更加直观的展示,让你第一眼就只要数 ...

  6. Python离线断网情况下安装numpy、pandas和matplotlib等常用第三方包

    联网情况下在命令终端CMD中输入“pip install numpy”即可自动安装,pandas和matplotlib同理一样方法进行自动安装. 工作的电脑不能上外网,所以不能通过直接输入pip命令来 ...

  7. python2.7安装numpy、pandas、matplotlib库

    我装的是python2.7 然后pip的版本是18.1,最近使用pip install **安装包的时候总是会提示 You are using pip version 18.1, however ve ...

  8. 其它课程中的python---5、Pandas处理数据和读取数据

    其它课程中的python---5.Pandas处理数据和读取数据 一.总结 一句话总结: 记常用和特例:慢慢慢慢的就熟了,不用太着急,慢慢来 库的使用都很简单:就是库的常用函数就这几个,后面用的时候学 ...

  9. windows下如何安装Python、pandas

    windows下如何安装Python.pandas 本篇主要涵盖以下三部分内容: Python.Pycharm的安装 使用Pycharm创建.运行Python程序 安装pandas 1.Python. ...

  10. 网络爬虫、Pandas

    网络爬虫.Pandas Pandas 是 Python 语言的一个扩展程序库,用于数据分析. Pandas 是一个开放源码.BSD 许可的库,提供高性能.易于使用的数据结构和数据分析工具. Panda ...

随机推荐

  1. Shell脚本语法---在Makefile等文件…

    1. Shell脚本语法 1.1. 条件测试:test [ 命令test或[可以测试一个条件是否成立,如果测试结果为真,则该命令的Exit Status为0,如果测试结果为假,则命令的Exit Sta ...

  2. 当property遇上category

    [当property遇上category] @property可以在类定义中,以及extension定义中使用,编译器会自动为@property生成代码,并在变量列表(ivar_list_t)中添加相 ...

  3. 【原创】4. MYSQL++ 之 SQLTypeAdapter类型、SQLQueryParms类型 与 SQLBuffer

    1. mysqlpp::SQLBuffer 该类型其实就是SQLTypeAdapter传入的各种类型(int, string, double, long, String, …) 的包装,包装的结果就是 ...

  4. 值得一做》关于一道DP+SPFA的题 BZOJ1003 (BZOJ第一页计划) (normal-)

    这是一道数据范围和评测时间水的可怕的题,只是思路有点难想,BUT假如你的思路清晰,完全了解怎么该做,那就算你写一个反LLL和反SLE都能A,如此水的一道题,你不心动吗? 下面贴出题目 Descript ...

  5. sql server 错误总结

    1>无法访问sql server2000数据库 1.1>安装sql server2000 sp1的补丁包. 1.2>sql server 数据库开启了允许远程访问. 1.3>s ...

  6. VS运行release版本正常,直接执行exe文件会出现问题

    博客转载自:https://blog.csdn.net/weixinhum/article/details/39962483 检测了一下自己的程序,发现程序先后开启了两个线程,并且对两个线程的启动顺序 ...

  7. 一步一步带你构建第一个 Laravel 项目

    参考链接:https://laravel-news.com/your-first-laravel-application 简介 按照以下的步骤,你会创建一个简易的链接分享网站. 安装 Laravel ...

  8. CentOS7下源码包方式安装rabbitmq

    1.先安装erlang http://www.cnblogs.com/justphp/p/6093880.html 2.下载rabbitmq rpm包: wget http://www.rabbitm ...

  9. WndProc

    主要用在拦截并处理系统消息和自定义消息. form窗体的一个重载方法. protected override void WndProc(ref Message m) { //拦截窗体最小化按钮消息 i ...

  10. 微信运动数据抓取(Python)

    "微信运动"能够向朋友分享一个包含有运动数据的网页,网页中就有我们需要的数据.url类似于:http://hw.weixin.qq.com/steprank/step/person ...