十分钟搞定pandas

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。

习惯上,我们做以下导入

Python

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

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

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

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

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

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

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

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

In [12]: df2.dtypes
Out[12]:
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object

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

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

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 A, B, C, 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

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

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

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 ]])

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In [29]: df.loc['20130102',['A','B']]
Out[29]:
A 1.212112
B -0.173215
Name: 2013-01-02 00:00:00, dtype: float64

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

按位置赋值

Python

In [49]: df.iat[0,1] = 0

1	In [49]: df.iat[0,1] = 0

通过numpy数组分配赋值

Python

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

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

之前的操作结果

Python

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

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

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

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

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

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

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

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

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

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

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

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

In [61]: df.mean()
Out[61]:
A -0.004474
B -0.383981
C -0.687758
D 5.000000
F 3.000000
dtype: float64

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In [88]: df.groupby('A').sum()
Out[88]:
C D
A
bar -2.802588 2.42611
foo 3.146492 -0.63958

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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]

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

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

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

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]

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

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

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

In [129]: df.groupby("grade").size()
Out[129]:
grade
very bad 1
bad NaN
medium NaN
good 2
very good 3
dtype: float64

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

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>

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

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>

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

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

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

读取csv文件

Python

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]

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

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

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

读取HDF5存储

Python

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]

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

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

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

读取excel文件

Python

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]

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

>>> 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().

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.