Pandas

创建对象

可视化数据

Geting数据

文件输入输出获取数据（Getting Data In/Out）

Pandas

这是一个简短的介绍pandas用法，主要面向新用户。在Cookbook你可以看到更复杂的方法。

通常，我们导入以下模块：

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: import matplotlib.pyplot as plt

创建对象

创建一个Series对象：

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numpy数组创建一个DateFrame对象，包括索引和列标签：

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

通过字典方式创建DataFrame对象：

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

查看各列的类型：

In [12]: df2.dtypes

Out[12]:

A float64

B datetime64[ns]

C float32

D int32

E category

F object

dtype: object

如果使用IPython，带有自动补全功能，可以利用tab键查看所有的列的名字和公有属性

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

可视化数据

查看首尾行数：

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，列标签和底层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([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 ]])

describe方法显示数据的快速统计汇总结果：

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

按索引排序：

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

按指定列的值排序：

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

选择数据

Note：标准Python/Numpy的数据选择和设置很直观和方便，但是在生产环境，我们推荐优化的pandas方法，如at, .iat, .loc, .iloc 和 .ix

Geting数据

选择一列数据，返回Series数据类型，和 df.A 命令等价：

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

通过 [] 选择行数据：

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

列标签选择数据

通过date索引获取一个横截面（cross section）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

多个标签获取数据：

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

切片数据：

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

在切片数据上精简维度：

In [29]: df.loc['20130102',['A','B']]

Out[29]:

A 1.212112

B -0.173215

Name: 2013-01-02 00:00:00, dtype: float64

获取一个标量数据：

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

Out[30]: 0.46911229990718628

一个更快速获取标量数据的方法（和上一个方法等同）：

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

Out[31]: 0.46911229990718628

通过位置获取数据

通过传递一个整数值定位：

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

类似Numpy/python，通过切片定位：

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

通过整数列表定位：

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

指定行切片：

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

指定列切片：

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

通过位置获取值：

In [37]: df.iloc[1,1]

Out[37]: -0.17321464905330858

类似的iat方法：

In [38]: df.iat[1,1]

Out[38]: -0.17321464905330858

布尔索引

通过单列值取数：

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

一个where操作取值：

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

isin（）方法：

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

相同索引赋值一列数据：

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

通过标签赋值：

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

位置赋值：

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

numpy数组赋值：

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

前面操作的结果展示：

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

where操作赋值：

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

缺失数据处理

pandas 主要用np.nan表示缺失数据，默认不列入计算。

reindex方法允许在指定的轴上增/删/改原索引，返回一个副本：

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

删除有缺失值的行：

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

在缺失值位置填充：

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

判断是否缺失，返回布尔集：

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

数据操作

Operations 通常排除缺失数据

描述统计：

In [61]: df.mean()

Out[61]:

A -0.004474

B -0.383981

C -0.687758

D 5.000000

F 3.000000

dtype: float64

同样操作在标签维度：

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

pandas操作不同维度的数据需要对齐，另外它会按指定的维度方向计算：

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

apply方法

applying 函数：

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

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中的字符处理方法和Python中的str方法一样。另外str方法默认在模式匹配的时候默认使用正则表达。

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

查询聚合

合并(merge)

concat方法：

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方法

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

append方法

在DataFrame中增加一列：

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

分组（grouping）

在”group by”的时候涉及到以下几步：

Spliting 按条件分割数据

Applying 在每组上应用函数

Combing 合并成一个数据集

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

先分组然后应用sum函数：

In [88]: df.groupby('A').sum()

Out[88]:

C D

bar -2.802588 2.42611

foo 3.146492 -0.63958

通过多列分组并生成层次索引，然后应用函数：

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（reshape）

stack方法

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

stack方法用列标签新增一层索引：

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

stack方法的逆操作为unstack，默认解压最后一层：

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

透视表（Pivot table）

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

可以通过pivot_table方法很轻松的透视数据：

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（Time Series）

pandas 拥有简单，强大，高效的函数用来处理频率转换中的重采样问题（例如将秒数据转换为5分钟数据）。

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').sum()

Out[105]:

2012-01-01 25083

Freq: 5T, dtype: int64

时区表示：

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

转换时区：

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

时区跨度转换：

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

period和timestamp之间的转换让某些算术函数应用起来非常方便。下面的例子将一个quarterly frequency with year ending in November 转化成 9am of the end of the month following the quarter end:

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

画图

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 0x10efd5a90>

在DataFrame中画出所有列：

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 0x112854d90>

Categoricals

从0.15版开始，DateFrame已经包含了categorical类型

将原始数据转换为categorical类型：

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]

重命名categorical类型：

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

重新排列并新增缺失数据：

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]

排序：

In [128]: df.sort_values(by="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

分组：

In [129]: df.groupby("grade").size()

Out[129]:

grade

very bad 1

bad 0

medium 0

good 2

very good 3

dtype: int64

文件输入输出获取数据（Getting Data In/Out）

csv

将数据写入一个csv文件：

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

读取csv数据文件：

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

写入HDF5：

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

读取HDF5文件：

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

写入excel：

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

读取Excel：

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]

python-获取当前工作路径

sys.argv[0]
import sys
print sys.argv[0]

#获得的是当前执行脚本的位置（若在命令行执行的该命令，则为空）

运行结果（在python脚本中执行的结果）：

F:/SEG/myResearch/myProject_2/test.py

os模块

import os

print os.getcwd()#获得当前工作目录

print os.path.abspath('.')#获得当前工作目录

print os.path.abspath('..')#获得当前工作目录的父目录

print os.path.abspath(os.curdir)#获得当前工作目录

运行结果：

F:\SEG\myResearch\myProject_2
F:\SEG\myResearch\myProject_2
F:\SEG\myResearch
F:\SEG\myResearch\myProject_2

注：argv[0]只是得到的是当前脚本的绝对位置；而os模块中的几种获得路径的方法，得到的是当前的工作目录，如:open('1.txt','r')，则会在当前工作目录查找该文件。即大部分的文件操作都是相对于当前工作路径。

若要改变当前工作路径，可以用：os.chdir(path) 。

如os.chdir(E:\Program Files),则大部分的文件操作就会是相对于E:\dir1。

fobj = open('Hello.txt')，实际会打开E:\Program Files\Hello.txt文件。

Pandas用法总结（详细）

Pandas

创建对象

可视化数据

Geting数据

赋值

缺失数据处理

数据操作

字符串方法

查询聚合

重塑（reshape）

透视表（Pivot table）

时间序列（Time Series）

画图

Categoricals

文件输入输出获取数据（Getting Data In/Out）

csv

HDF5

Excel

python-获取当前工作路径

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Pandas用法总结 （详细）

Pandas

创建对象

可视化数据

Geting数据

赋值

缺失数据处理

数据操作

字符串方法

查询聚合

重塑（reshape）

透视表（Pivot table）

时间序列（Time Series）

画图

Categoricals

文件输入输出获取数据（Getting Data In/Out）

csv

HDF5

Excel

python-获取当前工作路径

猜你喜欢

Pandas用法总结（详细）