Pandas基本操作笔记

Python Data Analysis Library 或 pandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现，它是使Python成为强大而高效的数据分析环境的重要因素之一。

#encoding=utf-8
import numpy as np
import pandas as pd
def main():
    #生成表格
    dates=pd.date_range("20170801",periods=8)
    df=pd.DataFrame(np.random.randn(8,5),index=dates,columns=list("ABCDE"))#生成框架
    print df
    dff=pd.DataFrame({"A":np.random.randint(1,10,8),"B":pd.date_range("20170707",periods=8)})
    print dff

    #基本操作
    print df.head(3)#打印前三行
    print df.tail(3)#打印后三行
    print df.index
    print df.values
    print df.T#转置
    print df.sort_values("C")#排序C列 升序
    print df.sort_index(axis=1,ascending=False)#用属性值排序 降序
    print df.describe()#数据的相关信息
    print df["A"]#切片
    print df[:3]
    print df["20170801":"20170806"]
    print df.loc[dates[0]]
    print df.loc["20170802":"20170806",["B","D"]]
    print df.at[dates[0],"C"]
    print df.iloc[1:3,2:4]
    print df.iloc[1,4]#2行第5列
    print df[df.B>0]
    print df[df<0]
    print df[df["E"].isin([1,2])]

    #基本设置
    sl=pd.Series(list(range(10,18)),index=pd.date_range("20170801",periods=8))
    print sl
    df["F"]=sl
    print df
    df.at[dates[0],"A"]=0
    print df
    df.iat[1,4]=666
    df.loc[:,"D"]=np.array([4]*len(df))
    print df
    df2=df.copy()#拷贝
    df2[df2<0]=-df2#负数变为正数
    print df2


if __name__ == '__main__':
    main()

#encoding=utf-8

import numpy as np

import pandas as pd

def main():

#生成表格

dates=pd.date_range("20170801",periods=8)

df=pd.DataFrame(np.random.randn(8,5),index=dates,columns=list("ABCDE"))#生成框架

print df

dff=pd.DataFrame({"A":np.random.randint(1,10,8),"B":pd.date_range("20170707",periods=8)})

print dff

#基本操作

print df.head(3)#打印前三行

print df.tail(3)#打印后三行

print df.index

print df.values

print df.T#转置

print df.sort_values("C")#排序C列升序

print df.sort_index(axis=1,ascending=False)#用属性值排序降序

print df.describe()#数据的相关信息

print df["A"]#切片

print df[:3]

print df["20170801":"20170806"]

print df.loc[dates[0]]

print df.loc["20170802":"20170806",["B","D"]]

print df.at[dates[0],"C"]

print df.iloc[1:3,2:4]

print df.iloc[1,4]#2行第5列

print df[df.B>0]

print df[df<0]

print df[df["E"].isin([1,2])]

#基本设置

sl=pd.Series(list(range(10,18)),index=pd.date_range("20170801",periods=8))

print sl

df["F"]=sl

print df

df.at[dates[0],"A"]=0

print df

df.iat[1,4]=666

df.loc[:,"D"]=np.array([4]*len(df))

print df

df2=df.copy()#拷贝

df2[df2<0]=-df2#负数变为正数

print df2

if __name__ == '__main__':

main()

output:

                   A         B         C         D         E
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477
   A          B
0  9 2017-07-07
1  3 2017-07-08
2  3 2017-07-09
3  6 2017-07-10
4  6 2017-07-11
5  4 2017-07-12
6  5 2017-07-13
7  6 2017-07-14
                   A         B         C         D         E
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
                   A         B         C         D         E
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477
DatetimeIndex(['2017-08-01', '2017-08-02', '2017-08-03', '2017-08-04',
               '2017-08-05', '2017-08-06', '2017-08-07', '2017-08-08'],
              dtype='datetime64[ns]', freq='D')
[[ 1.07524464 -0.60505755  0.71275536  2.41815902 -0.02894002]
 [-1.51331977 -0.51708246  1.36323759  0.6185221  -0.45040032]
 [-2.26771071 -2.23593917  2.28891947  2.60836214 -1.1816333 ]
 [-0.27608484 -1.13021474 -0.12282251 -0.54247504 -0.33188383]
 [-0.23136532 -0.48824379  1.75736371 -0.96758439 -1.44857541]
 [ 0.37751303  0.05175454  0.32217176 -0.46223914  0.82356261]
 [ 1.00914409 -0.67055311  0.26280966 -0.86527427 -0.72155023]
 [-0.25735124  0.98478455 -0.29728085 -0.23039814  0.69747694]]
   2017-08-01  2017-08-02  2017-08-03  2017-08-04  2017-08-05  2017-08-06  \
A    1.075245   -1.513320   -2.267711   -0.276085   -0.231365    0.377513   
B   -0.605058   -0.517082   -2.235939   -1.130215   -0.488244    0.051755   
C    0.712755    1.363238    2.288919   -0.122823    1.757364    0.322172   
D    2.418159    0.618522    2.608362   -0.542475   -0.967584   -0.462239   
E   -0.028940   -0.450400   -1.181633   -0.331884   -1.448575    0.823563   

   2017-08-07  2017-08-08  
A    1.009144   -0.257351  
B   -0.670553    0.984785  
C    0.262810   -0.297281  
D   -0.865274   -0.230398  
E   -0.721550    0.697477  
                   A         B         C         D         E
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
                   E         D         C         B         A
2017-08-01 -0.028940  2.418159  0.712755 -0.605058  1.075245
2017-08-02 -0.450400  0.618522  1.363238 -0.517082 -1.513320
2017-08-03 -1.181633  2.608362  2.288919 -2.235939 -2.267711
2017-08-04 -0.331884 -0.542475 -0.122823 -1.130215 -0.276085
2017-08-05 -1.448575 -0.967584  1.757364 -0.488244 -0.231365
2017-08-06  0.823563 -0.462239  0.322172  0.051755  0.377513
2017-08-07 -0.721550 -0.865274  0.262810 -0.670553  1.009144
2017-08-08  0.697477 -0.230398 -0.297281  0.984785 -0.257351
              A         B         C         D         E
count  8.000000  8.000000  8.000000  8.000000  8.000000
mean  -0.260491 -0.576319  0.785894  0.322134 -0.330243
std    1.158991  0.919133  0.928070  1.436732  0.812523
min   -2.267711 -2.235939 -0.297281 -0.967584 -1.448575
25%   -0.585394 -0.785469  0.166402 -0.623175 -0.836571
50%   -0.244358 -0.561070  0.517464 -0.346319 -0.391142
75%    0.535421 -0.353244  1.461769  1.068431  0.152664
max    1.075245  0.984785  2.288919  2.608362  0.823563
2017-08-01    1.075245
2017-08-02   -1.513320
2017-08-03   -2.267711
2017-08-04   -0.276085
2017-08-05   -0.231365
2017-08-06    0.377513
2017-08-07    1.009144
2017-08-08   -0.257351
Freq: D, Name: A, dtype: float64
                   A         B         C         D         E
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
                   A         B         C         D         E
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
A    1.075245
B   -0.605058
C    0.712755
D    2.418159
E   -0.028940
Name: 2017-08-01 00:00:00, dtype: float64
                   B         D
2017-08-02 -0.517082  0.618522
2017-08-03 -2.235939  2.608362
2017-08-04 -1.130215 -0.542475
2017-08-05 -0.488244 -0.967584
2017-08-06  0.051755 -0.462239
0.71275536229
                   C         D
2017-08-02  1.363238  0.618522
2017-08-03  2.288919  2.608362
-0.45040032497
                   A         B         C         D         E
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477
                   A         B         C         D         E
2017-08-01       NaN -0.605058       NaN       NaN -0.028940
2017-08-02 -1.513320 -0.517082       NaN       NaN -0.450400
2017-08-03 -2.267711 -2.235939       NaN       NaN -1.181633
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884
2017-08-05 -0.231365 -0.488244       NaN -0.967584 -1.448575
2017-08-06       NaN       NaN       NaN -0.462239       NaN
2017-08-07       NaN -0.670553       NaN -0.865274 -0.721550
2017-08-08 -0.257351       NaN -0.297281 -0.230398       NaN
Empty DataFrame
Columns: [A, B, C, D, E]
Index: []
2017-08-01    10
2017-08-02    11
2017-08-03    12
2017-08-04    13
2017-08-05    14
2017-08-06    15
2017-08-07    16
2017-08-08    17
Freq: D, dtype: int64
                   A         B         C         D         E   F
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940  10
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400  11
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633  12
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884  13
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575  14
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563  15
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550  16
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477  17
                   A         B         C         D         E   F
2017-08-01  0.000000 -0.605058  0.712755  2.418159 -0.028940  10
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400  11
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633  12
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884  13
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575  14
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563  15
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550  16
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477  17
                   A         B         C  D           E   F
2017-08-01  0.000000 -0.605058  0.712755  4   -0.028940  10
2017-08-02 -1.513320 -0.517082  1.363238  4  666.000000  11
2017-08-03 -2.267711 -2.235939  2.288919  4   -1.181633  12
2017-08-04 -0.276085 -1.130215 -0.122823  4   -0.331884  13
2017-08-05 -0.231365 -0.488244  1.757364  4   -1.448575  14
2017-08-06  0.377513  0.051755  0.322172  4    0.823563  15
2017-08-07  1.009144 -0.670553  0.262810  4   -0.721550  16
2017-08-08 -0.257351  0.984785 -0.297281  4    0.697477  17
                   A         B         C  D           E   F
2017-08-01  0.000000  0.605058  0.712755  4    0.028940  10
2017-08-02  1.513320  0.517082  1.363238  4  666.000000  11
2017-08-03  2.267711  2.235939  2.288919  4    1.181633  12
2017-08-04  0.276085  1.130215  0.122823  4    0.331884  13
2017-08-05  0.231365  0.488244  1.757364  4    1.448575  14
2017-08-06  0.377513  0.051755  0.322172  4    0.823563  15
2017-08-07  1.009144  0.670553  0.262810  4    0.721550  16
2017-08-08  0.257351  0.984785  0.297281  4    0.697477  17

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A B C D E

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477

A B

0 9 2017-07-07

1 3 2017-07-08

2 3 2017-07-09

3 6 2017-07-10

4 6 2017-07-11

5 4 2017-07-12

6 5 2017-07-13

7 6 2017-07-14

A B C D E

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633

A B C D E

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477

DatetimeIndex(['2017-08-01', '2017-08-02', '2017-08-03', '2017-08-04',

'2017-08-05', '2017-08-06', '2017-08-07', '2017-08-08'],

dtype='datetime64[ns]', freq='D')

[[ 1.07524464 -0.60505755 0.71275536 2.41815902 -0.02894002]

[-1.51331977 -0.51708246 1.36323759 0.6185221 -0.45040032]

[-2.26771071 -2.23593917 2.28891947 2.60836214 -1.1816333 ]

[-0.27608484 -1.13021474 -0.12282251 -0.54247504 -0.33188383]

[-0.23136532 -0.48824379 1.75736371 -0.96758439 -1.44857541]

[ 0.37751303 0.05175454 0.32217176 -0.46223914 0.82356261]

[ 1.00914409 -0.67055311 0.26280966 -0.86527427 -0.72155023]

[-0.25735124 0.98478455 -0.29728085 -0.23039814 0.69747694]]

2017-08-01 2017-08-02 2017-08-03 2017-08-04 2017-08-05 2017-08-06 \

A 1.075245 -1.513320 -2.267711 -0.276085 -0.231365 0.377513

B -0.605058 -0.517082 -2.235939 -1.130215 -0.488244 0.051755

C 0.712755 1.363238 2.288919 -0.122823 1.757364 0.322172

D 2.418159 0.618522 2.608362 -0.542475 -0.967584 -0.462239

E -0.028940 -0.450400 -1.181633 -0.331884 -1.448575 0.823563

2017-08-07 2017-08-08

A 1.009144 -0.257351

B -0.670553 0.984785

C 0.262810 -0.297281

D -0.865274 -0.230398

E -0.721550 0.697477

A B C D E

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633

E D C B A

2017-08-01 -0.028940 2.418159 0.712755 -0.605058 1.075245

2017-08-02 -0.450400 0.618522 1.363238 -0.517082 -1.513320

2017-08-03 -1.181633 2.608362 2.288919 -2.235939 -2.267711

2017-08-04 -0.331884 -0.542475 -0.122823 -1.130215 -0.276085

2017-08-05 -1.448575 -0.967584 1.757364 -0.488244 -0.231365

2017-08-06 0.823563 -0.462239 0.322172 0.051755 0.377513

2017-08-07 -0.721550 -0.865274 0.262810 -0.670553 1.009144

2017-08-08 0.697477 -0.230398 -0.297281 0.984785 -0.257351

A B C D E

count 8.000000 8.000000 8.000000 8.000000 8.000000

mean -0.260491 -0.576319 0.785894 0.322134 -0.330243

std 1.158991 0.919133 0.928070 1.436732 0.812523

min -2.267711 -2.235939 -0.297281 -0.967584 -1.448575

25% -0.585394 -0.785469 0.166402 -0.623175 -0.836571

50% -0.244358 -0.561070 0.517464 -0.346319 -0.391142

75% 0.535421 -0.353244 1.461769 1.068431 0.152664

max 1.075245 0.984785 2.288919 2.608362 0.823563

2017-08-01 1.075245

2017-08-02 -1.513320

2017-08-03 -2.267711

2017-08-04 -0.276085

2017-08-05 -0.231365

2017-08-06 0.377513

2017-08-07 1.009144

2017-08-08 -0.257351

Freq: D, Name: A, dtype: float64

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A B C D E

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2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

A 1.075245

B -0.605058

C 0.712755

D 2.418159

E -0.028940

Name: 2017-08-01 00:00:00, dtype: float64

B D

2017-08-02 -0.517082 0.618522

2017-08-03 -2.235939 2.608362

2017-08-04 -1.130215 -0.542475

2017-08-05 -0.488244 -0.967584

2017-08-06 0.051755 -0.462239

0.71275536229

C D

2017-08-02 1.363238 0.618522

2017-08-03 2.288919 2.608362

-0.45040032497

A B C D E

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477

A B C D E

2017-08-01 NaN -0.605058 NaN NaN -0.028940

2017-08-02 -1.513320 -0.517082 NaN NaN -0.450400

2017-08-03 -2.267711 -2.235939 NaN NaN -1.181633

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884

2017-08-05 -0.231365 -0.488244 NaN -0.967584 -1.448575

2017-08-06 NaN NaN NaN -0.462239 NaN

2017-08-07 NaN -0.670553 NaN -0.865274 -0.721550

2017-08-08 -0.257351 NaN -0.297281 -0.230398 NaN

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Index: []

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2017-08-02 11

2017-08-03 12

2017-08-04 13

2017-08-05 14

2017-08-06 15

2017-08-07 16

2017-08-08 17

Freq: D, dtype: int64

A B C D E F

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2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400 11

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633 12

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884 13

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575 14

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563 15

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550 16

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477 17

A B C D E F

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2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400 11

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633 12

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884 13

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575 14

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563 15

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550 16

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477 17

A B C D E F

2017-08-01 0.000000 -0.605058 0.712755 4 -0.028940 10

2017-08-02 -1.513320 -0.517082 1.363238 4 666.000000 11

2017-08-03 -2.267711 -2.235939 2.288919 4 -1.181633 12

2017-08-04 -0.276085 -1.130215 -0.122823 4 -0.331884 13

2017-08-05 -0.231365 -0.488244 1.757364 4 -1.448575 14

2017-08-06 0.377513 0.051755 0.322172 4 0.823563 15

2017-08-07 1.009144 -0.670553 0.262810 4 -0.721550 16

2017-08-08 -0.257351 0.984785 -0.297281 4 0.697477 17

A B C D E F

2017-08-01 0.000000 0.605058 0.712755 4 0.028940 10

2017-08-02 1.513320 0.517082 1.363238 4 666.000000 11

2017-08-03 2.267711 2.235939 2.288919 4 1.181633 12

2017-08-04 0.276085 1.130215 0.122823 4 0.331884 13

2017-08-05 0.231365 0.488244 1.757364 4 1.448575 14

2017-08-06 0.377513 0.051755 0.322172 4 0.823563 15

2017-08-07 1.009144 0.670553 0.262810 4 0.721550 16

2017-08-08 0.257351 0.984785 0.297281 4 0.697477 17

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