Pandas 选择数据

import pandas as pd
import numpy as np

dates = pd.date_range('20130101', periods = 6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index = dates, columns = ['A','B','C','D'])

df

    A   B   C   D
2013-01-01  0   1   2   3
2013-01-02  4   5   6   7
2013-01-03  8   9   10  11
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23

简单选择

1. 选择某列

print(df['A'])
print(df.A)

2013-01-01     0
2013-01-02     4
2013-01-03     8
2013-01-04    12
2013-01-05    16
2013-01-06    20
Freq: D, Name: A, dtype: int64
2013-01-01     0
2013-01-02     4
2013-01-03     8
2013-01-04    12
2013-01-05    16
2013-01-06    20
Freq: D, Name: A, dtype: int64

2. 切片选择

print(df[0:3])
print(df['20130102':'20130104'])

            A  B   C   D
2013-01-01  0  1   2   3
2013-01-02  4  5   6   7
2013-01-03  8  9  10  11
             A   B   C   D
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15

select by label:loc 按标签来选择

print(df.loc['20130102'])
print(df.loc[:,['A','B']])
print(df.loc['20130102',['A','B']])

A    4
B    5
C    6
D    7
Name: 2013-01-02 00:00:00, dtype: int64
             A   B
2013-01-01   0   1
2013-01-02   4   5
2013-01-03   8   9
2013-01-04  12  13
2013-01-05  16  17
2013-01-06  20  21
A    4
B    5
Name: 2013-01-02 00:00:00, dtype: int64

select by position:iloc 按位置来选择 与numpy相似

print(df.iloc[3])
print(df.iloc[3,1])
print(df.iloc[3:5,1:3])
print(df.iloc[[1,3,5],1:3])

A    12
B    13
C    14
D    15
Name: 2013-01-04 00:00:00, dtype: int64
13
             B   C
2013-01-04  13  14
2013-01-05  17  18
             B   C
2013-01-02   5   6
2013-01-04  13  14
2013-01-06  21  22

mixed selection:ix 标签与位置混合选择

print(df.ix[:3,['A','C']])

            A   C
2013-01-01  0   2
2013-01-02  4   6
2013-01-03  8  10

boolean indexing 是或否的选择

print(df[df.A > 8])

            A   B   C   D
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23

Pandas学习教程来源请戳这里