pandas中根据列的值选取多行数据

# 选取等于某些值的行记录 用 ==
df.loc[df['column_name'] == some_value]

# 选取某列是否是某一类型的数值 用 isin
df.loc[df['column_name'].isin(some_values)]

# 多种条件的选取 用 &
df.loc[(df['column'] == some_value) & df['other_column'].isin(some_values)]

# 选取不等于某些值的行记录 用 ！=
df.loc[df['column_name'] != some_value]

# isin返回一系列的数值,如果要选择不符合这个条件的数值使用~
df.loc[~df['column_name'].isin(some_values)]

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
    'B': 'one one two three two two one three'.split(),
    'C': np.arange(8), 'D': np.arange(8) * 2})
print(df)

     A      B  C   D
0  foo    one  0   0
1  bar    one  1   2
2  foo    two  2   4
3  bar  three  3   6
4  foo    two  4   8
5  bar    two  5  10
6  foo    one  6  12
7  foo  three  7  14

print(df.loc[df['A'] == 'foo'])

     A      B  C   D
0  foo    one  0   0
2  foo    two  2   4
4  foo    two  4   8
6  foo    one  6  12
7  foo  three  7  14

# 如果你想包括多个值，把它们放在一个list里面，然后使用isin
print(df.loc[df['B'].isin(['one','three'])])

     A     B      C   D
0  foo    one  0   0
1  bar    one  1   2
3  bar  three  3   6
6  foo    one  6  12
7  foo  three  7  14

df = df.set_index(['B'])
print(df.loc['one'])

 A   B   C     D
one  foo  0   0
one  bar  1   2
one  foo  6  12

A   B   C   D
one foo 0   0
one bar 1   2
two foo 2   4
two foo 4   8
two bar 5   10
one foo 6   12

来自：https://www.cnblogs.com/everfight/p/pandas_select_rows.html