1、通过loc使用isin、==或!=查询方法#一般查询
df.loc[df[‘column_name’] == some_value]
df.loc[df[‘column_name’] != some_value]
查询多个值
df.loc[df[‘column_name’].isin(some_values)]
选择值不在some_values的行,使用~来取反
df.loc[~df[‘column_name’].isin(some_values)]
2、根据列(column)值选择查找行(row)示例代码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’])
如果要包含多个值,请将它们放在列表中,并使用isin:
print(df.loc[df[‘B’].isin([‘one’,’three’])])
如果希望多次执行此操作,则先创建索引然后再使用df.loc会更高效:
df = df.set_index([‘B’])
print(df.loc[‘one’])
或者,要包含多个值,可以使用df.index.isin:
print(df.loc[df.index.isin([‘one’,’two’])])
Original: https://blog.csdn.net/weixin_31317351/article/details/113493550
Author: 麟翛
Title: python row column_Python DataFrame 根据列(column)值选择查找行(row)的方法及示例代码
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