Python 在 Pandas DataFrame 上选择具有条件的列
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Selecting columns with condition on Pandas DataFrame
提问by user3368526
I have a dataframe looking like this.
我有一个看起来像这样的数据框。
col1 col2
0 something1 something1
1 something2 something3
2 something1 something1
3 something2 something3
4 something1 something2
I'm trying to filter all rows that have something1either on col1or col2. If I just need the condition logic on a column, I can do it with df[df.col1 == 'something1']but would there be a way to do it with multiple columns?
我正在尝试过滤所有具有something1oncol1或 的行col2。如果我只需要列上的条件逻辑，我可以用它来做，df[df.col1 == 'something1']但有没有办法用多列来做？
采纳答案by jezrael
You can use allwith boolean indexing:
你可以用all与boolean indexing：
print ((df == 'something1').all(1))
0 True
1 False
2 True
3 False
4 False
dtype: bool
print (df[(df == 'something1').all(1)])
col1 col2
0 something1 something1
2 something1 something1
EDIT:
编辑：
If need select only some columns you can use isinwith boolean indexingfor selecting desired columnsand then use subset- df[cols]:
如果需要选择某些列，您可以使用isin与boolean indexing用于选择所需columns，然后使用subset- df[cols]：
print (df)
col1 col2 col3
0 something1 something1 a
1 something2 something3 s
2 something1 something1 r
3 something2 something3 a
4 something1 something2 a
cols = df.columns[df.columns.isin(['col1','col2'])]
print (cols)
Index(['col1', 'col2'], dtype='object')
print (df[(df[cols] == 'something1').all(1)])
col1 col2 col3
0 something1 something1 a
2 something1 something1 r
回答by Naomi Fridman
Why not:
为什么不：
df[(df.col1 == 'something1') | (df.col2 == 'something1')]
outputs:
输出：
col1 col2
0 something1 something1
2 something1 something1
4 something1 something2
回答by Sincole Brans
To apply one condition to the whole dataframe
将一个条件应用于整个数据框
df[(df == 'something1').any(axis=1)]
