[英]Selecting columns with condition on Pandas DataFrame
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 something1
either on col1
or col2
.我正在尝试过滤在
col1
或col2
上有something1
所有行。 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?如果我只需要列上的条件逻辑,我可以用
df[df.col1 == 'something1']
来做,但是有没有办法用多列来做?
You can use all
with boolean indexing
:您可以将
all
与boolean indexing
一起使用:
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 isin
with boolean indexing
for selecting desired columns
and then use subset
- df[cols]
:如果需要选择某些列,您可以使用
isin
与boolean indexing
选择所需的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
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
将一个条件应用于整个数据框
df[(df == 'something1').any(axis=1)]
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