pandas 如何将条件应用于熊猫 iloc
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How to apply a condition to pandas iloc
提问by Googlebot
I select columns 2 - endfrom a pandas DataFrame with ilocas
我选择列2 - end从Pandas数据帧与iloc作为
d=c.iloc[:,2:]
now how can I apply a condition to this selection? For example, if column1==1.
现在如何将条件应用于此选择？例如，如果column1==1.
回答by jezrael
You can use DataFrame.ilocif need filter first column select by position, :means here select all rows:
您可以使用DataFrame.ilocif 需要过滤第一列按位置选择，:表示这里选择所有行：
c[c.iloc[:, 0] == 1]
Sample:
样品：
c = pd.DataFrame({'A':list('abcdef'),
'B':[4,5,4,5,5,4],
'C':[7,8,9,4,2,3],
'D':[1,3,5,7,1,0],
'E':[5,3,6,9,2,4],
'F':list('aaabbb')})
print (c)
A B C D E F
0 a 4 7 1 5 a
1 b 5 8 3 3 a
2 c 4 9 5 6 a
3 d 5 4 7 9 b
4 e 5 2 1 2 b
5 f 4 3 0 4 b
df = c[c.iloc[:, 3] == 1]
print (df)
A B C D E F
0 a 4 7 1 5 a
4 e 5 2 1 2 b
回答by piRSquared
This is referred to as mixed indexing in that you want to index by boolean results in rows and position in columns. I'd use locin order to take advantage of boolean indexing for the rows. But that implies that you need column names values for the column slice.
这被称为混合索引，因为您希望按行中的布尔结果和列中的位置进行索引。我会使用loc以利用行的布尔索引。但这意味着您需要列切片的列名值。
d.loc[d.column1 == 1, d.columns[2:]]
If your column names are not unique then you can resort to the dreaded chained index.
如果您的列名不是唯一的，那么您可以求助于可怕的链式索引。
d.loc[d.column1 == 1].iloc[:, 2:]
What might also be intuitive is to use queryafterwards:
可能也很直观的是query之后使用：
d.iloc[:, 2:].query('column1 == 1')
