[英]Use loc and iloc together in pandas
Say I have the following dataframe, and I want to change the two elements in column c
that correspond to the first two elements in column a
that are equal to 1
to equal 2
.说我有以下数据帧,我想在列中的两个元件改变
c
对应于在列中的前两个元素a
是等于1
为等于2
。
>>> df = pd.DataFrame({"a" : [1,1,1,1,2,2,2,2], "b" : [2,3,1,4,5,6,7,2], "c" : [1,2,3,4,5,6,7,8]})
>>> df.loc[df["a"] == 1, "c"].iloc[0:2] = 2
>>> df
a b c
0 1 2 1
1 1 3 2
2 1 1 3
3 1 4 4
4 2 5 5
5 2 6 6
6 2 7 7
7 2 2 8
The code in the second line doesn't work because iloc sets a copy, so the original dataframe is not modified.第二行中的代码不起作用,因为 iloc 设置了副本,因此不会修改原始数据帧。 How would I do this?
我该怎么做?
一种肮脏的方式是:
df.loc[df[df['a'] == 1][:2].index, 'c'] = 2
You can use Index.isin
:您可以使用
Index.isin
:
import pandas as pd
df = pd.DataFrame({"a" : [1,1,1,1,2,2,2,2],
"b" : [2,3,1,4,5,6,7,2],
"c" : [1,2,3,4,5,6,7,8]})
#more general index
df.index = df.index + 10
print (df)
a b c
10 1 2 1
11 1 3 2
12 1 1 3
13 1 4 4
14 2 5 5
15 2 6 6
16 2 7 7
17 2 2 8
print (df.index.isin(df.index[:2]))
[ True True False False False False False False]
df.loc[(df["a"] == 1) & (df.index.isin(df.index[:2])), "c"] = 2
print (df)
a b c
10 1 2 2
11 1 3 2
12 1 1 3
13 1 4 4
14 2 5 5
15 2 6 6
16 2 7 7
17 2 2 8
If index is nice
(starts from 0
without duplicates):如果索引
nice
(从0
开始,没有重复):
df.loc[(df["a"] == 1) & (df.index < 2), "c"] = 2
print (df)
a b c
0 1 2 2
1 1 3 2
2 1 1 3
3 1 4 4
4 2 5 5
5 2 6 6
6 2 7 7
7 2 2 8
Another solution:另一种解决方案:
mask = df["a"] == 1
mask = mask & (mask.cumsum() < 3)
df.loc[mask.index[:2], "c"] = 2
print (df)
a b c
0 1 2 2
1 1 3 2
2 1 1 3
3 1 4 4
4 2 5 5
5 2 6 6
6 2 7 7
7 2 2 8
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