在python pandas中将多个列值合并为一列 [英] Merge multiple column values into one column in python pandas
问题描述
我有一个像这样的熊猫数据框:
I have a pandas data frame like this:
Column1 Column2 Column3 Column4 Column5
0 a 1 2 3 4
1 a 3 4 5
2 b 6 7 8
3 c 7 7
我现在想要做的是获取一个包含 Column1 和一个新 columnA 的新数据框.此 columnA 应包含从第 2 列到(到)n(其中 n 是从第 2 列到行尾的列数)的所有值,如下所示:
What I want to do now is getting a new dataframe containing Column1 and a new columnA. This columnA should contain all values from columns 2 -(to) n (where n is the number of columns from Column2 to the end of the row) like this:
Column1 ColumnA
0 a 1,2,3,4
1 a 3,4,5
2 b 6,7,8
3 c 7,7
我怎样才能最好地解决这个问题?任何意见将是有益的.提前致谢!
How could I best approach this issue? Any advice would be helpful. Thanks in advance!
推荐答案
你可以调用 apply
通过 axis=1
到 apply
行-明智的做法是将 dtype 转换为 str
和 join
:
You can call apply
pass axis=1
to apply
row-wise, then convert the dtype to str
and join
:
In [153]:
df['ColumnA'] = df[df.columns[1:]].apply(
lambda x: ','.join(x.dropna().astype(str)),
axis=1
)
df
Out[153]:
Column1 Column2 Column3 Column4 Column5 ColumnA
0 a 1 2 3 4 1,2,3,4
1 a 3 4 5 NaN 3,4,5
2 b 6 7 8 NaN 6,7,8
3 c 7 7 NaN NaN 7,7
这里我调用 dropna
来去掉 NaN
,但是我们需要再次转换为 int
所以我们不会结束以浮点数作为 str.
Here I call dropna
to get rid of the NaN
, however we need to cast again to int
so we don't end up with floats as str.
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