将多个列值合并到python pandas的一列中

问题描述：

我有一个这样的熊猫数据框:

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

我现在要做的是获取一个包含Column1和新columnA的新数据框.像这样:columnA应该包含第2列-(to)n的所有值(其中n是从Column2到行末的列数):

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

我如何最好地解决这个问题?任何意见将是有益的.预先感谢！

How could I best approach this issue? Any advice would be helpful. Thanks in advance!

答

您可以逐行调用apply传递axis=1到apply，然后将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

在这里，我打电话给dropna以摆脱NaN，但是我们需要再次强制转换为int，因此我们不会以浮点数作为str结束.

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.

将多个列值合并到python pandas的一列中

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