在python pandas中将多个列值合并为一列
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Merge multiple column values into one column in python pandas
提问by sequence_hard
I have a pandas data frame like this:
我有一个像这样的熊猫数据框：
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
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:
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 Column2 到行尾的列数）中的所有值，如下所示：
Column1 ColumnA
0 a 1,2,3,4
1 a 3,4,5
2 b 6,7,8
3 c 7,7
How could I best approach this issue? Any advice would be helpful. Thanks in advance!
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！
采纳答案by EdChum
You can call applypass axis=1to applyrow-wise, then convert the dtype to strand join:
您可以按行调用applypass ，然后将 dtype 转换为and ：axis=1applystrjoin
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
Here I call dropnato get rid of the NaN, however we need to cast again to intso we don't end up with floats as str.
在这里，我呼吁dropna摆脱NaN，但是我们需要再次int强制转换为，这样我们就不会以浮点数作为 str 结束。
回答by Amin Salgado
I propose to use .assign
我建议使用 .assign
df2 = df.assign(ColumnA = df.Column2.astype(str) + ', ' + \
df.Column3.astype(str) + ', ' df.Column4.astype(str) + ', ' \
df.Column4.astype(str) + ', ' df.Column5.astype(str))
it's simple, maybe long but it worked for me
这很简单，也许很长，但对我有用
回答by Om Prakash
If you have lot of columns say - 1000 columns in dataframe and you want to merge few columns based on particular column namee.g. -Column2in question and arbitrary no. of columns after that column (e.g. here 3 columns after 'Column2inclusive of Column2as OP asked).
如果您有很多列说 - 数据框中的 1000 列，并且您想基于particular column name例如 -Column2有问题和任意否合并几列。该列之后的列数（例如，此处'Column2包含Column2OP 要求的3 列之后）。
We can get position of column using .get_loc()- as answered here
我们可以使用.get_loc()-获取列的位置- 正如这里所回答的
source_col_loc = df.columns.get_loc('Column2') # column position starts from 0
df['ColumnA'] = df.iloc[:,source_col_loc+1:source_col_loc+4].apply(
lambda x: ",".join(x.astype(str)), axis=1)
df
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
To remove NaN, use .dropna()or .fillna()
Hope it helps!
希望能帮助到你！
