How to re-write dataframe with new rows per column?

Question

I have a pandas data frame that looks like the following

Name	Col 1	Col 2
A	1	2
B	3	4

I’d like to change the dataset so that it has 2 columns, name and value. But I’d like to create a new row for each existing row combined with each column.

Like this:

Name	Val
A-Col1	1
A-Col2	2
B-Col1	3
B-Col2	4

Asked By: DHutch00

||

Source

Answer 1

try this one



 def combine_columns(df):
     df['combined'] = df.apply(lambda row: row['name'] + ' ' + row['value'], axis=1)
     return df

Answered By: Mike garseia

Answer 2

You can get pretty close very quickly using unstack and setting the index to Name:

df.set_index("Name").unstack().swaplevel()

gives

    Name
A     Col 1    1
B     Col 1    3
A     Col 2    3
B     Col 2    4

Now you have the right structure you just need to sort and merge. Combining in the sort and merge steps you can use:

tmp = (
    df.set_index("Name")
    .unstack()
    .swaplevel()
    .sort_index()
    .reset_index()
    .rename({0: "val"}, axis=1)
)
tmp["Name"] = tmp["Name"] + "-" + tmp["level_1"]
df = tmp.drop("level_1", axis=1)

to get

      Name  val
0  A-Col 1    1
1  A-Col 2    3
2  B-Col 1    3
3  B-Col 2    4

From your expected output you could use:

tmp["Name"] = tmp["Name"] + "-" + tmp["level_1"].str.replace(" ", "")

for the Name line as this will remove the space to give

     Name  val
0  A-Col1    1
1  A-Col2    3
2  B-Col1    3
3  B-Col2    4

Answered By: Kraigolas

Answer 3

here is one way do it, using melt

df2=df.melt(id_vars='Name')
df2['Name'] = df2['Name'] + '-' + df2['variable']
df2=df2.drop(columns='variable')
df2

    Name    value
0   A-Col 1     1
1   B-Col 1     3
2   A-Col 2     2
3   B-Col 2     4

Answered By: Naveed

How to re-write dataframe with new rows per column?

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