I had a dataframe that looked like this:

To combine stu1 and stu2 into a single column with each name on a new row (and duplicating id for each name) I used this:

stu_df = stu_df.set_index('id').stack().reset_index(level=1, drop=True).to_frame('stu').reset_index()

Which gave this result:

The original dataframe has been updated to include dates and looks like this:

I want to combine stu1 and stu2 in the same way (duplicating the date and id for each name) but can't get it to work.

The desired outcome is this:

I'm new to Pandas and am struggling. Can anyone help? Thanks

CodePudding user response：

I would recommend using panda's melt function as follows :

import pandas as pd

# a sample of your dataframe
df = pd.DataFrame({
    'date' : ['03/06/2022', '03/06/2022', '03/06/2022'],
    'id' : [11,22,33], 
    'stu1' : ['James', 'Bob', 'Ryan' ], 
    'stu2' : ['James', 'Rob', 'Tom' ]})

# melt the dataframe
df_melted = df.melt(id_vars=['date', 'id'], value_vars= ['stu1', 'stu2'])

print(df_melted)

>>>
       date     id  variable    value
0   03/06/2022  11  stu1        James
1   03/06/2022  22  stu1         Bob
2   03/06/2022  33  stu1         Ryan
3   03/06/2022  11  stu2        James
4   03/06/2022  22  stu2         Rob
5   03/06/2022  33  stu2         Tom

If you do not care at all about the column variable that helps identify the origin of the name (from which column it came) then you can simply drop it.

You can also customize the name of the output variables (instead of variable and value). Here's an example to get your exact desired output :

df.melt(id_vars=['date', 'id'], value_vars= ['stu1', 'stu2'], value_name='stu').drop(['variable'], axis = 1)

Take a look at the original documentation pandas.melt for more info!