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I have the following DataFrame built:

 Movies Cost Tickets 0 1158 0.000000 2.000000 1 1158 0.000000 0.000000 2 1158 0.000000 0.000000 3 1158 0.000000 3.000000

I've used stack() to change my configuration to:

 Event 1 0 Movies 1158 1 Cost 0.000000 2 Tickets 2.000000 3 Movies 1158 4 Cost 0.000000 5 Tickets 0.000000 6 Movies 1158 7 Cost 0.000000 8 Tickets 0.000000 9 Movies 1158 10 Cost 0.000000 11 Tickets 3.000000

but this is stacking the data top of each other, I was looking to create a new column with the end goal being:

 Event 1 Event 2 Event 3 Event 4 0 Movies 1158 1158 1158 1158 1 Cost 0.000000 0.000000 0.000000 0.000000 2 Tickets 2.000000 0.000000 0.000000 3.000000

here is my current configuration:

df = pd.DataFrame({ 'Tickets': pd.Series(Tickets), 'Movies': pd.Series(Movies), 'Cost': pd.Series(costs)}) print(df) df.columns.name = '' stackEvent3 = df.stack() stackEvent3 = df.stack().reset_index(level=0, drop=True).reset_index(name='Event1')

Any guidance is greatly appreciated, thank you!

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  • 3
    Would a transposition not work? df.T Commented Feb 21, 2020 at 22:32
  • 1
    I really new to Pandas, care to share an example? Commented Feb 21, 2020 at 22:33
  • please check my answer Commented Feb 21, 2020 at 22:45
  • I looked up transposition and this makes sense. Thank you! Commented Feb 21, 2020 at 22:53

1 Answer 1

Reset to default
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Use DataFrame.transpose with DataFrame.add_prefix:

new_df = df.T.rename(columns=dict(zip(df.index, df.index+1))).add_prefix('Event ') Event 1 Event 2 Event 3 Event 4 Movies 1158.0 1158.0 1158.0 1158.0 Cost 0.0 0.0 0.0 0.0 Tickets 2.0 0.0 0.0 3.0

to set columns name like you want DataFrame.rename building a dict to replace:

df.T.rename(columns=dict(zip(df.index, ['brown', 'purple', 'yellow', 'blue'])))

or 

new_df = df.T new_df.columns = ['brown', 'purple', 'yellow', 'blue']
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5 Comments

What if I wanted to change the Event Name to something more unique? Like brown, purple, yellow and blue. I notice this is adding a number to the columns that are being generated. Btw, thank you for the quick response!
then use new_df.columns = ['brown', 'purple', 'yellow', 'blue'] and drop add_prefix simply you could use : df.T.rename(columns=dict(zip(df.index, ['brown', 'purple', 'yellow', 'blue'])))
You sir have saved me from breaking my head. I am new to Pandas and been searching for half the morning. But I don't know those keywords to search. Again thank you so much for all help!
I am glad to help:) please consider accept the answer!
Sorry, got one more question. Let's say I want to do math by dividing Movies by Tickets and captures its result by adding a new row or column on depending on what's best. Would you recommend doing math before the transpose or after?

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