How to use column containing values as dataframe column headers

Question

I have a df:

 Field Value Name Fred Age 69 Gender M

I want to use the values in 'Field' as the new dataframe column headers to have a df shaped like so:

Name Age Gender Fred 69 M

Right now I call

df.pivot(columns='Field', values='Value')

but that leaves me with a dataframe of multiple rows containing all NaN values except for one value for that specific row (such as Name).

this example is so small it's hard to know how the reshape is actually happening. For example, df.set_index('Field').T fits the bar for this example. can you include a couple more rows/columns so we understand what's actually happening in the reshape? I assume you don't just have a single 1:1 mapping between Field and Value? — Michael Delgado
– Michael Delgado, Commented Jun 28, 2022 at 16:35

paime · Accepted Answer · 2022-06-28 16:55:46Z

You can use set_index and transposition (.T):

In [4]: import pandas as pd ...: ...: df = pd.DataFrame( ...: { ...: "Field": ("Name", "Age", "Gender"), ...: "Value": ("Fred", 69, "M"), ...: } ...: ) In [5]: df Out[5]: Field Value 0 Name Fred 1 Age 69 2 Gender M In [6]: df.set_index("Field") Out[6]: Value Field Name Fred Age 69 Gender M In [7]: df.set_index("Field").T Out[7]: Field Name Age Gender Value Fred 69 M

To get rid of the index column do:

In [8]: df.set_index("Field").T.reset_index(drop=True).rename_axis(columns="") Out[8]: Name Age Gender 0 Fred 69 M

This will set Field/Value as the index column, but I don't want those in the final df... is there a way around this?

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