Convert pandas pivot table to regular dataframe
How can I convert a pandas pivot table to a regular framework? For example:
amount
categories A B C
date deposit
2017-01-15 6220140.00 5614354.16 0.00 0.00
2017-01-16 7384354.00 6247300.22 0.00 0.00
2017-01-17 6783939.00 10630021.37 0.00 0.00
2017-01-18 67940.00 4659384.47 0.00 0.00
to a regular time date, for example:
date deposit A B C
0 2017-01-15 6220140.00 5614354.16 0.00 0.00
1 2017-01-16 7384354.00 6247300.22 0.00 0.00
2 2017-01-17 6783939.00 10630021.37 0.00 0.00
3 2017-01-18 67940.00 4659384.47 0.00 0.00
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1 answer
Use droplevel
+ index name
before None
+ reset_index
:
df.columns = df.columns.droplevel(0) #remove amount
df.columns.name = None #remove categories
df = df.reset_index() #index to columns
Alternatively use rename_axis
:
df.columns = df.columns.droplevel(0)
df = df.reset_index().rename_axis(None, axis=1)
EDIT:
Perhaps it will also help to remove []
in the parameter values
- see.
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