Convert multiple columns into a single column in Python

Question

Here is the dataset I have:

Employee	Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar	Apr	May	Jun	Year	Index	Program	Status	Bid Category	Account
E_123456789	#missing	#missing	#missing	#missing	1	1	1	1	1	1	1	1	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead
E_123456799	#missing	0	0	0	0	0	0	.5	.9	.1	0	0	FY22	I_1255	P12345123	Extended	Cook 3	Utilization

This is what I'd like to have:

Employee	Month	Year	Index	Program	Status	Bid Category	Account	Percentage
E_123456789	Jul	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	#missing
E_123456789	Aug	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	#missing
E_123456789	Sep	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	#missing
E_123456789	Oct	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	#missing
E_123456789	Nov	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	1
E_123456789	Dec	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	1
E_123456789	Jan	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	1
E_123456789	Feb	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	1
E_123456789	Mar	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	1
E_123456789	Apr	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	1
E_123456789	May	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	1
E_123456789	Jun	FY22	I_1234	NA Entity	Final	Janitor 1	Overhead	1
E_123456799	Jul	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	#missing
E_123456799	Aug	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	0
E_123456799	Sep	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	0
E_123456799	Oct	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	0
E_123456799	Nov	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	0
E_123456799	Dec	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	0
E_123456799	Jan	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	0
E_123456799	Feb	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	.5
E_123456799	Mar	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	.9
E_123456799	Apr	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	.1
E_123456799	May	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	0
E_123456799	Jun	FY22	I_1255	P12345123	Extended	Cook 3	Utilization	0

I found steps that seemed to do the reverse of this (convert the "Month" column into Jul/Aug/Sep/etc) but I can't find anything related to this. I am very new to Python (read: a couple weeks of use) and am sure there is a relatively easy way to do this that I just don't know.

Here is my file:

import pandas as pd

data = pd.read_csv('SMART_Export_Test.csv')

Using pivot does the opposite of what I want (it spreads the values in the Jul column into new column names):

data.pivot(columns = 'Jul', values='Jul')

Any help/direction would be greatly appreciated!

Answer 1

Try with melt :

>>> df.melt(["Employee", "Year", "Index", "Program", "Status", "Bid Category", "Account"], 
            ["Jan", "Feb", "Mar", "Apr", "May", "Jun", 
             "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
            "Percentage")

       Employee  Year   Index  ...      Account Percentage     value
0   E_123456789  FY22  I_1234  ...     Overhead        Jan         1
1   E_123456799  FY22  I_1255  ...  Utilization        Jan         0
2   E_123456789  FY22  I_1234  ...     Overhead        Feb       1.0
3   E_123456799  FY22  I_1255  ...  Utilization        Feb       0.5
4   E_123456789  FY22  I_1234  ...     Overhead        Mar       1.0
5   E_123456799  FY22  I_1255  ...  Utilization        Mar       0.9
6   E_123456789  FY22  I_1234  ...     Overhead        Apr       1.0
7   E_123456799  FY22  I_1255  ...  Utilization        Apr       0.1
8   E_123456789  FY22  I_1234  ...     Overhead        May         1
9   E_123456799  FY22  I_1255  ...  Utilization        May         0
10  E_123456789  FY22  I_1234  ...     Overhead        Jun         1
11  E_123456799  FY22  I_1255  ...  Utilization        Jun         0
12  E_123456789  FY22  I_1234  ...     Overhead        Jul  #missing
13  E_123456799  FY22  I_1255  ...  Utilization        Jul  #missing
14  E_123456789  FY22  I_1234  ...     Overhead        Aug  #missing
15  E_123456799  FY22  I_1255  ...  Utilization        Aug         0
16  E_123456789  FY22  I_1234  ...     Overhead        Sep  #missing
17  E_123456799  FY22  I_1255  ...  Utilization        Sep         0
18  E_123456789  FY22  I_1234  ...     Overhead        Oct  #missing
19  E_123456799  FY22  I_1255  ...  Utilization        Oct         0
20  E_123456789  FY22  I_1234  ...     Overhead        Nov         1
21  E_123456799  FY22  I_1255  ...  Utilization        Nov         0
22  E_123456789  FY22  I_1234  ...     Overhead        Dec         1
23  E_123456799  FY22  I_1255  ...  Utilization        Dec         0

[24 rows x 9 columns]