How to restructure in a specific way a pandas dataframe?

Question

I have the following df:

              Year           b      c     Monthly Flow (2018)    First thing  Second thing Third thing  
1             2018   -0.041619  43.91               -0.041619           2000          1000           6
2             2018    0.011913  43.91               -0.041619           4000          120            8
3             2018   -0.048801  43.91               -0.041619           2000          1000           6
4             2018    0.002857  43.91               -0.041619           2000          1000           6

And I would like restructure it in order to achieve this output:

              Year           b      c     Monthly Flow (2018)    Process name    Value  
1             2018   -0.041619  43.91               -0.041619     First thing     2000
1             2018    0.011913  43.91               -0.041619    Second thing     1000
1             2018   -0.048801  43.91               -0.041619     Third thing        6
2             2018   -0.041619  43.91               -0.041619     First thing     4000
2             2018    0.011913  43.91               -0.041619    Second thing      120
2             2018   -0.048801  43.91               -0.041619     Third thing        8 
...

I tried to pivot but I can't get this output.

Mayank Porwal · Accepted Answer · 2020-06-01 18:26:12Z

You can use df.melt:

df.melt(id_vars=['Year','b','c','Monthly Flow (2018)'], var_name='Process name',value_name='Value')

Output:

    Year     b     c  Monthly_Flow_(2018)  Process name  Value
0   2018 -0.04 43.91                -0.04   First_thing   2000
1   2018  0.01 43.91                -0.04   First_thing   4000
2   2018 -0.05 43.91                -0.04   First_thing   2000
3   2018  0.00 43.91                -0.04   First_thing   2000
4   2018 -0.04 43.91                -0.04  Second_thing   1000
5   2018  0.01 43.91                -0.04  Second_thing    120
6   2018 -0.05 43.91                -0.04  Second_thing   1000
7   2018  0.00 43.91                -0.04  Second_thing   1000
8   2018 -0.04 43.91                -0.04   Third_thing      6
9   2018  0.01 43.91                -0.04   Third_thing      8
10  2018 -0.05 43.91                -0.04   Third_thing      6
11  2018  0.00 43.91                -0.04   Third_thing      6

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