Turn certain columns to key-value rows ( and the reverse )

pandas_melt.py

"""
Turn this

location    name    Jan-2010    Feb-2010    March-2010
A           "test"  12          20          30
B           "foo"   18          20          25

into this

location    name    Date        Value
A           "test"  Jan-2010    12       
A           "test"  Feb-2010    20
A           "test"  March-2010  30
B           "foo"   Jan-2010    18       
B           "foo"   Feb-2010    20
B           "foo"   March-2010  25

Reference: https://stackoverflow.com/questions/28654047/pandas-convert-some-columns-into-rows
"""

##########  Using pandas.melt
import pandas as pd

df = pd.DataFrame({"location": ["A", "B"], "name": ["test", "foo"], 
                   "Jan-2010": [12, 18], "Feb-2010": [20, 20], "Mar-2010": [30, 25]})
df2 = pd.melt(df, id_vars=["location", "name"], var_name="Date", value_name="Value")
df2 = df2.sort_values(["location", "name"])

##############
# TODO: how to do the reverse process

The reverse process: pandas.DataFrame.pivot

From the doc string of DataFrame.pivot:

Reshape data (produce a "pivot" table) based on column values. Uses
unique values from index / columns to form axes of the resulting
DataFrame.

So the code is like this:

df3 = df2.pivot(index='location', columns='Date', values='Value')
df3 = df3.reset_index()
df3['name'] = ['test', 'foo']
df3

Not so good?