Usage of DataFrame.melt()¶

unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)
Description from pandas docs

In [2]:

import pandas as pd
import numpy as np

In [6]:

data={
    'year':[2000,2001,2002],
    'KPMG':[105,110,120],
    'PwC':[80,85,100],
    'Deloitte':[80,85,90]
}
df=pd.DataFrame(data)
df

Out[6]:

	year	KPMG	PwC	Deloitte
0	2000	105	80	80
1	2001	110	85	85
2	2002	120	100	90

Turn the data to auditor-year data¶

In [9]:

pd.melt(df, id_vars= 'year', var_name='auditor_name')

Out[9]: