To make analysis of data in table easier, we can reshape the data into a more computer-friendly form using Pandas in Python. Pandas.melt() is one of the function to do so..
Pandas.melt() unpivots a DataFrame from wide format to long format.
melt() function is useful to massage a DataFrame into a format where one or more columns are identifier variables, while all other columns, considered measured variables, 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)
Parameters:
frame : DataFrame
id_vars[tuple, list, or ndarray, optional] : Column(s) to use as identifier variables.
value_vars[tuple, list, or ndarray, optional]: Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_name[scalar]: Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
value_name[scalar, default ‘value’]: Name to use for the ‘value’ column.
col_level[int or string, optional]: If columns are a MultiIndex then use this level to melt.
Example:
Python3
# Create a simple dataframe # importing pandas as pdimport pandas as pd # creating a dataframedf = pd.DataFrame({'Name': {0: 'John', 1: 'Bob', 2: 'Shiela'}, 'Course': {0: 'Masters', 1: 'Graduate', 2: 'Graduate'}, 'Age': {0: 27, 1: 23, 2: 21}})df |
Python3
# Name is id_vars and Course is value_varspd.melt(df, id_vars =['Name'], value_vars =['Course']) |
Python3
# multiple unpivot columnspd.melt(df, id_vars =['Name'], value_vars =['Course', 'Age']) |
Python3
# Names of ‘variable’ and ‘value’ columns can be customizedpd.melt(df, id_vars =['Name'], value_vars =['Course'], var_name ='ChangedVarname', value_name ='ChangedValname') |
