Pandas: Data Manipulation - melt() function

Last update on August 19 2022 21:50:33 (UTC/GMT +8 hours)

melt() function

Unpivot a DataFrame from wide format to long format, optionally leaving identifier variables 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)

Parameters:

Name	Description	Type	Required / Optional
frame	DataFrame		Required
id_vars	Column(s) to use as identifier variables.	tuple, list, or ndarray	Optional
value_vars	Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.	tuple, list, or ndarray	Optional
var_name	Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.	scalar	Required
value_name	Name to use for the ‘value’ column.	scalar, default ‘value’	Required
col_level	If columns are a MultiIndex then use this level to melt.	int or string	Optional

Returns: Unpivoted DataFrame.

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