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Pandas: Data Manipulation - melt() function

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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