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2 changes: 1 addition & 1 deletion doc/source/user_guide/reshaping.rst
Original file line number Diff line number Diff line change
Expand Up @@ -321,7 +321,7 @@ The missing value can be filled with a specific value with the ``fill_value`` ar
.. image:: ../_static/reshaping_melt.png

The top-level :func:`~pandas.melt` function and the corresponding :meth:`DataFrame.melt`
are useful to massage a :class:`DataFrame` into a format where one or more columns
are useful to reshape a :class:`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". The names of those columns can be customized
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4 changes: 2 additions & 2 deletions pandas/core/reshape/melt.py
Original file line number Diff line number Diff line change
Expand Up @@ -51,9 +51,9 @@ def 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
This function is useful to reshape 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
columns are considered measured variables (`value_vars`), and are "unpivoted" to
the row axis, leaving just two non-identifier columns, 'variable' and
'value'.

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