melt(self, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level: 'Level | None' = None, ignore_index: 'bool' = True) -> 'DataFrame'
This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (:None:None:`id_vars`), while all other columns, considered measured variables (:None:None:`value_vars`), are "unpivoted" to the row axis, leaving just two non-identifier columns, 'variable' and 'value'.
Reference the user guide <reshaping.melt>
for more examples.
Column(s) to use as identifier variables.
Column(s) to unpivot. If not specified, uses all columns that are not set as :None:None:`id_vars`.
Name to use for the 'variable' column. If None it uses frame.columns.name
or 'variable'.
Name to use for the 'value' column.
If columns are a MultiIndex then use this level to melt.
If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.
Unpivoted DataFrame.
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
DataFrame.explode
Explode a DataFrame from list-like columns to long format.
DataFrame.pivot
Return reshaped DataFrame organized by given index / column values.
melt
Identical method.
pivot_table
Create a spreadsheet-style pivot table as a DataFrame.
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},This example is valid syntax, but we were not able to check execution
... 'B': {0: 1, 1: 3, 2: 5},
... 'C': {0: 2, 1: 4, 2: 6}})
... df A B C 0 a 1 2 1 b 3 4 2 c 5 6
>>> df.melt(id_vars=['A'], value_vars=['B']) A variable value 0 a B 1 1 b B 3 2 c B 5This example is valid syntax, but we were not able to check execution
>>> df.melt(id_vars=['A'], value_vars=['B', 'C']) A variable value 0 a B 1 1 b B 3 2 c B 5 3 a C 2 4 b C 4 5 c C 6
The names of 'variable' and 'value' columns can be customized:
This example is valid syntax, but we were not able to check execution>>> df.melt(id_vars=['A'], value_vars=['B'],
... var_name='myVarname', value_name='myValname') A myVarname myValname 0 a B 1 1 b B 3 2 c B 5
Original index values can be kept around:
This example is valid syntax, but we were not able to check execution>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False) A variable value 0 a B 1 1 b B 3 2 c B 5 0 a C 2 1 b C 4 2 c C 6
If you have multi-index columns:
This example is valid syntax, but we were not able to check execution>>> df.columns = [list('ABC'), list('DEF')]This example is valid syntax, but we were not able to check execution
... df A B C D E F 0 a 1 2 1 b 3 4 2 c 5 6
>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B']) A variable value 0 a B 1 1 b B 3 2 c B 5This example is valid syntax, but we were not able to check execution
>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')]) (A, D) variable_0 variable_1 value 0 a B E 1 1 b B E 3 2 c B E 5See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.frame.DataFrame.explode
pandas.core.reshape.pivot.pivot_table
pandas.core.frame.DataFrame.pivot_table
pandas.core.reshape.melt.melt
pandas.core.series.Series.explode
pandas.core.frame.DataFrame.melt
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