pandas.melt#
- pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)[source]#
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 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’.
- Parameters
- id_varstuple, list, or ndarray, optional
Column(s) to use as identifier variables.
- value_varstuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
- var_namescalar
Name to use for the ‘variable’ column. If None it uses
frame.columns.name
or ‘variable’.- value_namescalar, default ‘value’
Name to use for the ‘value’ column.
- col_levelint or str, optional
If columns are a MultiIndex then use this level to melt.
- ignore_indexbool, default True
If True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary.
New in version 1.1.0.
- Returns
- DataFrame
Unpivoted DataFrame.
See also
DataFrame.melt
Identical method.
pivot_table
Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.pivot
Return reshaped DataFrame organized by given index / column values.
DataFrame.explode
Explode a DataFrame from list-like columns to long format.
Notes
Reference the user guide for more examples.
Examples
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'}, ... '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
>>> pd.melt(df, id_vars=['A'], value_vars=['B']) A variable value 0 a B 1 1 b B 3 2 c B 5
>>> pd.melt(df, 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:
>>> pd.melt(df, 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:
>>> pd.melt(df, 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:
>>> df.columns = [list('ABC'), list('DEF')] >>> df A B C D E F 0 a 1 2 1 b 3 4 2 c 5 6
>>> pd.melt(df, col_level=0, id_vars=['A'], value_vars=['B']) A variable value 0 a B 1 1 b B 3 2 c B 5
>>> pd.melt(df, 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 5