pandas.DataFrame.melt — pandas 3.0.0rc0+52.gb43b95d2b4 documentation (original) (raw)
DataFrame.melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)[source]#
Unpivot 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_varsscalar, tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
value_varsscalar, tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_namescalar, default None
Name to use for the ‘variable’ column. If None it usesframe.columns.name or ‘variable’.
value_namescalar, default ‘value’
Name to use for the ‘value’ column, can’t be an existing column label.
col_levelscalar, optional
If columns are a MultiIndex then use this level to melt.
ignore_indexbool, default True
If True, original index is ignored. If False, original index is retained. Index labels will be repeated as necessary.
Returns:
DataFrame
Unpivoted DataFrame.
See also
Identical method.
Create a spreadsheet-style pivot table as a DataFrame.
Return reshaped DataFrame organized by given index / column values.
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
df.melt(id_vars=["A"], value_vars=["B"]) A variable value 0 a B 1 1 b B 3 2 c B 5
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:
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:
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:
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
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 5
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 5