pandas.melt — pandas 3.0.0.dev0+2095.g2e141aaf99 documentation (original) (raw)

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 reshape a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns are considered measured variables (value_vars), and are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

Parameters:

frameDataFrame

The DataFrame to unpivot.

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, tuple, list, or ndarray, optional

Name to use for the ‘variable’ column. If None it usesframe.columns.name or ‘variable’. Must be a scalar if columns are a MultiIndex.

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, the original index is retained. Index labels will be repeated as necessary.

Returns:

DataFrame

Unpivoted DataFrame.

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