cudf.melt#
- cudf.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index: bool = True)[source]#
Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set.
- Parameters:
- frameDataFrame
- id_varstuple, list, or ndarray, optional
Column(s) to use as identifier variables. default: None
- value_varstuple, list, or ndarray, optional
Column(s) to unpivot. default: all columns that are not set as id_vars.
- var_namescalar
Name to use for the variable column. default: frame.columns.name or ‘variable’
- value_namestr
Name to use for the value column. default: ‘value’
- Returns:
- outDataFrame
Melted result
- Difference from pandas:
Does not support ‘col_level’ because cuDF does not have multi-index
Examples
>>> import cudf >>> df = cudf.DataFrame({'A': ['a', 'b', 'c'], ... 'B': [1, 3, 5], ... 'C': [2, 4, 6]}) >>> df A B C 0 a 1 2 1 b 3 4 2 c 5 6 >>> cudf.melt(df, id_vars=['A'], value_vars=['B']) A variable value 0 a B 1 1 b B 3 2 c B 5 >>> cudf.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:
>>> cudf.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