xorbits.pandas.lreshape#

xorbits.pandas.lreshape(data: DataFrame, groups, dropna: bool = True, label=None) → DataFrame[source]#

Reshape wide-format data to long. Generalized inverse of DataFrame.pivot.

Accepts a dictionary, groups, in which each key is a new column name and each value is a list of old column names that will be “melted” under the new column name as part of the reshape.

Parameters

data (DataFrame) – The wide-format DataFrame.
groups (dict) – {new_name : list_of_columns}.
dropna (bool, default True) – Do not include columns whose entries are all NaN.
label (None) –
Not used.

Deprecated since version 1.0.0.

Returns

Reshaped DataFrame.

Return type

DataFrame

See also

melt: Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
pivot: Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.pivot: Pivot without aggregation that can handle non-numeric data.
DataFrame.pivot_table: Generalization of pivot that can handle duplicate values for one index/column pair.
DataFrame.unstack: Pivot based on the index values instead of a column.
wide_to_long: Wide panel to long format. Less flexible but more user-friendly than melt.

Examples

>>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],  
...                      'team': ['Red Sox', 'Yankees'],
...                      'year1': [2007, 2007], 'year2': [2008, 2008]})
>>> data  
   hr1  hr2     team  year1  year2
0  514  545  Red Sox   2007   2008
1  573  526  Yankees   2007   2008

>>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})  
      team  year   hr
0  Red Sox  2007  514
1  Yankees  2007  573
2  Red Sox  2008  545
3  Yankees  2008  526

This docstring was copied from pandas.