pandas.lreshape

pandas.lreshape(data, groups, dropna=True)[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.

Returns:

DataFrame

Reshaped 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