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:

dataDataFrame: The wide-format DataFrame.
groupsdict: {new_name : list_of_columns}.
dropnabool, 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