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