lreshape and wide_to_long documentation (Closes #33417)
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name: Docs
name: Reshaping
description: Concat, Merge/Join, Stack/Unstack, Explode

Comments:
Out from from: `python scripts/validate_docstrings.py pandas.lreshape`

```
################################################################################
######################### Docstring (pandas.lreshape)  #########################
################################################################################

Reshape wide-format data to long. Generalized inverse of DataFrame.pivot.

Similar to ``pd.melt`` but more user-friendly. 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:: 1.0.0

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

################################################################################
################################## Validation ##################################
################################################################################
```
Output from `python scripts/validate_docstrings.py pandas.wide_to_long`:

```
################################################################################
####################### Docstring (pandas.wide_to_long)  #######################
################################################################################

Wide panel to long format. Less flexible but more user-friendly than melt.

With stubnames ['A', 'B'], this function expects to find one or more
group of columns with format
A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,...
You specify what you want to call this suffix in the resulting long format
with `j` (for example `j='year'`)

Each row of these wide variables are assumed to be uniquely identified by
`i` (can be a single column name or a list of column names)

All remaining variables in the data frame are left intact.

Parameters
----------
df : DataFrame
    The wide-format DataFrame.
stubnames : str or list-like
    The stub name(s). The wide format variables are assumed to
    start with the stub names.
i : str or list-like
    Column(s) to use as id variable(s).
j : str
    The name of the sub-observation variable. What you wish to name your
    suffix in the long format.
sep : str, default ""
    A character indicating the separation of the variable names
    in the wide format, to be stripped from the names in the long format.
    For example, if your column names are A-suffix1, A-suffix2, you
    can strip the hyphen by specifying `sep='-'`.
suffix : str, default '\\d+'
    A regular expression capturing the wanted suffixes. '\\d+' captures
    numeric suffixes. Suffixes with no numbers could be specified with the
    negated character class '\\D+'. You can also further disambiguate
    suffixes, for example, if your wide variables are of the form
    A-one, B-two,.., and you have an unrelated column A-rating, you can
    ignore the last one by specifying `suffix='(!?one|two)'`.

    .. versionchanged:: 0.23.0
        When all suffixes are numeric, they are cast to int64/float64.

Returns
-------
DataFrame
    A DataFrame that contains each stub name as a variable, with new index
    (i, j).

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.
lreshape : Reshape wide-format data to format. Generalized inverse of
    DataFrame.pivot.

Notes
-----
All extra variables are left untouched. This simply uses
`pandas.melt` under the hood, but is hard-coded to "do the right thing"
in a typical case.

Examples
--------
>>> np.random.seed(123)
>>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
...                    "A1980" : {0 : "d", 1 : "e", 2 : "f"},
...                    "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
...                    "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
...                    "X"     : dict(zip(range(3), np.random.randn(3)))
...                   })
>>> df["id"] = df.index
>>> df
  A1970 A1980  B1970  B1980         X  id
0     a     d    2.5    3.2 -1.085631   0
1     b     e    1.2    1.3  0.997345   1
2     c     f    0.7    0.1  0.282978   2
>>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
... # doctest: +NORMALIZE_WHITESPACE
                X  A    B
id year
0  1970 -1.085631  a  2.5
1  1970  0.997345  b  1.2
2  1970  0.282978  c  0.7
0  1980 -1.085631  d  3.2
1  1980  0.997345  e  1.3
2  1980  0.282978  f  0.1

With multiple id columns

>>> df = pd.DataFrame({
...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
...     'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
...     'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
   famid  birth  ht1  ht2
0      1      1  2.8  3.4
1      1      2  2.9  3.8
2      1      3  2.2  2.9
3      2      1  2.0  3.2
4      2      2  1.8  2.8
5      2      3  1.9  2.4
6      3      1  2.2  3.3
7      3      2  2.3  3.4
8      3      3  2.1  2.9
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
>>> l
... # doctest: +NORMALIZE_WHITESPACE
                  ht
famid birth age
1     1     1    2.8
            2    3.4
      2     1    2.9
            2    3.8
      3     1    2.2
            2    2.9
2     1     1    2.0
            2    3.2
      2     1    1.8
            2    2.8
      3     1    1.9
            2    2.4
3     1     1    2.2
            2    3.3
      2     1    2.3
            2    3.4
      3     1    2.1
            2    2.9

Going from long back to wide just takes some creative use of `unstack`

>>> w = l.unstack()
>>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
>>> w.reset_index()
   famid  birth  ht1  ht2
0      1      1  2.8  3.4
1      1      2  2.9  3.8
2      1      3  2.2  2.9
3      2      1  2.0  3.2
4      2      2  1.8  2.8
5      2      3  1.9  2.4
6      3      1  2.2  3.3
7      3      2  2.3  3.4
8      3      3  2.1  2.9

Less wieldy column names are also handled

>>> np.random.seed(0)
>>> df = pd.DataFrame({'A(weekly)-2010': np.random.rand(3),
...                    'A(weekly)-2011': np.random.rand(3),
...                    'B(weekly)-2010': np.random.rand(3),
...                    'B(weekly)-2011': np.random.rand(3),
...                    'X' : np.random.randint(3, size=3)})
>>> df['id'] = df.index
>>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
   A(weekly)-2010  A(weekly)-2011  B(weekly)-2010  B(weekly)-2011  X  id
0        0.548814        0.544883        0.437587        0.383442  0   0
1        0.715189        0.423655        0.891773        0.791725  1   1
2        0.602763        0.645894        0.963663        0.528895  1   2

>>> pd.wide_to_long(df, ['A(weekly)', 'B(weekly)'], i='id',
...                 j='year', sep='-')
... # doctest: +NORMALIZE_WHITESPACE
         X  A(weekly)  B(weekly)
id year
0  2010  0   0.548814   0.437587
1  2010  1   0.715189   0.891773
2  2010  1   0.602763   0.963663
0  2011  0   0.544883   0.383442
1  2011  1   0.423655   0.791725
2  2011  1   0.645894   0.528895

If we have many columns, we could also use a regex to find our
stubnames and pass that list on to wide_to_long

>>> stubnames = sorted(
...     set([match[0] for match in df.columns.str.findall(
...         r'[A-B]\(.*\)').values if match != []])
... )
>>> list(stubnames)
['A(weekly)', 'B(weekly)']

All of the above examples have integers as suffixes. It is possible to
have non-integers as suffixes.

>>> df = pd.DataFrame({
...     'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
...     'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
...     'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
...     'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
   famid  birth  ht_one  ht_two
0      1      1     2.8     3.4
1      1      2     2.9     3.8
2      1      3     2.2     2.9
3      2      1     2.0     3.2
4      2      2     1.8     2.8
5      2      3     1.9     2.4
6      3      1     2.2     3.3
7      3      2     2.3     3.4
8      3      3     2.1     2.9

>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
...                     sep='_', suffix='\w+')
>>> l
... # doctest: +NORMALIZE_WHITESPACE
                  ht
famid birth age
1     1     one  2.8
            two  3.4
      2     one  2.9
            two  3.8
      3     one  2.2
            two  2.9
2     1     one  2.0
            two  3.2
      2     one  1.8
            two  2.8
      3     one  1.9
            two  2.4
3     1     one  2.2
            two  3.3
      2     one  2.3
            two  3.4
      3     one  2.1
            two  2.9

################################################################################
################################## Validation ##################################
################################################################################
```
@tpanza can you merge master and get this passing
@jreback @WillAyd please review, thank you
> should just deprecate lreshape (separate PR). ok on wide_to_long, but even that we should decide if we want to deprecate.

opened #34313 and #34314 for discussion on deprecation. #15003 remains open for api consolidation. pd.melt could potentially replace both methods with some additionally functionality, https://github.com/pandas-dev/pandas/issues/15003#issuecomment-324674882.
@tpanza can you resolve conflicts.
Thanks @tpanza