DOC: Add missing :ref: to a link in a docstring

[jnclt]

• closes DOC: fix incorrect link in DataFrame.drop docstring noatamir/pyladies-berlin-sprints#18
• Tests added and passed if fixing a bug or adding a new feature
• All code checks passed.
• Added type annotations to new arguments/methods/functions.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.

Docstring fix.
Validation outcome:

>>> python scripts/validate_docstrings.py pandas.DataFrame.drop
Matplotlib created a temporary config/cache directory at /tmp/matplotlib-e6oti6qf because the default path (/home/gitpod/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.

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###################### Docstring (pandas.DataFrame.drop)  ######################
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Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding
axis, or by specifying directly index or column names. When using a
multi-index, labels on different levels can be removed by specifying
the level. See the :ref:`user guide <advanced.shown_levels>`
for more information about the now unused levels.

Parameters
----------
labels : single label or list-like
    Index or column labels to drop. A tuple will be used as a single
    label and not treated as a list-like.
axis : {0 or 'index', 1 or 'columns'}, default 0
    Whether to drop labels from the index (0 or 'index') or
    columns (1 or 'columns').
index : single label or list-like
    Alternative to specifying axis (``labels, axis=0``
    is equivalent to ``index=labels``).
columns : single label or list-like
    Alternative to specifying axis (``labels, axis=1``
    is equivalent to ``columns=labels``).
level : int or level name, optional
    For MultiIndex, level from which the labels will be removed.
inplace : bool, default False
    If False, return a copy. Otherwise, do operation
    inplace and return None.
errors : {'ignore', 'raise'}, default 'raise'
    If 'ignore', suppress error and only existing labels are
    dropped.

Returns
-------
DataFrame or None
    DataFrame without the removed index or column labels or
    None if ``inplace=True``.

Raises
------
KeyError
    If any of the labels is not found in the selected axis.

See Also
--------
DataFrame.loc : Label-location based indexer for selection by label.
DataFrame.dropna : Return DataFrame with labels on given axis omitted
    where (all or any) data are missing.
DataFrame.drop_duplicates : Return DataFrame with duplicate rows
    removed, optionally only considering certain columns.
Series.drop : Return Series with specified index labels removed.

Examples
--------
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
...                   columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2

Drop a specific index combination from the MultiIndex
DataFrame, i.e., drop the combination ``'falcon'`` and
``'weight'``, which deletes only the corresponding row

>>> df.drop(index=('falcon', 'weight'))
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        length  0.3     0.2

>>> df.drop(index='cow', columns='small')
                big
lama    speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3

>>> df.drop(index='length', level=1)
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8

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################################## Validation ##################################
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Docstring for "pandas.DataFrame.drop" correct. :)

[jorisvandenbossche]

Thanks!