[WIP] DOC Fixes #8447 created new example and fixed issues

Author: mabelvj

pandas/plotting/_core.py

@@ -1995,58 +1995,87 @@ def plot_series(data, kind='line', ax=None,                    # Series unique
 
 
 _shared_docs['boxplot'] = """
-    Make a box-and-whisker plot from DataFrame column optionally grouped
-    by some columns or other inputs. The box extends from the Q1 to Q3
-    quartile values of the data, with a line at the median (Q2).
-    The whiskers extend from the edges of box to show the range of the data.
-    Flier points (outliers) are those past the end of the whiskers.
-    The position of the whiskers is set by default to 1.5 IQR (`whis=1.5``)
-    from the edge of the box.
+    Make a box plot from DataFrame columns.
+
+    Make a box-and-whisker plot from DataFrame columns optionally grouped
+    by some other columns. A box plot is a method for graphically depicting
+    groups of numerical data through their quartiles.
+    The box extends from the Q1 to Q3 quartile values of the data,
+    with a line at the median (Q2).The whiskers extend from the edges
+    of box to show the range of the data. The position of the whiskers
+    is set by default to 1.5*IQR (IQR = Q3 - Q1) from the edges of the box.
+    Outlier points are those past the end of the whiskers.
 
     For further details see
-    Wikipedia's entry for `boxplot <https://en.wikipedia.org/wiki/Box_plot/>`_.
+    Wikipedia's entry for `boxplot <https://en.wikipedia.org/wiki/Box_plot>`_.
 
     Parameters
     ----------
-    column : column name or list of names, or vector
+    column : str or list of str, optional
+        Column name or list of names, or vector.
         Can be any valid input to groupby.
-    by : string or sequence
+    by : str or array-like
         Column in the DataFrame to groupby.
-    ax :  Matplotlib axes object, (default `None`)
+    ax : object of class matplotlib.axes.Axes, default `None`
         The matplotlib axes to be used by boxplot.
-    fontsize : int or string
-        The font-size used by matplotlib.
-    rot : label rotation angle
-        The rotation angle of labels.
-    grid : boolean( default `True`)
+    fontsize : float or str
+        Tick label font size in points or as a string (e.g., ‘large’)
+        (see `matplotlib.axes.Axes.tick_params
+        <https://matplotlib.org/api/_as_gen/
+        matplotlib.axes.Axes.tick_params.html>`_).
+    rot : int or float, default 0
+        The rotation angle of labels (in degrees)
+        with respect to the screen coordinate sytem.
+    grid : boolean, default `True`
         Setting this to True will show the grid.
     figsize : A tuple (width, height) in inches
-        The size of the figure to create in inches by default.
-    layout : tuple (optional)
-        Tuple (rows, columns) used for the layout of the plot.
-    return_type : {None, 'axes', 'dict', 'both'}, default None
-        The kind of object to return. The default is ``axes``
-        'axes' returns the matplotlib axes the boxplot is drawn on;
-        'dict' returns a dictionary whose values are the matplotlib
-        Lines of the boxplot;
-        'both' returns a namedtuple with the axes and dict.
-        When grouping with ``by``, a Series mapping columns to ``return_type``
-        is returned, unless ``return_type`` is None, in which case a NumPy
-        array of axes is returned with the same shape as ``layout``.
-        See the prose documentation for more.
-    kwds : Keyword Arguments (optional)
+        The size of the figure to create in matplotlib.
+    layout : tuple (rows, columns) (optional)
+        For example, (3, 5) will display the subplots
+        using 3 columns and 5 rows, starting from the top-left.
+    return_type : {None, 'axes', 'dict', 'both'}, default 'axes'
+        The kind of object to return. The default is ``axes``.
+
+        * 'axes' returns the matplotlib axes the boxplot is drawn on.
+        * 'dict' returns a dictionary whose values are the matplotlib
+          Lines of the boxplot.
+        * 'both' returns a namedtuple with the axes and dict.
+        * when grouping with ``by``, a Series mapping columns to
+          ``return_type`` is returned (i.e.
+          ``df.boxplot(column=['Col1','Col2'], by='var',return_type='axes')``
+          may return ``Series([AxesSubplot(..),AxesSubplot(..)],
+          index=['Col1','Col2'])``).
+
+          If ``return_type`` is `None`, a NumPy array
+          of axes with the same shape as ``layout`` is returned
+          (i.e. ``df.boxplot(column=['Col1','Col2'],
+          by='var',return_type=None)`` may return a
+          ``array([<matplotlib.axes._subplots.AxesSubplot object at ..>,
+          <matplotlib.axes._subplots.AxesSubplot object at ..>],
+          dtype=object)``).
+    **kwds : Keyword Arguments (optional)
         All other plotting keyword arguments to be passed to
-        matplotlib's function.
+        `matplotlib.pyplot.boxplot <https://matplotlib.org/api/_as_gen/
+        matplotlib.pyplot.boxplot.html#matplotlib.pyplot.boxplot>`_.
 
     Returns
     -------
-    lines : dict
-    ax : matplotlib Axes
-        (ax, lines): namedtuple
+    result:
+        Options:
+
+        * ax : object of class
+          matplotlib.axes.Axes (for ``return_type='axes'``)
+        * lines : dict (for ``return_type='dict'``)
+        * (ax, lines): namedtuple (for ``return_type='both'``)
+        * :class:`~pandas.Series` (for ``return_type != None``
+          and data grouped with ``by``)
+        * :class:`~numpy.array` (for ``return_type=None``
+          and data grouped with ``by``)
 
     See Also
     --------
     matplotlib.pyplot.boxplot: Make a box and whisker plot.
+    matplotlib.pyplot.hist: Make a hsitogram.
 
     Notes
     -----
@@ -2056,72 +2085,57 @@ def plot_series(data, kind='line', ax=None,                    # Series unique
 
     Examples
     --------
+
+    Boxplots can be created for every column in the dataframe
+    by ``df.boxplot()`` or indicating the columns to be used:
+
     .. plot::
         :context: close-figs
 
         >>> np.random.seed(1234)
+        >>> df = pd.DataFrame(np.random.rand(10,4),
+        ...                   columns=['Col1', 'Col2', 'Col3', 'Col4'])
+        >>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3'])
 
-        >>> df = pd.DataFrame({
-        ...     u'stratifying_var': np.random.uniform(0, 100, 20),
-        ...     u'price': np.random.normal(100, 5, 20),
-        ...     u'demand': np.random.normal(100, 10, 20)})
-
-        >>> df[u'quartiles'] = pd.qcut(
-        ...     df[u'stratifying_var'], 4,
-        ...     labels=[u'0-25%%', u'25-50%%', u'50-75%%', u'75-100%%'])
-
-        >>> df
-            stratifying_var       price      demand quartiles
-        0         19.151945  106.605791  108.416747     0-25%%
-        1         62.210877   92.265472  123.909605    50-75%%
-        2         43.772774   98.986768  100.761996    25-50%%
-        3         78.535858   96.720153   94.335541   75-100%%
-        4         77.997581  100.967107  100.361419    50-75%%
-        5         27.259261  102.767195   79.250224     0-25%%
-        6         27.646426  106.590758  102.477922     0-25%%
-        7         80.187218   97.653474   91.028432   75-100%%
-        8         95.813935  103.377770   98.632052   75-100%%
-        9         87.593263   90.914864  100.182892   75-100%%
-        10        35.781727   99.084457  107.554140     0-25%%
-        11        50.099513  105.294846  102.152686    25-50%%
-        12        68.346294   98.010799  108.410088    50-75%%
-        13        71.270203  101.687188   85.541899    50-75%%
-        14        37.025075  105.237893   85.980267    25-50%%
-        15        56.119619  105.229691   98.990818    25-50%%
-        16        50.308317  104.318586   94.517576    25-50%%
-        17         1.376845   99.389542   98.553805     0-25%%
-        18        77.282662  100.623565  103.540203    50-75%%
-        19        88.264119   98.386026   99.644870   75-100%%
-
-    To plot the boxplot of the ``demand`` just put:
+    Boxplots of variables distributions grouped by a third variable values
+    can be created using the option ``by``. For instance:
 
     .. plot::
         :context: close-figs
 
-        >>> boxplot = df.boxplot(column=u'demand', by=u'quartiles')
+        >>> df = pd.DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'] )
+        >>> df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])
+        >>> boxplot = df.boxplot(by='X')
 
-    Use ``grid=False`` to hide the grid:
+    A list of strings (i.e. ``['X','Y']``) containing can be passed to boxplot
+    in order to group the data by combination of the variables in the x-axis:
 
     .. plot::
         :context: close-figs
 
-        >>> boxplot = df.boxplot(column=u'demand', by=u'quartiles', grid=False)
+        >>> df = pd.DataFrame(np.random.rand(10,3),
+        ...                   columns=['Col1', 'Col2', 'Col3'])
+        >>> df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])
+        >>> df['Y'] = pd.Series(['A','B','A','B','A','B','A','B','A','B'])
+        >>> boxplot = df.boxplot(column=['Col1','Col2'], by=['X','Y'])
 
-    Optionally, the layout can be changed by setting ``layout=(rows, cols)``:
+    The layout of boxplot can be adjusted giving a tuple to ``layout``:
 
     .. plot::
         :context: close-figs
 
-        >>> boxplot = df.boxplot(column=[u'price',u'demand'],
-        ...                      by=u'quartiles', layout=(1,2),
-        ...                      figsize=(8,5))
+        >>> df = pd.DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'])
+        >>> df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])
+        >>> boxplot = df.boxplot(by='X', layout=(2,1))
+
+    Additional formatting can be done to the boxplot, like suppressing the grid
+    (``grid=False``), rotating the labels in the x-axis (i.e. ``rot=45``)
+    or changing the fontsize (i.e. ``fontsize=15``):
 
     .. plot::
         :context: close-figs
 
-        >>> boxplot = df.boxplot(column=[u'price',u'demand'],
-        ...                      by=u'quartiles', layout=(2,1),
-        ...                      figsize=(5,8))
+        >>> boxplot = df.boxplot(grid=False, rot=45, fontsize=15)
     """
 
 @Appender(_shared_docs['boxplot'] % _shared_doc_kwargs)