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Minor docstring updates on binning related plot functions #10831

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Mar 21, 2018
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Minor docstring updates on binning related plot functions #10831

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50 changes: 25 additions & 25 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4191,11 +4191,10 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
of the observations at (x[i],y[i]).

If *C* is specified, it specifies values at the coordinate
(x[i],y[i]). These values are accumulated for each hexagonal
(x[i], y[i]). These values are accumulated for each hexagonal
bin and then reduced according to *reduce_C_function*, which
defaults to numpy's mean function (np.mean). (If *C* is
specified, it must also be a 1-D sequence of the same length
as *x* and *y*.)
defaults to `numpy.mean`. (If *C* is specified, it must also
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two lines earlier: space after comma

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Done.

be a 1-D sequence of the same length as *x* and *y*.)

Parameters
----------
Expand All @@ -4211,7 +4210,7 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
tuple with two elements specifying the number of hexagons
in the *x*-direction and the *y*-direction.

bins : {'log'} or int or sequence, optional, default is *None*
bins : 'log' or int or sequence, optional, default is *None*
If *None*, no binning is applied; the color of each hexagon
directly corresponds to its count value.

Expand Down Expand Up @@ -4287,11 +4286,9 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,

Returns
-------
object
a :class:`~matplotlib.collections.PolyCollection` instance; use
:meth:`~matplotlib.collections.PolyCollection.get_array` on
this :class:`~matplotlib.collections.PolyCollection` to get
the counts in each hexagon.
polycollection
A `.PolyCollection` instance; use `.PolyCollection.get_array` on
this to get the counts in each hexagon.

If *marginals* is *True*, horizontal
bar and vertical bar (both PolyCollections) will be attached
Expand Down Expand Up @@ -6058,11 +6055,11 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
----------
x : (n,) array or sequence of (n,) arrays
Input values, this takes either a single array or a sequence of
arrays which are not required to be of the same length
arrays which are not required to be of the same length.

bins : integer or sequence or 'auto', optional
bins : int or sequence or str, optional
If an integer is given, ``bins + 1`` bin edges are calculated and
returned, consistent with :func:`numpy.histogram`.
returned, consistent with `numpy.histogram`.

If `bins` is a sequence, gives bin edges, including left edge of
first bin and right edge of last bin. In this case, `bins` is
Expand All @@ -6079,9 +6076,12 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,

Unequally spaced bins are supported if *bins* is a sequence.

If Numpy 1.11 is installed, may also be ``'auto'``.
With Numpy 1.11 or newer, you can alternatively provide a string
describing a binning strategy, such as 'auto', 'sturges', 'fd',
'doane', 'scott', 'rice', 'sturges' or 'sqrt', see
`numpy.histogram`.

Default is taken from the rcParam ``hist.bins``.
The default is taken from :rc:`hist.bins`.

range : tuple or None, optional
The lower and upper range of the bins. Lower and upper outliers
Expand All @@ -6094,7 +6094,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,

Default is ``None``

density : boolean, optional
density : bool, optional
If ``True``, the first element of the return tuple will
be the counts normalized to form a probability density, i.e.,
the area (or integral) under the histogram will sum to 1.
Expand All @@ -6118,7 +6118,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,

Default is ``None``

cumulative : boolean, optional
cumulative : bool, optional
If ``True``, then a histogram is computed where each bin gives the
counts in that bin plus all bins for smaller values. The last bin
gives the total number of datapoints. If *normed* or *density*
Expand Down Expand Up @@ -6178,7 +6178,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,

Default is ``None``

log : boolean, optional
log : bool, optional
If ``True``, the histogram axis will be set to a log scale. If
*log* is ``True`` and *x* is a 1D array, empty bins will be
filtered out and only the non-empty ``(n, bins, patches)``
Expand All @@ -6192,14 +6192,14 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,

Default is ``None``

label : string or None, optional
label : str or None, optional
String, or sequence of strings to match multiple datasets. Bar
charts yield multiple patches per dataset, but only the first gets
the label, so that the legend command will work as expected.

default is ``None``

stacked : boolean, optional
stacked : bool, optional
If ``True``, multiple data are stacked on top of each other If
``False`` multiple data are arranged side by side if histtype is
'bar' or on top of each other if histtype is 'step'
Expand Down Expand Up @@ -6553,10 +6553,10 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,

Parameters
----------
x, y: array_like, shape (n, )
x, y : array_like, shape (n, )
Input values

bins: [None | int | [int, int] | array_like | [array, array]]
bins : None or int or [int, int] or array_like or [array, array]
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(int, int) or (array, array)? (feels like tuples, nor arrays)

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This description came from
numpy.histogram2d. I would leave it like this.


The bin specification:

Expand All @@ -6580,7 +6580,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
xmax], [ymin, ymax]]. All values outside of this range will be
considered outliers and not tallied in the histogram.

normed : boolean, optional, default: False
normed : bool, optional, default: False
Normalize histogram.

weights : array_like, shape (n, ), optional, default: None
Expand Down Expand Up @@ -6610,7 +6610,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,

Other Parameters
----------------
cmap : {Colormap, string}, optional
cmap : Colormap or str, optional
A :class:`matplotlib.colors.Colormap` instance. If not set, use rc
settings.

Expand All @@ -6619,7 +6619,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
scale luminance data to ``[0, 1]``. If not set, defaults to
``Normalize()``.

vmin/vmax : {None, scalar}, optional
vmin/vmax : None or scalar, optional
Arguments passed to the `Normalize` instance.

alpha : ``0 <= scalar <= 1`` or ``None``, optional
Expand Down