Summary
The **kwargs are not typically listed in the function, but instead the final destination of the **kwargs is mentioned. For example:
**kwargs
Instructions on how to decorate your plots.
The keyword arguments are passed to `matplotlib.axes.Axes.plot()`
- If there are multiple possible targets, they are all listed (see below)
- If you happen to use some automation tool to interpolate and link your documentation, then you might list the possible keyword arguments in
**kwargsfor the convenience of the end users. This kind of approach is used in matplotlib, for example. (see below)
How and when document **kwargs (Numpydoc)
1) When to use **kwargs?
First thing to note here is that **kwargs should be used to pass arguments to underlying functions and methods. If the argument inside **kwargs would be used in the function (and not passed down), it should be written out as normal keyword argument, instead.
2) Where to put **kwargs decription?
The location of **kwargs description is in the Parameters section. Sometimes it is appropriate to list them in the Other Parameters section, but remember: Other Parameters should only be used if a function has a large number of keyword parameters, to prevent cluttering the Parameters section.
matplotlib.axes.Axes.gridhas**kwargsinParameterssection.matplotlib.axes.Axes.plothas**kwargsinOther Parameterssection (reasoning probably to large number of keyword arguments).
3) Syntax for **kwargs decription
The syntax for the description for the **kwargs is, following Numpydoc styleguide
Parameters
----------
... (other lines)
**kwargs : sometype
Some description on what the kwargs are
used for.
or
Parameters
----------
... (other lines)
**kwargs
Some description on what the kwargs are
used for.
The one describing the type is more appropriate, as [source].
For the parameter types, be as precise as possible
One exception for this is for example when the **kwargs could be passed to one of many functions based on other parameter values, as in seaborn.kdeplot. Then, the line for the type would become too long for describing all the types and it would be cleaner to use a bullet point list, which also describes the conditions on when the **kwargs are forwarded to where. Eg.:
Parameters
----------
fill: bool or None
If True, fill in the area under univariate density curves or between
bivariate contours. If None, the default depends on multiple.
**kwargs
Other keyword arguments are passed to one of the following matplotlib
functions:
* matplotlib.axes.Axes.plot() (univariate, fill=False),
* matplotlib.axes.Axes.fill_between() (univariate, fill=True),
* matplotlib.axes.Axes.contour() (bivariate, fill=False),
* matplotlib.axes.contourf() (bivariate, fill=True).
You may also add listing of the valid keyword arguments in **kwargs like in matplotlib.axes.Axes.grid. Here is the interpolated python doc/text version:
Parameters
----------
... (other lines)
**kwargs : `.Line2D` properties
Define the line properties of the grid, e.g.::
grid(color='r', linestyle='-', linewidth=2)
Valid keyword arguments are:
Properties:
agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha: float or None
animated: bool
antialiased or aa: bool
clip_box: `.Bbox`
clip_on: bool
clip_path: Patch or (Path, Transform) or None
color or c: color
contains: unknown
dash_capstyle: {'butt', 'round', 'projecting'}
dash_joinstyle: {'miter', '
... (more lines)
This is convenient for the user, but challenging for the developer. In matplotlib this kind of luxury is made possible with the automatization using some special documentation decorators and linking1. Manual writing of allowed kwargs will surely become a code maintenance nightmare.
4) Notes related to **kwargs / Extended help
Some additional info about the **kwargs could be included in the Notes section.
For example matplotlib.axes.Axes.plot discusses marker styles, line styles and colors in the Notes section. [2]
[1] They use a @docstring.dedent_interpd decorator which pulls the meaning of the kwargs to the final docs. So that is happening in place of %(Line2D:kwdoc)s, for example.
[2] See: help(ax.plot) where ax is instance of matplotlib.axes.Axes.
Summary
The **kwargs are not typically listed in the function, but instead the final destination of the **kwargs is mentioned. For example:
**kwargs
Instructions on how to decorate your plots.
The keyword arguments are passed to `matplotlib.axes.Axes.plot()`
- If there are multiple possible targets, they are all listed (see below)
- If you happen to use some automation tool to interpolate and link your documentation, then you might list the possible keyword arguments in
**kwargsfor the convenience of the end users. This kind of approach is used in matplotlib, for example. (see below)
How and when document **kwargs (Numpydoc)
1) When to use **kwargs?
First thing to note here is that **kwargs should be used to pass arguments to underlying functions and methods. If the argument inside **kwargs would be used in the function (and not passed down), it should be written out as normal keyword argument, instead.
2) Where to put **kwargs decription?
The location of **kwargs description is in the Parameters section. Sometimes it is appropriate to list them in the Other Parameters section, but remember: Other Parameters should only be used if a function has a large number of keyword parameters, to prevent cluttering the Parameters section.
matplotlib.axes.Axes.gridhas**kwargsinParameterssection.matplotlib.axes.Axes.plothas**kwargsinOther Parameterssection (reasoning probably to large number of keyword arguments).
3) Syntax for **kwargs decription
The syntax for the description for the **kwargs is, following Numpydoc styleguide
Parameters
----------
... (other lines)
**kwargs : sometype
Some description on what the kwargs are
used for.
or
Parameters
----------
... (other lines)
**kwargs
Some description on what the kwargs are
used for.
The one describing the type is more appropriate, as [source].
For the parameter types, be as precise as possible
One exception for this is for example when the **kwargs could be passed to one of many functions based on other parameter values, as in seaborn.kdeplot. Then, the line for the type would become too long for describing all the types and it would be cleaner to use a bullet point list, which also describes the conditions on when the **kwargs are forwarded to where. Eg.:
Parameters
----------
fill: bool or None
If True, fill in the area under univariate density curves or between
bivariate contours. If None, the default depends on multiple.
**kwargs
Other keyword arguments are passed to one of the following matplotlib
functions:
* matplotlib.axes.Axes.plot() (univariate, fill=False),
* matplotlib.axes.Axes.fill_between() (univariate, fill=True),
* matplotlib.axes.Axes.contour() (bivariate, fill=False),
* matplotlib.axes.contourf() (bivariate, fill=True).
You may also add listing of the valid keyword arguments in **kwargs like in matplotlib.axes.Axes.grid. Here is the interpolated python doc/text version:
Parameters
----------
... (other lines)
**kwargs : `.Line2D` properties
Define the line properties of the grid, e.g.::
grid(color='r', linestyle='-', linewidth=2)
Valid keyword arguments are:
Properties:
agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha: float or None
animated: bool
antialiased or aa: bool
clip_box: `.Bbox`
clip_on: bool
clip_path: Patch or (Path, Transform) or None
color or c: color
contains: unknown
dash_capstyle: {'butt', 'round', 'projecting'}
dash_joinstyle: {'miter', '
... (more lines)
This is convenient for the user, but challenging for the developer. In matplotlib this kind of luxury is made possible with the automatization using some special documentation decorators and linking1. Manual writing of allowed kwargs will surely become a code maintenance nightmare.
4) Notes related to **kwargs / Extended help
Some additional info about the **kwargs could be included in the Notes section.
For example matplotlib.axes.Axes.plot discusses marker styles, line styles and colors in the Notes section. [2]
[1] They use a @docstring.dedent_interpd decorator which pulls the meaning of the kwargs to the final docs. So that is happening in place of %(Line2D:kwdoc)s, for example.
[2] See: help(ax.plot) where ax is instance of matplotlib.axes.Axes.
Usually kwargs that need to be described in the Parameters section would typically be handled like other named arguments and the **kwargs is left unexpanded. However, the numpy style guide also has an Other Parameters section than can be used for providing descriptions of kwargs without cluttering the Parameters section. The style guide describes it as:
An optional section used to describe infrequently used parameters. It should only be used if a function has a large number of keyword parameters, to prevent cluttering the Parameters section.
The numpydoc repo gives this example:
"""
Other Parameters
----------------
only_seldom_used_keyword : int, optional
Infrequently used parameters can be described under this optional
section to prevent cluttering the Parameters section.
**kwargs : dict
Other infrequently used keyword arguments. Note that all keyword
arguments appearing after the first parameter specified under the
Other Parameters section, should also be described under this
section.
"""
So, the additional kwargs could be added as
"""
Other Parameters
----------------
first_kwarg: int
This is an integer
second_kwarg: str
This is a string
**kwargs : dict
Other infrequently used keyword arguments.
"""