matplotlib.pyplot.grid#
- matplotlib.pyplot.grid(visible=None,which='major',axis='both',**kwargs)[source]#
Configure the grid lines.
- Parameters:
- visiblebool or None, optional
Whether to show the grid lines. If anykwargs are supplied, itis assumed you want the grid on andvisible will be set to True.
Ifvisible isNone and there are nokwargs, this toggles thevisibility of the lines.
- which{'major', 'minor', 'both'}, optional
The grid lines to apply the changes on.
- axis{'both', 'x', 'y'}, optional
The axis to apply the changes on.
- **kwargs
Line2D
properties Define the line properties of the grid, e.g.:
grid(color='r',linestyle='-',linewidth=2)
Valid keyword arguments are:
Property
Description
a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image
float or None
bool
bool
BboxBase
or Nonebool
Patch or (Path, Transform) or None
CapStyle
or {'butt', 'projecting', 'round'}JoinStyle
or {'miter', 'round', 'bevel'}sequence of floats (on/off ink in points) or (None, None)
(2, N) array or two 1D arrays
{'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'
{'full', 'left', 'right', 'bottom', 'top', 'none'}
color or None
str
bool
object
{'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
float
marker style string,
Path
orMarkerStyle
float
markersize
orms
float
None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]
bool
list of
AbstractPathEffect
float or callable[[Artist, Event], tuple[bool, dict]]
float
bool
(scale: float, length: float, randomness: float)
bool or None
CapStyle
or {'butt', 'projecting', 'round'}JoinStyle
or {'miter', 'round', 'bevel'}unknown
str
bool
1D array
1D array
float
Notes
Note
This is thepyplot wrapper for
axes.Axes.grid
.The axis is drawn as a unit, so the effective zorder for drawing thegrid is determined by the zorder of each axis, not by the zorder of the
Line2D
objects comprising the grid. Therefore, to set grid zorder,useset_axisbelow
or, for more control, call theset_zorder
method of each axis.
Examples usingmatplotlib.pyplot.grid
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SkewT-logP diagram: using transforms and custom projections