Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.
matplotlib.axes.Axes.scatter() Function
The Axes.scatter() function in axes module of matplotlib library is used to plot a scatter of y vs. x with varying marker size and/or color.
Syntax: Axes.scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=, edgecolors=None, *, plotnonfinite=False, data=None, **kwargs)
Parameters: This method accept the following parameters that are described below:
- x, y: These parameter are the horizontal and vertical coordinates of the data points.
- s: This parameter is an optional parameter and it contains the marker with size of points**2.
- c: This parameter is an optional parameter and it contains the sequence of colors.
- marker: This parameter is also an optional parameter. And it contains the marker style.
- cmap: This parameter is also an optional parameter which contains the registered colormap name.Its default value is NONE.
- norm: This parameter is also an optional parameter. And it is used to scale luminance data to 0, 1.Its default value is NONE.
- vmin, vmax: These parameter are used in conjunction with norm to normalize luminance data with default value None.
- alpha: This parameter are also an optional parameter. They blending values between 0 (transparent) and 1 (opaque).
- linewidths: This parameter is also an optional parameter. It is the linewidth of the marker edges.Its default value is None.
- edgecolors: This parameter is also an optional parameter. It is the sequence of color or {‘face’, ‘none’, None}.
- plotnonfiniteboolean: This parameter is also an optional parameter. It is the linewidth of the marker edges.Its default value is None.
Returns: This returns the container and it is comprises of the following:
- plotline:This returns the Line2D instance of x, y plot markers and/or line.
- caplines:This returns the tuple of Line2D instances of the error bar caps.
- barlinecols:This returns the tuple of LineCollection with the horizontal and vertical error ranges.
Below examples illustrate the matplotlib.axes.Axes.errorbar() function in matplotlib.axes:
Example-1:
# Implementation of matplotlib function import matplotlib.pyplot as pltimport numpy as np # unit value1 ellipserx, ry = 3., 1.value1 = rx * ry * np.pivalue2 = np.arange(0, 3 * np.pi + 0.01, 0.2) value3 = np.column_stack([rx / value1 * np.cos(value2), ry / value1 * np.sin(value2)]) x, y, s, c = np.random.rand(4, 99)s *= 10**2. fig, ax = plt.subplots()ax.scatter(x, y, s, c, marker = value3)ax.set_title("matplotlib.axes.Axes.scatter Example1")plt.show() |
Output:
Example-2:
# Implementation of matplotlib function import numpy as npimport matplotlib.pyplot as plt # first define the ratiosr1 = 0.2r2 = r1 + 0.3r3 = r2 + 0.7 # define some sizes of the# scatter markersizes = np.array([60, 80, 120, 50]) # calculate the points of the# first pie markerx1 = np.cos(2 * np.pi * np.linspace(0, r1))y1 = np.sin(2 * np.pi * np.linspace(0, r1)) xy1 = np.row_stack([[0, 0], np.column_stack([x1, y1])]) s1 = np.abs(xy1).max() x2 = np.cos(2 * np.pi * np.linspace(r1, r2))y2 = np.sin(2 * np.pi * np.linspace(r1, r2)) xy2 = np.row_stack([[0, 0], np.column_stack([x2, y2])]) s2 = np.abs(xy2).max() x3 = np.cos(2 * np.pi * np.linspace(r2, r3))y3 = np.sin(2 * np.pi * np.linspace(r2, r3))xy3 = np.row_stack([[0, 0], np.column_stack([x3, y3])]) s3 = np.abs(xy3).max() x4 = np.cos(2 * np.pi * np.linspace(r3, 1))y4 = np.sin(2 * np.pi * np.linspace(r3, 1))xy4 = np.row_stack([[0, 0], np.column_stack([x4, y4])]) s4 = np.abs(xy4).max() fig, ax = plt.subplots()ax.scatter(range(3), range(3), marker = xy1, s = s1**2 * sizes, facecolor ='blue') ax.scatter(range(3), range(3), marker = xy2, s = s2**2 * sizes, facecolor ='green') ax.scatter(range(3), range(3), marker = xy3, s = s3**2 * sizes, facecolor ='red') ax.scatter(range(3), range(3), marker = xy4, s = s4**2 * sizes, facecolor ='black') ax.set_title("matplotlib.axes.Axes.scatter Example2")plt.show() |
Output:
