matplotlib Axes.scatter 函数学习笔记

函数头：

def Axes.scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, **kwargs)

返回：: PathCollection

要求：x，y必须是相同长度序列

RewrappedMake a scatter plot of x vs y, where x and y are sequence like objects of the same lengths.

Parameters（参数）

----------

x,y:输入组，类似数组

x, y : array_like, shape (n, )

    Input data

实验：画三个点

fig1 = plt.figure(num='fig1',figsize=(6,3),dpi=75,facecolor='#FFFFFF',edgecolor='#FF0000')
plt.xlim(0,20)
plt.ylim(0,10)

x = [2,4,6]
y = [2,2,2]
plt.scatter(x,y)

plt.show()
plt.close()

s:点的大小（面积），标量或数组，缺省值为 (20*20)

s : scalar or array_like, shape (n, ), optional, default: 20

    size in points^2.

实验：

x = [2,4,6,8,10,14,18]
y = [2,2,2,2,2,2,2]
s = []
for i in x:
  s.append(i*i)
plt.scatter(x,y,s=s)

运行结果 ：

注意：s实际显示的最小尺寸受到linewidth的影响

c:颜色

c : color or sequence of color, optional, default : 'b'
    `c` can be a single color format string, or a sequence of color
    specifications of length `N`, or a sequence of `N` numbers to be
    mapped to colors using the `cmap` and `norm` specified via kwargs
    (see below). Note that `c` should not be a single numeric RGB or
    RGBA sequence because that is indistinguishable from an array of
    values to be colormapped. `c` can be a 2-D array in which the
    rows are RGB or RGBA, however.

marker：点的形状

marker : `~matplotlib.markers.MarkerStyle`, optional, default: 'o'
    See `~matplotlib.markers` for more information on the different
    styles of markers scatter supports.

实验：

#'.'       point marker
plt.scatter(1,1,s=30,marker='.')
#','       pixel marker
plt.scatter(2,1,s=30,marker=',')
#'o'       circle marker
plt.scatter(5,1,s=30,marker='o')
#'v'       triangle_down marker
plt.scatter(6,1,s=30,marker='v')
#'^'       triangle_up marker
plt.scatter(8,1,s=30,marker='^')
#'<'       triangle_left marker
plt.scatter(10,1,s=30,marker='<')
#'>'       triangle_right marker
plt.scatter(12,1,s=30,marker='>')
#'1'       tri_down marker
plt.scatter(15,1,s=30,marker='1')
#'2'       tri_up marker
plt.scatter(16,1,s=30,marker='2')
#'3'       tri_left marker
plt.scatter(18,1,s=30,marker='3')
#'4'       tri_right marker
plt.scatter(1,2,s=30,marker='4')
#'s'       square marker
#plt.scatter(2,2,s=30,marker='5')
#'p'       pentagon marker
plt.scatter(5,2,s=30,marker='p')
#'*'       star marker
plt.scatter(6,2,s=30,marker='*')
#'h'       hexagon1 marker
plt.scatter(8,2,s=30,marker='h')
#'H'       hexagon2 marker
plt.scatter(10,2,s=30,marker='H')
#'+'       plus marker
plt.scatter(12,2,s=30,marker='+')
#'x'       x marker
plt.scatter(15,2,s=30,marker='x')
#'D'       diamond marker
plt.scatter(16,2,s=30,marker='D')
#'d'       thin_diamond marker
plt.scatter(18,2,s=30,marker='d')
#'|'       vline marker
plt.scatter(1,3,s=30,marker='|')
#'_'       hline marker

plt.scatter(2,3,s=30,marker='_')

运行结果：

cmap：颜色调色板

cmap : `~matplotlib.colors.Colormap`, optional, default: None
    A `~matplotlib.colors.Colormap` instance or registered name.
    `cmap` is only used if `c` is an array of floats. If None,
    defaults to rc `image.cmap`.

norm：颜色灰度，值在0，1之间

norm : `~matplotlib.colors.Normalize`, optional, default: None
    A `~matplotlib.colors.Normalize` instance is used to scale
    luminance data to 0, 1. `norm` is only used if `c` is an array of
    floats. If `None`, use the default :func:`normalize`.

vmi,vmax:标量，同norm结合使用，调节颜色的亮度

vmin, vmax : scalar, optional, default: None
    `vmin` and `vmax` are used in conjunction with `norm` to normalize
    luminance data. If either are `None`, the min and max of the
    color array is used. Note if you pass a `norm` instance, your
    settings for `vmin` and `vmax` will be ignored.

alpha:透明度，值在0（透明）和1（不透明）之间

alpha : scalar, optional, default: None
    The alpha blending value, between 0 (transparent) and 1 (opaque)

linewidths:线宽

linewidths : scalar or array_like, optional, default: None
    If None, defaults to (lines.linewidth,). Note that this is a
    tuple, and if you set the linewidths argument you must set it as a
    sequence of floats, as required by

    `~matplotlib.collections.RegularPolyCollection`.

实验：

plt.scatter(1,2,s=50,linewidths=1.0,marker='o',c='w')
plt.scatter(4,2,s=50,linewidths=2.0,marker='o',c='w')
plt.scatter(8,2,s=50,linewidths=4.0,marker='o',c='w')
plt.scatter(12,2,s=50,linewidths=8.0,marker='o',c='w')
plt.scatter(16,2,s=50,linewidths=16.0,marker='o',c='w')

运行结果：

线宽越大，“o"点也越大 

Returns
-------
paths : `~matplotlib.collections.PathCollection`

Other parameters
----------------
kwargs : `~matplotlib.collections.Collection` properties

Notes（注意）

------

'x','y','s','c'参数可以被 masked，只有unmasked的点才会被绘制

Any or all of `x`, `y`, `s`, and `c` may be masked arrays, in
which case all masks will be combined and only unmasked points
will be plotted.

Examples
--------

.. plot:: mpl_examples/shapes_and_collections/scatter_demo.py

随机点图

# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt

fig1 = plt.figure(num='fig1',figsize=(6,3),dpi=100,facecolor='#FFFFFF',edgecolor='#FF0000')
plt.xlim(0,600)
plt.ylim(0,300)

N = input('Please input a number:')
c = ['r','g','b','m','y','c']
for cc in c:
    x =np.random.randint(0,600,N)
    y =np.random.randint(0,300,N)
    s =np.random.randint(1,500,N)
    a =np.random.rand()
    plt.scatter(x,y,s=s,linewidths=0,marker='.',c=cc,alpha=a)

plt.show()
plt.close()

运行结果：