Python画图之散点图（plt.scatter）_python plt.scatter

        散点图的应用很广泛，以前介绍过很多画图方法：Python画图（直方图、多张子图、二维图形、三维图形以及图中图），漏掉了这个，现在补上，用法很简单，我们可以help(plt.scatter)看下它的用法：

Help on function scatter in module matplotlib.pyplot:
 
scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, 
vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None, **kwargs)
    Make a scatter plot of `x` vs `y`
 
    Marker size is scaled by `s` and marker color is mapped to `c`
 
    Parameters
    ----------
    x, y : array_like, shape (n, )
        Input data
 
    s : scalar or array_like, shape (n, ), optional
        size in points^2.  Default is `rcParams['lines.markersize'] ** 2`. 
 
    c : color, sequence, 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, including the case of a single      
        row to specify the same color for all points.
 
    marker : `~matplotlib.markers.MarkerStyle`, optional, default: 'o'     
        See `~matplotlib.markers` for more information on the different    
        styles of markers scatter supports. `marker` can be either
        an instance of the class or the text shorthand for a particular    
        marker.
 
    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 : `~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`.
 
    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 : scalar, optional, default: None
        The alpha blending value, between 0 (transparent) and 1 (opaque)   
 
    linewidths : scalar or array_like, optional, default: None
        If None, defaults to (lines.linewidth,).
 
    verts : sequence of (x, y), optional
        If `marker` is None, these vertices will be used to
        construct the marker.  The center of the marker is located
        at (0,0) in normalized units.  The overall marker is rescaled      
        by ``s``.
 
    edgecolors : color or sequence of color, optional, default: None       
        If None, defaults to 'face'
 
        If 'face', the edge color will always be the same as
        the face color.
 
        If it is 'none', the patch boundary will not
        be drawn.
 
        For non-filled markers, the `edgecolors` kwarg
        is ignored and forced to 'face' internally.
 
    Returns
    -------
    paths : `~matplotlib.collections.PathCollection`
 
    Other parameters
    ----------------
    kwargs : `~matplotlib.collections.Collection` properties
 
    See Also
    --------
    plot : to plot scatter plots when markers are identical in size and    
        color
 
    Notes
    -----
 
    * The `plot` function will be faster for scatterplots where markers    
      don't vary in size or color.
    * 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.
      Fundamentally, scatter works with 1-D arrays; `x`, `y`, `s`, and `c` 
      may be input as 2-D arrays, but within scatter they will be
      flattened. The exception is `c`, which will be flattened only if its 
      size matches the size of `x` and `y`.

我们可以看到参数比较多，平时主要用到的就是大小、颜色、样式这三个参数

s：形状的大小，默认 20，也可以是个数组，数组每个参数为对应点的大小，数值越大对应的图中的点越大。
c：形状的颜色，"b"：blue   "g"：green    "r"：red   "c"：cyan(蓝绿色，青色)  "m"：magenta(洋红色，品红色) "y"：yellow "k"：black  "w"：white
marker：常见的形状有如下
"."：点                   ","：像素点           "o"：圆形
"v"：朝下三角形   "^"：朝上三角形   "<"：朝左三角形   ">"：朝右三角形
"s"：正方形           "p"：五边星          "*"：星型
"h"：1号六角形     "H"：2号六角形 

"+"：+号标记      "x"：x号标记
"D"：菱形              "d"：小型菱形 
"|"：垂直线形         "_"：水平线形

我们来看几个示例(在一张图显示了)

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
x=np.array([3,5])
y=np.array([7,8])
 
x1=np.random.randint(10,size=(25,))
y1=np.random.randint(10,size=(25,))
 
plt.scatter(x,y,c='r')
plt.scatter(x1,y1,s=100,c='b',marker='*')
 
#使用pandas来读取
x2=[]
y2=[]
rdata=pd.read_table('1.txt',header=None)
for i in range(len(rdata[0])):
    x2.append(rdata[0][i].split(',')[0])
    y2.append(rdata[0][i].split(',')[1])
 
plt.scatter(x2,y2,s=200,c='g',marker='o')
plt.show()

 其中文档1.txt内容如下（上面图中的4个绿色大点）

5,6
7,9
3,4
2,7