Python画图之散点图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,),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`,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`,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`,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,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,default: None
        The alpha blending value,between 0 (transparent) and 1 (opaque)   

    linewidths : scalar or array_like,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,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`,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,))

plt.scatter(x,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