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 datas : scalar or array_like, shape (n, ), optionalsize 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 eitheran instance of the class or the text shorthand for a particular    marker.cmap : `~matplotlib.colors.Colormap`, optional, default: NoneA `~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: NoneA `~matplotlib.colors.Normalize` instance is used to scaleluminance 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: NoneThe alpha blending value, between 0 (transparent) and 1 (opaque)   linewidths : scalar or array_like, optional, default: NoneIf None, defaults to (lines.linewidth,).verts : sequence of (x, y), optionalIf `marker` is None, these vertices will be used toconstruct the marker.  The center of the marker is locatedat (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 asthe face color.If it is 'none', the patch boundary will notbe drawn.For non-filled markers, the `edgecolors` kwargis ignored and forced to 'face' internally.Returns-------paths : `~matplotlib.collections.PathCollection`Other parameters----------------kwargs : `~matplotlib.collections.Collection` propertiesSee Also--------plot : to plot scatter plots when markers are identical in size and    colorNotes-----* 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 beflattened. 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 pdx=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

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