通过一文入门Matplotlib

野性酷女 2022-04-18 01:35 231阅读 0赞

## **1、开始** ##

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(-1,1,50)#从(-1,1)均匀取50个点
    y = 2 * x
    
    plt.plot(x,y)
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70][]

## **2、Figure对象** ##

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(-1,1,50)
    y1 = x ** 2 
    y2 = x * 2
    #这个是第一个figure对象,下面的内容都会在第一个figure中显示
    plt.figure()
    plt.plot(x,y1)
    #这里第二个figure对象
    plt.figure(num = 3,figsize = (10,5))
    plt.plot(x,y2)
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 1][]

1.  我们看上面的每个图像的窗口，可以看出figure并没有从1开始然后到2，这是因为我们在创建第二个figure对象的时候，指定了一个num = 3的参数，所以第二个窗口标题上显示的figure3。
2.  对于每一个窗口，我们也可以对他们分别去指定窗口的大小。也就是figsize参数。
3.  若我们想让他们的线有所区别，我们可以用下面语句进行修改。

plt.plot(x,y2,color = 'red',linewidth = 3.0,linestyle = '--')

![20181106214801933.png][]

## **3、设置坐标轴** ##

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(-1,1,50)
    y = x *2
    
    plt.plot(x,y)
    plt.show()

默认的横坐标：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 2][]

#在plt.show()之前添加
    plt.xlim((0,2))
    plt.ylim((-2,2))

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 3][]

给横纵坐标设置名称：

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(-1,1,50)
    y = x * 2
    
    plt.xlabel("x'slabel")#x轴上的名字
    plt.ylabel("y's;abel")#y轴上的名字
    plt.plot(x,y,color='green',linewidth = 3)
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 4][]

把坐标轴换成不同的单位：

new_ticks = np.linspace(-1,2,5)
    plt.xticks(new_ticks)
    #在对应坐标处更换名称
    plt.yticks([-2,-1,0,1,2],['really bad','b','c','d','good'])

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 5][]

那么如果我想把坐标轴上的字体更改成数学的那种形式：

#在对应坐标处更换名称
    plt.yticks([-2,-1,0,1,2],[r'$really\ bad$',r'$b$',r'$c\ \alpha$','d','good'])

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 6][]

注意：

1.  我们如果要使用空格的话需要进行对空格的转义"\\ "这种转义才能输出空格；
2.  我们可以在里面加一些数学的公式，如"\\alpha"来表示 。

如何去更换坐标原点，坐标轴呢？我们在plt.show()之前：

#gca = 'get current axis'
    #获取当前的这四个轴
    ax = plt.gca()
    #设置脊梁(也就是包围在图标四周的默认黑线)
    #所以设置脊梁的时候，一共有四个方位
    ax.spines['right'].set_color('r')
    ax.spines['top'].set_color('none')
    
    #将底部脊梁作为x轴
    ax.xaxis.set_ticks_position('bottom')
    #ACCEPTS:['top' | 'bottom' | 'both'|'default'|'none']
    
    #设置x轴的位置(设置底的时候依据的是y轴)
    ax.spines['bottom'].set_position(('data',0))
    #the 1st is in 'outward' |'axes' | 'data'
    #axes : precentage of y axis
    #data : depend on y data
    
    ax.yaxis.set_ticks_position('left')
    # #ACCEPTS:['top' | 'bottom' | 'both'|'default'|'none']
    
    #设置左脊梁(y轴)依据的是x轴的0位置
    ax.spines['left'].set_position(('data',0))

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 7][]

## **4.legend图例** ##

我们很多时候会再一个figures中去添加多条线，那我们如何去区分多条线呢？这里就用到了legend。

#简单的使用
    l1, = plt.plot(x, y1, label='linear line')
    l2, = plt.plot(x, y2, color='red', linewidth=1.0, linestyle='--', label='square line')
    
    #简单的设置legend(设置位置)
    #位置在右上角
    plt.legend(loc = 'upper right')

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 8][]

l1, = plt.plot(x, y1, label='linear line')
    l2, = plt.plot(x, y2, color='red', linewidth=1.0, linestyle='--', label='square line')
    
    
    plt.legend(handles = [l1,l2],labels = ['up','down'],loc = 'best')
    #the ',' is very important in here l1, = plt...and l2, = plt...for this step
    """legend( handles=(line1, line2, line3),
               labels=('label1', 'label2', 'label3'),
               'upper right')
        shadow = True 设置图例是否有阴影
        The *loc* location codes are::
              'best' : 0,         
              'upper right'  : 1,
              'upper left'   : 2,
              'lower left'   : 3,
              'lower right'  : 4,
              'right'        : 5,
              'center left'  : 6,
              'center right' : 7,
              'lower center' : 8,
              'upper center' : 9,
              'center'       : 10,"""

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 9][]

这里需要注意的是：

1.  如果我们没有在legend方法的参数中设置labels，那么就会使用画线的时候，也就是plot方法中的指定的label参数所指定的名称，当然如果都没有的话就会抛出异常；
2.  其实我们plt.plot的时候返回的是一个线的对象，如果我们想在handle中使用这个对象，就必须在返回的名字的后面加一个","号；

legend = plt.legend(handles = [l1,l2],labels = ['hu','tang'],loc = 'upper center',shadow = True)
    frame = legend.get_frame()
    frame.set_facecolor('r')#或者0.9...

![20181106220037230.png][]

## **5.在图片上加一些标注annotation** ##

在图片上加注解有两种方式：

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(-3,3,50)
    y = 2*x + 1
    
    plt.figure(num = 1,figsize =(8,5))
    plt.plot(x,y)
    
    ax = plt.gca()
    ax.spines['right'].set_color('none')
    ax.spines['top'].set_color('none')
    
    #将底下的作为x轴
    ax.xaxis.set_ticks_position('bottom')
    #并且data，以y轴的数据为基本
    ax.spines['bottom'].set_position(('data',0))
    
    #将左边的作为y轴
    ax.yaxis.set_ticks_position('left')
    ax.spines['left'].set_position(('data',0))
    
    print("-----方式一-----")
    x0 = 1
    y0 = 2*x0 + 1
    plt.plot([x0,x0],[0,y0],'k--',linewidth = 2.5)
    plt.scatter([x0],[y0],s = 50,color='b')
    plt.annotate(r'$2x+1 = %s$'% y0,xy = (x0,y0),xycoords = 'data',
                 xytext=(+30,-30),textcoords = 'offset points',fontsize = 16
                 ,arrowprops = dict(arrowstyle='->',
                                    connectionstyle="arc3,rad=.2"))
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 10][]

plt.annotate(r'$2x+1 = %s$'% y0,xy = (x0,y0),xycoords = 'data',
                 xytext=(+30,-30),textcoords = 'offset points',fontsize = 16
                 ,arrowprops = dict(arrowstyle='->',
                                    connectionstyle="arc3,rad=.2"))

注意：

1.  xy就是需要进行注释的点的横纵坐标；
2.  xycoords = 'data'说明的是要注释点的xy的坐标是以横纵坐标轴为基准的；
3.  xytext=(+30,-30)和textcoords='data'说明了这里的文字是基于标注的点的x坐标的偏移+30以及标注点y坐标-30位置，就是我们要进行注释文字的位置；
4.  fontsize = 16就说明字体的大小；
5.  arrowprops = dict()这个是对于这个箭头的描述，arrowstyle='->'这个是箭头的类型，connectionstyle="arc3,rad=.2"这两个是描述我们的箭头的弧度以及角度的。

print("-----方式二-----")
    plt.text(-3.7,3,r'$this\ is\ the\ some\ text. \mu\ \sigma_i\ \alpha_t$',
             fontdict={'size':16,'color':'r'})

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 11][]

这里先介绍一下plot中的一个参数：

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(-3,3,50)
    y1 = 0.1*x
    y2 = x**2
    
    plt.figure()
    #zorder控制绘图顺序
    plt.plot(x,y1,linewidth = 10,zorder = 2,label = r'$y_1\ =\ 0.1*x$')
    plt.plot(x,y2,linewidth = 10,zorder = 1,label = r'$y_2\ =\ x^{2}$')
    
    plt.legend(loc = 'lower right')
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 12][]

如果改成：

#zorder控制绘图顺序
    plt.plot(x,y1,linewidth = 10,zorder = 1,label = r'$y_1\ =\ 0.1*x$')
    plt.plot(x,y2,linewidth = 10,zorder = 2,label = r'$y_2\ =\ x^{2}$')

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 13][]

下面我们看一下这个图：

import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.linspace(-3,3,50)
    y1 = 0.1*x
    y2 = x**2
    
    plt.figure()
    #zorder控制绘图顺序
    plt.plot(x,y1,linewidth = 10,zorder = 1,label = r'$y_1\ =\ 0.1*x$')
    plt.plot(x,y2,linewidth = 10,zorder = 2,label = r'$y_2\ =\ x^{2}$')
    
    plt.ylim(-2,2)
    
    ax = plt.gca()
    ax.spines['right'].set_color('none')
    ax.spines['top'].set_color('none')
    
    ax.xaxis.set_ticks_position('bottom')
    ax.spines['bottom'].set_position(('data',0))
    ax.yaxis.set_ticks_position('left')
    ax.spines['left'].set_position(('data',0))
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 14][]

从上面看，我们可以看见我们轴上的坐标被掩盖住了，那么我们怎么去修改他呢？

print(ax.get_xticklabels())
    print(ax.get_yticklabels())
    
    for label in ax.get_xticklabels() + ax.get_yticklabels():
        label.set_fontsize(12)
        label.set_bbox(dict(facecolor = 'white',edgecolor='none',alpha = 0.8,zorder = 2))
    
    <a list of 9 Text xticklabel objects>
    <a list of 9 Text yticklabel objects>

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 15][]

这里需要注意：

1.  ax.get\_xticklabels()获取得到就是坐标轴上的数字；
2.  set\_bbox()这个bbox就是那坐标轴上的数字的那一小块区域，从结果我们可以很明显的看出来；
3.  facecolor = 'white',edgecolor='none，第一个参数表示的这个box的前面的背景，边上的颜色。

## **6.画图的种类** ##

**1.scatter散点图**

import matplotlib.pyplot as plt
    import numpy as np
    
    n = 1024
    X = np.random.normal(0,1,n)
    Y = np.random.normal(0,1,n)
    T = np.arctan2(Y,X)#for color later on
    
    plt.scatter(X,Y,s = 75,c = T,alpha = .5)
    
    plt.xlim((-1.5,1.5))
    plt.xticks([])#ignore xticks
    plt.ylim((-1.5,1.5))
    plt.yticks([])#ignore yticks
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 16][]

**2.柱状图**

import matplotlib.pyplot as plt
    import numpy as np
    
    n = 12
    X = np.arange(n)
    Y1 = (1 - X/float(n)) * np.random.uniform(0.5,1.0,n)
    Y2 = (1 - X/float(n)) * np.random.uniform(0.5,1.0,n)
    #facecolor:表面的颜色;edgecolor:边框的颜色
    plt.bar(X,+Y1,facecolor = '#9999ff',edgecolor = 'white')
    plt.bar(X,-Y2,facecolor = '#ff9999',edgecolor = 'white')
    #描绘text在图表上
    # plt.text(0 + 0.4, 0 + 0.05,"huhu")
    for x,y in zip(X,Y1):
        #ha : horizontal alignment
        #va : vertical alignment
        plt.text(x + 0.01,y+0.05,'%.2f'%y,ha = 'center',va='bottom')
    
    for x,y in zip(X,Y2):
        # ha : horizontal alignment
        # va : vertical alignment
        plt.text(x+0.01,-y-0.05,'%.2f'%(-y),ha='center',va='top')
    
    plt.xlim(-.5,n)
    plt.yticks([])
    plt.ylim(-1.25,1.25)
    plt.yticks([])
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 17][]

**3.Contours等高线图**

import matplotlib.pyplot as plt
    import numpy as np
    
    def f(x,y):
        #the height function
        return (1-x/2 + x**5+y**3) * np.exp(-x **2 -y**2)
    
    n = 256
    x = np.linspace(-3,3,n)
    y = np.linspace(-3,3,n)
    #meshgrid函数用两个坐标轴上的点在平面上画网格。
    X,Y = np.meshgrid(x,y)
    
    #use plt.contourf to filling contours
    #X Y and value for (X,Y) point
    #这里的8就是说明等高线分成多少个部分，如果是0则分成2半
    #则8是分成10半
    #cmap找对应的颜色，如果高=0就找0对应的颜色值，
    plt.contourf(X,Y,f(X,Y),8,alpha = .75,cmap = plt.cm.hot)
    
    #use plt.contour to add contour lines
    C = plt.contour(X,Y,f(X,Y),8,colors = 'black',linewidth = .5)
    
    #adding label
    plt.clabel(C,inline = True,fontsize = 10)
    
    #ignore ticks
    plt.xticks([])
    plt.yticks([])
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 18][]

**4.image图片**

import matplotlib.pyplot as plt
    import numpy as np
    
    #image data
    a = np.array([0.313660827978, 0.365348418405, 0.423733120134,
                  0.365348418405, 0.439599930621, 0.525083754405,
                  0.423733120134, 0.525083754405, 0.651536351379]).reshape(3,3)
    
    '''
    for the value of "interpolation"，check this:
    http://matplotlib.org/examples/images_contours_and_fields/interpolation_methods.html
    for the value of "origin"= ['upper', 'lower'], check this:
    http://matplotlib.org/examples/pylab_examples/image_origin.html
    '''
    #显示图像
    #这里的cmap='bone'等价于plt.cm.bone
    plt.imshow(a,interpolation = 'nearest',cmap = 'bone' ,origin = 'up')
    #显示右边的栏
    plt.colorbar(shrink = .92)
    
    #ignore ticks
    plt.xticks([])
    plt.yticks([])
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 19][]

**5.3D数据**

import numpy as np
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D
    
    fig = plt.figure()
    ax = Axes3D(fig)
    #X Y value
    X = np.arange(-4,4,0.25)
    Y = np.arange(-4,4,0.25)
    X,Y = np.meshgrid(X,Y)
    R = np.sqrt(X**2 + Y**2)
    #hight value
    Z = np.sin(R)
    
    ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))
    """
    ============= ================================================
            Argument      Description
            ============= ================================================
            *X*, *Y*, *Z* Data values as 2D arrays
            *rstride*     Array row stride (step size), defaults to 10
            *cstride*     Array column stride (step size), defaults to 10
            *color*       Color of the surface patches
            *cmap*        A colormap for the surface patches.
            *facecolors*  Face colors for the individual patches
            *norm*        An instance of Normalize to map values to colors
            *vmin*        Minimum value to map
            *vmax*        Maximum value to map
            *shade*       Whether to shade the facecolors
            ============= ================================================
    """
    
    # I think this is different from plt12_contours
    ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.get_cmap('rainbow'))
    """
    ==========  ================================================
            Argument    Description
            ==========  ================================================
            *X*, *Y*,   Data values as numpy.arrays
            *Z*
            *zdir*      The direction to use: x, y or z (default)
            *offset*    If specified plot a projection of the filled contour
                        on this position in plane normal to zdir
            ==========  ================================================
    """
    ax.set_zlim(-2, 2)
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 20][]

## **7.多图合并展示** ##

**1.使用subplot函数**

import matplotlib.pyplot as plt
    
    plt.figure(figsize = (6,5))
    
    ax1 = plt.subplot(3,1,1)
    ax1.set_title("ax1 title")
    plt.plot([0,1],[0,1])
    
    #这种情况下如果再数的话以334为标准了，
    #把上面的第一行看成是3个列
    ax2 = plt.subplot(334)
    ax2.set_title("ax2 title")
    
    ax3 = plt.subplot(335)
    ax4 = plt.subplot(336)
    ax5 = plt.subplot(325)
    ax6 = plt.subplot(326)
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 21][]

import matplotlib.pyplot as plt
    
    plt.figure(figsize = (6,4))
    #plt.subplot(n_rows,n_cols,plot_num)
    plt.subplot(211)
    # figure splits into 2 rows, 1 col, plot to the 1st sub-fig
    plt.plot([0, 1], [0, 1])
    
    plt.subplot(234)
    # figure splits into 2 rows, 3 col, plot to the 4th sub-fig
    plt.plot([0, 1], [0, 2])
    
    plt.subplot(235)
    # figure splits into 2 rows, 3 col, plot to the 5th sub-fig
    plt.plot([0, 1], [0, 3])
    
    plt.subplot(236)
    # figure splits into 2 rows, 3 col, plot to the 6th sub-fig
    plt.plot([0, 1], [0, 4])
    
    plt.tight_layout()
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 22][]

**2.分格显示**

#method 1: subplot2grid
    import matplotlib.pyplot as plt
    plt.figure()
    #第一个参数shape也就是我们网格的形状
    #第二个参数loc,位置,这里需要注意位置是从0开始索引的
    #第三个参数colspan跨多少列,默认是1
    #第四个参数rowspan跨多少行,默认是1
    ax1 = plt.subplot2grid((3,3),(0,0),colspan = 3,rowspan = 1)
    #如果为他设置一些属性的话，如plt.title,则用ax1的话
    #ax1.set_title(),同理可设置其他属性
    ax1.set_title("ax1_title")
    
    ax2 = plt.subplot2grid((3,3),(1,0),colspan = 2,rowspan = 1)
    ax3 = plt.subplot2grid((3,3),(1,2),colspan = 1,rowspan = 2)
    ax4 = plt.subplot2grid((3,3),(2,0),colspan = 1,rowspan = 1)
    ax5 = plt.subplot2grid((3,3),(2,1),colspan = 1,rowspan = 1)
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 23][]

#method 2:gridspec
    import matplotlib.pyplot as plt
    import matplotlib.gridspec as gridspec
    
    plt.figure()
    gs = gridspec.GridSpec(3,3)
    #use index from 0
    ax1 = plt.subplot(gs[0,:])
    ax1.set_title("ax1 title")
    
    ax2 = plt.subplot(gs[1,:2])
    ax2.plot([1,2],[3,4],'r')
    
    ax3 = plt.subplot(gs[1:,2:])
    ax4 = plt.subplot(gs[-1,0])
    ax5 = plt.subplot(gs[-1,-2])
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 24][]

#method 3 :easy to define structure
    #这种方式不能生成指定跨行列的那种
    import matplotlib.pyplot as plt
    #(ax11,ax12),(ax13,ax14)代表了两行
    #f就是figure对象,
    #sharex：是否共享x轴
    #sharey:是否共享y轴
    f,((ax11,ax12),(ax13,ax14)) = plt.subplots(2,2,sharex = True,sharey = True)
    ax11.set_title("a11 title")
    ax12.scatter([1,2],[1,2])
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 25][]

**3.图中图**

import matplotlib.pyplot as plt
    
    fig = plt.figure()
    x = [1,2,3,4,5,6,7]
    y = [1,3,4,2,5,8,6]
    
    #below are all percentage
    left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
    #使用plt.figure()显示的是一个空的figure
    #如果使用fig.add_axes会添加轴
    ax1 = fig.add_axes([left, bottom, width, height])# main axes
    ax1.plot(x,y,'r')
    ax1.set_xlabel('x')
    ax1.set_ylabel('y')
    ax1.set_title('title')
    
    ax2 = fig.add_axes([0.2, 0.6, 0.25, 0.25])  # inside axes
    ax2.plot(y, x, 'b')
    ax2.set_xlabel('x')
    ax2.set_ylabel('y')
    ax2.set_title('title inside 1')
    
    # different method to add axes
    ####################################
    plt.axes([0.6, 0.2, 0.25, 0.25])
    plt.plot(y[::-1], x, 'g')
    plt.xlabel('x')
    plt.ylabel('y')
    plt.title('title inside 2')
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 26][]

**4.次坐标轴**

# 使用twinx是添加y轴的坐标轴
    # 使用twiny是添加x轴的坐标轴
    import matplotlib.pyplot as plt
    import numpy as np
    
    x = np.arange(0,10,0.1)
    y1 = 0.05 * x ** 2
    y2 = -1 * y1
    
    fig,ax1 = plt.subplots()
    
    ax2 = ax1.twinx()
    ax1.plot(x,y1,'g-')
    ax2.plot(x,y2,'b-')
    
    ax1.set_xlabel('X data')
    ax1.set_ylabel('Y1 data',color = 'g')
    ax2.set_ylabel('Y2 data',color = 'b')
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 27][]

## **8.animation动画** ##

import numpy as np
    from matplotlib import pyplot as plt
    from matplotlib import animation
    
    fig,ax = plt.subplots()
    
    x = np.arange(0,2*np.pi,0.01)
    #因为这里返回的是一个列表，但是我们只想要第一个值
    #所以这里需要加,号
    line, = ax.plot(x,np.sin(x))
    
    def animate(i):
        line.set_ydata(np.sin(x + i/10.0))#updata the data
        return line,
    
    def init():
        line.set_ydata(np.sin(x))
        return line,
    
    
    # call the animator.  blit=True means only re-draw the parts that have changed.
    # blit=True dose not work on Mac, set blit=False
    # interval= update frequency
    #frames帧数
    ani = animation.FuncAnimation(fig=fig, func=animate, frames=100, init_func=init,
                                  interval=20, blit=False)
    
    plt.show()

![20181106221246638.gif][]

以上内容均学习自莫烦教程。

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[20181106221246638.gif]: /images/20220418/d243038277984d81b11171471b42382b.png