先自行安装两个依赖库matplotlib，numpy

1、开始

import matplotlib.pyplot as pltimport 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()

2、Figure对象

import matplotlib.pyplot as pltimport 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()

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。
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。
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 = '--')

3、设置坐标轴

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

默认的横坐标：

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

给横纵坐标设置名称：

import matplotlib.pyplot as pltimport 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()

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'])

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'])

注意：

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；
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))

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')

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,"""

这里需要注意的是：

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；
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...

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 pltimport 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 = 1y0 = 2*x0 + 1plt.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()

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：

xy就是需要进行注释的点的横纵坐标；
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；
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；
fontsize = 16就说明字体的大小；
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'})

这里先介绍一下plot中的一个参数：

import matplotlib.pyplot as pltimport numpy as np x = np.linspace(-3,3,50)y1 = 0.1*xy2 = 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()

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'$y_2 = x^{2}$')

下面我们看一下这个图：

import matplotlib.pyplot as pltimport numpy as np x = np.linspace(-3,3,50)y1 = 0.1*xy2 = 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()

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>

这里需要注意：

ax.get_xticklabels()获取得到就是坐标轴上的数字；
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；
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 pltimport numpy as np n = 1024X = 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 xticksplt.ylim((-1.5,1.5))plt.yticks([])#ignore yticksplt.show()

2.柱状图

import matplotlib.pyplot as pltimport numpy as np n = 12X = 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()

3.Contours等高线图

import matplotlib.pyplot as pltimport 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 = 256x = 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 linesC = plt.contour(X,Y,f(X,Y),8,colors = 'black',linewidth = .5) #adding labelplt.clabel(C,inline = True,fontsize = 10) #ignore ticksplt.xticks([])plt.yticks([]) plt.show()

4.image图片

import matplotlib.pyplot as pltimport numpy as np #image dataa = 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.htmlfor 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.boneplt.imshow(a,interpolation = 'nearest',cmap = 'bone' ,origin = 'up')#显示右边的栏plt.colorbar(shrink = .92) #ignore ticksplt.xticks([])plt.yticks([]) plt.show()

5.3D数据

import numpy as npimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D fig = plt.figure()ax = Axes3D(fig)#X Y valueX = 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 valueZ = 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_contoursax.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()

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()

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-figplt.plot([0, 1], [0, 1]) plt.subplot(234)# figure splits into 2 rows, 3 col, plot to the 4th sub-figplt.plot([0, 1], [0, 2]) plt.subplot(235)# figure splits into 2 rows, 3 col, plot to the 5th sub-figplt.plot([0, 1], [0, 3]) plt.subplot(236)# figure splits into 2 rows, 3 col, plot to the 6th sub-figplt.plot([0, 1], [0, 4]) plt.tight_layout()plt.show()

2.分格显示

#method 1: subplot2gridimport matplotlib.pyplot as pltplt.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1ax1 = 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()

#method 2:gridspecimport matplotlib.pyplot as pltimport matplotlib.gridspec as gridspec plt.figure()gs = gridspec.GridSpec(3,3)#use index from 0ax1 = 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()

#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()

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 percentageleft, 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 axesax1.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 axesax2.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()

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 pltimport numpy as np x = np.arange(0,10,0.1)y1 = 0.05 * x ** 2y2 = -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()

8.animation动画

import numpy as npfrom matplotlib import pyplot as pltfrom 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()