自学教程：python通过Matplotlib绘制常见的几种图形（推荐）

import numpy as np import matplotlib.pyplot as plt%matplotlib inline #写了这个就可以不用写plt.show()plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签plt.rcParams['axes.unicode_minus']=False #用来正常显示负号  X = np.linspace(0, 2*np.pi,100)# 均匀的划分数据Y = np.sin(X)Y1 = np.cos(X) plt.title("Hello World!!")plt.plot(X,Y)plt.plot(X,Y1)

X = np.linspace(0, 2*np.pi,100)  Y = np.sin(X)Y1 = np.cos(X)plt.subplot(211) # 等价于 subplot(2,1,1)  #一个图版画两个图plt.plot(X,Y) plt.subplot(212)plt.plot(X,Y1,color = 'r')

data = [5,25,50,20]plt.bar(range(len(data)),data)

data = [5,25,50,20]plt.barh(range(len(data)),data)

data = [[5,25,50,20],        [4,23,51,17],        [6,22,52,19]]X = np.arange(4) plt.bar(X + 0.00, data[0], color = 'b', width = 0.25,label = "A")plt.bar(X + 0.25, data[1], color = 'g', width = 0.25,label = "B")plt.bar(X + 0.50, data[2], color = 'r', width = 0.25,label = "C") # 显示上面设置的 lableplt.legend()

data = [[5,25,50,20],        [4,23,51,17],        [6,22,52,19]]X = np.arange(4) plt.bar(X, data[0], color = 'b', width = 0.25)plt.bar(X, data[1], color = 'g', width = 0.25,bottom = data[0])plt.bar(X, data[2], color = 'r', width = 0.25,bottom = np.array(data[0]) + np.array(data[1])) plt.show()

N = 50x = np.random.rand(N)y = np.random.rand(N) plt.scatter(x, y)

N = 50x = np.random.rand(N)y = np.random.rand(N)colors = np.random.randn(N) # 颜色可以用数值表示area = np.pi * (15 * np.random.rand(N))**2  #  调整大小 plt.scatter(x, y, c=colors, alpha=0.5, s = area)

N = 50x = np.random.rand(N)y = np.random.rand(N)colors = np.random.randint(0,2,size =50)plt.scatter(x, y, c=colors, alpha=0.5,s = area)

a = np.random.rand(100)plt.hist(a,bins= 20)plt.ylim(0,15)

a = np.random.randn(10000)plt.hist(a,bins=50)plt.title("标准正太分布")

x = np.random.randint(20,100,size = (30,3))plt.boxplot(x)plt.ylim(0,120)# 在x轴的什么位置填一个 label，我们这里制定在 1，2，3 位置，写上 A，B，Cplt.xticks([1,2,3],['A','B','C'])  plt.hlines(y = np.median(x,axis = 0)[0] ,xmin =0,xmax=3)

# 设置画布颜色为 bluefig, ax = plt.subplots(facecolor='blue') # y 轴数据data = [[5,25,50,20],        [4,23,51,17],        [6,22,52,19]]X = np.arange(4) plt.bar(X+0.00, data[0], color = 'darkorange', width = 0.25,label = 'A')plt.bar(X+0.25, data[1], color = 'steelblue', width = 0.25,label="B")plt.bar(X+0.50, data[2], color = 'violet', width = 0.25,label = 'C') ax.set_title("Figure 2")plt.legend() # 添加文字描述 方法一W = [0.00,0.25,0.50]for i in range(3):    for a,b in zip(X+W[i],data[i]):        plt.text(a,b,"%.0f"% b,ha="center",va= "bottom")        plt.xlabel("Group")plt.ylabel("Num")plt.text(0.0,48,"TEXT")

X = np.linspace(0, 2*np.pi,100)# 均匀的划分数据Y = np.sin(X)Y1 = np.cos(X) plt.plot(X,Y)plt.plot(X,Y1) plt.annotate('Points',         xy=(1, np.sin(1)),         xytext=(2, 0.5), fontsize=16,         arrowprops=dict(arrowstyle="->")) plt.title("这是一副测试图！")

%pylab inlinepylab.rcParams['figure.figsize'] = (10, 6) # 调整图片大小 # np.random.seed(19680801) n_bins = 10x = np.random.randn(1000, 3) fig, axes = plt.subplots(nrows=2, ncols=2) ax0, ax1, ax2, ax3 = axes.flatten() colors = ['red', 'tan', 'lime']ax0.hist(x, n_bins, normed=1, histtype='bar', color=colors, label=colors)ax0.legend(prop={'size': 10})ax0.set_title('bars with legend') ax1.hist(x, n_bins, normed=1, histtype='bar', stacked=True)ax1.set_title('stacked bar') ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)ax2.set_title('stack step (unfilled)') # Make a multiple-histogram of data-sets with different length.x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]ax3.hist(x_multi, n_bins, histtype='bar')ax3.set_title('different sample sizes')

import pandas as pddf = pd.DataFrame(np.random.rand(50, 2), columns=['a', 'b'])# 散点图df.plot.scatter(x='a', y='b')

df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])# 绘制柱状图df.plot.bar()

# 堆积的柱状图df.plot.bar(stacked=True)

# 水平的柱状图df.plot.barh(stacked=True)

df = pd.DataFrame({'a':np.random.randn(1000)+1,'b':np.random.randn(1000),'c':np.random.randn(1000) - 1}, columns=['a', 'b', 'c']) # 直方图df.plot.hist(bins=20)

# 箱线图df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])df.plot.box()