python通过matplotlib绘制常见的几种图形
一、使用matplotlib对几种常见的图形进行绘制
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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) |
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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') |
1、柱状图
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data = [5,25,50,20]plt.bar(range(len(data)),data) |
2、水平绘制柱状图
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data = [5,25,50,20]plt.barh(range(len(data)),data) |
3、多个柱状图
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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() |
4、叠加型柱状图
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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() |
5、散点图
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n = 50x = np.random.rand(n)y = np.random.rand(n) plt.scatter(x, y) |
6、气泡图
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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) |
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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) |
7、直方图
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a = np.random.rand(100)plt.hist(a,bins= 20)plt.ylim(0,15) |
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a = np.random.randn(10000)plt.hist(a,bins=50)plt.title("标准正太分布") |
8、箱线图
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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) |
二、添加文字描述
1、文字描述一
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# 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") |
2、文字描述二
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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！") |
三、多个图形描绘 subplots
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%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') |
四、使用pandas 绘图
1、散点图
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import pandas as pddf = pd.dataframe(np.random.rand(50, 2), columns=['a', 'b'])# 散点图df.plot.scatter(x='a', y='b') |
2、绘制柱状图
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df = pd.dataframe(np.random.rand(10,4),columns=['a','b','c','d'])# 绘制柱状图df.plot.bar() |
3、堆积的柱状图
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# 堆积的柱状图df.plot.bar(stacked=true) |
4、水平的柱状图
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# 水平的柱状图df.plot.barh(stacked=true) |
5、直方图
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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) |
6、箱线图
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# 箱线图df = pd.dataframe(np.random.rand(10, 5), columns=['a', 'b', 'c', 'd', 'e'])df.plot.box() |
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原文链接：https://blog.51cto.com/u_11949039/3603586
