python通过Matplotlib绘制常见的几种图形

Posted 2021-08-24 孤独的明月

使用matplotlib对几种常见的图形进行绘制

Matplotlib官网 如果想了解更多可查看官网。

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\')

柱状图

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

水平绘制柱状图

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

多个柱状图

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 lable
plt.legend()

叠加型柱状图

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()

散点图

N = 50
x = np.random.rand(N)
y = np.random.rand(N)

plt.scatter(x, y)

气泡图

N = 50
x = 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 = 50
x = 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)

直方图

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")

箱线图

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，C
plt.xticks([1,2,3],[\'A\',\'B\',\'C\']) 

plt.hlines(y = np.median(x,axis = 0)[0] ,xmin =0,xmax=3)

添加文字描述

# 设置画布颜色为 blue
fig, 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")

添加文字描述 方法二

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！")

多个图形描绘 subplots

%pylab inline
pylab.rcParams[\'figure.figsize\'] = (10, 6) # 调整图片大小

# np.random.seed(19680801)

n_bins = 10
x = 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\')

使用Pandas 绘图

import pandas as pd
df = 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()