matplotlib绘图
import matplotlib.pyplot as plt import numpy as np
plt.plot()绘制线性图
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绘制单条线形图
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绘制多条线形图
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设置坐标系的比例plt.figure(figsize=(a,b))
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设置图例legend()
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设置轴的标识
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图例保存
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fig = plt.figure()
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plt.plot(x,y)
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figure.savefig（）
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曲线的样式和风格（自学）
#绘制单条线形图 x = np.array([1,2,3,4,5]) y = x + 3
plt.plot(x,y)#线性图
#绘制多条线形图 # 第一种方式 plt.plot(x,y) plt.plot(x+1,y-2)
# 第二种方式 plt.plot(x,y,x+1,y-2)
#设置坐标系的比例plt.figure(figsize=(a,b)) 将线型图等比例拉伸或缩小 plt.figure(figsize=(5,9))#放置在绘图的plot方法之前 plt.plot(x,y)
#设置图例legend() plt.plot(x,y,label='x,y') plt.plot(x+1,y-2,label='x+1,y-2') plt.legend() #图例生效
#设置轴的标识
plt.plot(x,y)
plt.xlabel('temp')
plt.ylabel('dist')
plt.title('dist&temp')
#图例保存
fig = plt.figure() #对象实例化 该对象的创建一定要放置在plot绘图之前
plt.plot(x,y,label='x,y')# 绘图
fig.savefig('./123.png')# 保存 当前目录下./
##曲线的样式和风格（自学） plt.plot(x,y,c='red',alpha=0.5)
柱状图：plt.bar()
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参数：第一个参数是索引。第二个参数是数据值。第三个参数是条形的宽度
plt.bar(x,y)
直方图
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是一个特殊的柱状图，又叫做密度图
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plt.hist()的参数
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bins 可以是一个bin数量的整数值，也可以是表示bin的一个序列。默认值为10
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normed 如果值为True，直方图的值将进行归一化处理，形成概率密度，默认值为False
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color 指定直方图的颜色。可以是单一颜色值或颜色的序列。如果指定了多个数据集合,例如DataFrame对象，颜色序列将会设置为相同的顺序。如果未指定，将会使用一个默认的线条颜色
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orientation 通过设置orientation为horizontal创建水平直方图。默认值为vertical
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data = [1,1,2,2,2,3,4,5,6,6,6,6,6,6,7,8,9,0] plt.hist(data,bins=20)#bins=20 20个柱子有的柱子高为0
(array([1., 0., 2., 0., 3., 0., 1., 0., 1., 0., 0., 1., 0., 6., 0., 1., 0.,
1., 0., 1.]),
array([0. , 0.45, 0.9 , 1.35, 1.8 , 2.25, 2.7 , 3.15, 3.6 , 4.05, 4.5 ,
4.95, 5.4 , 5.85, 6.3 , 6.75, 7.2 , 7.65, 8.1 , 8.55, 9. ]),
<a list of 20 Patch objects>)
饼图
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pie()，饼图也只有一个参数x
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饼图适合展示各部分占总体的比例，条形图适合比较各部分的大小
arr=[11,22,31,15] plt.pie(arr)
([<matplotlib.patches.Wedge at 0x1178be1d0>, <matplotlib.patches.Wedge at 0x1178be6a0>, <matplotlib.patches.Wedge at 0x1178beb70>, <matplotlib.patches.Wedge at 0x1178c60f0>], [Text(0.996424,0.465981,''), Text(-0.195798,1.08243,''), Text(-0.830021,-0.721848,''), Text(0.910034,-0.61793,'')])
arr=[0.2,0.3,0.1] plt.pie(arr)
([<matplotlib.patches.Wedge at 0x1177d0e80>, <matplotlib.patches.Wedge at 0x1177da390>, <matplotlib.patches.Wedge at 0x1177da8d0>], [Text(0.889919,0.646564,''), Text(-0.646564,0.889919,''), Text(-1.04616,-0.339919,'')])
arr=[11,22,31,15] plt.pie(arr,labels=['a','b','c','d'])
([<matplotlib.patches.Wedge at 0x11794aa90>, <matplotlib.patches.Wedge at 0x11794af60>, <matplotlib.patches.Wedge at 0x1179544e0>, <matplotlib.patches.Wedge at 0x117954a20>], [Text(0.996424,0.465981,'a'), Text(-0.195798,1.08243,'b'), Text(-0.830021,-0.721848,'c'), Text(0.910034,-0.61793,'d')])
arr=[11,22,31,15] plt.pie(arr,labels=['a','b','c','d'],labeldistance=0.3)
([<matplotlib.patches.Wedge at 0x1179e2278>, <matplotlib.patches.Wedge at 0x1179e2748>, <matplotlib.patches.Wedge at 0x1179e2c18>, <matplotlib.patches.Wedge at 0x1179eb198>], [Text(0.271752,0.127086,'a'), Text(-0.0533994,0.295209,'b'), Text(-0.226369,-0.196868,'c'), Text(0.248191,-0.168526,'d')])
arr=[11,22,31,15] plt.pie(arr,labels=['a','b','c','d'],labeldistance=0.3,autopct='%.6f%%')
([<matplotlib.patches.Wedge at 0x117a709e8>, <matplotlib.patches.Wedge at 0x117a7a128>, <matplotlib.patches.Wedge at 0x117a7a898>, <matplotlib.patches.Wedge at 0x117a83048>], [Text(0.271752,0.127086,'a'), Text(-0.0533994,0.295209,'b'), Text(-0.226369,-0.196868,'c'), Text(0.248191,-0.168526,'d')], [Text(0.543504,0.254171,'13.924050%'), Text(-0.106799,0.590419,'27.848101%'), Text(-0.452739,-0.393735,'39.240506%'), Text(0.496382,-0.337053,'18.987341%')])
arr=[11,22,31,15] plt.pie(arr,labels=['a','b','c','d'],labeldistance=0.3,shadow=True,explode=[0.2,0.3,0.2,0.4])
([<matplotlib.patches.Wedge at 0x117ab2390>, <matplotlib.patches.Wedge at 0x117ab2b38>, <matplotlib.patches.Wedge at 0x117abb390>, <matplotlib.patches.Wedge at 0x117abbba8>], [Text(0.45292,0.21181,'a'), Text(-0.106799,0.590419,'b'), Text(-0.377282,-0.328113,'c'), Text(0.579113,-0.393228,'d')])
散点图scatter()
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因变量随自变量而变化的大致趋势
x = np.array([1,3,5,7,9]) y = x ** 2 - 3 plt.scatter(x,y)
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标签：plot,arr,plt,figure,Text,pie,matplotlib,绘图 From： https://www.cnblogs.com/erhuoyuan/p/16936705.html