matplotlib 数据可视化 - 线性图

作者: 东南有大树 | 来源:发表于2018-11-27 12:33 被阅读7次

matplotlib 数据可视化 - 线性图
数据分析入门
（建议收藏）Python可视化16matplotlib&sebo
#Matplotlib 练手# 西安人口数量变化
matplotlib
numpy数组知识点
Python数据分析：matplotlib！
Python库之数据可视化
Python - Matplotlib用法说明
笔记1_绘图与可视化

线性图、条状图、饼状图

import numpy as np
import matplotlib.pyplot as plt

线性图

线性图的各个数据点是由一条线来连接的。

如，将函数 y=sin(3*x)/x 展示在图表上。

'''创建x数据'''
x = np.arange(-2*np.pi,2*np.pi,0.01)  # 因为是正弦函数，所以x的值应该是pi的倍数或因数
y = np.sin(3*x)/x
plt.plot(x,y)
plt.show()

对上例进行扩展

显示y=sin(n*x)/x图像

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y)
plt.plot(x,y2)
plt.plot(x,y3)
plt.show()

上例中，我们绘制了三条线，可以看出，matplotlib会自动为不同的线赋予不同的颜色

尝试使用关键字参数color来指定线条的颜色，用linestyle来指定线型

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,'k--',linewidth=3)  # 这是我们之前了解到的
plt.plot(x,y2,'m-.')
plt.plot(x,y3,color='#87a3cc',linestyle='--')
plt.show()

之前，在plot()的第三个参数中，用r可以表示红色，g表示绿色，b表示蓝色，这里列举一些常用的颜色字符代表：

b 蓝色
g 绿色
r 红色
c 蓝绿色
m 洋红
y 黄色
k 黑色
w 白色

将上图中的x轴变成π的倍数

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,'k--',linewidth=3)
plt.plot(x,y2,'m-.')
plt.plot(x,y3,color='#87a3cc',linestyle='--')
plt.xticks([-2*np.pi,-np.pi,0,np.pi,2*np.pi],['$-2\pi$','$-\pi$','$0$','$1\pi$','$2\pi$'])
plt.show()

改变y轴的刻度值

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,'k--',linewidth=3)
plt.plot(x,y2,'m-.')
plt.plot(x,y3,color='#87a3cc',linestyle='--')
plt.xticks([-2*np.pi,-np.pi,0,np.pi,2*np.pi],['$-2\pi$','$-\pi$','$0$','$1\pi$','$2\pi$'])
plt.yticks([-1,0,1,2,3],['$-1$','$0$','$1$','$2$','$3$'])
plt.show()

总结：在设置x/y轴刻度的时候，ticks()中的参数，第一个列表表示刻度范围，第二个参数表示标签，用LeTeX表达式书写

接下来，尝试改变轴的位置

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,color='b')
plt.plot(x,y2,color='r')
plt.plot(x,y3,color='g')
plt.xticks([-2*np.pi,-np.pi,0,np.pi,2*np.pi],['$-2\pi$','$-\pi$','$0$','$1\pi$','$2\pi$'])
plt.yticks([-1,0,1,2,3],['$-1$','$0$','$1$','$2$','$3$'])

'''通过gac区取Axes对象，即获取画轴的对象'''
ax = plt.gca()
print(ax)

plt.show()

AxesSubplot(0.125,0.125;0.775x0.755)

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,color='b')
plt.plot(x,y2,color='r')
plt.plot(x,y3,color='g')
plt.xticks([-2*np.pi,-np.pi,0,np.pi,2*np.pi],['$-2\pi$','$-\pi$','$0$','$1\pi$','$2\pi$'])
plt.yticks([-1,0,1,2,3],['$-1$','$0$','$1$','$2$','$3$'])

'''通过gac区取Axes对象，即获取画轴的对象'''
ax = plt.gca()
'''spines表示画轴的边对象，这里设置右边和上边轴为无色'''
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
print(ax.spines)

plt.show()

OrderedDict([('left', <matplotlib.spines.Spine object at 0x7f35587482b0>), ('right', <matplotlib.spines.Spine object at 0x7f3558609a90>), ('bottom', <matplotlib.spines.Spine object at 0x7f3558622630>), ('top', <matplotlib.spines.Spine object at 0x7f35585a16a0>)])

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,color='b')
plt.plot(x,y2,color='r')
plt.plot(x,y3,color='g')
plt.xticks([-2*np.pi,-np.pi,0,np.pi,2*np.pi],['$-2\pi$','$-\pi$','$0$','$1\pi$','$2\pi$'])
plt.yticks([-1,0,1,2,3],['$-1$','$0$','$1$','$2$','$3$'])

'''通过gac区取Axes对象，即获取画轴的对象'''
ax = plt.gca()
'''spines表示画轴的边对象，这里设置右边和上边轴为无色'''
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
'''set_position指定某个轴在哪个位置，这里指定在数据的0，即穿过0'''
ax.spines['bottom'].set_position(('data',0))

plt.show()

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,color='b')
plt.plot(x,y2,color='r')
plt.plot(x,y3,color='g')
plt.xticks([-2*np.pi,-np.pi,0,np.pi,2*np.pi],['$-2\pi$','$-\pi$','$0$','$1\pi$','$2\pi$'])
plt.yticks([-1,0,1,2,3],['$-1$','$0$','$1$','$2$','$3$'])

'''通过gac区取Axes对象，即获取画轴的对象'''
ax = plt.gca()
'''spines表示画轴的边对象，这里设置右边和上边轴为无色'''
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
'''set_position指定某个轴在哪个位置，这里指定在数据的0，即穿过0'''
ax.spines['bottom'].set_position(('data',0))
'''然后设置y轴的名称，并让它也穿过0'''
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

plt.show()

完整代码：

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,color='b')
plt.plot(x,y2,color='r')
plt.plot(x,y3,color='g')
plt.xticks([-2*np.pi,-np.pi,0,np.pi,2*np.pi],['$-2\pi$','$-\pi$','$0$','$1\pi$','$2\pi$'])
plt.yticks([-1,0,1,2,3],['$-1$','$0$','$1$','$2$','$3$'])

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()

用箭头将公式标签指向某一点

使用到的函数是annotate()

x = np.arange(-2*np.pi,2*np.pi,0.01)
y = np.sin(3*x)/x
y2 = np.sin(2*x)/x
y3 = np.sin(1*x)/x
plt.plot(x,y,color='b')
plt.plot(x,y2,color='r')
plt.plot(x,y3,color='g')
plt.xticks([-2*np.pi,-np.pi,0,np.pi,2*np.pi],['$-2\pi$','$-\pi$','$0$','$1\pi$','$2\pi$'])
plt.yticks([-1,0,1,2,3],['$-1$','$0$','$1$','$2$','$3$'])

plt.annotate(r'$\lim_{x\to 0}\frac{\sin(x)}{x}=1$',  # 表达式
             xy=[0,1],  # 坐标
             xycoords='data',  
             xytext=[30,30],  # 文本坐标
             fontsize=16,  # 字体大小 
             textcoords='offset points',
             arrowprops=dict(arrowstyle="->",connectionstyle="arc3, rad=.2"))  # 指定箭头

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()

为pandas数据结构绘制线性图

import pandas as pd

data = {'one':[1,3,4,5,5],
       'two':[2,4,5,2,7],
       'three':[3,2,4,8,9]}
df = pd.DataFrame(data)
x = np.arange(5)
plt.axis([0,5,0,7])
plt.plot(x,df)
plt.legend(data, loc=2)
plt.show()