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()
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