【数据分析可视化】公式、坐标轴

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# 公式
plt.title('equals',fontsize=15)
plt.xlim([1,8])
plt.ylim([1,5])
plt.text(2,4,r'$ \alpha \beta \pi \lambda \omega $',size=15)
plt.text(4,4,r'$ \sin(0)=\cos(\frac{\pi}{2}) $',size=20)
plt.text(2,2,r'$ \lim_{x \rightarrow y} \frac{1}{x^3} $',size=8)
plt.text(4,2,r'$ \sqrt[4]{x}=\sqrt{y} $',size=20)
plt.show()

# 公式
plt.title('equals',fontsize=15)
plt.xlim([-4,4])
plt.ylim([-4,4])
plt.text(-2,2,r'NWNU',alpha=0.5,size=25,bbox=dict(facecolor='red', alpha=0.1))
plt.text(-2,0,r'y=sin(x)',size=20,bbox=dict(facecolor='red'))
plt.text(0,-2,r'y=cos(x)',size=20,bbox=dict(facecolor='BLUE'))

plt.show()

# xy
x = np.linspace(0,10,1000)
y = np.sin(x)
z = np.cos(x**2)

x

array([ 0.        ,  0.01001001,  0.02002002,  0.03003003,  0.04004004,
        0.05005005,  0.06006006,  0.07007007,  0.08008008,  0.09009009,
        0.1001001 ,  0.11011011,  0.12012012,  0.13013013,  0.14014014,
        0.15015015,  0.16016016,  0.17017017,  0.18018018,  0.19019019,
        0.2002002 ,  0.21021021,  0.22022022,  0.23023023,  0.24024024,
        0.25025025,  0.26026026,  0.27027027,  0.28028028,  

y

array([ 0.        ,  0.01000984,  0.02001868,  0.03002552,  0.04002934,
        0.05002916,  0.06002396,  0.07001275,  0.07999452,  

# 线段图
plt.plot(x,y,'r',linewidth=2,label='sin(x)')
plt.legend()

<matplotlib.legend.Legend at 0x1207d1a50>

plt.plot(x,z,'g--',linewidth=3,label='cos(x^2)')
plt.legend()

<matplotlib.legend.Legend at 0x120916850>

# 坐标轴https://www.cnblogs.com/ahochen/p/10702817.html
x = np.linspace(-1, 1, 10)
y1 = 2*x
y2 = x*x
plt.figure()
plt.plot(x, y1)
plt.plot(x, y2, color="blue", linestyle="--", linewidth=1.0)

plt.xlabel("x label")
plt.ylabel("y label")

x_stick = np.linspace(-1, 1, 5)
plt.xticks(x_stick)
plt.yticks([1, 2, 3, 4], ["$bad\ xixi$", "$ordinary$", "$good$", "$best$"])

ax = plt.gca()
#使用spines设置边框，共有（"left","right","top","bottom"）四种选项，使用set_color()设置颜色
ax.spines["right"].set_color("none")
ax.spines["top"].set_color("none")
#将底部边框放大y=0的位置
ax.spines["bottom"].set_position(("data", 0))
ax.spines["left"].set_position(("data", 0))
# 所有位置：top，bottom，both，default，none
ax.xaxis.set_ticks_position('top')
ax.xaxis.set_label_position('top')
# 所有位置：left，right，both，default，none
ax.yaxis.set_ticks_position('right')
ax.yaxis.set_label_position('right')

plt.show()