import matplotlib.pyplot as plt
import numpy as np
x=np.arange(-1000,1001)
x=x*(np.pi/500)
print(x)
y=np.cos(x)
plt.plot(x,y)
plt.show()

#example 
import matplotlib.pyplot as plt
import numpy as np
age=[2,4,6,8,10,12,14,16,18,20]
taille=[50,85,114,125,130,138,146,158,160,162]
plt.plot(age,taille,color='red',linestyle='--',marker='o',linewidth=2)
plt.title('les variations de taille avec age')
plt.xlabel('age')
plt.ylabel('taille')
age=[2,4,6,8,10,12,14,16,18,20]
poids=[10,12,20,25,30,38,48,52,54,56]
plt.plot(age,poids,marker='*',color='green')
plt.legend(['les variations de taille avec age','les variations de poids avec age'])
plt.show()

#histograms
#example
import matplotlib.pyplot as plt
import numpy as np
ages=[10,30,45,30,31,46,12,16,32,23,28,29]
bins=[10,20,30,40,50,60]
mean=np.mean(ages)
print(mean)
plt.hist(ages,bins=bins,edgecolor='black')
plt.axvline(mean,color='red')
plt.title('age distribution')
plt.xlabel('age')
plt.ylabel('frequancy')
plt.show()

#subplot
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('seaborn')
dev-x=[25,26,27,28,29,30,31,32,33,34,34]
dev-y=[38496,42000,46752,49320,53200,56000,62316,64928,67317,68748,73752]
fig,ax=plt.subplots(nrows=1,ncols=2)
axes[0].set-title('salary for each age')
axes[0].set-xlabel('age')
axes[0].set-ylabel('salary')
axes[0].plot(dev-x,dev-y,color='red',linestyle='--',marker='o',label='mean dev salary')
py-dev-x=[25,26,27,28,29,30,31,32,33,34,35]
py-dev-y=[23600,65999,45312,97845,32155,32645,20036,10099,78462,42366,16532]
axes[1].plot(py-dev-x,py-dev-y,color='black',linewidth=5,label='mean python salary')
axes[1].set-title('python salary for each age')
axes[1].set-xlabe('age')
axes[1].set-ylabel('salary')
axes[0].legend()
axes[1].legend()
plt.tight-layout()
plt.show()

#boxplot
import matplotlib.pyplot as plt
import numpy as np
x1=np.arange(2,50,20)
plt.boxplot(x1)