import matplotlib.pyplot as plt
import numpy as np
x=[0,1,2,3,4,5]
y=[0,1,4,9,16,25]
plt.plot(x,y)
plt.show()

x1=[1,2,3]
y1=[2,4,1]
plt.plot(x1,y1,label="line 1")
x2=[1,2,3]
y2=[4,1,3]
plt.plot(x2,y2,label="line 2")
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Multi-Line Chart')
plt.legend()
plt.show()

x=[1,2,3,4,5]
y1=[10,12,14,16,18]
y2=[5,7,9,11,13]
y3=[2,4,6,8,10]
plt.fill_between(x,y1,color='blue')
plt.fill_between(x,y2,color='green')
plt.plot(x,y1,color='red')
plt.plot(x,y2,color='black')
plt.legend(['y1','y2'])
plt.show()

plt.stackplot(x,y1,y2,y3,labels=['Line 1','Line 2','Line 3'])
plt.legend(loc='upper left')
plt.title('Stacked Line Chart')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

from scipy.interpolate import make_interp_spline
x=np.array([1,2,3,4,5,6,7,8,9,10])
y=np.array([2,4,5,7,8,8,9,10,11,12])
spl=make_interp_spline(x,y)
x1=np.linspace(x.min(),x.max(),100)
y1=spl(x1)
plt.plot(x,y,'*',label='data')
plt.plot(x1,y1,'-',label="spline")
plt.legend()
plt.show()

val=[5,6,3,7,2]
names=["A","B","C","D","E"]
plt.bar(names,val,color="blue")
plt.show()

x=[0,1,2,3,4,5]
y=[0,1,4,9,16,25]
plt.scatter(x,y,s=30,color="red")
plt.show()

x = [1, 2, 3, 4, 5]
y = [10, 15, 20, 25, 30]
sizes = [100, 200, 300, 400, 500]
plt.scatter(x, y, s=sizes, alpha=0.5)
plt.xlabel('x_values')
plt.ylabel('y_values')
plt.title('Bubble Chart')
plt.show()

ages=[2,5,70,40,30,45,50,45,43,40,44,60,7,13,57,18,90,77,32,21,20,40]
range=(0,100)
bins=10
plt.hist(ages,bins,range,color='purple',histtype='bar',rwidth=0.8)
plt.xlabel('age')
plt.ylabel('No. Of People')
plt.title('Histogram')
plt.show()

np.random.seed(0)
data=np.random.normal(loc=0,scale=1,size=100)
data
fig,ax=plt.subplots()
ax.boxplot(data)
ax.set_xlabel('Data')
ax.set_ylabel('Values')
ax.set_title('Box Plot')

data = [np.random.normal(loc=0, scale=1, size=100),
        np.random.normal(loc=2, scale=1, size=100),
        np.random.normal(loc=1, scale=2, size=100)]
plt.violinplot(data)
plt.xlabel('Groups')
plt.ylabel('Values')
plt.title('Violin Plot')
plt.xticks([1, 2, 3], ['Group 1', 'Group 2', 'Group 3'])
plt.show()

data = np.random.normal(0, 1, 1000)
plt.hist(data, bins=30, density=True, alpha=0.5)
plt.title('Density Plot Example')
plt.xlabel('Values')
plt.ylabel('Density')
from scipy.stats import gaussian_kde
kde = gaussian_kde(data)
x_vals = np.linspace(min(data), max(data), 1000)
plt.plot(x_vals, kde(x_vals), 'r')
plt.show()

act=['eat','sleep','work','play']
slices=[3,7,8,6]
color=['r','y','g','b']
plt.pie(slices,labels=act,colors=color,startangle=90,shadow=True,explode=(0.1,0.1,0.1,0.1),radius=1.2,
autopct='%1.1f%%')
plt.legend()
plt.show()