import matplotlib.pyplot as pd
labels=['saheli','kesla','sukhtawa','podar']
sizes=[215,180,241,130]
colors=['green','red','blue','cyan']
explode=(0.2,0,0,0) #explode 2nd slice
pd.pie(sizes,explode=explode,labels=labels,colors=colors,shadow=True)
pd.axis('equal')
pd.show()

import matplotlib.pyplot as np
factories=['yadavproductions','patelorg','winefactory','milkianswaleorg']
sizes=[290,130,102,240]
colors=['blue','lightgreen','grey','yellow']
explode=(0.1,0,0,0) #explode 1st slice
np.pie(sizes,labels=factories,explode=explode,colors=colors,shadow=True)
np.title('factories associated in our village')
np.axis('equal')
np.show()

import matplotlib.pyplot as plt
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
languages=['c','c++','java','python','php']
students=[23,17,35,29,12]
ax.bar(languages,students)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
data=[[30,25,50,20],[40,23,51,17],[35,22,45,19]]
x=np.arange(5)
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.bar(x+0.15,data[1],color='blue',width=0.25)
ax.bar(x+0.25,data[2],color='green',width=0.25)
ax.bar(x+0.50,data[3],color='red',width=0.25)
pyplot.legend()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
n=5
menmeans=(20,35,30,35,27)
womenmeans=(25,32,34,20,25)
ind=np.arange(n) #the x locations for the groups
width=0.35
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.bar(ind,menmeans,width,color='r')
ax.bar(ind,womenmeans,width,bottom=menmeans,color='b')
ax.set_ylabel('scores')
ax.set_title('scores by group and gender')
ax.set_xticks(ind,('g1','g2','g3','g4','g5'))
ax.set_yticks(np.arange(0,81,10))
ax.legend(labels=['men','women'])
plt.show()

from matplotlib import pyplot as plt
import numpy as np
fig,ax=plt.subplots(1,1)
a=np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])
ax.hist(a,bins=[0,25,50,75,100])
ax.set_title("histogram of result")
ax.set_xticks([0,25,50,75,100])
ax.set_xlabel('marks')
ax.set_ylabel('no. of students')
plt.show()

from matplotlib import pyplot as plt
import numpy as np
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.axis('equal')
languages=['c','c++','java','python','php']
students=[23,17,35,29,12]
ax.pie(students,labels=languages,autopct='%1.2f%%')
plt.show()

import matplotlib.pyplot as plt
girls_grades=[89,90,70,89,100,80,90,100,80,34]
boys_grades=[30,29,49,48,100,48,38,45,20,30]
grades_range=[10,20,30,40,50,60,70,80,90,100]
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.scatter(grades_range,girls_grades,color='r')
ax.scatter(grades_range,boys_grades,color='b')
ax.set_xlabel('grades range')
ax.set_ylabel('grades scored')
ax.set_title('scatter plot')
plt.show()

import numpy as np
import matplotlib.pyplot as plt
xlist=np.linspace(-3.0,3.0,100)
ylist=np.linspace(-3.0,3.0,100)
x,y=np.meshgrid(xlist,ylist)
z=np.sqrt(x**2+y**2)
fig,ax=plt.subplots(1,1)
cp=ax.contourf(x,y,z)
fig.colorbar(cp) #add a colorbar to a plot
ax.set_title('filled contours plot')
#ax.set_xlabel('x(cm)')
ax.set_ylabel('y(cm)')
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x,y=np.meshgrid(np.arange(-2,2,.2),np.arange(-2,2,.25))
z=x*np.exp(-x**2-y**2)
v,u=np.gradient(z,.2,.2)
fig,ax=plt.subplots()
q=ax.quiver(x,y,u,v)
plt.show()

from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
fig=plt.figure()
ax=plt.axes(projection='3d')
z=np.linspace(0,1,100)
x=z*np.sin(20*z)
y=z*np.cos(20*z)
c=x+y
ax.scatter(x,y,z,c=c)
ax.set_title('3d scatter plot')
plt.show()

from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
def f(x,y):
    return np.sin(np.sqrt(x**2+y**2))
x=np.linspace(-6,6,30)
y=np.linspace(-6,6,30)
x,y=np.meshgrid(x,y)
z=f(x,y)
fig=plt.figure()
ax=plt.axes(projection='3d')
ax.contour3D(x,y,z,50,cmap='binary')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.set_title('3d contour')
plt.show()
 

from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
x=np.outer(np.linspace(-2,2,30),np.ones(30))
y=x.copy().T #transpose
z=np.cos(x**2+y**2)
fig=plt.figure()
ax=plt.axes(projection='3d')
ax.plot_surface(x,y,z,cmap='viridis',edgecolor='none')
ax.set_title('surface plot')
plt.show()

print("hello matplotlib")

from matplotlib import pyplot as plt
import numpy as np
import math #needed for definition of pi
x=np.arange(0,math.pi*2,0.05)
y=np.sin(x)
plt.plot(x,y)
plt.xlabel("angle")
plt.ylabel("sine")
plt.title('sine wave')
plt.show()

print('hello anand')

print('yadav')

import pandas as pd
from matplotlib import pyplot as plt

data.head()
df=pd.dataframe(data)
name=df['car'].head(12)
price=df['price'].head(12)
fig,ax=plt.subplots(figsize=(16,9))
ax.barh(name,price)
for s in['top','bottom','left','right']:
    ax.spines[s].set_visible(false)
    ax.xaxis.set_tick_position('none')
    ax.yaxis.set_tick_position('none')
    ax.xaxis.set_tick_params(pad=5)
    ax.yaxis.set_tick_params(pad=10)
    ax.grid(b=True,color='grey',linestyle='-.',linewidth=0.5,alpha=0.2)
    ax.invert_yaxis()
    for i in ax.patches:
        plt.text(i.get_width()+0.2,i.get_y()+0.5,str(round((i.get_width()),2)),fontsize=10,fontweight='bold',color='grey')
        ax.set_title('sports car and their price in crore',loc='left',)
        fig.text(0.9,0.15,'AnandYadav03',fontsize=12,color='green',ha='right',va='bottom',alpha=0.7)
        plt.show()

import pandas as pd
data={"calories":[480,320,290],"duration":[60,50,30]}
df=pd.DataFrame(data)
print(df)
print(df.loc[0])
print(df.loc[[0,1]])

import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pandas import DataFrame
fig=plt.figure()
fig=plt.figure(figsize=(12,8),dpi=80)
ax=fig.add_subplot(111,projection='3d')
pnt3d=ax.scatter3D(data_with_dummies_without_2017
['Year'],data_with_dummies_without_2017['Machine'],
data_with_dummies_without_2017['Grinding_Volume'],c=data_with_dummies_without_2017['Grinding_volume'])
cbar=plt.color(pnt3d)
cbar.set_label('Grinding Volume(cm3)')
fig.set_facecolor('white')
ax.set_facecolor('white')
plt.xticks(np.arange(2018,2021,1))
plt.yticks(np.arange(1,3,1))
ax.set_xlabel('Year')
ax.set_ylabel('Machine')
ax.set_zlabel('Grinding Volume(cm3)')
plt.show()