Data visualization with Matplotlib

# importing required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


# Generation of variables 
x=np.arange(0,10) #Array of range 0 to 9
y=x**3

# Printing the variables
print(x)
print(y)

plt.plot(x,y) # Function to plot
plt.title('Line Chart') # Function to give title
  
# Functions to give x and y labels
plt.xlabel('X-Axis') 
plt.ylabel('Y-Axis')

# Functionn to show the graph  
plt.show()

# importing required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


# Generation of 1 set of variables 
x = np.arange(0,11)
y = x**3

# Generation of 1 set of variables
x2 = np.arange(0,11)
y2 = (x**3)/2

# Printing all variables
print(x,y,x2,y2,sep="\n")

# "linewidth" is used to specify the width of the lines
# "color" is used to specify the colour of the lines
# "label"is used to specify the name of axes to represent in the lengend 
plt.plot(x,y,color='r',label='first data', linewidth=5) 
plt.plot(x2,y2,color='y',linewidth=5,label='second data')
plt.title('Multiline Chart')

# Uses the label attribute to display reference in legend
plt.ylabel('Y axis')
plt.xlabel('X axis')

# Shows the legend in the best postion with respect to the graph
plt.legend()
plt.show()

# Importing required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Generation of variables 
x = ["India",'USA',"Japan",'Australia','Italy']
y = [6,7,8,9,2]

# Printing the variables
print(x)
print(y)

plt.bar(x,y, label='Bars1', color ='r') # Function to plot
  
# Function to give x and y labels 
plt.xlabel("Country")
plt.ylabel("Inflation Rate%")

# Function to give heading of the chart
plt.title("Bar Graph")

# Function to show the chart
plt.show()

# importing required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Generation of 1 set of variables 
x = ["India",'USA',"Japan",'Australia','Italy']
y = [6,7,8,9,5]

# Generation of 2 set of variables
x2 = ["India",'USA',"Japan",'Australia','Italy']
y2 = [5,1,3,4,2]

# Printing all variables
print(x,y,x2,y2,sep="\n")

# Functions to plot 
plt.bar(x,y, label='Inflation', color ='y')
plt.bar(x2,y2, label='Growth', color ='g')

# Functions to give x and y labels
plt.xlabel("Country")
plt.ylabel("Inflation & Growth Rate%")
  
plt.title("Multiple Bar Graph")
plt.legend()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


# Generation of variable
stock_prices = [32,67,43,56,45,43,42,46,48,53,73,55,54,56,43,55,54,20,33,65,62,51,79,31,27]

# Function to show the chart
plt.figure(figsize = (8,5))
plt.hist(stock_prices, bins = 5)

# Importing required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Generation of x and y variables
x = [1,2,3,4,5,6,7,8]
y = [5,2,4,2,1,4,5,2]

# Function to plot the graph
plt.scatter(x,y)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Scatter Plot')

# Importing required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Collection of raw data
raw_data={'names':['Nick','Sani','John','Rubi','Maya'],
'jan_score':[123,124,125,126,128],
'feb_score':[23,24,25,27,29],
'march_score':[3,5,7,6,9]}

# Segregating the raw data into usuable form/variables
df=pd.DataFrame(raw_data,columns=['names','jan_score','feb_score','march_score'])
df['total_score']=df['jan_score']+df['feb_score']+df['march_score']

# Printing the data
print(df)

# Function to plot the graph
plt.pie(df['total_score'],labels=df['names'],autopct='%.2f%%')
plt.axis('equal')
plt.axis('equal')
plt.show()

# Importing required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Defining the sixe og the figures
plt.figure(figsize=(10,10))

# Generation of variables
x = np.array([1,2,3,4,5,6,7,8])
y = np.array([5,2,4,2,1,4,5,2])

# Generating 4 subplots in form of 2x2 matrix
# In the line below the arguments of plt.subplot are as follows:
# 2- no. of rows
# 2- no. of columns
# 1- position in matrix
# Position (0,0)
plt.subplot(2,2,1)
plt.plot(x,y,'g')
plt.title('Sub Plot 1')
plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')

# Position (0,1)
plt.subplot(2,2,2)
plt.plot(y,x,'b')
plt.title('Sub Plot 2')
plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')

# Position (1,0)
plt.subplot(2,2,3)
plt.plot(y*2,x*2,'y')
plt.title('Sub Plot 3')
plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')

# Position (1,1)
plt.subplot(2,2,4)
plt.plot(x*2,y*2,'m')
plt.title('Sub Plot 4')
plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')

# Function for layout and spacing
plt.tight_layout(h_pad=5, w_pad=10)