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Data visualization with Matplotlib

Likith Narukurthi

Matplotlib is a popular Python plotting library that plays a major role in subjects like data science, machine learning, etc. Visualization gives us access to huge amounts of data in easily digestible visuals.

Installation

 pip install matplotlib

Importing matplotlib

import matplotlib.pyplot as plt`

Applications

  • Line Chart
  • Bar Chart
  • Histograms
  • Scatter Plots
  • Pie Charts
  • Sub Plots

Line chart

# 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()

Multiple line chart

# 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()

Bar chart

# 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()

Multiple bar chart

# 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()

Histogram

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)

Scatter plot

# 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')

Pie chart

# 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()

Sub plots

# 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)

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data visualization
community creator
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