How to plot data from a text file using Matplotlib?

Perquisites: Matplotlib, NumPy

In this article, we will see how to load data files for Matplotlib. Matplotlib is a 2D Python library used for Date Visualization. We can plot different types of graphs using the same data like:

• Bar Graph
• Line Graph
• Scatter Graph
• Histogram Graph and many.

In this article, we will learn how we can load data from a file to make a graph using the "Matplotlib" python module. Here we will also discuss two different ways to extract data from a file.  In the First Module, we will discuss extracting data using the inbuilt CVS module and In the Second Module, we will use a third-party "NumPy" Module to extract data from a file. 

Requirement:

A text file from where data should be extracted.  Let the file name = GFG.txt

Method 1: In this method, we will extract data using CSV module to load CVS files.
  
Step 1:

Import all required modules.

import matplotlib.pyplot as plt
import csv

Step 2: Create X and Y variables to store X-axis data and Y-axis data from a text file.  

import matplotlib.pyplot as plt
import csv

X = []
Y = []

Step 3: Open text file in read mode. Pass 'file_name' and delimiter in reader function and store returned data in a new variable. 

import matplotlib.pyplot as plt
import csv

X = []
Y = []

with open('GFG.txt', 'r') as datafile:
    plotting = csv.reader(datafile, delimiter=',')

Step 4: Create a loop, that will append the data in X and Y variable.

import matplotlib.pyplot as plt
import csv

X = []
Y = []

with open('GFG.txt', 'r') as datafile:
    plotting = csv.reader(datafile, delimiter=',')
    
    for ROWS in plotting:
        X.append(int(ROWS[0]))
        Y.append(int(ROWS[1]))

Step 5: Now pass all the parameter in their respective functions.

import matplotlib.pyplot as plt
import csv

X = []
Y = []

with open('GFG.txt', 'r') as datafile:
    plotting = csv.reader(datafile, delimiter=',')
    
    for ROWS in plotting:
        X.append(int(ROWS[0]))
        Y.append(int(ROWS[1]))

plt.plot(X, Y)
plt.title('Line Graph using CSV')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

Output:

Method 2: In this method, we will extract data using numpy module to load files. Here you will notice that Step 2,3 and 4 are replaced by np.loadtxt( )

import matplotlib.pyplot as plt
import numpy as np

X, Y = np.loadtxt('GFG.txt', delimiter=',', unpack=True)

plt.bar(X, Y)
plt.title('Line Graph using NUMPY')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

Output:

You can also try other different graphs by just changing 1 line

plt.plot(X,Y) to plt.scatter(X,Y) or plt.plot(X,Y)

• Using plt.bar()

import matplotlib.pyplot as plt
import numpy as np

X, Y = np.loadtxt('GFG.txt', delimiter=',', unpack=True)

plt.plot(X, Y)
plt.title('Line Graph using NUMPY')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

Output:

• Using plt.scatter()

import matplotlib.pyplot as plt
import numpy as np

X, Y = np.loadtxt('GFG.txt', delimiter=',', unpack=True)

plt.scatter(X, Y)
plt.title('Line Graph using NUMPY')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

Output:

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Article Tags :

• Data Visualization
• AI-ML-DS
• Python-matplotlib
• AI-ML-DS With Python
• Python Data Visualization

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