CSV stands for “comma separated values”, that means the values are distinguished by putting commas and newline characters. A CSV file provides a table like format that can be read by almost every spreadsheet reader like Microsoft Excel and Google Spreadsheet.
A Bar Graph uses labels and values where label is the name of a particular bar and value represent the height of the bar. A Bar Graph is commonly used in data analytics where we want to compare the data and extract the most common or highest groups.
In this post, we will learn how to plot a bar graph using a CSV file. There are plenty of modules available to read a .csv file like csv, pandas, etc. But in this post we will manually read the .csv file to get an idea of how things work.
Functions Used
- Pandas read_csv() function is used to read a csv file.
Syntax:
read_csv(“file path”)
- Matplotlib’s bar() function is used to create a bar graph
Syntax:
plt.bar(x, height, width, bottom, align)
Method 1: Using pandas
Approach
- Import module
- Read file using read_csv() function
- Plot bar graph
- Display graph
Example:
Dataset in use: Click here
Python3
# Import the necessary modulesimport matplotlib.pyplot as pltimport pandas as pd # Initialize the lists for X and Ydata = pd.read_csv('C:\\Users\\Vanshi\\Desktop\\data.csv') df = pd.DataFrame(data) X = list(df.iloc[:, 0])Y = list(df.iloc[:, 1]) # Plot the data using bar() methodplt.bar(X, Y, color='g')plt.title("Students over 11 Years")plt.xlabel("Years")plt.ylabel("Number of Students") # Show the plotplt.show() |
Output:
Method 2: Using Matplotlib
Approach
- Import module
- Open file
- Read data
- Plot bar graph
- Display graph
Program:
Dataset in use: Click here to download this file.
Python3
# Import the modulesimport matplotlib.pyplot as plt # Initialize a dictionary for monthsdata = dict() # Read the datawith open('electronics.csv', 'r') as f: for line in f.readlines(): # Store each line in the dictionary month, item, quantity = line.split(',') if month not in data: data[month] = [] data[month].append((item, int(quantity))) # Position of each subplot where 221 means 2 row,# 2 columns, 1st indexpositions = [221, 222, 223, 224] # Colors to distinguish the plotcolors = ['r', 'g', 'b', 'y'] # Plot the subgraphsfor i, l in enumerate(data.keys()): plt.subplot(positions[i]) data_i = dict(data[l]) plt.bar(data_i.keys(), data_i.values(), color=colors[i]) plt.xlabel(l) # Show the plotsplt.show() |
Output:
