Automate matplotlib for Bar Charts

Creating a Real-Time Bar Chart with Python and Matplotlib

Creating a Real-Time Bar Chart with Python and Matplotlib

If you're a beginner in Python and are keen to explore data visualization, this post will guide you through creating a real-time bar chart using Python's Matplotlib library.

Prerequisites

  • Python installed
  • Matplotlib library installed

Code Overview

This code accomplishes the following:

  1. Imports the necessary libraries
  2. Initializes an array for bar heights
  3. Sets up the plotting window
  4. Iterates 50 times, updating the bar heights and re-plotting

Step-by-Step Code Walkthrough

Step 1: Import Matplotlib and Random Libraries

import matplotlib.pyplot as plt
import random

Step 2: Initialize Array

values = [0]*50

Step 3: Set Up the Plot

plt.xlim(0, 50)
plt.ylim(0, 100)

Step 4: Main Loop for Real-Time Plotting

for i in range(50):
    values[i] = random.randint(0, 50)
    plt.bar(list(range(50)), values)
    plt.pause(0.001)

plt.show()

Understanding the Code

The code initializes a list called values with 50 zeroes. The loop runs 50 times, each time replacing one of the zeroes with a random value between 0 and 50. The bar chart updates to reflect the new heights, occurring 50 times in total.

Conclusion

This example provides an introductory guide to real-time data visualization using Python and Matplotlib. In a few lines of code, we were able to create a dynamic bar chart that updates its bars in real-time.

This post made possible by NeuralNine

Comments

Post a Comment

Popular posts from this blog

Drawing Tables with ReportLab: A Comprehensive Example

Image
Tables play an integral part in visualizing and summarizing complex data in a clear, readable format. ReportLab offers a powerful object called `Table` for rendering data in table form within a PDF. By pairing this with `TableStyle`, users can customize tables to fit any styling requirements. Let's break down an example to understand the power and flexibility of ReportLab's table creation capabilities. The first step is to know your table and cast it to a list of list.  I didn't know how to do this off the top of my head, so some quick searching gave me the results! It is a common task for ReportLab users so there are many example out there.  Here is my example:  #Start working with the table budgetinlist = [budget.columns.tolist()] + budget.values.tolist() # Format numbers with commas for i in range(1, len(budgetinlist)):     for j in range(1, len(budgetinlist[i])):         try:             budgetinlist[i][j] = ...
Read more

Blog Topics

Image
This platform aims to systematically curate and preserve my favored Python techniques for future reference. This digital journal, accessible globally wherever an internet connection is available, is a convenient and effective learning tool.  Going down a wooded path at night, alone, with your trusted companion by your side, can be scary and daunting.  However, with support from your friends, documenting your path (curating a code base like a blog), and continuously working on the code base can make the process appear as a walk in the park!  In a world dominated by sophisticated language models like OpenAI's GPT, blogging may seem like a relic of the past. But here's a nuanced perspective: this blog serves as an innovative instrument for both teaching and mastering new programming languages. It's about immersing in syntax, applying it, and then translating the information into layman's terms for broader understanding. Each topic presented here is elaborated in a separate...
Read more

fillna

Image
Handling Missing Data with Pandas: A Comprehensive Guide Handling Missing Data with Pandas: A Comprehensive Guide Dealing with missing data is an essential part of the data cleaning process in Python programming. The Pandas library provides various methods to fill or drop missing values, depending on the nature of the data and the desired outcome. In this guide, we'll explore different techniques to handle missing data, including hard-coded values, filling specific columns, forward fill, and using rolling averages. 1. Filling All Missing Values with a Fixed Number You can simply use the fillna() function to replace all NaNs with a specific value: import pandas as pd df = pd.DataFrame({'A': [1, 2, None], 'B': [None, 5, 6]}) df.fillna(0) 2. Filling Specific Columns with Different Values df.fillna({1: 0.5, 2: 0}) 3. Forward Fill Method df.fillna(method='ff...
Read more