Grouped bar charts in Matplotlib are hard to understand. Here’s an attempt at making them easier to understand and create.
As I was working on freeCodeCamp’s Data Analysis with Python certification, I came across a tricky Matplotlib visualization: a grouped bar chart. I’ve been making my way through the projects, but the guidance is minimal. This is good because it makes you put in the work to arrive at the desired solution, but it is awful if you don’t have much experience with Matplotlib, pandas and Numpy, or even if you’re just having difficulties with the current exercise.
So, I’m writing this article to share my solution on how to create the grouped bar chart from the “Page View Time Series Visualizer” project. I had a hard time understanding how to create this visualization in Matplotlib so I hope this article is enlightening for your data analysis projects.
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Many a time, I have seen beginners in data science skip exploratory data analysis (EDA) and jump straight into building a hypothesis function or model. In my opinion, this should not be the case.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
Python for Data Science, you will be working on an end-to-end case study to understand different stages in the data science life cycle. This will mostly deal with "data manipulation" with pandas and "data visualization" with seaborn. After this, an ML model will be built on the dataset to get predictions. You will learn about the basics of the sci-kit-learn library to implement the machine learning algorithm.