In this article, I will use real datasets named iris and movies. You can download these data sets here. Let’s load the famous iris dataset.
Data visualization is one of the most enjoyable stages of data analysis. You can draw graphics very easily with the recently developed open source and free libraries. The Pandas library is one of the most used Python libraries for data preprocessing and data cleaning. Libraries such as Matplotlib and Seaborn are often used to visualize data. But with Pandas, you can easily visualize Series and DataFrame data. In my last article, I explained the bar, histogram and box graphics with Pandas. In summary, I will explain the following topics in this article.
Data visualization is the graphical representation of data in a graph, chart or other visual formats. It shows relationships of the data with images.
So here is my first blog regarding the data visualization with matplotlib in python. In this article we will cover the basic of the visualization with matplotlib.
Learning the basics of Exploratory Data Analysis (EDA) using Python with Numpy, Matplotlib, and Pandas. EDA in Python uses data visualization to draw meaningful patterns and insights. EDA is an approach of analyzing datasets to summarize their main characteristics, often with visual methods.
Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories.
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.