All the Basic Types of Visualization That Is Available in Pandas and Some Advanced Visualization That Are Extremely Useful and Time Saver
We use python’s pandas' library primarily for data manipulation in data analysis. But we can use Pandas for data visualization as well. You even do not need to import the Matplotlib library for that. Pandas itself can use Matplotlib in the backend and render the visualization for you. It makes it really easy to makes a plot using a DataFrame or a Series. Pandas use a higher-level API than Matplotlib. So, it can make plots using fewer lines of code.
I will start with the very basic plots using random data and then move to the more advanced one with a real dataset.
I will use a Jupyter notebook environment for this tutorial. If you do not have that installed, you can simply use a Google Colab notebook. You even won’t have to install pandas on it. It already has that installed for us.
If you want a Jupyter notebook installed that’s also a great idea. Please go ahead and install the anaconda package.
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