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The Pandas Workshop

The Pandas Workshop

By : Blaine Bateman, Saikat Basak , Thomas Joseph, William So
4.8 (16)
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The Pandas Workshop

The Pandas Workshop

4.8 (16)
By: Blaine Bateman, Saikat Basak , Thomas Joseph, William So

Overview of this book

The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects. You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you’ll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services. By the end of this data analytics book, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.
Table of Contents (21 chapters)
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1
Part 1 – Introduction to pandas
6
Part 2 – Working with Data
11
Part 3 – Data Modeling
15
Part 4 – Additional Use Cases for pandas

Understanding the basics of pandas visualization

pandas has built-in plot generation capabilities that can be used to visualize both DataFrames and series alike. pandas comes with a built-in plot function that acts as a wrapper on top of the matplotlib plot function. This means that pandas is actually using the matplotlib library but with a simplified syntax. This presents the advantage of being much easier to use (less code and simpler syntax) compared to matplotlib. It provides a wide range of functionality and flexibility to plot data analytics charts with given data.

To start off using pandas in-built visualizations, you will need to know several key parameters for the .plot() function, which can be called from a DataFrame. Some of these are listed as follows:

  • kind: This is the type of plot (bar, barh, pie, scatter, kde, and so on).
  • color: This is the color of the plot.
  • linestyle: This is the style of the line used in the plot (solid, dotted, and dashed).
  • ...

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