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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

Exploring regression modeling

You have already used regression models in Chapter 9 and Chapter 10. Here, we will go deeper into regression modeling and compare linear and non-linear models for data modeling. A famous early example of regression analysis was produced by Sir Francis Galton, who lived in England from 1822 to 1911. Among many activities, Galton collected data on the heights of fathers and mothers and their adult children. It is notable that today, the data would be considered biased, as the sample was most likely from more affluent families that had access to better nutrition and living conditions than the average for the time in England. Nonetheless, the data serves as a good introduction to regression:

  1. Here, we load a simplified version of the data (adapted from the original) into a pandas DataFrame and plot the heights of all the children and the fathers:
    galton_heights = pd.read_csv('Datasets/galton.csv')
    galton_heights.head()

This produces...

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