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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

Summary

In this final chapter, you got hands-on practice with different data processing tasks done on real-world datasets. In the first dataset, you explored different methods of data processing. Some of the key methods implemented were for converting from wide format to long format, merging two DataFrames, and imputing missing data using the interpolate method.

With the second dataset, you practiced preprocessing tasks before plotting, such as grouping and aggregation, and converting continuous data into categorical data using binning. You also answered questions about the data using line plots and bar charts.

Using the third dataset, you extracted geolocations from latitude and longitude information. After extracting geolocation information, you also answered some questions on the service level of bus routes.

Finally, with the fourth dataset, we used different methods to preprocess data to build a classification model. You should now be able to confidently tackle most data...

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