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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

The world of data

In today's digitally driven world, data is being generated at an ever-increasing pace. The World Economic Forum reports that, by 2025, 463 exabytes of data will be created each day globally. An exabyte is a 1 followed by 18 zeros. That is just a little less than 5,359 TB per second, or 5.3 million GB per second. Unsurprisingly, not all of this data is in the form of simple text files. While CSV files are very common and highly useful, they are just one of the many possible data formats we may want to work with while using pandas. In this chapter, we will explore more options to bring data into pandas DataFrames and Series, and store data back in memory. Such data operations are called input/output or I/O operations. For example, data from your finance team might be from software such as SAS or Stata. Furthermore, if you work with "big data," you may need to access Parquet or HDF data. Depending on your business requirements and the complexity of the...

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