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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

Subsetting by data types

How can we extract specific data within data using its type? Through subsetting the data with pandas, we can locate and manipulate datasets easily. Subsetting is used frequently during a real analysis as it gives you the power to change data dynamically.

For text data (dtype = object or dtype = category), pandas provides methods to perform string transformation that are referred to as "on the fly." These string transforming methods can be accessed through the str attribute when they're used in a pandas Series or Index.

Let's look at the most important strings methods by covering a few examples:

  • We will start by defining a series with a string:
    import pandas as pd
    s = pd.Series(['pandas is awesome'])
    s

The output will be as follows:

0     pandas is awesome
dtype:  object
  • Now, let's use the split() method to split the series into a list of three strings:
    s.str...

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