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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

Data selection in pandas DataFrames

In Chapter 3, Data Structures, we studied the two core pandas data structures, DataFrames and Series. There, we did some very basic data selection without digging into the details of how it works. In this section, we will do a deeper dive and explore the index, which is fundamental to many pandas operations.

As you may recall when we introduced the idea of DataFrames, we drew analogies to spreadsheets. Let's revisit that analogy. Here is the same figure from Chapter 2, Data Structures (which is the data from Figure 5.1 but in a spreadsheet):

Figure 5.2 – The industry GDP data in a spreadsheet

Here, we can see the same three columns of data that were shown in Figure 5.1, but we have annotated the key differences. In pandas, the standard row index starts at 0, while for most spreadsheets, it starts at row 1. This "0 indexing" is standard for Python. An index in pandas is a series of numbers or strings...

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