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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

Understanding the differences between base Python and pandas data selection

For the most part, once you have learned a bit of pandas notation for slicing and indexing, pandas objects work nearly transparently with core Python. Since the indexing of some different object types looks similar, here, we'll touch on some of the differences so that you can avoid surprises in the future.

Lists versus Series access

Python lists look superficially like Series. When you're using bracket notation to index a Series, it works much the same way as indexing a list. Here, we make a simple list using the range() function, then print out 11 values within the list:

my_list = list(range(100))
print(my_list[12:33])

This will produce the following output:

[12  13,  14,  15,  16,  17,  18,  19,  20,  21,  22]

Now, let's attempt the same thing, but using .iloc[]:

print(my_list...

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