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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

Datetime math operations

We have shown already some operations that can be applied to datetime/Timestamp objects. In this section, we will go a little deeper and show the use of the origin parameter, which is useful if converting dates in some integer formats (such as might come from Excel).

Date ranges

Suppose you were provided with some data that is daily temperatures from 1/1/2019 to 6/30/2020 but it is provided as simply a series of values without the corresponding dates. You would like to be able to work with the data corresponding to actual dates, for example, to look for repeating patterns or seasonal variations. You can add the dates easily using the pandas date_range method:

  1. Here, you create a temperatures series beginning with just an integer series, using the NumPy sin() function and a period of 180 days to generate variation over time, and adding noise to represent the hypothetical data. The first step is to create the integer series:
    x_values = pd.Series...

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