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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

Introduction to the case studies and datasets

Data cleaning and preparation usually take up to 80% of the time in a data analytics life cycle. Transactional datasets can have multiple failure modes, some of the prominent ones being missing data points, incompatible formats, variability in data types, incorrect spellings in data, and unwanted characters and white spaces in data.

These are just some examples of how data can be messy. The success of a data analyst will depend on how well they are able to traverse these quagmires of messy data and transform the data into the required format. A sure-shot way to be adept at this all-too-important process is to get hands-on experience with multiple real-world datasets. In this chapter, you will analyze four different datasets, with each analysis focusing on different facets of data wrangling. The following list offers a snapshot of the datasets we will be dealing with in this chapter and the different techniques we will be applying to...

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