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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

Exploring dependent and independent variables

In this chapter, you will learn about dependent and independent variables. You will learn about the need for scaling and normalization of data, in addition to performing those operations. You will also use some basic modeling methods to analyze your data.

At a high level, we can say a dependent variable is related to one or more independent variables in a linear or non-linear way. Linear models are easy to understand. A linear model relating one Y to one X is just a line. With multiple X variables, each one has a coefficient that gives its effect on Y, and since all those effects are independent, we just add all the effects together in a multivariate linear model. In a non-linear model, Y depends on X in a more complex way, such as Y being a function of X2. We can create non-linear models nearly as easily as linear models in pandas using some simple additional modules. We'll explore how to do that in the following chapter.

Much...

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