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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

Activity 10.01 – Normalizing and smoothing data

Suppose you are an analyst in a financial advisory firm. Your manager has given three stock symbols to you and requested your input on how they may be correlated with their price behavior. You are provided a stocks.csv data file, which contains the symbols, closing prices, trading volumes, and a sentiment indicator (some view of the quality of the stocks, but you are not told the exact definition). Your initial goal here is to determine whether all three stocks show similar market characteristics or not, and if any or all of them do, make an initial visualization using smoothing. The long-term goal is to try to build some predictive models, so you will split the data into train and test sets. As it is time series, it's important to split on time, not randomly. For this activity, all you will need is the pandas library, a scaling module from sklearn, and matplotlib. Load them in the first cell of the notebook:

ximport pandas...

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