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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 14.01 – analyzing air quality data

Consider that you're working as a data analyst for your city's municipality. The Department for the Environment needs your help in getting answers to some questions related to emissions. The following are the questions the department wants answers to:

  • Which day of the week has the highest NO2(GT) emissions?
  • At what time of the day are NMHC(GT) emissions highest?
  • Which month has the lowest CO(GT) emissions?

    Note

    The emissions dataset has been sourced from the following link:

    https://archive.ics.uci.edu/ml/machine-learning-databases/00360/

    You can find the dataset in the GitHub repository for this book. Download the data, unzip the data, and then load the CSV file in a data folder of your local machine. The department needs the answers through good visualizations.

The following steps will help you complete this activity:

  1. Open a new Jupyter notebook.
  2. Download the data and then read the data using...

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