Book Image

The Pandas Workshop

By : Blaine Bateman, Saikat Basak, Thomas V. Joseph, William So
5 (1)
Book Image

The Pandas Workshop

5 (1)
By: Blaine Bateman, Saikat Basak, Thomas V. 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)
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 2.01 – Working with pandas data structures

In this activity, you will read a DataFrame from the US_GDP.csv file, which contains information about the GDP of the US, from the first financial quarter of 2017 to the last financial quarter of 2019. The data is stored in two columns, date and GDP, and the date is read in (by default) as the object type. The goal of this activity is to first convert the date column into a timestamp and then set this column as the index. Finally, you'll save the updated dataset to a new file:

Note

You can download the file from

  1. Import the pandas library.
  2. Read the US_GDP.csv file from the Datasets directory into a DataFrame named GDP_data. The data is stored as dates and values, and you wish to use the dates as the index, so that in future work you may apply pandas time series methods to this data.
  3. Display the head of GDP_data so that you can see the formats of the data in the file.
  4. Inspect the object types of...