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 9.01 – Data splitting, scaling, and modeling

You are charged with analyzing the performance of a combined cycle power plant and are given data on the full-load electrical power production along with environmental variables (such as temperature or humidity). In the first part of the activity, you will split the data manually and with sklearn, then you will scale the data, construct a simple linear model, and output the results:

  1. For this activity, all you will need is the Pandas library, the modules from sklearn, and numpy. Load them in the first cell of the notebook.
  2. Use the power_plant.csv dataset – 'Datasets\\power_plant.csv'. Read the data into a Pandas DataFrame, print out the shape, and list the first five rows.

The independent variables are as follows:

  • AT – ambient temperature
  • V – exhaust vacuum level
  • AP – ambient pressure
  • RH – relative humidity

The dependent variable is...