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

Predicting future values of time series

You have seen how smoothing can be used to uncover important information in a series that might be hidden by noise. It might be tempting to think that smoothing is a very easy data modeling method, so why not use it to make predictions? The issue that arises is, in many cases, the process of smoothing data and aligning it to the original series means you are using information for any given point in the smoothed series that includes future values. Therefore, using such values as predictions is an example of data leakage, discussed in Chapter 9, Data Modeling – Preprocessing in the Avoiding information leakage section.

Suppose you are again analyzing the SPX index data you saw in Chapter 9, Data Modeling – Preprocessing:

  1. Here, you read the data, convert the dates to datetimes, and make a simple plot over a limited time range:
    SPX = pd.read_csv('Datasets/spx.csv')
    SPX['date'] = pd.to_datetime(SPX[&apos...