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

The time series as an index

In many of the examples so far, we have had a column in a DataFrame containing dates or datetime information, and we've manipulated that. In many cases, when we want to perform operations on time-stamped data, it is simpler and more natural to have a time-based index. In general, you may want to consider time series to refer to a data structure with a time-based index and one or more columns of data. Let's explore a bit more what we can do with such a time series.

Time series periods/frequencies

We've seen the use of the pandas .date_range() method to generate a sequence of dates. The method is intuitive; we simply provide the start, end, and optional frequency (freq) arguments. The latter is the key to a lot of the convenience provided by pandas. The freq argument can take many values, and we've summarized them here.

Figure 13.1 – The possible values and meanings of the freq argument for date_range...