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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

What are datetimes?

You probably already understand that in the computer memory, all numeric information is represented as ones and zeros, so at the most basic level, there isn't anything special about dates or times. However, when working with real data in business and technical projects, we tend to think about time or dates in their own units, differently from other numbers. Time is most often thought of as hours, minutes, or seconds, and dates are usually years, months, and days. Other common patterns are the weekdays, day of the week, business days, and quarters. We often group data into bins of days, weeks, months, or quarters. Within these bins, there might be data every second, minute, hour, or on some other or even random period. Because it is natural to think of dates and time of day together, Python in general, and pandas in particular, provides objects to make it easy to work this way. The most fundamental time component in pandas is Timestamp, and it is equivalent...

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