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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

Activity 6.01 – Series data selection

In this activity, you will read some US population data for large cities for the years 2010 and 2019 and analyze it. The goal is to determine the population growth for the top three cities compared to all the top 20 from 2010 to 2019. To do this, you must compute the population of the three largest cities for 2010 and 2019, as well as the population of the 20 largest cities for both years. Using these values, you can compute the growth rates and compare them.

Follow these steps to complete this activity:

  1. For this activity, all you will need is the pandas library. Load it into the first cell of the notebook.
  2. Read in a pandas Series from the US_Census_SUB-IP-EST2019-ANNRNK_top_20_2010.csv file. This data is from the US Census Bureau (source: https://www2.census.gov/programs-surveys/popest/datasets/2010/2010-eval-estimates/). The city names are in the first column, so read them so that they are used as the indexes. List the resulting...

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