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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

Preparing Series from DataFrames and vice versa

In Chapter 5, Data Selection – DataFrames, we saw examples of getting a Series by slicing the column of a DataFrame. Let's review this. You have been provided with a dataset (adapted from https://archive.ics.uci.edu/ml/datasets/Water+Treatment+Plant) regarding a water treatment facility and you've been asked to analyze its performance. The data contains various chemical measurements for the input, two settling stages, and the output, plus some performance indicators. We will begin by reading the water-treatment.csv file. After reading the data, we will use the .fillna() method, which replaces any missing values, which are converted into NaN values during the file read, into the value that's passed to .fillna(). We will use a value of -9999 here:

water_data = pd.read_csv('Datasets\\water-treatment.csv')
water_data.fillna(-9999, inplace = True)
water_data

Note

Please change the path of the dataset...

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