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The Data Science Workshop

You're reading from   The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800566927
Length 824 pages
Edition 2nd Edition
Languages
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Authors (5):
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Robert Thas John Robert Thas John
Author Profile Icon Robert Thas John
Robert Thas John
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Dr. Samuel Asare Dr. Samuel Asare
Author Profile Icon Dr. Samuel Asare
Dr. Samuel Asare
Andrew Worsley Andrew Worsley
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Andrew Worsley
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Toc

Table of Contents (16) Chapters Close

Preface
1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning

Summarizing Numerical Variables

Now, let's have a look at a numerical column and get a good understanding of its content. We will use some statistical measures that summarize a variable. All of these measures are referred to as descriptive statistics. In this chapter, we will introduce you to the most popular ones.

With the pandas package, a lot of these measures have been implemented as methods. For instance, if we want to know what the highest value contained in the 'Quantity' column is, we can use the .max() method:

df['Quantity'].max()

You should get the following output:

80995

We can see that the maximum quantity of an item sold in this dataset is 80995, which seems extremely high for a retail business. In a real project, this kind of unexpected value will have to be discussed and confirmed with the data owner or key stakeholders to see whether this is a genuine or an incorrect value. Now, let's have a look at the lowest value for ...

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