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Practical Data Analysis Using Jupyter Notebook

You're reading from   Practical Data Analysis Using Jupyter Notebook Learn how to speak the language of data by extracting useful and actionable insights using Python

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838826031
Length 322 pages
Edition 1st Edition
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Author (1):
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Marc Wintjen Marc Wintjen
Author Profile Icon Marc Wintjen
Marc Wintjen
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Data Analysis Essentials
2. Fundamentals of Data Analysis FREE CHAPTER 3. Overview of Python and Installing Jupyter Notebook 4. Getting Started with NumPy 5. Creating Your First pandas DataFrame 6. Gathering and Loading Data in Python 7. Section 2: Solutions for Data Discovery
8. Visualizing and Working with Time Series Data 9. Exploring, Cleaning, Refining, and Blending Datasets 10. Understanding Joins, Relationships, and Aggregates 11. Plotting, Visualization, and Storytelling 12. Section 3: Working with Unstructured Big Data
13. Exploring Text Data and Unstructured Data 14. Practical Sentiment Analysis 15. Bringing It All Together 16. Works Cited
17. Other Books You May Enjoy

Learning how to restrict, sort, and sift through data

Now that we have the data available in a DataFrame, we can walk through how to restrict, sort, and sift through data with a few Python commands. The concepts we are going to walk through using pandas are also common using SQL, so I will also include the equivalent SQL commands for reference.

Restricting

The concept of restricting data, which is also known as filtering data, is all about isolating one or more records based on conditions. Simple examples are when you are only retrieving results based on matching a specific field and value. For example, you only want to see results for one user or a specific point in time. Other requirements for restricting data can be more complicated, including explicit conditions that require elaborate logic, business rules, and multiple steps. I will not be covering complex examples that require complex logic but will add some references in the Further reading section. However...

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