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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

Understanding pandas and DataFrames

Now that we have a better understanding of tabular data and we have provided some background about panel data and the origins of why the pandas library was created, let's dive into some examples using pandas and explain how DataFrames are used.

The pandas library is a powerful Python library used for changing and analyzing data. A pandas DataFrame is a feature available in the library and is defined as a two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A DataFrame is a two-dimensional data structure—that is, data is aligned in a tabular fashion in rows and columns. It is commonly known that pandas DataFrame consists of three principal components: the data, the rows, and the columns. Being a visual learner myself, I created an example of this with the following diagram, which we can go through now:

DataFrames...

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