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The Pandas Workshop

You're reading from   The Pandas Workshop A comprehensive guide to using Python for data analysis with real-world case studies

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781800208933
Length 744 pages
Edition 1st Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
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Blaine Bateman
William So William So
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William So
Saikat Basak Saikat Basak
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Saikat Basak
Thomas Joseph Thomas Joseph
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Thomas Joseph
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Table of Contents (21) Chapters Close

Preface 1. Part 1 – Introduction to pandas
2. Chapter 1: Introduction to pandas FREE CHAPTER 3. Chapter 2: Working with Data Structures 4. Chapter 3: Data I/O 5. Chapter 4: Pandas Data Types 6. Part 2 – Working with Data
7. Chapter 5: Data Selection – DataFrames 8. Chapter 6: Data Selection – Series 9. Chapter 7: Data Exploration and Transformation 10. Chapter 8: Understanding Data Visualization 11. Part 3 – Data Modeling
12. Chapter 9: Data Modeling – Preprocessing 13. Chapter 10: Data Modeling – Modeling Basics 14. Chapter 11: Data Modeling – Regression Modeling 15. Part 4 – Additional Use Cases for pandas
16. Chapter 12: Using Time in pandas 17. Chapter 13: Exploring Time Series 18. Chapter 14: Applying pandas Data Processing for Case Studies 19. Chapter 15: Appendix 20. Other Books You May Enjoy

Indexes and columns

We have already referred to indexes and columns without fully defining them. An index contains references to the rows of a DataFrame. The index of a pandas DataFrame is analogous to the row numbers you might see in a spreadsheet. In spreadsheets, it's common to use the so-called A1 notation, where A refers to the columns, which usually begin with A, and 1 refers to the rows, which usually begin with 1.

We will start by looking at the index, and continue with the sample_df_from_lists DataFrame created earlier. You can use the .index method to display information about the index, as follows:

sample_df_from_lists.index

This line of code produces the following output:

RangeIndex(start=0, stop=100, step=1)

You may recall that ranges in Python are inclusive of the start value and exclusive of the end value. You see that the index for sample_df_from_lists runs from 0 to 99, which matches the rows. As you will learn in detail in Chapter 5, Data Selection...

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