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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
Author Profile Icon Matthew Harrison
Matthew Harrison
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Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Selecting Series data

Series and DataFrames are complex data containers that have multiple attributes that use an index operation to select data in different ways. In addition to the index operator itself, the .iloc and .loc attributes are available and use the index operator in their own unique ways.

Series and DataFrames allow selection by position (like Python lists) and by label (like Python dictionaries). When we index off of the .iloc attribute, pandas selects only by position and works similarly to Python lists. The .loc attribute selects only by index label, which is similar to how Python dictionaries work.

The .loc and .iloc attributes are available on both Series and DataFrames. This recipe shows how to select Series data by position with .iloc and by label with .loc. These indexers accept scalar values, lists, and slices.

The terminology can get confusing. An index operation is when you put brackets, [], following a variable. For instance, given a Series s,...

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