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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
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Matthew Harrison
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Toc

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

Developing a data analysis routine

Although there is no standard approach when beginning a data analysis, it is typically a good idea to develop a routine for yourself when first examining a dataset. Similar to everyday routines that we have for waking up, showering, going to work, eating, and so on, a data analysis routine helps you to quickly get acquainted with a new dataset. This routine can manifest itself as a dynamic checklist of tasks that evolves as your familiarity with pandas and data analysis expands.

Exploratory Data Analysis (EDA) is a term used to describe the process of analyzing datasets. Typically it does not involve model creation, but summarizing the characteristics of the data and visualizing them. This is not new and was promoted by John Tukey in his book Exploratory Data Analysis in 1977.

Many of these same processes are still applicable and useful to understand a dataset. Indeed, they can also help with creating machine learning models later...

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