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Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Modern techniques and Python tools to detect and remove dirty data and extract key insights

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565661
Length 436 pages
Edition 1st Edition
Languages
Tools
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Authors (2):
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Michael B Walker Michael B Walker
Author Profile Icon Michael B Walker
Michael B Walker
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Anticipating Data Cleaning Issues when Importing Tabular Data into pandas 2. Chapter 2: Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas FREE CHAPTER 3. Chapter 3: Taking the Measure of Your Data 4. Chapter 4: Identifying Missing Values and Outliers in Subsets of Data 5. Chapter 5: Using Visualizations for the Identification of Unexpected Values 6. Chapter 6: Cleaning and Exploring Data with Series Operations 7. Chapter 7: Fixing Messy Data when Aggregating 8. Chapter 8: Addressing Data Issues When Combining DataFrames 9. Chapter 9: Tidying and Reshaping Data 10. Chapter 10: User-Defined Functions and Classes to Automate Data Cleaning 11. Other Books You May Enjoy

Importing R data

We will use pyreadr to read an R data file into pandas. Since pyreadr cannot capture the metadata, we will write code to reconstruct value labels (analogous to R factors) and column headings. This is similar to what we did in the Importing data from SQL databases recipe.

The R statistical package is, in many ways, similar to the combination of Python and pandas, at least in its scope. Both have strong tools across a range of data preparation and data analysis tasks. Some data scientists work with both R and Python, perhaps doing data manipulation in Python and statistical analysis in R, or vice-versa, depending on their preferred packages. But there is currently a scarcity of tools for reading data saved in R, as rds or rdata files, into Python. The analyst often saves the data as a CSV file first, and then loads the CSV file into Python. We will use pyreadr, from the same author as pyreadstat, because it does not require an installation of R.

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