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Python Data Cleaning and Preparation Best Practices

You're reading from   Python Data Cleaning and Preparation Best Practices A practical guide to organizing and handling data from various sources and formats using Python

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781837634743
Length 456 pages
Edition 1st Edition
Languages
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Author (1):
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Maria Zervou Maria Zervou
Author Profile Icon Maria Zervou
Maria Zervou
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1: Upstream Data Ingestion and Cleaning
2. Chapter 1: Data Ingestion Techniques FREE CHAPTER 3. Chapter 2: Importance of Data Quality 4. Chapter 3: Data Profiling – Understanding Data Structure, Quality, and Distribution 5. Chapter 4: Cleaning Messy Data and Data Manipulation 6. Chapter 5: Data Transformation – Merging and Concatenating 7. Chapter 6: Data Grouping, Aggregation, Filtering, and Applying Functions 8. Chapter 7: Data Sinks 9. Part 2: Downstream Data Cleaning – Consuming Structured Data
10. Chapter 8: Detecting and Handling Missing Values and Outliers 11. Chapter 9: Normalization and Standardization 12. Chapter 10: Handling Categorical Features 13. Chapter 11: Consuming Time Series Data 14. Part 3: Downstream Data Cleaning – Consuming Unstructured Data
15. Chapter 12: Text Preprocessing in the Era of LLMs 16. Chapter 13: Image and Audio Preprocessing with LLMs 17. Index 18. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “The delete_entry() function is used to remove an entry, showing how data can be deleted from the store

A block of code is set as follows:

def process_in_batches(data, batch_size): 
    for i in range(0, len(data), batch_size): 
        yield data[i:i + batch_size]

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

user_satisfaction_scores = [random.randint(1, 5) for _ in range(num_users)]

Any command-line input or output is written as follows:

$ mkdir data
pip install pandas

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “It involves storing data on remote servers accessed from anywhere via the internet, rather than on local devices

Tips or important notes

Appear like this.

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