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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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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

Profiling data with pandas’ ydata_profiling

Let’s see an example in Python that showcases data profiling using the ProfileReport class from the ydata-profiling library.

Let’s start with installing a few libraries first:

pip install pandas
pip install ydata-profiling
pip install ipywidgets

In the following code example, we will use the iris dataset from the seaborn library, which is an open source dataset.

Next, we are going to read the dataset and perform some initial EDA with minimal code!

  1. We’ll start by importing the libraries and loading the dataset directly from its URL using the read_csv() function from pandas:
    import pandas as pd
    import ydata_profiling as pp
  2. Load the iris dataset from the seaborn library:
    iris_data = pd.read_csv('https: //raw. githubusercontent .com/mwaskom/ seaborn-data/master/ iris.csv')
  3. Next, we’ll perform data profiling by creating a profile report using the ProfileReport() function from...
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