Python Data Cleaning and Preparation Best Practices

Table of Contents

1. Data Ingestion Techniques
2. Importance of Data Quality
3. Data Profiling – Understanding Data Structure, Quality, and Distribution
4. Cleaning Messy Data and Data Manipulation
5. Data Transformation – Merging and Concatenating
6. Data Grouping, Aggregation, Filtering, and Applying Functions
7. Data Sinks
8. Detecting and Handling Missing Values and Outliers
9. Normalization and Standardization
10. Handling Categorical Features
11. Consuming Time Series Data
12. Text Preprocessing in the Era of LLMs
13. Image and Audio Preprocessing with LLMs