Once you’ve cleaned and filtered your dataset, it’s time to reshape and enrich it. This hub guides you through powerful techniques to transform DataFrames, create new features, manipulate strings, and work with categorical values. Data transformation is key to preparing data for modeling, analysis, or reporting.
🔄 Key Transformation Topics
- Reshaping Data (Pivoting and Melting) – Convert wide to long format and vice versa using melt, pivot, and stack/unstack.
- Applying Functions to Data – Use apply(), map(), and vectorized operations to modify values or columns.
- Creating New Columns – Derive new fields from existing ones using arithmetic, conditions, and functions.
- String Manipulation – Clean, parse, and extract string data using str accessor.
- Handling Categorical Data – Convert and optimize text features using astype and category dtype.
🛠Useful Transformation Tutorials
- Apply Function to a Column
- Replace Part of a String
- Replace List of Values
- Handle Text Data
- Cast to String
- Handle Numerical Data
- Drop All Columns Except One
- Replace NaN by Mean
- MinMaxScaler in Pandas
- Calculate Cumulative Sum
- Use Explode on List Columns
🧯 Errors While Transforming Data
- Fix NotImplementedError for Compression
- Fix ValueError: Indexes Have Overlapping Values
- Fix IndexError: Too Many Levels
- Fix KeyError While Transforming Columns
🧠Real-World Use Cases
Use Case 1: Creating clean labels from raw text? Start with text handling and string replacement.
Use Case 2: Preparing features for machine learning? Use type casting, scaling, and column-level functions.
Use Case 3: Need to un-nest list data? Try explode() and learn how to handle irregular structures.