Applying Functions
3 Topics
Apply Basics
8 Sub-topics
Using apply() on DataFrame Columns
Using apply() on DataFrame Rows
Lambda Functions with apply()
Applying Functions with Arguments
Using applymap() for Element-wise Application
Vectorized Operations vs apply()
Map and Transform
4 Sub-topics
Using map() for Series Transformation
Using replace() with Dictionaries
Using transform() in Groupby
Using pipe() for Method Chaining
Conditional Operations
4 Sub-topics
Conditional Application with where()
Conditional Application with mask()
Using np.where() with Pandas
Creating Calculated Columns
Performance Optimization
5 Topics
Memory Optimization
5 Sub-topics
Understanding Pandas Memory Usage
Reducing Memory with Appropriate dtypes
Using Category for String Columns
Execution Optimization
8 Sub-topics
Avoiding Iterrows and Itertuples
Using eval() for Efficient Computation
Using query() for Fast Filtering
Copy vs View Understanding
Avoiding Chained Indexing
Using inplace Parameter Wisely
Large Dataset Strategies
7 Sub-topics
Sampling Large DataFrames
Column Selection for Memory
Using Appropriate Data Types
Using Databases Instead of CSV
Iteration Strategies
8 Sub-topics
When Iteration is Necessary
Using itertuples() Efficiently
Vectorized Alternatives to Iteration
NumPy Operations for Speed
Benchmarking Iteration Methods
Parallel Processing Considerations
Performance Profiling
8 Sub-topics
Pandas Profiling Libraries