UsefulLinks
Computer Science
Data Science
NumPy Library
1. Introduction to NumPy
2. The NumPy ndarray Object
3. Data Types in NumPy
4. Array Creation Techniques
5. Indexing and Slicing
6. Array Manipulation and Reshaping
7. Universal Functions (ufuncs)
8. Array Aggregation and Statistics
9. Broadcasting
10. Linear Algebra Operations
11. Random Number Generation
12. File Input and Output
13. Advanced NumPy Features
14. NumPy Ecosystem Integration
15. Best Practices and Common Pitfalls
13.
Advanced NumPy Features
13.1.
Structured Arrays
13.1.1.
Creating Structured Arrays
13.1.1.1.
dtype Definition
13.1.1.2.
Field Names and Types
13.1.1.3.
Nested Structures
13.1.2.
Accessing Structured Data
13.1.2.1.
Field Access
13.1.2.2.
Multi-field Selection
13.1.2.3.
Record Arrays
13.1.3.
Operations on Structured Arrays
13.1.3.1.
Sorting by Fields
13.1.3.2.
Field Manipulation
13.2.
Memory Layout and Performance
13.2.1.
Array Memory Layout
13.2.1.1.
C-order vs. Fortran-order
13.2.1.2.
Strides and Memory Access
13.2.1.3.
flags Attribute
13.2.2.
Performance Optimization
13.2.2.1.
Contiguous Arrays
13.2.2.2.
Cache-friendly Operations
13.2.2.3.
In-place Operations
13.2.2.4.
out Parameter Usage
13.2.3.
Memory Views
13.2.3.1.
View vs. Copy Distinction
13.2.3.2.
Creating Views
13.2.3.3.
Shared Memory Implications
13.3.
Advanced Indexing Techniques
13.3.1.
Multi-dimensional Advanced Indexing
13.3.2.
Index Broadcasting
13.3.3.
Combining Index Types
13.3.4.
Performance Implications
13.4.
Masked Arrays
13.4.1.
numpy.ma Module
13.4.2.
Creating Masked Arrays
13.4.3.
Masked Array Operations
13.4.4.
Handling Invalid Data
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12. File Input and Output
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14. NumPy Ecosystem Integration