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Learn all about NumPy

Bog
  • Format
  • Bog, hæftet
  • Engelsk
  • 222 sider

Beskrivelse

Learn all about NumPy

NumPy, short for Numerical Python, is a powerful library in the Python ecosystem that provides support for efficient numerical computations, particularly with large multidimensional arrays and matrices. It serves as a fundamental building block for scientific computing and data analysis in Python.

The book covers the following:

1 Introduction to NumPy

What is NumPy?

History and background

Advantages and applications

Installing NumPy

2 NumPy Basics

NumPy arrays: creation, attributes, and operations

Data types and casting

Indexing and slicing arrays

Array manipulation: reshaping, resizing, and stacking

Array broadcasting

3 Array Computations and Mathematical Operations

Element-wise operations

Mathematical functions and operations

Linear algebra with NumPy

Random number generation with NumPy

4 Advanced Array Operations

Array sorting and searching

Fancy indexing and Boolean indexing

Array iteration and vectorization

Broadcasting rules and examples

5 Working with Structured Data

Structured arrays

Structured data manipulation

Record arrays



6 File Input and Output

Reading and writing arrays to files

File formats (CSV, text, binary)

Memory-mapping files

7 Performance and Optimization

Understanding array views and copies

Memory management and optimization techniques

Vectorization and avoiding loops

Profiling and benchmarking NumPy code

8 Integration with Other Libraries

Integration with pandas for data analysis

Visualization with Matplotlib and NumPy

SciPy: advanced scientific computing with NumPy

9 NumPy Best Practices and Tips

Writing efficient and readable code

Code organization and modularization

Debugging and error handling

Testing and documenting NumPy code

10 Case Studies and Examples

Solving common mathematical problems with NumPy

Image processing and manipulation with NumPy

Data analysis examples using NumPy

11 Advanced Topics and Future Directions

NumPy extensions and alternative libraries

GPU acceleration with NumPy

Distributed computing with NumPy

NumPy in machine learning and deep learning frameworks

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  • Vægt304 g
  • Dybde1,2 cm
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    10 cm
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    15,2 cm
    22,9 cm

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