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Numerical Computing with NumPy: A Comprehensive Guide to Arrays and Vectorization, Slides of Programming Languages

King Mongkut's University of Technology LadkrabangProgramming Languages

python programming language for finance fields

Typology: Slides

2022/2023

Uploaded on 11/07/2023

emelia-goh
emelia-goh 🇹🇭

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Numerical Computing with Numpy
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•Python interpreter has built-in data structures.
•NumPy adds valuable functionality to existing structures.
•This session focuses on NumPy. àProvides multidimensional array object which stores
homogeneous or heterogeneous data arrays.
•It also Supports code vectorization.
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Download Numerical Computing with NumPy: A Comprehensive Guide to Arrays and Vectorization and more Slides Programming Languages in PDF only on Docsity!

Numerical Computing with Numpy

  • Python interpreter has built-in data structures.
  • NumPy adds valuable functionality to existing structures.
  • This session focuses on NumPy. à Provides multidimensional array object which stores homogeneous or heterogeneous data arrays.
  • It also Supports code vectorization.

Arrays of Data

  • Python has useful and flexible general data structures, like lists.
  • Lists can have higher memory usage and slower performance.
  • Scientific and financial applications require high-performing operations on special data structures, like arrays.
  • Arrays structure objects of the same data type in rows and columns.
  • One-dimensional arrays represent vectors, while multidimensional arrays can represent matrices, cubes, or n-dimensional shapes.

Arrays with Python Lists

  • Arrays can be built using built-in data structures.
  • List objects are well-suited for this purpose.
  • A simple list can represent a one-dimensional array.
  • Since list objects can contain arbitrary other objects à they can also contain other list object and can be used to construct two- and higher-dimensional arrays

Arrays with Python Lists

  • Accessing the matrix elements
  • Nesting can be pushed further for even more general structures:

The Python array Class

  • Consider the following code, which instantiates an array object out of a list object:

The Python array Class

  • Trying to append an object of different data type than the one specified raises a TypeError:

The Python array Class

  • The data type of the array object is of importance when reading the data from disk:

Regular Numpy Arrays

  • List objects can be used for composing array structures.
  • Lists are not specifically designed for this purpose.
  • List class has a broader and more general scope.
  • Array class is more specialized for working with arrays of data.
  • A truly specialized class could greatly benefit array-type structures handling.
  • numpy.ndarray is such a class, built with the specific goal of handling dimensional arrays both conveniently and efficiently, i.e. in a highly performance manner

Regular Numpy Arrays

  • A major feature of the ndarray class is the multitude of built-in methods:

Regular Numpy Arrays

  • Another major feature is the (vectorized) mathematical operations defined on ndarray objects:

Regular Numpy Arrays

  • Universal functions are another important feature of the Numpy Package
  • Operate on ndarray objects and basic Python data types.
  • Applicable to Python float objects, but with reduced performance.
  • Performance advantage when using math module for basic Python data types.

Regular Numpy Arrays

  • Cont..

Multiple Dimensions

  • Cont.

Multiple Dimensions

  • Multiple ways to initialize ndarray objects.
  • np.array used when all elements are available.
  • Alternative approach: instantiate ndarray first.
  • Populate ndarray later with results from code execution.