Name: [6237] unpingco
Member: 76 months
Authored: 40 videos
Description: Python for scientific and large-scale computing. ...

Getting Started with Numpy Arrays in Python [ID:1037]

a series of video-tutorials by unpingco

Numpy is the foundation of most of the scientific computing in Python. In this series, we examine the many important features of numpy and how they can be used to facilitate scientific computing in Python.

Video Tutorials

1. Introducing Numpy Arrays

In this segment, we discuss how to create and use simple numpy arrays.

2. Using numpy dtypes

In this segment, we discuss additional ways to create numpy arrays and how to use powerful numpy dtypes (data types) in numpy arrays.

3. Numpy data input/output

In this segment, we discuss how you can read in numpy arrays from files under various conditions.

4. Using Numpy Arrays: Slicing, Logical Array Operations, and Math

In the segment, we discussed manipulating numpy arrays using slicing and logical operations, as well has how to use numpy's wealth of built-in optimized math functions.

5. Linear Algebra with Numpy

In this segment, we discuss how to do linear algebra with numpy matrices and arrays.

6. Numpy Masked Arrays

In this segment, we discuss the distinctive and powerful masked array numpy type that allows for selective suppression of array entries.

7. Broadcasting Numpy Arrays

In this segment, we discuss numpy broadcasting by which arrays of different shapes can be intuitively combined.

8. Using Numpy Temporary Variables

In this segment, we discuss how you can use numpy temporary variables to control memory constrained calculations.

9. Functional Programming Idioms in Numpy

In this segment, we examine some of the useful functional programming idioms available in numpy.

10. Using Polynomials in Numpy

In this segment, we examine some of the built-in features for creating, manipulating, and using polynomials in numpy.

11. Understanding and Using Strides in Numpy

In this segment, we explore Numpy strides and show how they can be effectively used to create overlapped arrays.

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