python.tar.gz

python3.6

Live3.6

If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra).

Join us as we venture into the vast world of NumPy in this comprehensive course. Each lesson dive into the actual implementation of concepts in both pure Python and then NumPy, exploring how NumPy vectorization compares to traditional Python that uses a procedural and object-oriented approach.

Practice and test yourself along the way with in-browser coding challenges, quizzes, and more.

This course is intended for users who are already familiar with intermediate level Python.

From Python to Numpy

## Introduction
One of the strengths of NumPy is that it can be used to build new objects or to
[subclass the ndarray](https://docs.scipy.org/doc/numpy/user/basics.subclassing.html) object. This
later process is a bit tedious but it is worth the effort because it allows you
to improve the `ndarray` object to suit your problem.


We'll examine in the following section two real-world cases (typed list and memory-aware array)
that are extensively used in the [glumpy](http://glumpy.github.io) project
(that I maintain).
$$$$

## What is a Typed list?

Typed list (also known as ragged array) is a list of items that all have the same data type (in the sense of NumPy). They offer both the list and the `ndarray` API (with some restriction of course) but because their respective APIs may not be compatible in some cases, we have to make choices. For example, concerning the `+` operator, we'll choose to use the NumPy API where the value is added to each individual item instead of expanding the list by appending a new item (1).


# Introduction
One of the strengths of NumPy is that it can be used to build new objects or to
[subclass the ndarray](https://docs.scipy.org/doc/numpy/user/basics.subclassing.html) object. This
later process is a bit tedious but it is worth the effort because it allows you
to improve the `ndarray` object to suit your problem.


We'll examine in the following section two real-world cases (typed list and memory-aware array)
that are extensively used in the [glumpy](http://glumpy.github.io) project
(that I maintain).
$$$$

# What is a Typed list?

Typed list (also known as ragged array) is a list of items that all have the same data type (in the sense of NumPy). They offer both the list and the `ndarray` API (with some restriction of course) but because their respective APIs may not be compatible in some cases, we have to make choices. For example, concerning the `+` operator, we'll choose to use the NumPy API where the value is added to each individual item instead of expanding the list by appending a new item (1).


This lesson is a brief introduction to Typed list and how it is implemented using the NumPy library. 

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Typed list

Introduction

What is a Typed list?