Beginner’s Guide to Python Arrays

Develop understanding of how Python Arrays work and what advantages they offer over other Data Structures

Create Arrays of Different Dimensions

Arrays Visualization – 2dD, 3D, 4D and higher dimensional Arrays

Array Attributes and how to use them to know more about Arrays

Use Arrays as Data containers for Common Data Operations


  • Basic knowledge of Python (including Data Types and Structures, For Loops, List Comprehension, etc.)


Arrays are a powerful means of storing variables of the same data type (Integer, Float, String, etc.). Compared to their counterpart Data Structures, they provide many benefits, be it:

  • Faster processing
  • Compact memory usage
  • Easy access to data elements, or
  • Simpler operations with less coding effort

To give you some context, if you have worked on Pandas DataFrames, which is a special case of 2 Dimensional Arrays, you would know what different operations you can perform and how you can handle datasets more effectively. Well with Arrays you can do most of that and much more and for that very reason they are used as the preferred Data Containers to run Machine Learning algorithms (in Modules such as Scipy and Scikit-learn).

To simply put, “A good command on Arrays will take your understanding of Data Structures and their use to the next level”, and this is exactly where this course comes in. Even if you’ve not worked on Arrays earlier, you can use this course to develop your understanding grounds-up.

Here we cover, “Arrays as Data Structures and how they get applied in Python”. Below are the key areas that this course addresses :

  1. Intuition of Arrays as Data Containers
  2. Visualizing 2D/3D and higher dimensional Arrays
  3. Array Indexing and Slicing – 2D/3D Arrays
  4. Performing basic operations using Numpy Arrays

By the end of this course, you will be able to use Arrays in common data operations and data analysis. This will also give you a platform and confidence to quickly scale up to learn more advanced topics related to Numpy.

Who this course is for:

  • Anyone who wants to learn in more depth, about Numpy Arrays and put them to practical use

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Course content

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