Python - Introduction to NumPy for Multi-dimensional Data

Python 3    |    Intermediate
  • 11 Videos | 1h 3m 34s
  • Includes Assessment
  • Earns a Badge
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ThisSkillsoft Aspire course explores NumPy, a Python library used in data science and big data. NumPy provides a framework to express data in the form of arrays, and is the fundamental building block for several other Python libraries. For this course, you will need to know basics of programming in Python3, and should also have some familiarity in working with Jupyter notebooks. You will learn how to create NumPy arrays and perform basic mathematical operations on them. Next you will see how to modify, index, slice, and reshape the arrays; and examine the NumPy library's universal array functions that operate on an element-by-element basis. Conclude by learning how to iterate various options through NumPy arrays.

WHAT YOU WILL LEARN

  • identify the applications of NumPy
    install NumPy and learn how to create basic NumPy arrays
    create specialized NumPy arrays
    describe how arrays of different shapes and sizes can be displayed
    explore the different mathematical operations available when working with arrays
  • work with functions which apply to each element of an array
    retrieve specific parts of an array using row and column indices
    describe the options available when iterating over 1-dimensional and multi-dimensional arrays
    perform reshape operations on arrays to visualize its contents in different ways
    utilize NumPy to perform basic array manipulation

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 6s
    UP NEXT
  • Playable
    2. 
    Introduction to NumPy and the NumPy Ecosystem
    2m 11s
  • Locked
    3. 
    Array Creation - Part 1
    6m 52s
  • Locked
    4. 
    Array Creation - Part 2
    5m 26s
  • Locked
    5. 
    Printing Arrays
    4m 29s
  • Locked
    6. 
    Basic Array Operations
    8m 10s
  • Locked
    7. 
    Universal Functions
    7m 56s
  • Locked
    8. 
    Indexing and Slicing
    6m 17s
  • Locked
    9. 
    Iterating Over Arrays
    6m 19s
  • Locked
    10. 
    Reshaping Arrays
    5m 15s
  • Locked
    11. 
    Exercise: Python NumPy Array Operations
    4m 4s

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