
Explore Numpy arrays' shape, size, and ndim, and learn how to inspect these properties in code with examples of one-, two-, and three-dimensional arrays.
Discover numpy array creation from existing python data, such as lists, and from your own data, enabling on-the-fly construction using size and shape. This section covers essential creation methods.
Learn how zeros() creates arrays of zeros with a shape, including 1d, 2d, and 3d forms, and how ones() creates arrays of ones, with memory layout determined by order parameter.
Arange creates an array of evenly spaced values within interval, starting at start and excluding stop. It uses a step to control spacing, like Python's range, but returns an array.
Master indexing and slicing in numpy arrays. Use single brackets with zero-based row and column indices to access values, and slice to extract subarrays of rows and columns.
Explore slicing in numpy by extracting rows and columns using start and end indices, including and excluding endpoints, with examples of first row, first column, and range-based selections.
Explore how NumPy arrays use ndim, shape and size to define structure, and learn to reshape data without changing total elements using the reshape method.
Learn method chaining in Numpy by creating ranges and shaping arrays, then using reshape to produce new arrays without altering the original; chain steps in a single command for efficiency.
Explore NumPy broadcasting and arithmetic operations between scalars and arrays. Learn how shapes and dimensions determine compatibility, and how to prepend ones to align differing dimensions.
Welcome to the most comprehensive course on the NumPy library of Python! This complete guide on Numpy helps you take your Python skills to the next level. At the end of the course, you shall have gained enough expertise in the Numpy library so as to venture into the field of data driven technologies which are hot and trending - data science, data analysis, and machine learning.
Numpy, or Numerical Python, works under the hood; is the backbone of many other Python libraries. Pandas, ScikitLearn, SciPy, Tkinter, Matplotlib - they all are built on top of NumPy;
So, if you know NumPy well, you will find it easy to explore these libraries of data science and data analysis and can join the hottest skill of the century: Data Science & Machine Learning.
And don't you worry! In this course, we'll cover every nooks and crannies the Numpy library offers. You won't find such an elaborate tutorial on Numpy on Udemy, for sure! Intended for absolute beginners who know Python , and want to venture into data science and machine learning, but don't know where to start, the course covers complete details of every important concept of Numpy, including
What is NumPy,
Where it is used
How and why it works with numbers so easily
Concept of numerical arrays
Creating arrays
Insert, update and delete Numpy arrays
Array manipulation using Numpy
Important functions (methods) in Numpy
Linear algebra with Numpy
You also get the opportunity to apply your newly-learned Numpy skills and knowledge of Python, on practical projects.
All this and more in the course!
This course is still in a draft mode. I am still adding more and more content, quiz, projects related to data processing with different functionalities of Numpy. So stay tuned and enroll now.