
Explore NumPy array basics by creating 1D, 2D, and 3D arrays with np.array, np.arange, and np.random.randint, and examine size, ndim, shape, and dtype attributes.
Reshape 1d arrays into 2d and 3d forms using arange, the shape attribute, and the reshape function. Ensure shapes are compatible, and use -1 to infer an unknown dimension.
Visualize 2D, 3D, 4D, and higher dimensional arrays by understanding axes, shapes, and how axis 0, axis 1, and beyond structure data for indexing and operations.
Demonstrates storing quarterly, product, and yearly sales in 2d and 3d arrays. Leverage numpy, pandas, and read_csv to create, reshape, and access data in DataFrame and arrays.
Explore indexing and slicing rules for Python arrays, using 0-based positions, end-exclusive ranges, and start or end omissions, then apply to 2-D arrays with axes and shapes like 5x4.
Explore slicing a 2-D array by axis, with axis 0, axis 1, and both axes, using index ranges and discrete selections, plus common broadcasting pitfalls.
Learn how to index and slice a 3d array in python, reshape a 1d array to 3d, and retrieve year, product, and quarter data across multiple axes.
Learn to perform basic operations on a two-dimensional array, using axis choices and reshape to compute total and minimum revenues, argmin positions, and boolean revenue > 5000 counts.
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 :
Intuition of Arrays as Data Containers
Visualizing 2D/3D and higher dimensional Arrays
Array Indexing and Slicing - 2D/3D Arrays
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.