🔢Numpy 2

Table of Contents

In the Numpy part 2, we have covered the following topics:

1. Indexing and Slicing:

Indexing refers to accessing individual elements of an array using their position. Slicing allows you to extract a portion of the array by specifying a range of indices. Both indexing and slicing are powerful tools for manipulating data efficiently in numpy arrays.

Slicing into numpy arrays: A piece of the Python cake

1. Expand and Squeeze:

The expand_dims function is used to add a new axis to an array, increasing its dimensionality. This can be helpful when performing operations that require arrays with matching shapes. Conversely, the squeeze function removes single-dimensional entries from the shape of an array, reducing its dimensionality and simplifying calculations.

Adjusting dimensions for better fit

1. Stack and Split:

The stack function is used to join arrays along a new axis, either horizontally or vertically. This allows for the creation of higher-dimensional arrays from multiple lower-dimensional ones. Conversely, the split function divides an array into smaller subarrays along a specified axis, facilitating data manipulation and organization.

Stitching data together with tailoring arrays

1. Mathematical / Statistical Functions:

Numpy provides a wide range of mathematical functions such as max, min, sum, exp, and log, allowing for efficient computation and manipulation of array elements. Additionally, NumPy offers statistical functions like correlation_coefficient, covariance, and variance to analyze and extract meaningful insights from data arrays. These functions play a crucial role in data analysis and scientific computing tasks

Numpy 2 Document 📚

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Numpy2.ipynb