NumPy Math Operations for Data Science — Element-Wise, Mean, Std, Log and More - Dr. Colleen Spiegel
-In this lesson, we cover NumPy math operations — one of the most powerful features of the NumPy library. Unlike regular Python lists, NumPy arrays let you perform mathematical operations across entire datasets in a single line of code, with no loops required. This is one of the core reasons NumPy is so widely used in data science and machine learning.
What we cover in this video:
Using basic math operators on a NumPy array — addition, subtraction, multiplication, division, and exponentiation — all applied element by element
Performing math operations between two arrays of the same shape
Calculating the mean of all elements in an array with np.mean()
Calculating the mean across a dimension — for example, the mean of each row using the axis parameter
Measuring spread in your data with np.std() for standard deviation
Summing elements along a dimension with np.sum() — for example, getting column sums with axis=0
Applying logarithmic transformation to every element with np.log()
Calculating the square root of every element with np.sqrt()
Understanding how to perform math on arrays efficiently is a critical skill in data science. Whether you are normalizing data before feeding it into a machine learning model, calculating summary statistics, or transforming skewed distributions with a log function — these are the operations you will use on nearly every project.
🗂️ This video is part of a foundational Python for Data Science series, originally recorded for an online data science school and now freely available here.