80/20 NumPy—NumPy for Data Science

Never miss a tutorial! Subscribe to the Project Data Science channel: https://bit.ly/3yTMdQV Go from zero to hero with our Data Science Specialization: https://courses.projectdatascience.co.... Or learn about all of data science in one blog post! https://www.projectdatascience.com/wh... ... The 20% of NumPy that you need to know to get 80% of the value. NumPy is a numeric computation library that is the foundation of many other Python data science libraries. Using pandas? It's built on NumPy. Using Scikit-Learn? Also built on NumPy. If you're going to be doing data science in Python, you're going to need to know how to use the primary object in NumPy—NumPy arrays. But, there are really only a handful of things you need to know to get up and running quickly in NumPy, and that's what we're going to cover in this video. --- If you need to get your professional data science environment set up, here's a guide on exactly how to do that: https://www.projectdatascience.com/st... (Or you can watch this video:    • Setting Up Your Python Data Science Enviro...  ) If you prefer a blog version of this NumPy tutorial, you can find that here: https://www.projectdatascience.com/80... And finally, here's a course that walks you through a whole machine learning / data science project from start to finish in Python: https://courses.projectdatascience.co... --- 00:00 Introduction 01:22 What is NumPy? 02:48 Launching Jupyter notebooks and creating a notebook 05:05 Importing NumPy 06:17 Primary NumPy data structure - ndarray 09:31 Loading NumPy array data from Scikit-Learn 11:03 Looking at the data in NumPy arrays 13:02 Looking at the shape of our NumPy array 14:22 Indexing and slicing our NumPy array 21:50 Using start-stop-step indexing 25:24 Mathematical operations on NumPy arrays 32:51 Filtering NumPy data using boolean masks 40:22 If-then logic using np.where 44:11 Sorting NumPy arrays 48:04 Getting NumPy arrays from pandas DataFrames 50:09 NumPy arrays are homogeneous 52:29 Creating ranges using np.arange and np.linspace 55:31 Reshaping NumPy arrays using np.reshape and np.flatten 01:00:15 Wrap-up and thank you!