Python NumPy Basic for Data Science

With NumPy, which makes it easy to handle multidimensional arrays
Take your first steps into data science!

Lecture Topics 📖

NumPy is a representative Python library that serves as the foundation for data science, machine learning, and deep learning operations, along with Python Pandas, SciPy, Matplotlib, and Pytorch.

It is one of the libraries that enables numerical operations for processing large multidimensional arrays and matrix data in Python, and is implemented internally in the C language, so it minimizes memory usage while also providing very fast operation speeds.

So why use multidimensional arrays ?

When processing data, it's stored as an array of numbers. When processing an image like the one above, the entire image is structured as a two-dimensional array of data, with each pixel being processed as a numeric value ranging from 0 to 255. This is because organizing data into an array is the most fundamental process for data processing .

Lecture Structure and Progress 🎢

We will conduct hands-on training in Google Colab , a Python development environment for data science.

The course will be divided into seven sections, spanning approximately 30 lectures. Each section and topic covers functions available in the NumPy module, along with simple examples. Initially, you'll follow the example code and write your own, then use the practice files to fully review the material for the day. (The practice files are explained below.)

Difficult and unfamiliar linear algebra concepts are learned additionally through materials.

In the final section, we'll work through examples based on the theories and functions learned, strengthening our Python and NumPy library code writing skills. (Practical examples may be added in the future.)

How to get 100% out of your lectures ✨

Download the practice file uploaded to Google Drive.

From the top navigation bar, click File - Save Copy to Drive. This will save the file to your drive. The file name will end with "Copy of ~practical_yjglab.ipynb." You can use the file by deleting the "Copy of" part from the file name, excluding the file format.

This file lists the sections and subtopics in which the lecture was given.
After attending the lecture, try writing codes that fit the topic based on what you learned that day.
For example, if you learned about functions A and B in Section 2 today, you would write examples for each function without looking at the theory directly.

It's okay if you don't remember. Write down as much as you can, review the lecture material for any areas you missed, and check that the code examples you wrote align with what you learned.

What you'll learn 📚

Section 0. Getting Started with NumPy

Starting with a brief introduction to NumPy, we will set up a Python practice environment and then cover the basic concepts of N-dimensional arrays: arrays, axes, dimensions, matrices, and tensors.

Section 1. Creating an N-dimensional array (ndarray)

We'll cover how to create N-dimensional arrays in various formats, data types, and how to easily visualize your data.

Section 2. Indexing an N-dimensional array

We will cover how to access values using array indexes and how to search through a specific range of values.

Section 3. Operations on N-dimensional arrays

It covers basic operations such as matrix arithmetic, inner product operation, comparison operation, and broadcasting operation process.

Section 4. Sorting an N-dimensional array

We will cover how to sort the elements of an array according to their order.

Section 5. Changing the shape of an N-dimensional array

We cover how to change the shape of an array and expand or reduce its dimensions.

Section 6. Merging N-dimensional arrays

Covers how to add and delete elements from an array, and how to merge and split data.

Section 7. Practical Examples

Let's solve a practical example based on what we've learned.

Expected Questions Q&A 💬