Introduction to NumPy

Welcome to this course on NumPy for Quants!

NumPy is a scientific computing library in Python that provides a high-performance multidimensional array object and tools for working with these arrays. It is one of the most important libraries used in the field of Quantitative Finance and Artificial Intelligence.

This course aims to introduce students to NumPy and its functions and teaches all the basic fundamental concepts needed to use NumPy.

The learning objectives of the course are set out as follows:

• Learn how to download and install NumPy in your system.
• Learn to create NumPy arrays using built-in functions and Python data structures.
• Learn about basics of NumPy arrays.
• Learn about arithmetic, statistical and transformative array operations in NumPy.
• Learn input/output operations in NumPy.

You can expect to have all of these objectives met by the time you reach the end of this course.

For this course, you need to be familiar with the Python Programming Language. If you are new to Python, you can check out our free course on the same.

NumPy, or Numerical Python, is an open-source Python library that helps you perform simple as well as complex computations on numerical data. It is the go-to scientific computation library for beginner as well as advanced Python programmers and it is used mostly by statisticians, data scientists, and engineers.

The popularity behind NumPy is credited to its in-built capability of working with arrays and matrix-like data structures. On top of that, the library provides a large set of functions that are optimized to work on multi-dimensional arrays of data, also known as, n-dimensional arrays.

Traditionally, Python programmers wrote explicit for-loops in a nested format to work on nested arrays. This was slow as well as inefficient and thus, NumPy addressed this problem by working on making these operations much faster.

As a result, NumPy started using vectorized forms of arrays (termed as, 'vectorization') and over the years, the library has been further improved and optimized to perform numerical operations on vectors. The benefits of vectorization in NumPy are as follows:

• Vectorized code is clear, concise, and easy to read.
• It removes the need for explicit for-loops to work on arrays. This makes the code feel more 'Pythonic'.
• The code resembles standard mathematical notation.
• The number of potential bug encounters decreases as only a few lines of code are needed to perform numerical computation.

NumPy can be installed using the Python Package Manager, called 'pip'. Using pip, you can run the following command in your command line/terminal to install NumPy:

To install NumPy while you are in Jupyter Notebook/Lab, you can use the exclamation (!) syntax to execute commands from the underlying operating system.

This will install the latest stable version of NumPy for you to import and work with.

Once you've installed NumPy, you can use your favorite IDE (PyCharm, Jupyter Notebook, etc.) or the Python shell to import the library and use it.

It is a general convention to import NumPy as np in your Python code and you will find that a lot of Python programmers do the same. The following block of code illustrates how to import NumPy in Python:

If running this line of code doesn't give you an error, then, you've successfully installed and imported NumPy in Python.

Next, you can check the version of the installed NumPy library by printing out the __version__ attribute off of the NumPy package.

If your installed version is 2.4.2 or newer, then, you are good to go with this course!