This lecture series will teach you to use Python for scientific computing, with

a focus on economics and finance.

The series is aimed at Python novices, although experienced users will also find useful content in later lectures.

The series is aimed at Python novices, although experienced users will also find

useful content in later lectures.

In this lecture we will

* introduce Python,

* showcase some of its abilities,

* discuss the connection between Python and AI,

* explain why Python is our favorite language for scientific computing, and

* point you to the next steps.

You do **not** need to understand everything you see in this lecture -- we will work through the details slowly later in the lecture series.

### Can't I Just Use ChatGPT?

### Can't I Just Use LLMs?

No!

It's tempting to think that in the age of AI we don't need to learn how to code.

Of course it's tempting to think that in the age of AI we don't need to learn how to code.

And it's true that AIs like [ChatGPT](https://chatgpt.com/) and other LLMs are wonderful productivity tools for coders.

And yes, we like to be lazy too sometimes.

In fact an AI can be a great companion for these lectures -- try copy-pasting some code from this series and ask the AI to explain it to you.

In addition, we agree that AIs are outstanding productivity tools for coders.

AIs will certainly help you write pieces of code that you can combine.

But AIs cannot reliably solve new problems that they haven't seen before.

But AIs cannot completely and reliably solve a new problem that they haven't seen before!

You will need to be the architect and the supervisor -- and for these tasks you need to

be able to read, write, and understand computer code.

You will need to be the supervisor -- and for that you need to be able to read, write, and understand computer code.

Having said that, a good LLM is a useful companion for these lectures -- try copy-pasting some

code from this series and asking for an explanation.

### Isn't MATLAB Better?

No, no, and one hundred times no.

For almost all modern problems, Python's scientific libraries are now far in advance of MATLAB's capabilities.

Nirvana was great (and Soundgarden [was better](https://www.youtube.com/watch?v=3mbBbFH9fAg&list=RD3mbBbFH9fAg)) but

it's time to move on from the '90s.

We will explain the benefits of Python's libraries throughout this lecture

series, as well as in our later series on [JAX](https://jax.quantecon.org/intro.html).

For most modern problems, Python's scientific libraries are now far in advance of MATLAB's capabilities.

This is particularly the case in fast-growing fields such as deep learning and reinforcement learning.

We will also explain how Python's elegant design helps you write clean, efficient code.

Moreover, all major LLMs are more proficient at writing Python code than MATLAB

code.

On top of these features, Python is more widely used, with a huge and helpful community, and free!

We will discuss relative merits of Python's libraries throughout this lecture

series, as well as in our later series on [JAX](https://jax.quantecon.org/intro.html).

## What's Python?

## Introducing Python

[Python](https://www.python.org) is a general-purpose programming language conceived in 1989 by [Guido van Rossum](https://en.wikipedia.org/wiki/Guido_van_Rossum).

* encourages reproducibility and [open science](https://en.wikipedia.org/wiki/Open_science).

### Common Uses

{index}`Python <single: Python; common uses>` is a general-purpose language used in almost all application domains, including

{index}`Python <single: Python; common uses>` is a general-purpose language used

in almost all application domains, including

* AI

* scientific computing

* AI and computer science

* other scientific computing

* communication

* web development

* CGI and graphical user interfaces

* multimedia

* etc.

It is used and supported extensively by tech firms including

It is used and supported extensively by large tech firms including

* [Google](https://www.google.com/)

* [OpenAI](https://openai.com/)

* [Netflix](https://www.netflix.com/)

* [Meta](https://opensource.fb.com/)

* [Dropbox](https://www.dropbox.com/)

* [Amazon](https://www.amazon.com/)

* [Reddit](https://www.reddit.com/)

* etc.

### Relative Popularity

Python is, without doubt, one of the [most popular programming languages](https://www.tiobe.com/tiobe-index/).

Python is one of the most -- if not the most -- [popular programming languages](https://www.tiobe.com/tiobe-index/).

Python libraries like [pandas](https://pandas.pydata.org/) and [Polars](https://pola.rs/) are replacing familiar tools like Excel and VBA as an essential skill in the fields of finance and banking.

Moreover, Python is extremely popular within the scientific community -- especially AI

The following chart, produced using Stack Overflow Trends, provides some evidence.

Moreover, Python is extremely popular within the scientific community -- especially those connected to AI

It shows the popularity of a Python AI library called [PyTorch](https://pytorch.org/) relative to MATLAB.

For example, the following chart from Stack Overflow Trends shows how the

popularity of a single Python deep learning library

([PyTorch](https://pytorch.org/)) has grown over the last few years.

```{figure} /_static/lecture_specific/about_py/pytorch_vs_matlab.png

```

Pytorch is just one of several Python libraries for deep learning and AI.

The chart shows that MATLAB's popularity has faded, while PyTorch is growing rapidly.

Moreover, PyTorch is just one of the thousands of Python libraries available for scientic computing.

### Features

Python is a [high-level language](https://en.wikipedia.org/wiki/High-level_programming_language), which means it is relatively easy to read, write and debug.

Python is a [high-level

language](https://en.wikipedia.org/wiki/High-level_programming_language), which

means it is relatively easy to read, write and debug.

It has a relatively small core language that is easy to learn.

This core is supported by many libraries, which you can learn to use as required.

Python is very beginner-friendly

* suitable for students learning programming

* used in many undergraduate and graduate programs

Other features of Python:

* multiple programming styles are supported (procedural, object-oriented, functional, etc.)

* [interpreted](https://en.wikipedia.org/wiki/Interpreter_(computing)) rather than [compiled](https://en.wikipedia.org/wiki/Compiler) ahead of time.

This core is supported by many libraries, which can be studied as required.

Python is flexible and pragmatic, supporting multiple programming styles (procedural, object-oriented, functional, etc.).

### Syntax and Design

```{index} single: Python; syntax and design

```

One reason for Python's popularity is its simple and elegant design --- we'll see many examples later on.

One reason for Python's popularity is its simple and elegant design.

To get a feeling for this, let's look at an example.

This Java code opens an imaginary file called `data.csv` and computes the mean

of the values in the second column.

Even without knowing Java, you can see that the program is long and complex.

Here's Python code that does the same thing.

Even if you don't yet know Python, you can see that the code is simpler and

easier to read.

Even if you don't yet know Python, you can see that the code is far simpler and easier to read.

```{code-cell} python3

:tags: [skip-execution]

```

The simplicity of Python and its neat design are a big factor in its popularity.

### The AI Connection

Unless you have been living under a rock and avoiding all contact with the

modern world, you will know that AI is rapidly advancing.

AI is already remarkably good at helping you write code, as discussed above.

No doubt AI will take over many tasks currently performed by humans,

just like other forms of machinery have done over the past few centuries.

AI is in the process of taking over many tasks currently performed by humans,

just as other forms of machinery have done over the past few centuries.

Python is playing a huge role in the advance of AI and machine learning.

Moreover, Python is playing a huge role in the advance of AI and machine learning.

This means that tech firms are pouring money into development of extremely

powerful Python libraries.

We have already discussed the importance of Python for AI, machine learning and data science

Let's take a look at the role of Python in other areas of scientific computing.

Python is either the dominant player or a major player in

Python is also one of the dominant players in

* astronomy

* chemistry

This section briefly showcases some examples of Python for general scientific programming.

### NumPy

```{index} single: scientific programming; numeric

* libraries like JAX directly extend NumPy functionality and hence are easier to

learn when you already know NumPy.

This lecture series will provide you with extensive background in NumPy.

### SciPy

The [SciPy](http://www.scipy.org) library is built on top of NumPy and provides additional functionality.

### Networks and Graphs

The study of networks and graphs becoming an important part of scientific work

The study of [networks](https://networks.quantecon.org/) is becoming an important part of scientific work

in economics, finance and other fields.

For example, we are interesting in studying

* friendship and social networks

* etc.

(We have a [book on economic networks](https://networks.quantecon.org/) if you would like to learn more.)

Python has many libraries for studying networks and graphs.

```{index} single: NetworkX

* [Dask](https://docs.dask.org/en/stable/) for parallelization

* [Numba](http://numba.pydata.org/) for making Python run at the same speed as native machine code

* [CVXPY](https://www.cvxpy.org/) for convex optimization

* [scikit-image](https://scikit-image.org/) and [OpenCV](https://opencv.org/) for processing and analysing image data

* [scikit-image](https://scikit-image.org/) and [OpenCV](https://opencv.org/) for processing and analyzing image data

* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) for extracting data from HTML and XML files