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Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant indentation. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.


apt-get install python

Install a specific version

  • Install dependencies

    sudo apt install wget software-properties-common build-essential libnss3-dev zlib1g-dev libgdbm-dev libncurses5-dev libssl-dev libffi-dev libreadline-dev libsqlite3-dev libbz2-dev

  • Select the version in and download it

    cd Python-3.9.2/
    ./configure --enable-optimizations
    sudo make altinstall


Generator functions are a special kind of function that return a lazy iterator. These are objects that you can loop over like a list. However, unlike lists, lazy iterators do not store their contents in memory.

An example would be an infinite sequence generator

def infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1

You can use it as a list:

for i in infinite_sequence():
...     print(i, end=" ")
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
30 31 32 33 34 35 36 37 38 39 40 41 42

Instead of using a for loop, you can also call next() on the generator object directly. This is especially useful for testing a generator in the console:.

>>> gen = infinite_sequence()
>>> next(gen)
>>> next(gen)
>>> next(gen)
>>> next(gen)

Understanding Generators

Generator functions look and act just like regular functions, but with one defining characteristic. Generator functions use the Python yield keyword instead of return.

yield indicates where a value is sent back to the caller, but unlike return, you don’t exit the function afterward.Instead, the state of the function is remembered. That way, when next() is called on a generator object (either explicitly or implicitly within a for loop), the previously yielded variable num is incremented, and then yielded again.

Interesting libraries to explore

  • di: a modern dependency injection system, modeled around the simplicity of FastAPI's dependency injection.
  • humanize: This modest package contains various common humanization utilities, like turning a number into a fuzzy human-readable duration ("3 minutes ago") or into a human-readable size or throughput.
  • tryceratops: A linter of exceptions.
  • schedule: Python job scheduling for humans. Run Python functions (or any other callable) periodically using a friendly syntax.
  • huey: a little task queue for python.
  • textual: Textual is a TUI (Text User Interface) framework for Python using Rich as a renderer.
  • parso: Parses Python code.
  • kivi: Create android/Linux/iOS/Windows applications with python. Use it with kivimd to make it beautiful, check the examples and the docs.

    For beginner tutorials check the real python's and towards data science (and part 2). * apprise: Allows you to send a notification to almost all of the most popular notification services available to us today such as: Linux, Telegram, Discord, Slack, Amazon SNS, Gotify, etc. Look at all the supported notifications (¬º-°)¬. * aiomultiprocess: Presents a simple interface, while running a full AsyncIO event loop on each child process, enabling levels of concurrency never before seen in a Python application. Each child process can execute multiple coroutines at once, limited only by the workload and number of cores available. * twint: An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations. Maybe use snscrape (is below) if twint doesn't work. * snscrape: A social networking service scraper in Python. * tweepy: Twitter for Python.

Interesting sources

  • Musa 550 looks like a nice way to learn how to process geolocation data.

Last update: 2022-11-11