Functional Python Programming (docs.python.org)

Learning a bit of clojure will really open your eyes to what functional programming means, and you can take some of those learnings back to python.

  class Recurse(Exception):
      def __init__(self, *args, **kwargs):
          self.args = args
          self.kwargs = kwargs
  
  class Terminate(Exception):
      def __init__(self, retval):
          self.retval = retval

  def tailrec(func):
      def wrapper(*args, **kwargs):
          while True:
              try:
                  func(*args, **kwargs)

              except Recurse as r:
                  args = r.args
                  kwargs = r.kwargs

              except Terminate as t:
                  return t.retval

      return wrapper

  @tailrec
  def fact(n, acc=1):
      if n == 0:
          raise Terminate(acc)

      else:
          raise Recurse(n - 1, acc * n)

Integers, floats, tuples, named tuples, and frozensets are all immutable, functions are values, etc. E.g.: https://joypy.osdn.io/notebooks/Derivatives_of_Regular_Expre... -or- https://github.com/calroc/xerblin/blob/master/xerblin/btree....

It's not fantastic, but it's not that bad.

I can't keep up, does this mean scheme is not a functional language anymore by modern definition?

I disagree since any paradigm can be done either with or without mutable state. So it is not something that defines functional programming.

In any case you can also use immutable data types with python anyway.

The main annoyance with python is in my opinion your last point: The lambda syntax is too limited. So it is necessary to define lots of nested small functions instead.

Also python syntax is not as nice in chaining function calls over multiple lines.

It's about functions being first class values, nothing more.

Lisp and Scheme are examples of functional languages that do not restrict mutability.

The term for what you are talking about is pure functional programming!

    import pandas as pd
    import functools
    
    (
        pd.Series(range(10))
        .apply(lambda x: x * 10)
        .where(lambda x: x % 2 == 0)
        .pipe(lambda s: functools.reduce(lambda x, y : x + y, s))
    )

I don't think so.

> During Van Rossum's stay at CNRI, he launched the Computer Programming for Everybody (CP4E) initiative, intending to make programming more accessible to more people, with a basic "literacy" in programming languages, similar to the basic English literacy and mathematics skills required by most employers. Python served a central role in this

In python the C and even C++ heritage is quite clear, but it is in no way designed like Miranda and Haskell, which actually target to some extent expressing particular mathematical constructs in code, something which neither C, C++ not python ever aspired to.

Neither of these is true. I actually still find it a little weird that Python became so widely adopted by data science people, and that definitely doesn’t seem to be the main user GvR originally had in mind when creating the language.

Practically? Yes, generators are often more memory-efficient, and thereby often more compute-efficient.

Generators allow one to easily transform code from non-buffered to buffered. Consider this example:

    for y in x:
        do(z)

Another use: generators are used to implement async / await. I personally find this idea ridiculously stupid, but a lot of people (and especially those who don't understand what it does) like it a lot. Yielding mechanism, which is a feature of generators, is the one that's used to communicate / switch between co-routines (tasks) of asyncio.

Another aspect, besides bufferization is that you might want to delegate control over how much looping you want to do to a separate chunk of code. I.e. you may want to separate the generation of elements (hence, generator) from eg. filtering them, or transforming them in some way, or reducing them etc. If you didn't have this ability, you'd have to generate the entire collection upfront, and if your computation takes multiple steps that you'd like to separate into different code chunks, you'd have to also generate intermediate collections, even though, potentially, you don't need some of the elements in those collections. Consider, for example:

    def powers(start, end, power):
        return (x ** power for x in range(start, end))

    def flt(a, b, c):
        return a + b == c

    def disprove_flt(upto, upto_power):
        for power in range(upto_power):
            for a, b, c in zip(powers(1, upto - 2, power), powers(2, upto - 1, power), powers(3, upto, power)):
                if flt(a, b, c):
                    return a, b, c
        return None, None, None

An exercise to the reader: rewrite the code above in such a way as to eliminate "upto" and "upto_power", i.e. to search until a counterexample is found (or indefinitely).

In fact, I’d wager that any Python programmer worth their salt would suggest using a list comprehension and a lambda over a loop in most data transformation situations. Also currying comes in handy often once you have it in your toolkit.

It’s a bit silly to call two very mature libraries that are part of the Python standard library a ‘fun project’ just because the language itself supports multiple programming paradigms.