Recursing

import itertools
import operator

# https://joelgrus.com/2015/07/07/haskell-style-fibonacci-in-python/
def tee_fibs():
    yield 1
    yield 1
    fibs1, fibs2 = itertools.tee(tee_fibs())
    next(fibs2)  # Offset fibs2
    yield from map(operator.add, fibs1, fibs2)

Recursing

from time import time  # For tracking how long everything is taking
import os

import matplotlib.pyplot as plt  # For plotting
import matplotlib.ticker as ticker

import geopandas as gpd  # For geographic data
import pandas as pd  # For population data
from shapely.geometry import Polygon  # Used to filter out empty polygons

Recursing

# pip install z3-solver
from z3 import Int, Solver, And, sat, If, Sum, Or
from typing import List, Tuple

width = 8  # Number of columns
height = 8  # Number of rows

# Matrix of integers (0 if grass, 1 if tent), using integer so we can use Sum to count them
X = [[Int(f"x_{i+1}_{j+1}") for j in range(width)] for i in range(height)]

Recursing

import z3
answers = [z3.Bool(f"answer{i}") for i in range(1,7)]
implications = [
  z3.And(answers[1:]),         # All of the below
  z3.Not(z3.Or(answers[2:])),  # None of the below
  z3.And(answers[:2]),         # All of the above
  z3.Or(answers[:3]),          # Any of the above
  z3.Not(z3.Or(answers[:4])),  # None of the above
  z3.Not(z3.Or(answers[:5]))]  # None of the above

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def print_pos(board):
    print()
    uni_pieces = {
      "r": "♜", "n": "♞", "b": "♝", "q": "♛", "k": "♚", "p": "♟",
      "R": "♖", "N": "♘", "B": "♗", "Q": "♕", "K": "♔", "P": "♙",  ".": "·"}
    colors = ["\x1b[48;5;255m", "\x1b[48;5;253m"]
    sc, ec = "\x1b[0;30;107m", "\x1b[0m"
    for i, row in enumerate(board.split()):
        colored = [colors[(i + c) % 2] + uni_pieces[p] for c, p in enumerate(row)]
        print(" ", sc, 8 - i, " ".join(colored), ec)

Recursing

from random import random


def truncated_given_exp(minimum, maximum, expected):
  if expected == minimum: return expected
  maximum -= minimum
  expected -= minimum
  k = maximum/expected - 1 # Questa si comporta peggio della prima se expected - min < max - expected
  return minimum + maximum * random()**k

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from collections import namedtuple
from datetime import date

Person = namedtuple('Person', 'cognome nome data_nascita sesso comune')


def reverse_cf(cf: str) -> Person:
    assert len(cf) == 16
    cf = cf.upper()
    cognome = cf[:3]