Typing

Iterable

The Iterable type represent every type that can be iterated (that can be looped through_): list, tuple, set, ...

from typing import Iterable

players_iterable: Iterator[str] = ['Player1, Player2, Player3']
players_iterable: Iterator[str] = ('Player1, Player2, Player3')
players_iterable: Iterator[str] = {'Player1, Player2, Player3'}

Iterator

When we get an information form an iterator with next() or list() it is removed from the iterator.

from typing import Iterator

players: list[str] = ['Player1, Player2, Player3']
players_iter: Iterator[str] = iter(players)

print(next(players_iter)) # Player1
print(next(players_iter)) # Player2
print(next(players_iter)) # Player3
print(list(players_iter)) # []

Generator

Basis

A generator as 3 parameters:

  • 1st is the type to yield
  • 2nd is the type that can be sent to the generator
  • 3rd is the type that is returned by the generator

Note: a Generator is under the hood an Iterator

Yield Example

from typing import Generator


def fibonacci_generator() -> Generator[int, None, None]
	a, b = 0, 1
	while True:
		yield a
		a, b = b, (a + b)


fib_gen: Generator[int, None, None] = fibonacci_generator()
n: int = 10
line_break: str = '-' * 20


while True:
	input(f'Press "enter" for the next {n} numbers of ficonnacci)
	for i in range(n):
		print(f'{next(fib_gen)}')
	print(line_break)

Source : Indently - 5 Useful Generator Functions In Python

Yield + Return Example

from typing import Generator


def read(path: string) -> Generator[str, None, str]:
	with open(path, 'r') as file:
		for line in file:
			yield line.strip()
	return 'This is the end of the file.'


reader: Generator[str, None, str] = read('file.txt')
while True:
	input(f'Press "enter for the next line") 
	try:
		print(next(reader))
	except StopIteration as e:
		# StopIteration the return of the Genenrator
		print('StopIteration:', e.value)
		sys.exit()

Source : Indently - 5 Useful Generator Functions In Python

Yield + Send Example

from typing import Generator


def cumulative_sum() -> Generator[float, float, None]:
	total: flaot = 0
	while True:
		total += yield total # yield act like retrive 

cumulative_generator: Generator[float, float, None] = cumulative_sum()
next(cumulative_generator) # start the generator
while True:
	value: float = float(input('Enter a value: '))
	current_sum: float = cumulative_generator.send(value)
	print(f'Cumulative sum: {current_sum}')

Source : Indently - 5 Useful Generator Functions In Python

Time

Import

import datetime
import locale

Basis

print(locale.getlocale())
#('en_US', 'UTF-8')

# Set local to get right format for the date
locale.setlocale(locale.LC_TIME, locale.getlocale())


print(f'{datetime.datetime.now()}')
#2024-08-02 22:30:04.850285

print(f'{datetime.datetime.now():%c}')
#Fri 02 Aug 2024 10:30:01 PM 

print(f'{datetime.datetime.now():%x}')
#08/02/2024

Pandas

Import

import pandas as pd

Create a DataFrame

# The data dictionary can be like this
data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
# or a list of dicts
data = [
	{'col1':'value1','col2':'value1'},
	{'col1':'value2','col2':'value2'}
]

# Create DataFrame form dict 
df = pd.DataFrame.from_dict(data)

Select Columns

# This can be used to rearrange the columns order
df = df[['my_column_name_3', 'my_column_name_1', 'my_column_name_4']]

Rename Columns

df = df.rename(columns={'column_name_1': 'my_column_name_1'})

Drop Columns

df = df.drop(columns=['my_column_name_3', 'my_column_name_4'])

Sort Values

df = df.sort_values(by='my_column_name_2')

Group By and Count

df = df.groupby(['my_column_name_2']).size().reset_index(name='count')

Left Join

merged_df = pd.merge(
	left_dataset,
	right_dataset,
	how='left',
	left_on='left_column_name',
	right_on='right_column_name'
)