Jupyter Snippet CB2nd 02_py3

2.2. Using the latest features of Python 3

my_list = list(range(10))

print(my_list)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

print(*my_list)

0 1 2 3 4 5 6 7 8 9

print(*my_list, sep=" + ", end=" = %d" % sum(my_list))

0 + 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 = 45

first, second, *rest, last = my_list

print(first, second, last)

0 1 9

rest

[2, 3, 4, 5, 6, 7, 8]

from math import pi, cos
α = 2
π = pi
cos(α * π)

1.000

a, b = 1, 2
f"The sum of {a} and {b} is {a + b}"

'The sum of 1 and 2 is 3'

def kinetic_energy(mass: 'kg',
                   velocity: 'm/s') -> 'J':
    """The annotations serve here as documentation."""
    return .5 * mass * velocity ** 2

annotations = kinetic_energy.__annotations__
print(*(f"{key} is in {value}"
        for key, value in annotations.items()),
      sep=", ")

mass is in kg, velocity is in m/s, return is in J

import numpy as np
M = np.array([[0, 1], [1, 0]])

M * M

array([[0, 1],
       [1, 0]])

M @ M

array([[1, 0],
       [0, 1]])

def gen1():
    for i in range(5):
        for j in range(i):
            yield j

def gen2():
    for i in range(5):
        yield from range(i)

list(gen1())

[0, 0, 1, 0, 1, 2, 0, 1, 2, 3]

list(gen2())

[0, 0, 1, 0, 1, 2, 0, 1, 2, 3]

import time

def f1(x):
    time.sleep(1)
    return x

%timeit -n1 -r1 f1(0)

1 s ± 0 ns per loop (mean ± std. dev. of 1 run,
    1 loop each)

%timeit -n1 -r1 f1(0)

1 s ± 0 ns per loop (mean ± std. dev. of 1 run,
    1 loop each)

from functools import lru_cache

@lru_cache(maxsize=32)  # keep the latest 32 calls
def f2(x):
    time.sleep(1)
    return x

%timeit -n1 -r1 f2(0)

1 s ± 0 ns per loop (mean ± std. dev. of 1 run,
    1 loop each)

%timeit -n1 -r1 f2(0)

6.14 µs ± 0 ns per loop (mean ± std. dev. of 1 run,
    1 loop each)

from pathlib import Path

p = Path('.')

sorted(p.glob('*.md'))

[PosixPath('00_intro.md'),
 PosixPath('01_py3.md'),
 PosixPath('02_workflows.md'),
 PosixPath('03_git.md'),
 PosixPath('04_git_advanced.md'),
 PosixPath('05_tips.md'),
 PosixPath('06_high_quality.md'),
 PosixPath('07_test.md'),
 PosixPath('08_debugging.md')]

_[0].read_text()

'# Introduction\n\n...\n'

[d for d in p.iterdir() if d.is_dir()]

[PosixPath('images'),
 PosixPath('.ipynb_checkpoints'),
 PosixPath('__pycache__'),

list((p / 'images').iterdir())

[PosixPath('images/github_new.png'),
 PosixPath('images/folder.png')]

import random as r
import statistics as st

my_list = [r.normalvariate(0, 1)
           for _ in range(100000)]

print(st.mean(my_list),
      st.median(my_list),
      st.stdev(my_list),
      )

0.00073 -0.00052 1.00050