# $ pip3 install tqdm
>>> from tqdm import tqdm
>>> from time import sleep
>>> for el in tqdm([1, 2, 3], desc='Processing'):
...     sleep(1)
Processing: 100%|████████████████████| 3/3 [00:03<00:00,  1.00s/it]

# $ pip3 install matplotlib
import matplotlib.pyplot as plt
plt.plot(<x_data>, <y_data> [, label=<str>])   # Or: plt.plot(<y_data>)
plt.legend()                                   # Adds a legend.
plt.savefig(<path>)                            # Saves the figure.
plt.show()                                     # Displays the figure.
plt.clf()                                      # Clears the figure.

# $ pip3 install tabulate
import csv, tabulate
with open('test.csv', encoding='utf-8', newline='') as file:
    rows   = csv.reader(file)
    header = next(rows)
    table  = tabulate.tabulate(rows, header)
print(table)

import curses, curses.ascii, os
from curses import A_REVERSE, KEY_DOWN, KEY_UP, KEY_LEFT, KEY_RIGHT, KEY_ENTER

def main(screen):
    ch, first, selected, paths = 0, 0, 0, os.listdir()
    while ch != curses.ascii.ESC:
        height, _ = screen.getmaxyx()
        screen.erase()
        for y, filename in enumerate(paths[first : first+height]):
            screen.addstr(y, 0, filename, A_REVERSE * (selected == first + y))
        ch = screen.getch()
        selected += (ch == KEY_DOWN) - (ch == KEY_UP)
        selected = max(0, min(len(paths)-1, selected))
        first += (first <= selected - height) - (first > selected)
        if ch in [KEY_LEFT, KEY_RIGHT, KEY_ENTER, 10, 13]:
            new_dir = '..' if ch == KEY_LEFT else paths[selected]
            if os.path.isdir(new_dir):
                os.chdir(new_dir)
                first, selected, paths = 0, 0, os.listdir()

if __name__ == '__main__':
    curses.wrapper(main)

# $ pip3 install loguru
from loguru import logger

logger.add('debug_{time}.log', colorize=True)  # Connects a log file.
logger.add('error_{time}.log', level='ERROR')  # Another file for errors or higher.
logger.<level>('A logging message.')           # Logs to file/s and prints to stderr.

try:
    ...
except <exception>:
    logger.exception('An error happened.')

rotation=<int>|<datetime.timedelta>|<datetime.time>|<str>

retention=<int>|<datetime.timedelta>|<str>

# $ pip3 install requests beautifulsoup4
import requests, bs4, os, sys

WIKI_URL = 'https://en.wikipedia.org/wiki/Python_(programming_language)'
try:
    html       = requests.get(WIKI_URL).text
    document   = bs4.BeautifulSoup(html, 'html.parser')
    table      = document.find('table', class_='infobox vevent')
    python_url = table.find('th', text='Website').next_sibling.a['href']
    version    = table.find('th', text='Stable release').next_sibling.strings.__next__()
    logo_url   = table.find('img')['src']
    logo       = requests.get(f'https:{logo_url}').content
    filename   = os.path.basename(logo_url)
    with open(filename, 'wb') as file:
        file.write(logo)
    print(f'{python_url}, {version}, file://{os.path.abspath(filename)}')
except requests.exceptions.ConnectionError:
    print("You've got problems with connection.", file=sys.stderr)

# $ pip3 install bottle
from bottle import run, route, static_file, template, post, request, response
import json

run(host='localhost', port=8080)        # Runs locally.
run(host='0.0.0.0', port=80)            # Runs globally.

@route('/img/<filename>')
def send_file(filename):
    return static_file(filename, root='img_dir/')

@route('/<sport>')
def send_html(sport):
    return template('<h1>{{title}}</h1>', title=sport)

@post('/<sport>/odds')
def send_json(sport):
    team = request.forms.get('team')
    response.headers['Content-Type'] = 'application/json'
    response.headers['Cache-Control'] = 'no-cache'
    return json.dumps({'team': team, 'odds': [2.09, 3.74, 3.68]})

# $ pip3 install requests
>>> import threading, requests
>>> threading.Thread(target=run, daemon=True).start()
>>> url = 'http://localhost:8080/football/odds'
>>> request_data = {'team': 'arsenal f.c.'}
>>> response = requests.post(url, data=request_data)
>>> response.json()
{'team': 'arsenal f.c.', 'odds': [2.09, 3.74, 3.68]}

from time import perf_counter
start_time = perf_counter()
...
duration_in_seconds = perf_counter() - start_time

>>> from timeit import timeit
>>> timeit("''.join(str(i) for i in range(100))",
...        number=10000, globals=globals(), setup='pass')
0.34986

# $ pip3 install line_profiler memory_profiler
@profile
def main():
    a = [*range(10000)]
    b = {*range(10000)}
main()

$ kernprof -lv test.py
Line #   Hits     Time  Per Hit   % Time  Line Contents
=======================================================
     1                                    @profile
     2                                    def main():
     3      1    955.0    955.0     43.7      a = [*range(10000)]
     4      1   1231.0   1231.0     56.3      b = {*range(10000)}

$ python3 -m memory_profiler test.py
Line #         Mem usage      Increment   Line Contents
=======================================================
     1        37.668 MiB     37.668 MiB   @profile
     2                                    def main():
     3        38.012 MiB      0.344 MiB       a = [*range(10000)]
     4        38.477 MiB      0.465 MiB       b = {*range(10000)}

# $ pip3 install pycallgraph2; apt/brew install graphviz
import pycallgraph2 as cg, datetime

filename = f'profile-{datetime.datetime.now():%Y%m%d_%H%M%S}.png'
drawer = cg.output.GraphvizOutput(output_file=filename)
with cg.PyCallGraph(drawer):
    <code_to_be_profiled>

# $ pip3 install numpy
import numpy as np

<array> = np.array(<list/list_of_lists>)                # Returns 1d/2d NumPy array.
<array> = np.zeros/ones(<shape>)                        # Also np.full(<shape>, <el>).
<array> = np.arange(from_inc, to_exc, ±step)            # Also np.linspace(start, stop, num).
<array> = np.random.randint(from_inc, to_exc, <shape>)  # Also np.random.random(<shape>).

<view>  = <array>.reshape(<shape>)                      # Also `<array>.shape = <shape>`.
<array> = <array>.flatten()                             # Collapses array into one dimension.
<view>  = <array>.squeeze()                             # Removes dimensions of length one.

<array> = <array>.sum/min/mean/var/std(axis)            # Passed dimension gets aggregated.
<array> = <array>.argmin(axis)                          # Returns indexes of smallest elements.
<array> = np.apply_along_axis(<func>, axis, <array>)    # Func can return a scalar or array.

<el>       = <2d_array>[row_index, column_index]        # <3d_a>[table_i, row_i, column_i]
<1d_view>  = <2d_array>[row_index]                      # <3d_a>[table_i, row_i]
<1d_view>  = <2d_array>[:, column_index]                # <3d_a>[table_i, :, column_i]

<1d_array> = <2d_array>[row_indexes, column_indexes]    # <3d_a>[table_is, row_is, column_is]
<2d_array> = <2d_array>[row_indexes]                    # <3d_a>[table_is, row_is]
<2d_array> = <2d_array>[:, column_indexes]              # <3d_a>[table_is, :, column_is]

<2d_bools> = <2d_array> ><== <el>                       # <3d_array> ><== <1d_array>
<1d_array> = <2d_array>[<2d_bools>]                     # <3d_array>[<2d_bools>]

left  = [[0.1], [0.6], [0.8]]                           # Shape: (3, 1)
right = [ 0.1 ,  0.6 ,  0.8 ]                           # Shape: (3,)

left  = [[0.1], [0.6], [0.8]]                           # Shape: (3, 1)
right = [[0.1 ,  0.6 ,  0.8]]                           # Shape: (1, 3) <- !

left  = [[0.1,  0.1,  0.1],                             # Shape: (3, 3) <- !
         [0.6,  0.6,  0.6],
         [0.8,  0.8,  0.8]]

right = [[0.1,  0.6,  0.8],                             # Shape: (3, 3) <- !
         [0.1,  0.6,  0.8],
         [0.1,  0.6,  0.8]]

>>> points = np.array([0.1, 0.6, 0.8])
 [ 0.1,  0.6,  0.8]
>>> wrapped_points = points.reshape(3, 1)
[[ 0.1],
 [ 0.6],
 [ 0.8]]
>>> distances = wrapped_points - points
[[ 0. , -0.5, -0.7],
 [ 0.5,  0. , -0.2],
 [ 0.7,  0.2,  0. ]]
>>> distances = np.abs(distances)
[[ 0. ,  0.5,  0.7],
 [ 0.5,  0. ,  0.2],
 [ 0.7,  0.2,  0. ]]
>>> i = np.arange(3)
[0, 1, 2]
>>> distances[i, i] = np.inf
[[ inf,  0.5,  0.7],
 [ 0.5,  inf,  0.2],
 [ 0.7,  0.2,  inf]]
>>> distances.argmin(1)
[1, 2, 1]

# $ pip3 install pillow
from PIL import Image

<Image> = Image.new('<mode>', (width, height))   # Also: `color=<int/tuple/str>`.
<Image> = Image.open(<path>)                     # Identifies format based on file contents.
<Image> = <Image>.convert('<mode>')              # Converts image to the new mode.
<Image>.save(<path>)                             # Selects format based on the path extension.
<Image>.show()                                   # Opens image in default preview app.

<int/tuple> = <Image>.getpixel((x, y))           # Returns a pixel.
<Image>.putpixel((x, y), <int/tuple>)            # Writes a pixel to the image.
<ImagingCore> = <Image>.getdata()                # Returns a flattened sequence of pixels.
<Image>.putdata(<list/ImagingCore>)              # Writes a flattened sequence of pixels.
<Image>.paste(<Image>, (x, y))                   # Writes passed image to the image.

<2d_array> = np.array(<Image_L>)                 # Creates NumPy array from greyscale image.
<3d_array> = np.array(<Image_RGB/A>)             # Creates NumPy array from color image.
<Image>    = Image.fromarray(np.uint8(<array>))  # Use <array>.clip(0, 255) to clip the values.

WIDTH, HEIGHT = 100, 100
n_pixels = WIDTH * HEIGHT
hues = (255 * i/n_pixels for i in range(n_pixels))
img = Image.new('HSV', (WIDTH, HEIGHT))
img.putdata([(int(h), 255, 255) for h in hues])
img.convert('RGB').save('test.png')

from random import randint
add_noise = lambda value: max(0, min(255, value + randint(-20, 20)))
img = Image.open('test.png').convert('HSV')
img.putdata([(add_noise(h), s, v) for h, s, v in img.getdata()])
img.convert('RGB').save('test.png')

from PIL import ImageDraw
<ImageDraw> = ImageDraw.Draw(<Image>)

<ImageDraw>.point((x, y))                        # Truncates floats into ints.
<ImageDraw>.line((x1, y1, x2, y2 [, ...]))       # To get anti-aliasing use Image's resize().
<ImageDraw>.arc((x1, y1, x2, y2), deg1, deg2)    # Always draws in clockwise direction.
<ImageDraw>.rectangle((x1, y1, x2, y2))          # To rotate use Image's rotate() and paste().
<ImageDraw>.polygon((x1, y1, x2, y2, ...))       # Last point gets connected to the first.
<ImageDraw>.ellipse((x1, y1, x2, y2))            # To rotate use Image's rotate() and paste().

# $ pip3 install imageio
from PIL import Image, ImageDraw
import imageio

WIDTH, HEIGHT, R = 126, 126, 10
frames = []
for velocity in range(1, 16):
    y = sum(range(velocity))
    frame = Image.new('L', (WIDTH, HEIGHT))
    draw  = ImageDraw.Draw(frame)
    draw.ellipse((WIDTH/2-R, y, WIDTH/2+R, y+R*2), fill='white')
    frames.append(frame)
frames += reversed(frames[1:-1])
imageio.mimsave('test.gif', frames, duration=0.03)

import wave

<Wave_read>  = wave.open('<path>', 'rb')        # Opens the WAV file.
framerate    = <Wave_read>.getframerate()       # Number of frames per second.
nchannels    = <Wave_read>.getnchannels()       # Number of samples per frame.
sampwidth    = <Wave_read>.getsampwidth()       # Sample size in bytes.
nframes      = <Wave_read>.getnframes()         # Number of frames.
<params>     = <Wave_read>.getparams()          # Immutable collection of above.
<bytes>      = <Wave_read>.readframes(nframes)  # Returns next 'nframes' frames.

<Wave_write> = wave.open('<path>', 'wb')        # Truncates existing file.
<Wave_write>.setframerate(<int>)                # 44100 for CD, 48000 for video.
<Wave_write>.setnchannels(<int>)                # 1 for mono, 2 for stereo.
<Wave_write>.setsampwidth(<int>)                # 2 for CD quality sound.
<Wave_write>.setparams(<params>)                # Sets all parameters.
<Wave_write>.writeframes(<bytes>)               # Appends frames to the file.

+-----------+-----------+------+-----------+
| sampwidth |    min    | zero |    max    |
+-----------+-----------+------+-----------+
|     1     |         0 |  128 |       255 |
|     2     |    -32768 |    0 |     32767 |
|     3     |  -8388608 |    0 |   8388607 |
+-----------+-----------+------+-----------+

def read_wav_file(filename):
    def get_int(bytes_obj):
        an_int = int.from_bytes(bytes_obj, 'little', signed=(sampwidth != 1))
        return an_int - 128 * (sampwidth == 1)
    with wave.open(filename, 'rb') as file:
        sampwidth = file.getsampwidth()
        frames = file.readframes(-1)
    bytes_samples = (frames[i : i+sampwidth] for i in range(0, len(frames), sampwidth))
    return [get_int(b) / pow(2, sampwidth * 8 - 1) for b in bytes_samples]

def write_to_wav_file(filename, float_samples, nchannels=1, sampwidth=2, framerate=44100):
    def get_bytes(a_float):
        a_float = max(-1, min(1 - 2e-16, a_float))
        a_float += sampwidth == 1
        a_float *= pow(2, sampwidth * 8 - 1)
        return int(a_float).to_bytes(sampwidth, 'little', signed=(sampwidth != 1))
    with wave.open(filename, 'wb') as file:
        file.setnchannels(nchannels)
        file.setsampwidth(sampwidth)
        file.setframerate(framerate)
        file.writeframes(b''.join(get_bytes(f) for f in float_samples))

from math import pi, sin
samples_f = (sin(i * 2 * pi * 440 / 44100) for i in range(100000))
write_to_wav_file('test.wav', samples_f)

from random import random
add_noise = lambda value: value + (random() - 0.5) * 0.03
samples_f = (add_noise(f) for f in read_wav_file('test.wav'))
write_to_wav_file('test.wav', samples_f)

# $ pip3 install simpleaudio
from simpleaudio import play_buffer
with wave.open('test.wav', 'rb') as file:
    p = file.getparams()
    frames = file.readframes(-1)
    play_buffer(frames, p.nchannels, p.sampwidth, p.framerate)

# $ pip3 install pyttsx3
import pyttsx3
engine = pyttsx3.init()
engine.say('Sally sells seashells by the seashore.')
engine.runAndWait()

# $ pip3 install simpleaudio
import itertools as it, math, struct, simpleaudio

F  = 44100
P1 = '71♩,69♪,,71♩,66♪,,62♩,66♪,,59♩,,'
P2 = '71♩,73♪,,74♩,73♪,,74♪,,71♪,,73♩,71♪,,73♪,,69♪,,71♩,69♪,,71♪,,67♪,,71♩,,'
get_pause   = lambda seconds: it.repeat(0, int(seconds * F))
sin_f       = lambda i, hz: math.sin(i * 2 * math.pi * hz / F)
get_wave    = lambda hz, seconds: (sin_f(i, hz) for i in range(int(seconds * F)))
get_hz      = lambda key: 8.176 * 2 ** (int(key) / 12)
parse_note  = lambda note: (get_hz(note[:2]), 1/4 if '♩' in note else 1/8)
get_samples = lambda note: get_wave(*parse_note(note)) if note else get_pause(1/8)
samples_f   = it.chain.from_iterable(get_samples(n) for n in f'{P1},{P1},{P2}'.split(','))
samples_b   = b''.join(struct.pack('<h', int(f * 30000)) for f in samples_f)
simpleaudio.play_buffer(samples_b, 1, 2, F)

# $ pip3 install pygame
import pygame as pg

pg.init()
screen = pg.display.set_mode((500, 500))
rect = pg.Rect(240, 240, 20, 20)
while all(event.type != pg.QUIT for event in pg.event.get()):
    deltas = {pg.K_UP: (0, -1), pg.K_RIGHT: (1, 0), pg.K_DOWN: (0, 1), pg.K_LEFT: (-1, 0)}
    for ch, is_pressed in enumerate(pg.key.get_pressed()):
        rect = rect.move(deltas[ch]) if ch in deltas and is_pressed else rect
    screen.fill((0, 0, 0))
    pg.draw.rect(screen, (255, 255, 255), rect)
    pg.display.flip()

<Rect> = pg.Rect(x, y, width, height)           # Floats get truncated into ints.
<int>  = <Rect>.x/y/centerx/centery/…           # Top, right, bottom, left. Allows assignments.
<tup.> = <Rect>.topleft/center/…                # Topright, bottomright, bottomleft. Same.
<Rect> = <Rect>.move((x, y))                    # Use move_ip() to move in-place.

<bool> = <Rect>.collidepoint((x, y))            # Checks if rectangle contains a point.
<bool> = <Rect>.colliderect(<Rect>)             # Checks if two rectangles overlap.
<int>  = <Rect>.collidelist(<list_of_Rect>)     # Returns index of first colliding Rect or -1.
<list> = <Rect>.collidelistall(<list_of_Rect>)  # Returns indexes of all colliding rectangles.

<Surf> = pg.display.set_mode((width, height))   # Returns a display surface.
<Surf> = pg.Surface((width, height))            # New RGB surface. RGBA if `flags=pg.SRCALPHA`.
<Surf> = pg.image.load('<path>')                # Loads the image. Format depends on source.
<Surf> = <Surf>.subsurface(<Rect>)              # Returns a subsurface.

<Surf>.fill(color)                              # Tuple, Color('#rrggbb[aa]') or Color(<name>).
<Surf>.set_at((x, y), color)                    # Updates pixel.
<Surf>.blit(<Surf>, (x, y))                     # Draws passed surface to the surface.

from pygame.transform import scale, ...
<Surf> = scale(<Surf>, (width, height))         # Returns scaled surface.
<Surf> = rotate(<Surf>, anticlock_degrees)      # Returns rotated and scaled surface.
<Surf> = flip(<Surf>, x_bool, y_bool)           # Returns flipped surface.

from pygame.draw import line, ...
line(<Surf>, color, (x1, y1), (x2, y2), width)  # Draws a line to the surface.
arc(<Surf>, color, <Rect>, from_rad, to_rad)    # Also: ellipse(<Surf>, color, <Rect>, width=0)
rect(<Surf>, color, <Rect>, width=0)            # Also: polygon(<Surf>, color, points, width=0)

<Font> = pg.font.SysFont('<name>', size)        # Loads the system font or default if missing.
<Font> = pg.font.Font('<path>', size)           # Loads the TTF file. Pass None for default.
<Surf> = <Font>.render(text, antialias, color)  # Background color can be specified at the end.

<Sound> = pg.mixer.Sound('<path>')              # Loads the WAV file.
<Sound>.play()                                  # Starts playing the sound.

import collections, dataclasses, enum, io, itertools as it, pygame as pg, urllib.request
from random import randint

P = collections.namedtuple('P', 'x y')          # Position
D = enum.Enum('D', 'n e s w')                   # Direction
W, H, MAX_S = 50, 50, P(5, 10)                  # Width, Height, Max speed

def main():
    def get_screen():
        pg.init()
        return pg.display.set_mode((W*16, H*16))
    def get_images():
        url = 'https://gto76.github.io/python-cheatsheet/web/mario_bros.png'
        img = pg.image.load(io.BytesIO(urllib.request.urlopen(url).read()))
        return [img.subsurface(get_rect(x, 0)) for x in range(img.get_width() // 16)]
    def get_mario():
        Mario = dataclasses.make_dataclass('Mario', 'rect spd facing_left frame_cycle'.split())
        return Mario(get_rect(1, 1), P(0, 0), False, it.cycle(range(3)))
    def get_tiles():
        border = [(x, y) for x in range(W) for y in range(H) if x in [0, W-1] or y in [0, H-1]]
        platforms = [(randint(1, W-2), randint(2, H-2)) for _ in range(W*H // 10)]
        return [get_rect(x, y) for x, y in border + platforms]
    def get_rect(x, y):
        return pg.Rect(x*16, y*16, 16, 16)
    run(get_screen(), get_images(), get_mario(), get_tiles())

def run(screen, images, mario, tiles):
    clock = pg.time.Clock()
    while all(event.type != pg.QUIT for event in pg.event.get()):
        keys = {pg.K_UP: D.n, pg.K_RIGHT: D.e, pg.K_DOWN: D.s, pg.K_LEFT: D.w}
        pressed = {keys.get(ch) for ch, is_prsd in enumerate(pg.key.get_pressed()) if is_prsd}
        update_speed(mario, tiles, pressed)
        update_position(mario, tiles)
        draw(screen, images, mario, tiles, pressed)
        clock.tick(28)

def update_speed(mario, tiles, pressed):
    x, y = mario.spd
    x += 2 * ((D.e in pressed) - (D.w in pressed))
    x -= (x > 0) - (x < 0)
    y += 1 if D.s not in get_boundaries(mario.rect, tiles) else (D.n in pressed) * -10
    mario.spd = P(x=max(-MAX_S.x, min(MAX_S.x, x)), y=max(-MAX_S.y, min(MAX_S.y, y)))

def update_position(mario, tiles):
    x, y = mario.rect.topleft
    n_steps = max(abs(s) for s in mario.spd)
    for _ in range(n_steps):
        mario.spd = stop_on_collision(mario.spd, get_boundaries(mario.rect, tiles))
        x, y = x + mario.spd.x / n_steps, y + mario.spd.y / n_steps
        mario.rect.topleft = x, y

def get_boundaries(rect, tiles):
    deltas = {D.n: P(0, -1), D.e: P(1, 0), D.s: P(0, 1), D.w: P(-1, 0)}
    return {d for d, delta in deltas.items() if rect.move(delta).collidelist(tiles) != -1}

def stop_on_collision(spd, bounds):
    return P(x=0 if (D.w in bounds and spd.x < 0) or (D.e in bounds and spd.x > 0) else spd.x,
             y=0 if (D.n in bounds and spd.y < 0) or (D.s in bounds and spd.y > 0) else spd.y)

def draw(screen, images, mario, tiles, pressed):
    def get_marios_image_index():
        if D.s not in get_boundaries(mario.rect, tiles):
            return 4
        return next(mario.frame_cycle) if {D.w, D.e} & pressed else 6
    screen.fill((85, 168, 255))
    mario.facing_left = (D.w in pressed) if {D.w, D.e} & pressed else mario.facing_left
    screen.blit(images[get_marios_image_index() + mario.facing_left * 9], mario.rect)
    for t in tiles:
        screen.blit(images[18 if t.x in [0, (W-1)*16] or t.y in [0, (H-1)*16] else 19], t)
    pg.display.flip()

if __name__ == '__main__':
    main()

# $ pip3 install pandas matplotlib
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt

>>> Series([1, 2], index=['x', 'y'], name='a')
x    1
y    2
Name: a, dtype: int64

<Sr> = Series(<list>)                          # Assigns RangeIndex starting at 0.
<Sr> = Series(<dict>)                          # Takes dictionary's keys for index.
<Sr> = Series(<dict/Series>, index=<list>)     # Only keeps items with keys specified in index.

<el> = <Sr>.loc[key]                           # Or: <Sr>.iloc[index]
<Sr> = <Sr>.loc[keys]                          # Or: <Sr>.iloc[indexes]
<Sr> = <Sr>.loc[from_key : to_key_inclusive]   # Or: <Sr>.iloc[from_i : to_i_exclusive]

<el> = <Sr>[key/index]                         # Or: <Sr>.key
<Sr> = <Sr>[keys/indexes]                      # Or: <Sr>[<key_range/range>]
<Sr> = <Sr>[bools]                             # Or: <Sr>.i/loc[bools]

<Sr> = <Sr> ><== <el/Sr>                       # Returns a Series of bools.
<Sr> = <Sr> +-*/ <el/Sr>                       # Items with non-matching keys get value NaN.

<Sr> = <Sr>.append(<Sr>)                       # Or: pd.concat(<coll_of_Sr>)
<Sr> = <Sr>.combine_first(<Sr>)                # Adds items that are not yet present.
<Sr>.update(<Sr>)                              # Updates items that are already present.

<Sr>.plot.line/area/bar/pie/hist()             # Generates a Matplotlib plot.
plt.show()                                     # Displays the plot. Also plt.savefig(<path>).

<el> = <Sr>.sum/max/mean/idxmax/all()          # Or: <Sr>.agg(lambda <Sr>: <el>)
<Sr> = <Sr>.rank/diff/cumsum/ffill/interpl()   # Or: <Sr>.agg/transform(lambda <Sr>: <Sr>)
<Sr> = <Sr>.fillna(<el>)                       # Or: <Sr>.agg/transform/map(lambda <el>: <el>)

>>> sr = Series([1, 2], index=['x', 'y'])
x    1
y    2

+-----------------+-------------+-------------+---------------+
|                 |    'sum'    |   ['sum']   | {'s': 'sum'}  |
+-----------------+-------------+-------------+---------------+
| sr.apply(…)     |      3      |    sum  3   |     s  3      |
| sr.agg(…)       |             |             |               |
+-----------------+-------------+-------------+---------------+

+-----------------+-------------+-------------+---------------+
|                 |    'rank'   |   ['rank']  | {'r': 'rank'} |
+-----------------+-------------+-------------+---------------+
| sr.apply(…)     |             |      rank   |               |
| sr.agg(…)       |     x  1    |   x     1   |    r  x  1    |
| sr.transform(…) |     y  2    |   y     2   |       y  2    |
+-----------------+-------------+-------------+---------------+

>>> DataFrame([[1, 2], [3, 4]], index=['a', 'b'], columns=['x', 'y'])
   x  y
a  1  2
b  3  4

<DF>    = DataFrame(<list_of_rows>)            # Rows can be either lists, dicts or series.
<DF>    = DataFrame(<dict_of_columns>)         # Columns can be either lists, dicts or series.

<el>    = <DF>.loc[row_key, column_key]        # Or: <DF>.iloc[row_index, column_index]
<Sr/DF> = <DF>.loc[row_key/s]                  # Or: <DF>.iloc[row_index/es]
<Sr/DF> = <DF>.loc[:, column_key/s]            # Or: <DF>.iloc[:, column_index/es]
<DF>    = <DF>.loc[row_bools, column_bools]    # Or: <DF>.iloc[row_bools, column_bools]

<Sr/DF> = <DF>[column_key/s]                   # Or: <DF>.column_key
<DF>    = <DF>[row_bools]                      # Keeps rows as specified by bools.
<DF>    = <DF>[<DF_of_bools>]                  # Assigns NaN to False values.

<DF>    = <DF> ><== <el/Sr/DF>                 # Returns DF of bools. Sr is treated as a row.
<DF>    = <DF> +-*/ <el/Sr/DF>                 # Items with non-matching keys get value NaN.

<DF>    = <DF>.set_index(column_key)           # Replaces row keys with values from a column.
<DF>    = <DF>.reset_index()                   # Moves row keys to a column named index.
<DF>    = <DF>.sort_index(ascending=True)      # Sorts rows by row keys.
<DF>    = <DF>.sort_values(column_key/s)       # Sorts rows by the passed column/s.

>>> l = DataFrame([[1, 2], [3, 4]], index=['a', 'b'], columns=['x', 'y'])
   x  y
a  1  2
b  3  4
>>> r = DataFrame([[4, 5], [6, 7]], index=['b', 'c'], columns=['y', 'z'])
   y  z
b  4  5
c  6  7

+------------------------+---------------+------------+------------+--------------------------+
|                        |    'outer'    |   'inner'  |   'left'   |       Description        |
+------------------------+---------------+------------+------------+--------------------------+
| l.merge(r, on='y',     |    x   y   z  | x   y   z  | x   y   z  | Joins/merges on column.  |
|            how=…)      | 0  1   2   .  | 3   4   5  | 1   2   .  | Also accepts left_on and |
|                        | 1  3   4   5  |            | 3   4   5  | right_on parameters.     |
|                        | 2  .   6   7  |            |            | Uses 'inner' by default. |
+------------------------+---------------+------------+------------+--------------------------+
| l.join(r, lsuffix='l', |    x yl yr  z |            | x yl yr  z | Joins/merges on row keys.|
|           rsuffix='r', | a  1  2  .  . | x yl yr  z | 1  2  .  . | Uses 'left' by default.  |
|           how=…)       | b  3  4  4  5 | 3  4  4  5 | 3  4  4  5 | If r is a Series, it is  |
|                        | c  .  .  6  7 |            |            | treated as a column.     |
+------------------------+---------------+------------+------------+--------------------------+
| pd.concat([l, r],      |    x   y   z  |     y      |            | Adds rows at the bottom. |
|           axis=0,      | a  1   2   .  |     2      |            | Uses 'outer' by default. |
|           join=…)      | b  3   4   .  |     4      |            | A Series is treated as a |
|                        | b  .   4   5  |     4      |            | column. Use l.append(sr) |
|                        | c  .   6   7  |     6      |            | to add a row instead.    |
+------------------------+---------------+------------+------------+--------------------------+
| pd.concat([l, r],      |    x  y  y  z |            |            | Adds columns at the      |
|           axis=1,      | a  1  2  .  . | x  y  y  z |            | right end. Uses 'outer'  |
|           join=…)      | b  3  4  4  5 | 3  4  4  5 |            | by default. A Series is  |
|                        | c  .  .  6  7 |            |            | treated as a column.     |
+------------------------+---------------+------------+------------+--------------------------+
| l.combine_first(r)     |    x   y   z  |            |            | Adds missing rows and    |
|                        | a  1   2   .  |            |            | columns. Also updates    |
|                        | b  3   4   5  |            |            | items that contain NaN.  |
|                        | c  .   6   7  |            |            | R must be a DataFrame.   |
+------------------------+---------------+------------+------------+--------------------------+

<Sr> = <DF>.sum/max/mean/idxmax/all()          # Or: <DF>.apply/agg(lambda <Sr>: <el>)
<DF> = <DF>.rank/diff/cumsum/ffill/interpl()   # Or: <DF>.apply/agg/transfrm(lambda <Sr>: <Sr>)
<DF> = <DF>.fillna(<el>)                       # Or: <DF>.applymap(lambda <el>: <el>)

>>> df = DataFrame([[1, 2], [3, 4]], index=['a', 'b'], columns=['x', 'y'])
   x  y
a  1  2
b  3  4

+-----------------+-------------+-------------+---------------+
|                 |    'sum'    |   ['sum']   | {'x': 'sum'}  |
+-----------------+-------------+-------------+---------------+
| df.apply(…)     |             |       x  y  |               |
| df.agg(…)       |     x  4    |  sum  4  6  |     x  4      |
|                 |     y  6    |             |               |
+-----------------+-------------+-------------+---------------+

+-----------------+-------------+-------------+---------------+
|                 |    'rank'   |   ['rank']  | {'x': 'rank'} |
+-----------------+-------------+-------------+---------------+
| df.apply(…)     |      x  y   |      x    y |        x      |
| df.agg(…)       |   a  1  1   |   rank rank |     a  1      |
| df.transform(…) |   b  2  2   | a    1    1 |     b  2      |
|                 |             | b    2    2 |               |
+-----------------+-------------+-------------+---------------+

<DF>.plot.line/bar/hist/scatter/box()          # Also: `x=column_key, y=column_key/s`.
plt.show()                                     # Displays the plot. Also plt.savefig(<path>).

<DF> = pd.read_json/html('<str/path/url>')     # Run `$ pip3 install beautifulsoup4 lxml`.
<DF> = pd.read_csv/pickle/excel('<path/url>')  # Use `sheet_name=None` to get all Excel sheets.
<DF> = pd.read_sql('<table/query>', <conn.>)   # Accepts SQLite3 or SQLAlchemy connection.
<DF> = pd.read_clipboard()                     # Reads a copied table from the clipboard.

<dict> = <DF>.to_dict(['d/l/s/…'])             # Returns columns as dicts, lists or series.
<str>  = <DF>.to_json/html/csv([<path>])       # Also to_markdown/latex([<path>]).
<DF>.to_pickle/excel(<path>)                   # Run `$ pip3 install openpyxl` for xlsx files.
<DF>.to_sql('<table_name>', <connection>)      # Accepts SQLite3 or SQLAlchemy connection.

>>> df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 6]], index=list('abc'), columns=list('xyz'))
>>> df.groupby('z').get_group(6)
   x  y
b  4  5
c  7  8

<GB> = <DF>.groupby(column_key/s)              # Splits DF into groups based on passed column.
<DF> = <GB>.apply(<func>)                      # Maps each group. Func can return DF, Sr or el.
<GB> = <GB>[column_key]                        # Single column GB. All operations return a Sr.

<DF> = <GB>.sum/max/mean/idxmax/all()          # Or: <GB>.agg(lambda <Sr>: <el>)
<DF> = <GB>.rank/diff/cumsum/ffill()           # Or: <GB>.transform(lambda <Sr>: <Sr>)
<DF> = <GB>.fillna(<el>)                       # Or: <GB>.transform(lambda <Sr>: <Sr>)

>>> gb = df.groupby('z')
      x  y  z
3: a  1  2  3
6: b  4  5  6
   c  7  8  6

+-----------------+-------------+-------------+-------------+---------------+
|                 |    'sum'    |    'rank'   |   ['rank']  | {'x': 'rank'} |
+-----------------+-------------+-------------+-------------+---------------+
| gb.agg(…)       |      x   y  |      x  y   |      x    y |        x      |
|                 |  z          |   a  1  1   |   rank rank |     a  1      |
|                 |  3   1   2  |   b  1  1   | a    1    1 |     b  1      |
|                 |  6  11  13  |   c  2  2   | b    1    1 |     c  2      |
|                 |             |             | c    2    2 |               |
+-----------------+-------------+-------------+-------------+---------------+
| gb.transform(…) |      x   y  |      x  y   |             |               |
|                 |  a   1   2  |   a  1  1   |             |               |
|                 |  b  11  13  |   b  1  1   |             |               |
|                 |  c  11  13  |   c  2  2   |             |               |
+-----------------+-------------+-------------+-------------+---------------+

<RSr/RDF/RGB> = <Sr/DF/GB>.rolling(win_size)   # Also: `min_periods=None, center=False`.
<RSr/RDF/RGB> = <RDF/RGB>[column_key/s]        # Or: <RDF/RGB>.column_key
<Sr/DF>       = <R>.mean/sum/max()             # Or: <R>.apply/agg(<agg_func/str>)

# $ pip3 install plotly kaleido
from plotly.express import line
<Figure> = line(<DF>, x=<col_name>, y=<col_name>)        # Or: line(x=<list>, y=<list>)
<Figure>.update_layout(margin=dict(t=0, r=0, b=0, l=0))  # Or: paper_bgcolor='rgba(0, 0, 0, 0)'
<Figure>.write_html/json/image('<path>')                 # Also: <Figure>.show()

covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv',
                    usecols=['iso_code', 'date', 'total_deaths', 'population'])
continents = pd.read_csv('https://gist.githubusercontent.com/stevewithington/20a69c0b6d2ff'
                         '846ea5d35e5fc47f26c/raw/country-and-continent-codes-list-csv.csv',
                         usecols=['Three_Letter_Country_Code', 'Continent_Name'])
df = pd.merge(covid, continents, left_on='iso_code', right_on='Three_Letter_Country_Code')
df = df.groupby(['Continent_Name', 'date']).sum().reset_index()
df['Total Deaths per Million'] = df.total_deaths * 1e6 / df.population
df = df[df.date > '2020-03-14']
df = df.rename({'date': 'Date', 'Continent_Name': 'Continent'}, axis='columns')
line(df, x='Date', y='Total Deaths per Million', color='Continent').show()

import pandas as pd
import plotly.graph_objects as go

def main():
    display_data(wrangle_data(*scrape_data()))

def scrape_data():
    def scrape_covid():
        url = 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
        df = pd.read_csv(url, usecols=['location', 'date', 'total_cases'])
        return df[df.location == 'World'].set_index('date').total_cases
    def scrape_yahoo(slug):
        url = f'https://query1.finance.yahoo.com/v7/finance/download/{slug}' + \
              '?period1=1579651200&period2=9999999999&interval=1d&events=history'
        df = pd.read_csv(url, usecols=['Date', 'Close'])
        return df.set_index('Date').Close
    out = scrape_covid(), scrape_yahoo('BTC-USD'), scrape_yahoo('GC=F'), scrape_yahoo('^DJI')
    return map(pd.Series.rename, out, ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones'])

def wrangle_data(covid, bitcoin, gold, dow):
    df = pd.concat([bitcoin, gold, dow], axis=1)  # Joins columns on dates.
    df = df.sort_index().interpolate()            # Sorts by date and interpolates NaN-s.
    df = df.loc['2020-02-23':]                    # Discards rows before '2020-02-23'.
    df = (df / df.iloc[0]) * 100                  # Calculates percentages relative to day 1.
    df = df.join(covid)                           # Adds column with covid cases.
    return df.sort_values(df.index[-1], axis=1)   # Sorts columns by last day's value.

def display_data(df):
    figure = go.Figure()
    for col_name in reversed(df.columns):
        yaxis = 'y1' if col_name == 'Total Cases' else 'y2'
        trace = go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis=yaxis)
        figure.add_trace(trace)
    figure.update_layout(
        yaxis1=dict(title='Total Cases', rangemode='tozero'),
        yaxis2=dict(title='%', rangemode='tozero', overlaying='y', side='right'),
        legend=dict(x=1.1),
        height=450
    ).show()

if __name__ == '__main__':
    main()

# $ pip3 install PySimpleGUI
import PySimpleGUI as sg
layout = [[sg.Text("What's your name?")], [sg.Input()], [sg.Button('Ok')]]
window = sg.Window('Window Title', layout)
event, values = window.read()
print(f'Hello {values[0]}!' if event == 'Ok' else '')

# $ pip3 install cython
import pyximport; pyximport.install()
import <cython_script>
<cython_script>.main()

cdef <ctype> <var_name> = <el>
cdef <ctype>[n_elements] <var_name> = [<el_1>, <el_2>, ...]
cdef <ctype/void> <func_name>(<ctype> <arg_name>): ...

cdef class <class_name>:
    cdef public <ctype> <attr_name>
    def __init__(self, <ctype> <arg_name>):
        self.<attr_name> = <arg_name>

cdef enum <enum_name>: <member_name_1>, <member_name_2>, ...

$ pip3 install pyinstaller
$ pyinstaller script.py                        # Compiles into './dist/script' directory.
$ pyinstaller script.py --onefile              # Compiles into './dist/script' console app.
$ pyinstaller script.py --windowed             # Compiles into './dist/script' windowed app.
$ pyinstaller script.py --add-data '<path>:.'  # Adds file to the root of the executable.

#!/usr/bin/env python3
#
# Usage: .py
#

from sys import argv, exit
from collections import defaultdict, namedtuple
from dataclasses import make_dataclass
from enum import Enum
import functools as ft, itertools as it, operator as op, re


def main():
    pass


###
##  UTIL
#

def read_file(filename):
    with open(filename, encoding='utf-8') as file:
        return file.readlines()


if __name__ == '__main__':
    main()