hughdbrown

#!/usr/bin/env python3

from pprint import pprint
from random import randint
from datetime import datetime
from uuid import uuid4

import pandas as pd

NUMS = 10000

hughdbrown

#!/usr/bin/env python3

from timeit import timeit

def naive(values, k):
    for i, v1 in enumerate(values):
        for v2 in values[i + 1:]:
            if v1 + v2 == k:
                return True
    return False

hughdbrown

from collections import defaultdict

def trie():
    return defaultdict(trie)

class Trie(object):
    TERMINATOR = "<END>"
    def __init__(self):
        self.t = trie()
    def insert_word(self, word):

hughdbrown

#!/usr/bin/env python3

import collections
import itertools
import random
import sys
import timeit

pop = [
    (b := random.randint(1900, 2000), random.randint(b + 1, b + 100))

hughdbrown

#!/usr/bin/env python3
# Validation of code in Mewdium article:
# https://medium.com/the-modern-scientist/make-pandas-code-120x-faster-a-forbidden-mathematical-jutsu-87103030eb9c
import timeit

import pandas as pd
import numpy as np

sizes = [1_000, 10_000, 100_000, 1_000_000]

hughdbrown

#!/usr/bin/env python3
from collections import deque

def possible(pos, legal_pos):
    r, c = pos
    can = {
        (r + 2, c - 1), (r + 2, c + 1),
        (r + 1, c - 2), (r + 1, c + 2),
        (r - 1, c - 2), (r - 1, c + 2),
        (r - 2, c - 1), (r - 2, c + 1)

hughdbrown

Sample output

~/twenty.py preventionthionalist
process dictionary 0:00:00.057675
lookup tables 0:00:00.354845
search solutions 0:00:00.004037
[(304, 'trephinations', 'violent'),
 (304, 'interventional', 'pithos'),
 (304, 'interpolative', 'tonnish'),
 (292, 'trephination', 'violents'),

hughdbrown

# O(n) alternative to:
# https://elisabethirgens.github.io/notes/2021/12/step-away/
from collections import defaultdict

mylist = [
    {'thing': 'A', 'count': 4},
    {'thing': 'B', 'count': 2},
    {'thing': 'A', 'count': 6},
]

hughdbrown

AZ: (0.3804329085751007, 0.6195670914248993, 332493.15068493155, 541493.1506849315, -209000.00000000003)
FL: (0.6779600278494492, 0.3220399721505508, 727637.3626373624, 345637.36263736244, 381999.99999999994)
GA: (0.5798898071625344, 0.42011019283746565, 1219448.2758620693, 883448.2758620695, 335999.99999999977)
IA: (0.43084455324357407, 0.5691554467564259, 352000.0, 465000.0, -112999.99999999997)
MI: (0.5424654351109562, 0.45753456488904376, 1628720.9302325586, 1373720.9302325582, 255000.0000000003)
NC: (0.6079855936413314, 0.3920144063586686, 208319.14893617015, 134319.14893617015, 74000.00000000003)
OH: (0.6330845907837039, 0.36691540921629606, 711172.8395061726, 412172.8395061726, 299000.0)
PA: (0.5496743418035043, 0.4503256581964957, 1997333.333333333, 1636333.333333333, 361000.0)
TX: (0.5517922189231381, 0.4482077810768619, 2002944.444444445, 1626944.444444445, 375999.99999999994)
WI: (0.5267461143892939, 0.47325388561070614, 718842.105263158, 645842.105263158, 73000.00000000007)

hughdbrown

>>> def chunk(items, size):
...     i, count = 0, len(items)
...     for j in reversed(range(1, size + 1)):
...         m, n = divmod(count, j)
...         k = m + int(bool(n))
...         yield items[i:i + k]
...         i += k
...         count -= k
...
>>> list(chunk(range(5), 5))