marskar

from functools import partial


def pipe(data, *funcs):
    for f in funcs:
        data = f(data)
    return data


pipe(

marskar

import pandas as pd
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler

# Get data
df = pd.read_csv("data/diamonds.csv")
df = df[df.cut.isin(["Ideal", "Good"])]
X = df.select_dtypes(["int64", "float64"])
y = df.cut

marskar

from functools import reduce
from pathlib import Path

import pandas as pd


def process_weather_data(filename):
    df = pd.read_csv(filename).dropna()
    first, *others = df.columns.to_list()
    return df.melt(id_vars=first,

marskar

from math import log10, log2, e, log

x = 3.0

# Log base 10
one_k = 10**x # 1000.0
log10(one_k) # 3.0

# Log base 2
eight = 2**x # 8.0

marskar

import numpy as np

r = list(range(-2, 3)) # [-2, -1,  0,  1,  2]

# Log base 10
tens = [10**i for i in r] # [0.01, 0.1, 1, 10, 100]
np.log10(tens).astype(int).tolist() # [-2, -1,  0,  1,  2]

# Log base 2
twos = [2**i for i in r] # [0.25, 0.5, 1, 2, 4]

marskar

Obtain the logistic function mathematically in 6 easy steps ;)

Step 1. Write out the linear regression equation

$\Huge y=\beta_0+\beta_1 x_1+...+\beta_n x_n$

Step 2. The logistic regression equation is the same as above except output is log odds

$\Huge log(odds)=\beta_0+\beta_1 x_1+...+\beta_n x_n$

Step 3. Exponentiate both sides of the logistic regression equation to get odds

$\Huge odds=e^{\beta_0+\beta_1 x_1+...+\beta_n x_n}$

Step 4. Write out the probability equation

$\Huge p=\frac{odds}{1+odds}$

marskar

import numpy as np


def get_singles(arr):
    while arr.size:
        first, arr = arr[0], arr[1:]
        if first in arr:
            arr = arr[arr != first]
        else:
            yield first

marskar

def get_singles(a_list):
    while a_list:
        x = a_list.pop(0)
        if x in a_list:
            a_list.remove(x)
        else:
            yield x


list(get_singles([0, 1, 1, 2, 3]))

marskar

from this import s as zen
from codecs import encode
from functools import partial


def get_alpha(string):
    return (char.lower() if char.isalpha() else " " for char in string)


def join_then_split(iterable):

marskar

def fib(n):
    a, b = 0, 1
    for _ in range(n):
        yield a
        a, b = b, a + b


def fizzbuzz(iterable):
    return (
        "fizz" * (i % 3 == 0)