marskar

from typing import Tuple, List

class MinMaxScaler:
    def __init__(self, values: Tuple) -> List:
        self.values = values
        self.min_val = min(values)
        self.max_val = max(values)
        self.max_min_diff = self.max_val - self.min_val

    def min_max_scale(self, x: Tuple):

marskar

from typing import Dict, Iterable, List, KeysView, ItemsView, ValuesView

import matplotlib.pyplot as plt
import pandas as pd
import requests


class Pokemon:
    def __init__(self, name: str = None, number: int = None) -> None:
        if name and number:

marskar

def median(vector):
    sorted_vector = sorted(vector)
    vector_length = len(vector)
    middle = vector_length // 2
    if vector_length % 2 != 0:
        return sorted_vector[middle]
    else:
        return (sorted_vector[middle] + sorted_vector[middle - 1]) / 2
​
    

marskar

from functools import partial


def pipe(data, *steps):
    for step in steps:
        if callable(step):
            data = step(data)
        elif hasattr(type(step), "__iter__"):
            func, *others = step
            for arg in others:

marskar

from sklearn.model_selection import KFold, GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston

boston = load_boston()
X = boston.data
y = boston.target

# Create kf instance 
kf = KFold(n_splits=5, shuffle=True, random_state=42)

marskar

from sklearn.model_selection import KFold, GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston

boston = load_boston()
X = boston.data
y = boston.target

# Create kf instance 
kf = KFold(n_splits=5, shuffle=True, random_state=42)

marskar

import pandas as pd
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_boston

boston = load_boston()
X = boston.data
y = boston.target

# Create kf instance 

marskar

import numpy as np
import pandas as pd
from statsmodels.formula.api import ols
from scipy.stats import ttest_ind, mannwhitneyu

def permut_concat(iterable):
    """Concatenate iterables of arrays then randomize."""
    return np.random.permutation(np.concatenate(iterable))

# Make two distributions: a, b

marskar

from this import s


# Without long strings
def rot13(x):
    abc = "".join(map(chr, range(65, 91)))
    a_to_m = x in abc[:13] + abc[:13].lower()
    n_to_z = x in abc[13:] + abc[13:].lower()
    return chr(ord(x) + 13 * (a_to_m or -n_to_z))

marskar

from pathlib import Path
from functools import partial


def chaine(data, *funcs,
           global_assign=False,
           print_results=False,
           write_results=False,
           **kwargs):
    chaine_options = any([global_assign, print_results, write_results])