BD T1

import itertools
import math
import operator

import numpy
import pandas
from sklearn.datasets import fetch_california_housing


def p(tri: tuple, rec: tuple, circ: tuple):
    s1 = tri[0] * (tri[1] / 2)
    s2 = rec[0] * rec[1]
    s3 = math.pi * (circ[0] ** 2)
    return dict(zip(["triangle", "rectangle", "circle"], [s1, s2, s3]))


# print(p((2, 3), (2, 3), (3, 2)))


def do(a, b, op):
    if op == "+":
        return operator.add(a, b)
    elif op == "-":
        return operator.sub(a, b)
    elif op == "*":
        return operator.mul(a, b)
    elif op == "/":
        return operator.truediv(a, b)
    elif op == "//":
        return operator.mod(a, b)
    elif op == 'abs':
        return operator.abs(a, b)
    elif op == "pow" or op == "**":
        return operator.pow(a, b)


# print(do(2, 3, "**"))


def l():
    a = list()
    s = 0
    while True:
        k = int(input())
        s += k
        a.append(k)
        if s == 0:
            for i in a:
                s += i ** 2
            print(s)
            break


# l()

def sc(n, al=0):
    m = list()
    for i in range(1, n):
        al += i
        if n >= al:
            m.extend(itertools.repeat(i, i))
    if len(m) < n:
        m.append(max(m) + 1)
        print(m)

    # sc(7)


def cnt():
    dct = dict()
    a = [1, 2, 3, 4, 2, 1, 3, 4, 5, 6, 5, 4, 3, 2]
    b = ['a', 'b', 'c', 'c', 'c', 'b', 'a', 'c', 'a', 'a', 'b', 'c', 'b', 'a']
    dct = dict(zip(list(sorted(set(b))), itertools.repeat(0, len(b))))

    for i in range(len(b)):
        bp = b.pop()
        ap = a.pop()
        match bp:
            case "a":
                dct.update({'a': dct["a"] + ap})
            case "b":
                dct.update({'b': dct["b"] + ap})
            case "c":
                dct.update({'c': dct["c"] + ap})
    print(dct)


cnt()

def sk():
    data = fetch_california_housing(as_frame=True)
    df = data.frame
    print(df.loc[(df['HouseAge'] > 50) & (df['Population'] > 2500)])
    print(df.loc[df['MedHouseVal'].idxmax(), 'MedHouseVal'])
    print(df.loc[df['MedHouseVal'].idxmin(), 'MedHouseVal'])
    print(df.apply(numpy.average, result_type='expand'))
sk()