{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af977a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37.2, 37.6, 36.8])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = np.array([[1.2, 1.5, 1.8],\n",
    "               [1.3, 1.4, 1.9],\n",
    "               [1.1, 1.6, 1.7]])\n",
    "y = np.array([5, 10, 9]).T\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X.dot(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9bdc397c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 10,  9])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0f0b081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e4391e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "790 ns ± 28.8 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit X.dot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20fe1bd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.7 µs ± 1.29 µs per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "total = []\n",
    "for i in range(X.shape[0]):\n",
    "    each_price = 0\n",
    "    for j in range(X.shape[1]):\n",
    "        each_price += X[i,j] * y[j]\n",
    "#         each_price = each_price + X[i,j] * y[j]\n",
    "    total.append(round(each_price, 1))\n",
    "total   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4334b3ce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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