{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1de8b57d",
   "metadata": {},
   "source": [
    "# Основы Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e442423a",
   "metadata": {},
   "source": [
    "## Основная информация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54ad0b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f127250",
   "metadata": {},
   "source": [
    "ndarray - основная единица в NumPy, представляет из себя n-мерный массив."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5df38ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data: np.ndarray = np.array(\n",
    "    [\n",
    "        [\n",
    "            [1, 2, 3],\n",
    "            [4, 5, 6],\n",
    "            [7, 8, 9],\n",
    "        ],\n",
    "        [\n",
    "            [1, 2, 3],\n",
    "            [4, 5, 6],\n",
    "            [7, 8, 9],\n",
    "        ],\n",
    "        [\n",
    "            [1, 2, 3],\n",
    "            [4, 5, 6],\n",
    "            [7, 8, 9],\n",
    "        ],\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b68b84b",
   "metadata": {},
   "source": [
    "Параметры и базовые функции:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "866a3fb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3, 3)\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# Размерность массива\n",
    "shape = data.shape\n",
    "print(shape)\n",
    "\n",
    "# Кол-во измерений\n",
    "dimentions = data.ndim\n",
    "print(dimentions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e167e93",
   "metadata": {},
   "source": [
    "## Измерения в Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9013d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(60)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0-D arrays - Scalars\n",
    "scalar = np.array(42)\n",
    "scalar2 = np.array(18)\n",
    "\n",
    "scalar + scalar2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f705611",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82\n",
      "[10 24 48]\n"
     ]
    }
   ],
   "source": [
    "# 1-D arrays - Vectors\n",
    "vec1 = np.array([1, 2, 3])\n",
    "vec2 = np.array((10, 12, 16))\n",
    "\n",
    "print(np.dot(vec1, vec2))\n",
    "print(vec1 * vec2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3a9d28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 12]\n",
      "[13 15]\n",
      "[4 8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([61, 81])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2-D arrays - Matrix\n",
    "matrix = np.array(\n",
    "    [\n",
    "        [10, 12],\n",
    "        [13, 15],\n",
    "        [4, 8],\n",
    "    ]\n",
    ")\n",
    "\n",
    "vec = np.array(\n",
    "    [\n",
    "        1, 3, 3,\n",
    "    ]\n",
    ")\n",
    "\n",
    "print(*matrix, sep=\"\\n\")\n",
    "\n",
    "# Умножение вектора на матрицу\n",
    "vec @ matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec7aafca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12 15  8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([12, 45, 24])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec2 = matrix[:, 1]\n",
    "print(vec2)\n",
    "\n",
    "vec * vec2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7732da7b",
   "metadata": {},
   "source": [
    "## Минимальное кол-во измеренений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "721daf56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сам допаковывает переданный массив в нужное кол-во измерений\n",
    "\n",
    "vec = np.array([1, 2], ndmin=2)\n",
    "vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eae8552a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3]\n",
      " [10 12 32]]\n"
     ]
    }
   ],
   "source": [
    "array = [[1, 2, 3], [10, 12, 32]]\n",
    "array = np.array(array, ndmin=2)\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd29be4",
   "metadata": {},
   "source": [
    "## Доступ по индексам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d139f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 14, 34])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[0, 1] + array[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "927cc500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 12])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[1, -3:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a861bf",
   "metadata": {},
   "source": [
    "## Типы данных\n",
    "* Накладываются на весь массив\n",
    "* Базовые типы:\n",
    "    * u - безнаковые числа\n",
    "    * i - целые числа\n",
    "    * f - дробные числа\n",
    "    * c - комплексные дробные числа\n",
    "    * m - timedelta\n",
    "    * M - datetime\n",
    "    * S - строки\n",
    "    * V - void, просто определенные куски памяти, выделенные под хранение\n",
    "    * U - строки unicode\n",
    "    * O - object, смешанный тип"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43bcb180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64 (3,) 1\n",
      "float64 (3,) 1\n",
      "int64 (3,) 1\n",
      "object (3,) 1\n"
     ]
    }
   ],
   "source": [
    "array = [1, 2, 3]\n",
    "\n",
    "numpy_array = np.array(array)\n",
    "print(numpy_array.dtype, numpy_array.shape, numpy_array.ndim)\n",
    "\n",
    "\n",
    "array = [1, 2, 3.]\n",
    "\n",
    "numpy_array = np.array(array)\n",
    "print(numpy_array.dtype, numpy_array.shape, numpy_array.ndim)\n",
    "\n",
    "numpy_array = np.array(array, dtype=int)\n",
    "print(numpy_array.dtype, numpy_array.shape, numpy_array.ndim)\n",
    "\n",
    "\n",
    "\n",
    "class Test:\n",
    "    def __init__(self): pass\n",
    "\n",
    "array = [Test(), True, 3]\n",
    "\n",
    "numpy_array = np.array(array, dtype=object)\n",
    "print(numpy_array.dtype, numpy_array.shape, numpy_array.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c12e579",
   "metadata": {},
   "source": [
    "Конвертация numpy массива в другой numpy массив"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6f6728c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.2  2.5  3.1] float64\n",
      "[-1  2  3] int16\n"
     ]
    }
   ],
   "source": [
    "array: np.ndarray = np.array([-1.2, 2.5, 3.1])\n",
    "print(array, array.dtype)\n",
    "\n",
    "new_array = np.ndarray = array.astype(\"i2\")\n",
    "print(new_array, new_array.dtype)"
   ]
  }
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