{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23ed41a0-7a05-493e-a640-4bfb10c42164",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa3799c5-d3a0-4967-98d4-a340d19dbfc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 11 12 13 14 15 16 17 18 19 20]\n",
      "(11,)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "#Zadanie 1.1\n",
    "# Tworzenie tablicy jednowymiarowej\n",
    "arr = np.array([10,11,12,13,14,15,16,17,18,19,20])\n",
    "print(arr)\n",
    "print(arr.shape)\n",
    "print(type(arr))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6b4fa7d-7ee5-416c-8060-39057b49d77b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10 20]\n",
      " [30 40]\n",
      " [50 60]]\n",
      "(3, 2)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Zadanie 1.2\n",
    "arr = np.array([[10, 20], [30, 40], [50, 60]])\n",
    "\n",
    "print(arr)\n",
    "\n",
    "print(arr.shape)\n",
    "\n",
    "print(type(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f96f774c-d6cd-440f-b6bf-a2d373404de3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "8\n",
      "[[7 8 9]]\n",
      "[3 6 9]\n",
      "[[5 6]\n",
      " [8 9]]\n"
     ]
    }
   ],
   "source": [
    "# Zadanie 2\n",
    "# Tworzenie dwuwymiarowej tablicy\n",
    "arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "\n",
    "print(arr)\n",
    "\n",
    "\n",
    "print(arr[2, 1])\n",
    "\n",
    "print(arr[2:])\n",
    "\n",
    "\n",
    "print(arr[:,2])\n",
    "\n",
    "print(arr[1:,1:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43216855-9d5d-4d03-9512-557f4d228571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 20 30 40]\n",
      "int64\n",
      "[10. 20. 30. 40.]\n",
      "float32\n",
      "['Python' 'NumPy' 'Coding']\n",
      "<U6\n"
     ]
    }
   ],
   "source": [
    "# Zadanie 3\n",
    "\n",
    "# Punkt 1\n",
    "arr = np.array([10, 20, 30, 40])\n",
    "print(arr)\n",
    "print(arr.dtype)\n",
    "\n",
    "# Punkt 2\n",
    "arr = arr.astype('float32')\n",
    "print(arr)\n",
    "print(arr.dtype)\n",
    "\n",
    "# Punkt 3\n",
    "arr = np.array([\"Python\", \"NumPy\", \"Coding\"])\n",
    "print(arr)\n",
    "print(arr.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a700a92-fb6e-498d-bf2f-d5f9758d0147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50  2  3  4  5]\n",
      "[50  2  3  4  5]\n",
      "[50  2  3  4  5]\n",
      "[1 2 3 4 5]\n",
      "[50  2  3  4  5]\n",
      "[[1 2 3 4]]\n",
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "### Zadanie 4\n",
    "import copy\n",
    "\n",
    "\n",
    "# Punkt 1 - Przypisanie do zmiennej\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "x = arr\n",
    "arr[0] = 50\n",
    "print(arr)  # Tablica arr po zmianie\n",
    "print(x)    # Tablica x po zmianie\n",
    "\n",
    "# Punkt 2 - Kopia tablicy\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "x = arr.copy()\n",
    "arr[0] = 50\n",
    "print(arr)  # Tablica arr po zmianie\n",
    "print(x)    # Tablica x po kopii\n",
    "\n",
    "# Punkt 3 - Głęboka kopia\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "x = copy.deepcopy(arr)\n",
    "arr[0] = 50\n",
    "print(arr)  # Tablica arr po zmianie\n",
    "\n",
    "arr2 = np.array([1, 2, 3, 4], ndmin=2)\n",
    "print(arr2)\n",
    "\n",
    "# Zmiana wymiaru na jednowymiarowy\n",
    "arr_squeezed = arr2.squeeze()\n",
    "print(arr_squeezed)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbf7f5ee-aace-47f6-9a73-14fdf7595ff4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 1],\n",
       "         [ 2]]],\n",
       "\n",
       "\n",
       "       [[[ 3],\n",
       "         [ 4]]],\n",
       "\n",
       "\n",
       "       [[[ 5],\n",
       "         [ 6]]],\n",
       "\n",
       "\n",
       "       [[[ 7],\n",
       "         [ 8]]],\n",
       "\n",
       "\n",
       "       [[[ 9],\n",
       "         [10]]]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Zadanie 5\n",
    "\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "\n",
    "arr\n",
    "\n",
    "arr.reshape(5,2)\n",
    "\n",
    "arr.reshape(10,1)\n",
    "\n",
    "arr.reshape(5,-1)\n",
    "\n",
    "arr.reshape(5,1,2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5afcad53-ce4a-408d-bc44-f33fd7b8e276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[53 65 77]\n",
      "[13 15 17]\n",
      "[15 25 35]\n",
      "[[ 3  5  7]\n",
      " [ 8 10 12]\n",
      " [10 10 10]]\n",
      "[119 135 151]\n",
      "940\n"
     ]
    }
   ],
   "source": [
    "### Zadanie 6\n",
    "\n",
    "\n",
    "x = np.array([3, 5, 7])\n",
    "y = np.array([50, 60, 70])\n",
    "\n",
    "print(x + y)\n",
    "print(x + 10)\n",
    "print(x * 5)\n",
    "z = np.array([[3, 5, 7], [8, 10, 12], [10,10,10]])\n",
    "print(z)\n",
    "\n",
    "print(x.dot(z))\n",
    "print(x.dot(y))"
   ]
  }
 ],
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