{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "56b06287-d1ba-409a-a207-2125edc31719", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "id": "fa9d3b45-d381-4cfa-91b3-701a7c2578f7", "metadata": {}, "source": [ "# NumPy" ] }, { "cell_type": "markdown", "id": "7d008b7b-b668-42bd-993e-64cc2de4ae17", "metadata": {}, "source": [ "## wymiary" ] }, { "cell_type": "code", "execution_count": 2, "id": "cb38fb13-3671-4a7b-89e7-247d8db91a13", "metadata": {}, "outputs": [], "source": [ "arr = np.array([1, 2, 3, 4, 5, 6, 7])" ] }, { "cell_type": "code", "execution_count": 3, "id": "445ebe15-088f-4811-96c6-cc0043fb5ab4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3 4 5 6 7]\n" ] } ], "source": [ "print(arr)" ] }, { "cell_type": "code", "execution_count": 4, "id": "9428e371-82e5-4f94-b6f6-e54808a7b72c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(arr)" ] }, { "cell_type": "code", "execution_count": 5, "id": "bea5efad-20cc-4284-8575-bd32becaddd7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(arr)" ] }, { "cell_type": "code", "execution_count": 6, "id": "cb12cac9-cf3b-4924-90e3-a82f5ca7274c", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "()" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(123).shape" ] }, { "cell_type": "code", "execution_count": 7, "id": "7e589615-ab19-4786-b2bc-49f46c706adc", "metadata": {}, "outputs": [], "source": [ "arr = np.array([[1, 2, 3], [4, 5, 6]])" ] }, { "cell_type": "code", "execution_count": 8, "id": "a0925cb2-1586-48a7-9256-2d07228547e1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 3)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr.shape" ] }, { "cell_type": "code", "execution_count": 9, "id": "10b857a9-0bf0-4ee4-9f86-dabef5452d59", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]]\n" ] } ], "source": [ "print(arr)" ] }, { "cell_type": "code", "execution_count": 10, "id": "0029c4f5-135b-4a37-bf95-7f8e9afed454", "metadata": {}, "outputs": [], "source": [ "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "6fe7be82-a5d9-493f-8410-c1201d49606d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 2, 3)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr.shape" ] }, { "cell_type": "code", "execution_count": 12, "id": "61686743-b034-4fb7-a891-e4aed1d7bd66", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[1 2 3]\n", " [4 5 6]]\n", "\n", " [[1 2 3]\n", " [4 5 6]]]\n" ] } ], "source": [ "print(arr)" ] }, { "cell_type": "markdown", "id": "ae9cd324-0297-42bc-8010-4ea0fa7074cc", "metadata": {}, "source": [ "### zadanie 1\n", "\n", "1. Utwórz jednowymiarową tablicę zawierającą liczby od 10 do 20 włącznie. Wyświetl:\n", " - Tablicę,\n", " - Jej kształt (`shape`),\n", " - Jej typ (`type`).\n", "\n", "2. Utwórz macierz 3x2 o elementach (10,20,30,40,50,60) i wyświetl:\n", " - Tablicę,\n", " - Jej kształt (`shape`),\n", " - Jej typ (`type`).\n" ] }, { "cell_type": "markdown", "id": "d38ccaec-ac11-4bef-8d48-7087931f4157", "metadata": {}, "source": [ "## dostęp do elementów" ] }, { "cell_type": "code", "execution_count": 13, "id": "a1cf151f-9128-414f-b065-2060b8a8a411", "metadata": {}, "outputs": [], "source": [ "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])" ] }, { "cell_type": "code", "execution_count": 14, "id": "89975e71-f996-48af-a50d-3dd5f86c6775", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 2 3 4 5]\n", " [ 6 7 8 9 10]]\n" ] } ], "source": [ "print(arr)" ] }, { "cell_type": "code", "execution_count": 15, "id": "3f3b6ab3-7c1f-491c-9b9d-648073e65378", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[1,2]" ] }, { "cell_type": "code", "execution_count": 16, "id": "66527b7d-b6a6-435a-b330-2890792ca56e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[1,-2]" ] }, { "cell_type": "code", "execution_count": 17, "id": "2bb01400-e860-4c75-9fd1-ce44268463b6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([8, 9])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[1,2:4]" ] }, { "cell_type": "code", "execution_count": 18, "id": "ed9309c6-9666-41fe-9310-0597408740f7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 8, 9, 10])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[1,2:]" ] }, { "cell_type": "code", "execution_count": 19, "id": "8b1a9f33-5e91-4ffc-ac7a-d79286dcf1da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 6, 7, 8, 9, 10])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[1,:]" ] }, { "cell_type": "code", "execution_count": 20, "id": "d9ec87f1-7c83-44f4-a71b-c9f291a51644", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[0,:]" ] }, { "cell_type": "code", "execution_count": 21, "id": "825d6cf5-4302-4b94-b00a-dd8b5c61f058", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3, 4])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[0,2:4]" ] }, { "cell_type": "markdown", "id": "0cb14fd3-88aa-43a8-a11d-a71fd9f8aeb6", "metadata": {}, "source": [ "### zadanie 2\n", "1. Utwórz dwuwymiarową tablicę NumPy o wymiarach (3,3) zawierającą liczby od 1 do 9.\n", "2. Wykonaj następujące operacje na tablicy:\n", " - Wyświetl element znajdujący się w drugim wierszu\n", " - Wyświetl wszystkie elementy znajdujące się w drugim wierszu\n", " - Wyświetl wszystkie elementy znajdujące się w drugiej kolumnie\n", " - Wyświetl macierz, ale bez pierwszego wiersza i bez pierwszej kolumny\n" ] }, { "cell_type": "markdown", "id": "4ad7bed0-d812-4683-a03b-033db0911197", "metadata": {}, "source": [ "## Typy danych" ] }, { "cell_type": "code", "execution_count": 22, "id": "c529565a-fc38-47ae-8649-352cd0525178", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "int64\n" ] } ], "source": [ "arr = np.array([1, 2, 3, 4])\n", "\n", "print(arr.dtype)" ] }, { "cell_type": "code", "execution_count": 23, "id": "64996026-ef86-4a43-bdb0-1dcb64e3228b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1\u001b[0m arr[\u001b[38;5;241m3\u001b[39m]\n", "\u001b[0;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 1" ] } ], "source": [ "arr[3]" ] }, { "cell_type": "code", "execution_count": 56, "id": "2563e811-a4e4-4f6d-bef7-379ff24b1624", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr.squeeze()" ] }, { "cell_type": "code", "execution_count": 57, "id": "9ca46070-ed05-4f09-8a51-bdd2ed121af9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr.squeeze()[3]" ] }, { "cell_type": "code", "execution_count": 58, "id": "ff357a34-afb3-4cb1-b898-33708b1ad9e0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3, 4]])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 59, "id": "63318611-23d1-413a-bda7-d09cdf240ae2", "metadata": {}, "outputs": [], "source": [ "arr = np.array([1, 2, 3, 4])" ] }, { "cell_type": "code", "execution_count": 60, "id": "245ae6b2-ee99-4537-bea6-3fbca70636ca", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4])" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 61, "id": "6a2c2e36-2b01-473e-b912-c1d042eaf4fe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3, 4]])" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.expand_dims(arr, axis=0)" ] }, { "cell_type": "code", "execution_count": 62, "id": "dab2689b-fefb-4495-a958-61472d562ff1", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[1],\n", " [2],\n", " [3],\n", " [4]])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.expand_dims(arr, axis=1)" ] }, { "cell_type": "markdown", "id": "ed9bae4d-900f-4f09-b2eb-09155bc3ef9e", "metadata": {}, "source": [ "### Zadanie 4" ] }, { "cell_type": "markdown", "id": "d92593f8-0ddc-493c-b033-da1fdf3263eb", "metadata": {}, "source": [ "### Zadanie 4\n", "\n", "\n", "1. Utwórz tablicę NumPy zawierającą liczby [1, 2, 3, 4, 5]. Przypisz ją do zmiennej `x` i zmień pierwszy element tablicy na 50. Wyświetl:\n", " - Tablicę `arr`,\n", " - Tablicę `x`.\n", "\n", "2. Zrób kopię tablicy `arr` za pomocą metody `.copy()` i zmień pierwszy element tablicy `arr` na 50. Wyświetl:\n", " - Tablicę `arr`,\n", " - Tablicę `x` (po kopii).\n", "\n", "3. Zrób kopię tablicy `arr` przy użyciu modułu `copy.deepcopy` i zmień pierwszy element tablicy `arr` na 50. Wyświetl:\n", " - Tablicę `arr`,\n", " - Tablicę `x` (po głębokiej kopii).\n", "\n", "4. Utwórz tablicę NumPy o wymiarach (1,4) zawierającą elementy \\( [1, 2, 3, 4, 5] \\) i wykonaj następujące operacje:\n", " - Wyświetl tablicę.\n", " - Zmień tablicę, aby była jednowymiarowa za pomocą metody `.squeeze()`\n" ] }, { "cell_type": "markdown", "id": "4562728a-8fea-4643-ac20-4880ee264e5c", "metadata": {}, "source": [ "### Reshape jeszcze raze" ] }, { "cell_type": "code", "execution_count": 64, "id": "6b10a6a1-2f81-41a4-8466-222990ac904c", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 2 3 4 5 6 7 8 9 10 11 12]\n", "[[ 1 2 3]\n", " [ 4 5 6]\n", " [ 7 8 9]\n", " [10 11 12]]\n" ] } ], "source": [ "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n", "print(arr)\n", "newarr = arr.reshape(4, 3)\n", "print(newarr)" ] }, { "cell_type": "code", "execution_count": 65, "id": "8c982360-8838-4036-ac05-92069f789a30", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "cannot reshape array of size 12 into shape (4,2)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[65], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m newarr \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(newarr)\n", "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 12 into shape (4,2)" ] } ], "source": [ "newarr = arr.reshape(4, 2)\n", "print(newarr)" ] }, { "cell_type": "code", "execution_count": 66, "id": "5ff60614-82d0-4421-9ebf-7b0598cda361", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 2 3]\n", " [ 4 5 6]\n", " [ 7 8 9]\n", " [10 11 12]]\n" ] } ], "source": [ "newarr = arr.reshape(4, -1)\n", "print(newarr)" ] }, { "cell_type": "code", "execution_count": 67, "id": "e951ca90-8d1e-4ca8-8196-f00db99cf866", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 2]\n", " [ 3 4]\n", " [ 5 6]\n", " [ 7 8]\n", " [ 9 10]\n", " [11 12]]\n" ] } ], "source": [ "newarr = arr.reshape(-1, 2)\n", "print(newarr)" ] }, { "cell_type": "markdown", "id": "d9ed66d4-9440-4076-8ee0-9855238762bf", "metadata": {}, "source": [ "### Zadanie 5\n", "\n", "1. Utwórz tablicę NumPy zawierającą liczby [1, 2, 3, ..., 10] o wymiarach (2,5). Wyświetl:\n", " - Tablicę (2,5)\n", "\n", "2. Zmień kształt tablicy na (5,2) za pomocą metody `reshape`. Wyświetl ją.\n", "\n", "3. Zmień kształt tablicy na (10,1) za pomocą metody `reshape`. Wyświetl ją\n", " \n", "4. Użyj wartości `-1` w jednym z wymiarów w metodzie `reshape`, aby automatycznie dostosować pozostałe wymiary na (5,-1)\n", "\n", "5. Utwórz tablicę o wymiarach (5,1,2,1) na podstawie powyższego przykładu" ] }, { "cell_type": "markdown", "id": "7927210e-feb0-4e61-87a4-7bb9b8b76b17", "metadata": {}, "source": [ "## Obliczenia wektorowe i macierzowe" ] }, { "cell_type": "code", "execution_count": 68, "id": "5b8fd161-d367-47b7-a558-2ac275a7a308", "metadata": {}, "outputs": [], "source": [ "x = np.array([1,2,4])\n", "y = np.array([100,101,102])" ] }, { "cell_type": "code", "execution_count": 69, "id": "ef246bbf-66dc-42cd-b851-c56d477a4879", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([101, 103, 106])" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x+y" ] }, { "cell_type": "code", "execution_count": 70, "id": "3085a99f-9461-4f7d-a141-d051fd670866", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([101, 102, 104])" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x+100" ] }, { "cell_type": "code", "execution_count": 71, "id": "25d19f04-240b-4054-a8d0-e2cbf3056db1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([100, 200, 400])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x*100" ] }, { "cell_type": "code", "execution_count": 72, "id": "6e71e5a0-427a-4d3d-93cf-f6e45cd69226", "metadata": {}, "outputs": [], "source": [ "x = np.array([[1,2,4], [10,11,12]])" ] }, { "cell_type": "code", "execution_count": 73, "id": "67e64578-3574-44c4-8f9d-d116412bc83e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 4],\n", " [10, 11, 12]])" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "markdown", "id": "cdf9ffb5-6ce3-400a-bf8f-0f364f17fefc", "metadata": {}, "source": [ "#### element-wise multiplication" ] }, { "cell_type": "code", "execution_count": 74, "id": "d182d4a9-d654-4f07-8577-b367a9892860", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 100, 200, 400],\n", " [1000, 1100, 1200]])" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x*100" ] }, { "cell_type": "code", "execution_count": 75, "id": "db4ec9ff-7ca9-4d63-90cd-a513b3bb59ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 4],\n", " [10, 11, 12]])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 76, "id": "505e245c-d4e6-45ea-b932-0e4de1327fc4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([100, 101, 102])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 77, "id": "7c80b3f2-4dc9-4662-9036-1d736ecb6af1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 4, 16],\n", " [100, 121, 144]])" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x*x" ] }, { "cell_type": "code", "execution_count": 78, "id": "a49e39b2-f64e-4cbe-9fe2-cac29f217637", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 100, 202, 408],\n", " [1000, 1111, 1224]])" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x*y" ] }, { "cell_type": "markdown", "id": "75af9e4a-2d8f-4c77-8dac-f25c09c549f5", "metadata": {}, "source": [ "#### mnożenie macierzy, iloczny skalarny\n", "dot product- iloczyn skaralny dla wektorów \n", "mnożenie macierzowe - dla macierzy" ] }, { "cell_type": "code", "execution_count": 79, "id": "79413af7-c8b2-4cae-9979-992782c5ad74", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([100, 101, 102])" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 80, "id": "6bc6192b-fbce-4eb0-95de-e6e6bd6729a2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30605" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.dot(y)" ] }, { "cell_type": "code", "execution_count": 81, "id": "8d2f6687-f4cc-450a-bd5c-ec9ddf220cb6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30605" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y@y" ] }, { "cell_type": "code", "execution_count": 82, "id": "fc5fd3fe-6b34-409f-ad34-25828988f0b2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30605" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.matmul(y,y)" ] }, { "cell_type": "code", "execution_count": 83, "id": "640e1c27-4ab9-4cb7-bd00-a895fdcc5476", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 4],\n", " [10, 11, 12]])" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 84, "id": "49d9ff43-3e93-4836-b87f-7095b2667854", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "shapes (2,3) and (2,3) not aligned: 3 (dim 1) != 2 (dim 0)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[84], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x\u001b[38;5;241m.\u001b[39mdot(x)\n", "\u001b[0;31mValueError\u001b[0m: shapes (2,3) and (2,3) not aligned: 3 (dim 1) != 2 (dim 0)" ] } ], "source": [ "x.dot(x)" ] }, { "cell_type": "code", "execution_count": 85, "id": "9e5a2c8f-d398-4246-b215-8c5fb5e1da00", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 3)" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.shape" ] }, { "cell_type": "code", "execution_count": 86, "id": "c31c73f0-4848-43fa-97a7-fb5522a87862", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3,)" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.shape" ] }, { "cell_type": "code", "execution_count": 87, "id": "47e8851e-47a2-4453-aee5-c1e3bdd01541", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 710, 3335])" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.dot(y)" ] }, { "cell_type": "code", "execution_count": 88, "id": "3b5ed3a6-591a-4181-b4eb-efe0c85a852c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 710, 3335])" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x @ y" ] }, { "cell_type": "code", "execution_count": 89, "id": "a1f68122-808c-40df-948f-08fa3993f336", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 710, 3335])" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.matmul(x,y)" ] }, { "cell_type": "code", "execution_count": 90, "id": "6d4ed5fa-6537-418e-b648-a64c14ae40cf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 4],\n", " [10, 11, 12]])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 91, "id": "13ed3203-b5b8-47dc-a471-0eda865a8eed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2, 4],\n", " [11, 12]])" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[:,1:]" ] }, { "cell_type": "code", "execution_count": 92, "id": "d7991eb9-2774-4424-aa19-373456fe18bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 48, 56],\n", " [154, 188]])" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.matmul(x[:,1:] , x[:,1:])" ] }, { "cell_type": "code", "execution_count": 93, "id": "74d2da83-8cf3-46f4-859d-6694fce7f194", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 4],\n", " [10, 11, 12]])" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 94, "id": "06981ab2-4b03-4dcd-87d2-f4f689e75f2d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 10],\n", " [ 2, 11],\n", " [ 4, 12]])" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.T" ] }, { "cell_type": "markdown", "id": "aaf38f60-7ef3-47c9-922c-64016e35196e", "metadata": {}, "source": [ "### Zadanie 6\n", "\n", "\n", "1. Utwórz dwie tablice jednowymiarowe `x` i `y`:\n", " - `x` zawiera liczby \\( [3, 5, 7] \\),\n", " - `y` zawiera liczby \\( [50, 60, 70] \\).\n", "\n", "2. Wykonaj następujące operacje i wyświetl wyniki:\n", " - Dodaj tablicę `x` i `y`.\n", " - Dodaj do każdego elementu tablicy `x` liczbę 10.\n", " - Pomnóż każdy element tablicy `x` przez 5.\n", "\n", "3. Utwórz dwuwymiarową tablicę `z` zawierającą:\n", " [[ 3 5 7 ], [8,10,12], [10,10,10]]\n", " \n", " Następnie wykonaj następujące operacje:\n", " - Pomnóż macierzowo x i z\n", " - Oblicz iloczyn skalrny x i y\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "eb34ddb2-6f92-4444-87dd-00fb392d416e", "metadata": {}, "outputs": [], "source": [ "## Tworzenie macierzy zerowych, jednostkowych, itp" ] }, { "cell_type": "code", "execution_count": 6, "id": "de2ca8df-31fe-4fef-ba7f-bd2fa2bfa766", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.zeros((10,10))" ] }, { "cell_type": "code", "execution_count": 7, "id": "e5850227-6736-4867-ba93-6d56685d6d64", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n", " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones((10,10))" ] }, { "cell_type": "code", "execution_count": 98, "id": "69f3a9e8-5ffa-4eb6-916f-d89bab25438a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.],\n", " [123., 123., 123., 123., 123., 123., 123., 123., 123., 123.]])" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones((10,10)) * 123" ] }, { "cell_type": "code", "execution_count": 99, "id": "7f9568c6-cb77-452c-9298-cfa4229157fc", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.eye(10)" ] }, { "cell_type": "code", "execution_count": 100, "id": "6fda600b-9422-45b2-a593-8afe96738a7c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n" ] } ], "source": [ "#\n", "arr = np.array([[1, 2, 3], [4, 5, 6]])\n", "\n", "for x in arr:\n", " for y in x:\n", " print(y)" ] }, { "cell_type": "code", "execution_count": 101, "id": "af0eb0d3-4ec1-41c4-9860-63cdd54b818b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n" ] } ], "source": [ "#\n", "arr = np.array([[1, 2, 3], [4, 5, 6]])\n", "\n", "for i in range(len(arr)):\n", " for j in range(len(arr[i])):\n", " print(arr[i,j])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }