312 lines
6.2 KiB
Plaintext
312 lines
6.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "23ed41a0-7a05-493e-a640-4bfb10c42164",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "fa3799c5-d3a0-4967-98d4-a340d19dbfc6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[10 11 12 13 14 15 16 17 18 19 20]\n",
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"(11,)\n",
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"<class 'numpy.ndarray'>\n"
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]
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}
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],
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"source": [
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"#Zadanie 1.1\n",
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"# Tworzenie tablicy jednowymiarowej\n",
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"arr = np.array([10,11,12,13,14,15,16,17,18,19,20])\n",
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"print(arr)\n",
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"print(arr.shape)\n",
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"print(type(arr))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b6b4fa7d-7ee5-416c-8060-39057b49d77b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[10 20]\n",
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" [30 40]\n",
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" [50 60]]\n",
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"(3, 2)\n",
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"<class 'numpy.ndarray'>\n"
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]
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}
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],
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"source": [
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"# Zadanie 1.2\n",
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"arr = np.array([[10, 20], [30, 40], [50, 60]])\n",
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"\n",
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"print(arr)\n",
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"\n",
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"print(arr.shape)\n",
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"\n",
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"print(type(arr))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f96f774c-d6cd-440f-b6bf-a2d373404de3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[1 2 3]\n",
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" [4 5 6]\n",
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" [7 8 9]]\n",
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"8\n",
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"[[7 8 9]]\n",
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"[3 6 9]\n",
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"[[5 6]\n",
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" [8 9]]\n"
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]
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}
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],
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"source": [
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"# Zadanie 2\n",
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"# Tworzenie dwuwymiarowej tablicy\n",
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"arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
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"\n",
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"print(arr)\n",
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"\n",
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"\n",
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"print(arr[2, 1])\n",
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"\n",
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"print(arr[2:])\n",
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"\n",
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"\n",
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"print(arr[:,2])\n",
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"\n",
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"print(arr[1:,1:])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "43216855-9d5d-4d03-9512-557f4d228571",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[10 20 30 40]\n",
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"int64\n",
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"[10. 20. 30. 40.]\n",
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"float32\n",
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"['Python' 'NumPy' 'Coding']\n",
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"<U6\n"
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]
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}
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],
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"source": [
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"# Zadanie 3\n",
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"\n",
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"# Punkt 1\n",
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"arr = np.array([10, 20, 30, 40])\n",
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"print(arr)\n",
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"print(arr.dtype)\n",
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"\n",
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"# Punkt 2\n",
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"arr = arr.astype('float32')\n",
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"print(arr)\n",
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"print(arr.dtype)\n",
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"\n",
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"# Punkt 3\n",
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"arr = np.array([\"Python\", \"NumPy\", \"Coding\"])\n",
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"print(arr)\n",
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"print(arr.dtype)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "0a700a92-fb6e-498d-bf2f-d5f9758d0147",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[50 2 3 4 5]\n",
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"[50 2 3 4 5]\n",
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"[50 2 3 4 5]\n",
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"[1 2 3 4 5]\n",
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"[50 2 3 4 5]\n",
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"[[1 2 3 4]]\n",
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"[1 2 3 4]\n"
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]
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}
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],
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"source": [
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"### Zadanie 4\n",
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"import copy\n",
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"\n",
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"\n",
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"# Punkt 1 - Przypisanie do zmiennej\n",
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"arr = np.array([1, 2, 3, 4, 5])\n",
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"x = arr\n",
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"arr[0] = 50\n",
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"print(arr) # Tablica arr po zmianie\n",
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"print(x) # Tablica x po zmianie\n",
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"\n",
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"# Punkt 2 - Kopia tablicy\n",
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"arr = np.array([1, 2, 3, 4, 5])\n",
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"x = arr.copy()\n",
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"arr[0] = 50\n",
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"print(arr) # Tablica arr po zmianie\n",
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"print(x) # Tablica x po kopii\n",
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"\n",
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"# Punkt 3 - Głęboka kopia\n",
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"arr = np.array([1, 2, 3, 4, 5])\n",
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"x = copy.deepcopy(arr)\n",
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"arr[0] = 50\n",
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"print(arr) # Tablica arr po zmianie\n",
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"\n",
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"arr2 = np.array([1, 2, 3, 4], ndmin=2)\n",
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"print(arr2)\n",
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"\n",
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"# Zmiana wymiaru na jednowymiarowy\n",
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"arr_squeezed = arr2.squeeze()\n",
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"print(arr_squeezed)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "fbf7f5ee-aace-47f6-9a73-14fdf7595ff4",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[[[ 1],\n",
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" [ 2]]],\n",
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"\n",
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"\n",
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" [[[ 3],\n",
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" [ 4]]],\n",
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"\n",
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"\n",
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" [[[ 5],\n",
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" [ 6]]],\n",
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"\n",
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"\n",
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" [[[ 7],\n",
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" [ 8]]],\n",
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"\n",
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"\n",
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" [[[ 9],\n",
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" [10]]]])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"## Zadanie 5\n",
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"\n",
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"\n",
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"arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
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"\n",
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"arr\n",
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"\n",
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"arr.reshape(5,2)\n",
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"\n",
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"arr.reshape(10,1)\n",
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"\n",
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"arr.reshape(5,-1)\n",
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"\n",
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"arr.reshape(5,1,2,1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "5afcad53-ce4a-408d-bc44-f33fd7b8e276",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[53 65 77]\n",
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"[13 15 17]\n",
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"[15 25 35]\n",
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"[[ 3 5 7]\n",
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" [ 8 10 12]\n",
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" [10 10 10]]\n",
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"[119 135 151]\n",
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"940\n"
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]
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}
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],
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"source": [
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"### Zadanie 6\n",
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"\n",
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"\n",
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"x = np.array([3, 5, 7])\n",
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"y = np.array([50, 60, 70])\n",
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"\n",
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"print(x + y)\n",
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"print(x + 10)\n",
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"print(x * 5)\n",
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"z = np.array([[3, 5, 7], [8, 10, 12], [10,10,10]])\n",
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"print(z)\n",
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"\n",
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"print(x.dot(z))\n",
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"print(x.dot(y))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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