855 lines
16 KiB
Plaintext
855 lines
16 KiB
Plaintext
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{
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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": "f3557a6d-106e-461c-bc75-7ddcef4f81e5",
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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": "markdown",
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"id": "80a3b70d-fea8-4a6f-ac02-73e30abc1f66",
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"metadata": {},
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"source": [
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"### ufunc\n",
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"ufuncs are used to implement vectorization in NumPy which is way faster than iterating over elements.\n",
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"\n",
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"They also provide broadcasting and additional methods like reduce, accumulate etc. that are very helpful for computation.\n",
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"\n",
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"ufuncs also take additional arguments, like:\n",
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"\n",
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"where boolean array or condition defining where the operations should take place.\n",
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"\n",
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"dtype defining the return type of elements.\n",
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"\n",
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"out output array where the return value should be copied."
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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": "60f2d36b-d334-4e19-9f63-36688cca1c20",
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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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"[5, 7, 9, 11]\n"
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]
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}
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],
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"source": [
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"# python\n",
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"x = [1, 2, 3, 4]\n",
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"y = [4, 5, 6, 7]\n",
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"z = []\n",
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"\n",
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"for i, j in zip(x, y):\n",
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" z.append(i + j)\n",
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"print(z)"
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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": "0272feaa-47e7-44b4-a66d-dafdffca4a7b",
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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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"[ 5 7 9 11]\n"
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]
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}
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],
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"source": [
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"# numpy\n",
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"x = [1, 2, 3, 4]\n",
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"y = [4, 5, 6, 7]\n",
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"z = np.add(x, y)\n",
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"\n",
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"print(z)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5e52ad13-9186-4866-b1c7-3f8a16901c9f",
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"metadata": {},
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"source": [
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"#### Own function"
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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": "3d004fc9-f52c-42fb-93a1-f48dd0849ba9",
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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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"[6 8 10 12]\n"
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]
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}
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],
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"source": [
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"def myadd(x, y):\n",
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" return x+y\n",
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"\n",
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"myadd = np.frompyfunc(myadd, 2, 1)\n",
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"\n",
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"print(myadd([1, 2, 3, 4], [5, 6, 7, 8]))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c034cd44-af9c-44ff-8d74-cf0d8d34b018",
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"metadata": {},
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"source": [
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"#### Simple Arithmetic"
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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": "428246a1-ea45-4c0b-bc94-b3f1ad27a0ac",
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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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"[30 32 34 36 38 40]\n"
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]
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}
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],
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"source": [
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"arr1 = np.array([10, 11, 12, 13, 14, 15])\n",
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"arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
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"\n",
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"newarr = np.add(arr1, arr2)\n",
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"\n",
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"print(newarr)"
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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": 9,
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"id": "ae349412-0d47-42b4-8c39-56d0839b3d34",
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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 -1 8 17 26 35]\n"
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]
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}
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],
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"source": [
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"arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
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"arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
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"\n",
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"newarr = np.subtract(arr1, arr2)\n",
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"\n",
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"print(newarr)"
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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": 10,
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"id": "f61b2129-e31b-4661-a12e-d7897955d163",
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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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"[ 200 420 660 920 1200 1500]\n"
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]
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}
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],
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"source": [
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"arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
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"arr2 = np.array([20, 21, 22, 23, 24, 25])\n",
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"\n",
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"newarr = np.multiply(arr1, arr2)\n",
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"\n",
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"print(newarr)"
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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": 11,
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"id": "67540798-c33e-4d1e-a1d9-47c00a121b36",
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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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"[ 3.33333333 4. 3. 5. 25. 1.81818182]\n"
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]
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}
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],
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"source": [
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"arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
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"arr2 = np.array([3, 5, 10, 8, 2, 33])\n",
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"\n",
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"newarr = np.divide(arr1, arr2)\n",
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"\n",
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"print(newarr)"
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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": 12,
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"id": "05c10eef-8c8e-4515-9aea-402821f05a02",
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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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"[ 1000 3200000 729000000 6553600000000 2500\n",
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" 0]\n"
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]
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}
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],
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"source": [
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"arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
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"arr2 = np.array([3, 5, 6, 8, 2, 33])\n",
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"\n",
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"newarr = np.power(arr1, arr2)\n",
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"\n",
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"print(newarr)"
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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": 13,
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"id": "c47b12e8-f0a0-45c9-b1e5-202fd2dfdaed",
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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 6 3 0 0 27]\n"
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]
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}
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],
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"source": [
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"arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
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"arr2 = np.array([3, 7, 9, 8, 2, 33])\n",
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"\n",
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"newarr = np.mod(arr1, arr2)\n",
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"\n",
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"print(newarr)"
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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": 14,
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"id": "7626b9ec-04f0-421b-a35d-8f30bad5a136",
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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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"(array([ 3, 2, 3, 5, 25, 1]), array([ 1, 6, 3, 0, 0, 27]))\n"
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]
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}
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],
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"source": [
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"# Quotient and Mod\n",
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"arr1 = np.array([10, 20, 30, 40, 50, 60])\n",
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"arr2 = np.array([3, 7, 9, 8, 2, 33])\n",
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"\n",
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"newarr = np.divmod(arr1, arr2)\n",
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"\n",
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"print(newarr)"
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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": 15,
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"id": "e436c31a-a796-4be1-9e7f-ddaf0f026dec",
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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 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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"arr = np.array([-1, -2, 1, 2, 3, -4])\n",
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"\n",
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"newarr = np.absolute(arr)\n",
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"\n",
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"print(newarr)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d55a17c4-1f67-4629-8a85-64f9faa32370",
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"metadata": {},
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"source": [
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"### Rounding Decimals\n",
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"- truncation\n",
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"- fix\n",
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"- rounding\n",
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"- floor\n",
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"- ceil"
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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": 18,
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"id": "11395b0f-660b-4c28-a2ed-1c722b560a71",
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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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"[-3. 3.]\n"
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]
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}
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],
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"source": [
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"# Truncation\n",
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"# Remove the decimals, and return the float number closest to zero. Use the trunc() and fix() functions.\n",
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"\n",
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"arr = np.trunc([-3.1666, 3.6667])\n",
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"\n",
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"print(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": 17,
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"id": "9d2c024e-07ca-43cd-978b-206a391e35f1",
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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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"[-3. 3.]\n"
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]
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}
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],
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"source": [
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"arr = np.fix([-3.1666, 3.6667])\n",
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"\n",
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"print(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": 19,
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"id": "59ca3a99-da45-4a5c-8a2f-bcb6b22a5998",
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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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"3.17\n"
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]
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}
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],
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"source": [
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"# rounding\n",
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"arr = np.around(3.1666, 2)\n",
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"\n",
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"print(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": 20,
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"id": "e213a874-e4b0-4a3e-831a-5e9ff93c3d94",
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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",
|
||
|
|
"text": [
|
||
|
|
"[-4. 3.]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr = np.floor([-3.1666, 3.6667])\n",
|
||
|
|
"\n",
|
||
|
|
"print(arr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"id": "fbe93a72-8e92-4b36-bfab-232c1617e4f1",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[-3. 4.]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr = np.ceil([-3.1666, 3.6667])\n",
|
||
|
|
"\n",
|
||
|
|
"print(arr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "225b1cd3-85cf-492d-8f1d-b2c94eb63783",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Logs"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"id": "995a441d-e78e-4ace-b6fc-b497cce04268",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[1 2 3 4 5 6 7 8 9]\n",
|
||
|
|
"[0. 1. 1.5849625 2. 2.32192809 2.5849625\n",
|
||
|
|
" 2.80735492 3. 3.169925 ]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# log2() function to perform log at the base 2.\n",
|
||
|
|
"arr = np.arange(1, 10)\n",
|
||
|
|
"print(arr)\n",
|
||
|
|
"print(np.log2(arr))\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "b386fbe7-54c7-48dd-8b08-10f053e00638",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Summations"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"id": "16f39361-d8a9-4d9a-970e-72367f2d4685",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[2 4 6]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr1 = np.array([1, 2, 3])\n",
|
||
|
|
"arr2 = np.array([1, 2, 3])\n",
|
||
|
|
"\n",
|
||
|
|
"newarr = np.add(arr1, arr2)\n",
|
||
|
|
"\n",
|
||
|
|
"print(newarr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"id": "bd241f37-75bf-4ee5-bcb4-8c72695eb75d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"12\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr1 = np.array([1, 2, 3])\n",
|
||
|
|
"arr2 = np.array([1, 2, 3])\n",
|
||
|
|
"\n",
|
||
|
|
"newarr = np.sum([arr1, arr2])\n",
|
||
|
|
"\n",
|
||
|
|
"print(newarr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"id": "7309e9d4-c239-4c42-8389-413df30d6abc",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[6 6]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# summation over axis\n",
|
||
|
|
"arr1 = np.array([1, 2, 3])\n",
|
||
|
|
"arr2 = np.array([1, 2, 3])\n",
|
||
|
|
"\n",
|
||
|
|
"newarr = np.sum([arr1, arr2], axis=1)\n",
|
||
|
|
"\n",
|
||
|
|
"print(newarr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "518cdbe0-417b-4085-9575-729595ae2a52",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Products"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"id": "0211ead9-2b18-4409-a4c6-179688bdd203",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"24\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr = np.array([1, 2, 3, 4])\n",
|
||
|
|
"\n",
|
||
|
|
"x = np.prod(arr)\n",
|
||
|
|
"\n",
|
||
|
|
"print(x)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "207af559-db09-48f5-b462-40fd52500bb4",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Differences"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"id": "5215ae9f-709b-4206-90f3-95b5dd391f68",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[ 5 10 -20]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr = np.array([10, 15, 25, 5])\n",
|
||
|
|
"\n",
|
||
|
|
"newarr = np.diff(arr)\n",
|
||
|
|
"\n",
|
||
|
|
"print(newarr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"id": "23aeea8b-3f92-490e-927d-a61a8426a365",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[ 5 -30]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Compute discrete difference of the following array twice\n",
|
||
|
|
"# 15-10=5, 25-15=10, and 5-25=-20 AND 10-5=5 and -20-10=-30\n",
|
||
|
|
"arr = np.array([10, 15, 25, 5])\n",
|
||
|
|
"\n",
|
||
|
|
"newarr = np.diff(arr, n=2)\n",
|
||
|
|
"\n",
|
||
|
|
"print(newarr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "0da9fba4-7ae2-4bfe-94b8-378d3d124b57",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"#### Other"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"id": "69a07fa4-546c-47cd-99b2-a14548b886e2",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"12\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Finding LCM (Lowest Common Multiple)\n",
|
||
|
|
"num1 = 4\n",
|
||
|
|
"num2 = 6\n",
|
||
|
|
"# (4*3=12 and 6*2=12)\n",
|
||
|
|
"x = np.lcm(num1, num2)\n",
|
||
|
|
"\n",
|
||
|
|
"print(x) "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"id": "9c874e84-1ca6-44f8-871b-0c57d05ac360",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"18\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr = np.array([3, 6, 9])\n",
|
||
|
|
"\n",
|
||
|
|
"x = np.lcm.reduce(arr)\n",
|
||
|
|
"\n",
|
||
|
|
"print(x)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 38,
|
||
|
|
"id": "d7f635b8-7915-452b-8ee1-e963ed5e8f54",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"3\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Finding GCD (Greatest Common Denominator)\n",
|
||
|
|
"num1 = 6\n",
|
||
|
|
"num2 = 9\n",
|
||
|
|
"\n",
|
||
|
|
"x = np.gcd(num1, num2)\n",
|
||
|
|
"\n",
|
||
|
|
"print(x)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"id": "d39331bf-062b-41b8-b7fe-082312dd5d6f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[1 2 3 4 5 6 7]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# unique\n",
|
||
|
|
"arr = np.array([1, 1, 1, 2, 3, 4, 5, 5, 6, 7])\n",
|
||
|
|
"\n",
|
||
|
|
"x = np.unique(arr)\n",
|
||
|
|
"\n",
|
||
|
|
"print(x)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 43,
|
||
|
|
"id": "126fdebd-0a45-4be7-a65d-b4e119ebef7d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[1 2 3 4 5 6]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr1 = np.array([1, 2, 3, 4])\n",
|
||
|
|
"arr2 = np.array([3, 4, 5, 6])\n",
|
||
|
|
"\n",
|
||
|
|
"newarr = np.union1d(arr1, arr2)\n",
|
||
|
|
"print(newarr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 44,
|
||
|
|
"id": "a84475b1-15c3-4c8f-8535-2913a4b8579d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[3 4]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"arr1 = np.array([1, 2, 3, 4])\n",
|
||
|
|
"arr2 = np.array([3, 4, 5, 6])\n",
|
||
|
|
"\n",
|
||
|
|
"newarr = np.intersect1d(arr1, arr2, assume_unique=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(newarr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"id": "771b4dc1-78e9-4819-af4f-b2bb3bc154e5",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[1 2]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"set1 = np.array([1, 2, 3, 4])\n",
|
||
|
|
"set2 = np.array([3, 4, 5, 6])\n",
|
||
|
|
"\n",
|
||
|
|
"newarr = np.setdiff1d(set1, set2, assume_unique=True)\n",
|
||
|
|
"\n",
|
||
|
|
"print(newarr)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 39,
|
||
|
|
"id": "95b8cec1-8b0e-43fb-acae-428d7778c03e",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"1.0\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Trigonometric Functions\n",
|
||
|
|
"x = np.sin(np.pi/2)\n",
|
||
|
|
"\n",
|
||
|
|
"print(x)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 40,
|
||
|
|
"id": "e3dbac10-d7bd-45bb-974b-56f8f6082de8",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[1.57079633 3.14159265 4.71238898 6.28318531]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# and sin(), cos() and tan()\n",
|
||
|
|
"# np.deg2rad(arr)\n",
|
||
|
|
"# Hyperbolic: sinh(), cosh() and tanh()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "67cab045-d1ef-4de1-9aaa-5257d5ba5571",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.10.8"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|