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‎.ipynb_checkpoints/Python Practice for Sem-checkpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b6c71ae6-7f42-473f-aec4-c51d37f021d7",
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"metadata": {},
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"source": [
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"## Odd & Even Number"
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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": "641a7c4c-0d05-4813-8d98-3b813a7d690b",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Enter a number: 10\n"
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]
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},
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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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"The number is even.\n"
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]
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}
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],
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"source": [
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"n = int(input(\"Enter a number: \"))\n",
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"\n",
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"def oddEven(num):\n",
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" if num % 2 == 0:\n",
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" print(\"The number is even.\")\n",
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" else:\n",
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" print(\"The number is odd.\")\n",
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" \n",
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"oddEven(n)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6abfb252-c34c-44a0-af2d-48553d15709b",
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"metadata": {},
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"source": [
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"## Prime numbers upto 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": 19,
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"id": "f04634ef-8682-4f23-8419-71791a497b6c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Enter a number: 19\n"
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]
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},
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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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"2\n",
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"3\n",
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"5\n",
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"7\n",
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"11\n",
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"13\n",
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"17\n",
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"19\n"
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]
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}
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],
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"source": [
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"n = int(input(\"Enter a number: \")) \n",
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"\n",
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"for num in range(1, n+1):\n",
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" if num > 1:\n",
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" for i in range(2, num):\n",
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" if num % i == 0:\n",
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" break\n",
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" else:\n",
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" print(num)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b1b14ebe-258e-4d02-9713-0be13b220b5d",
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"metadata": {},
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"source": [
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"## Read and Plot from File"
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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": 46,
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"id": "cfe4c568-ce6a-4969-b47e-1c11c147738c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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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": 48,
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"id": "2dfb676d-0519-402a-9a2a-936b5f8ace84",
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"metadata": {
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"tags": []
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},
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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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"[56, 84, 15, 35, 69, 24]\n",
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"[48, 65, 18, 35, 49, 20]\n"
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]
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}
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],
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"source": [
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"f = open(\"sample.txt\", \"r\")\n",
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"\n",
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"x = []\n",
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"a = f.readline().split()\n",
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"for item in a:\n",
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" x.append(int(item))\n",
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"print(x)\n",
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"\n",
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"y = []\n",
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"b = f.readline().split()\n",
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"for item in b:\n",
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" y.append(int(item))\n",
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"print(y)"
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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": 51,
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"id": "7ceaf6b8-e5be-4dba-8fbe-ec04af4522fe",
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"metadata": {
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"tags": []
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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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"<matplotlib.collections.PathCollection at 0x1a2e2913e50>"
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]
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},
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"execution_count": 51,
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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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"data": {
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"image/png": 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",
170+
"text/plain": [
171+
"<Figure size 640x480 with 1 Axes>"
172+
]
173+
},
174+
"metadata": {},
175+
"output_type": "display_data"
176+
}
177+
],
178+
"source": [
179+
"plt.scatter(x, y)"
180+
]
181+
}
182+
],
183+
"metadata": {
184+
"kernelspec": {
185+
"display_name": "Python 3 (ipykernel)",
186+
"language": "python",
187+
"name": "python3"
188+
},
189+
"language_info": {
190+
"codemirror_mode": {
191+
"name": "ipython",
192+
"version": 3
193+
},
194+
"file_extension": ".py",
195+
"mimetype": "text/x-python",
196+
"name": "python",
197+
"nbconvert_exporter": "python",
198+
"pygments_lexer": "ipython3",
199+
"version": "3.9.16"
200+
}
201+
},
202+
"nbformat": 4,
203+
"nbformat_minor": 5
204+
}

‎.ipynb_checkpoints/sample-checkpoint.txt

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