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Commit 854440a

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‎Day18(16may19).ipynb‎

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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Problem 1:\n",
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"\n",
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"### Statement\n",
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"Define a function to read data from text file\\\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": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Hello!\\nMr\\n'"
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]
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},
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"execution_count": 16,
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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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"def ReadFileData(filename):\n",
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" f=open(filename,'r')\n",
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" filedata=f.readline()\n",
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" filedata+=f.readline()\n",
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" \n",
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" f.close()\n",
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" return filedata\n",
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"\n",
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"ReadFileData('DataFiles/data.txt')"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Hello!\\nMr\\nThis\\nis\\nPython'"
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]
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},
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"execution_count": 17,
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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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"def readfiledata(filename):\n",
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" f = open(filename, 'r')\n",
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" filedata = f.read()\n",
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" \n",
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" f.close()\n",
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" return filedata\n",
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"\n",
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"readfiledata('DataFiles/data.txt')"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Hello!\\n'"
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]
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},
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"execution_count": 18,
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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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"def readfiledata(filename):\n",
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" with open(filename, 'r') as f:\n",
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" filedata = f.readline()\n",
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" \n",
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"\n",
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" return filedata\n",
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"\n",
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"readfiledata('DataFiles/data.txt')"
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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": 337,
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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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"Hello!\n",
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"\n",
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"Mr\n",
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"\n",
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"This\n",
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"\n",
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"is\n",
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"\n",
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"Python\n"
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]
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}
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],
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"source": [
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"def readfiledata(filename):\n",
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" with open(filename, 'r') as f:\n",
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" for line in f:\n",
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" print(line)\n",
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"\n",
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"readfiledata('DataFiles/data.txt')"
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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": 338,
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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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"Hello!\n",
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"Mr\n",
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"This\n",
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"is\n",
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"Python"
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]
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}
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],
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"source": [
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"def readfiledata(filename):\n",
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" with open(filename, 'r') as f:\n",
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" for line in f:\n",
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" print(line, end = '')\n",
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"\n",
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"readfiledata('DataFiles/data.txt')"
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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": 340,
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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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"Hello!\n",
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"Mr\n",
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"This\n",
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"is\n",
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"Python\n"
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]
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}
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],
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"source": [
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"def readfiledata(filename):\n",
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" with open(filename, 'r') as f:\n",
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" print(f.read())\n",
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"\n",
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"readfiledata('DataFiles/data.txt')"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Problem 2:\n",
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"Define a function to generate a Marks data file (text file) for 1300 students such that each mark is entered in anew line. Marks range from 0 to 100 (inclusive) random numbers."
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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": 347,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.009846191001997795"
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]
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},
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"execution_count": 347,
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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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"import random,timeit\n",
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"def GenerateMarksData(n,filename):\n",
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" start=timeit.default_timer()\n",
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" with open(filename,'w') as marksFile:\n",
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" for i in range(n):\n",
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" marks=random.randint(1,100)\n",
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" marksFile.write(str(marks)+'\\n')\n",
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" return timeit.default_timer()-start\n",
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"\n",
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"GenerateMarksData(1300,'MarksData.txt')"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Problem 3:\n",
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"Generate a report on marks data with the following indicators:\n",
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"* Highest mark :\n",
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"* Lowest Mark :\n",
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"* Average Mark :\n",
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"* No of students with distinction(>90) :\n",
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"* No of students with First class (>80):\n",
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"* No of students with second class (>70):\n",
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"* No of students with second class (>60):\n",
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"* No of students failed (<40) :\n",
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"### Test Cases\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": 349,
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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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"Max Marks= 57 Min Marks= 57 Avg Marks= 0\n",
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"Grade A-- 0 Grade B-- 0 Grade C-- 0 Grade F-- 0 "
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]
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},
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{
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"data": {
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"text/plain": [
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"0.004933976000756957"
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]
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},
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"execution_count": 349,
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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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"import timeit\n",
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"def ReadMarks(n,filename):\n",
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" start=timeit.default_timer()\n",
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" with open (filename,'r') as rip:\n",
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" l=[]\n",
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" ga=[]\n",
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" gc=[]\n",
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" gb=[]\n",
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" fl=[]\n",
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" for i in rip:\n",
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" l.append(int(i))\n",
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" print('Max Marks=',max(l),'Min Marks=',min(l),'Avg Marks=',(sum(l)//n),end='\\n')\n",
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" if int(i)>=90:\n",
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" ga.append(int(i))\n",
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" elif int(i)>=80 and int(i)<90:\n",
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" gb.append(int(i))\n",
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" elif int(i)>=70 and int(i)<80:\n",
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" gc.append(int(i))\n",
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" elif int(i)<=40:\n",
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" fl.append(int(i))\n",
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" print('Grade A--',len(ga),'Grade B--',len(gb),'Grade C--',len(gc),'Grade F--',len(fl), end = ' ')\n",
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" return timeit.default_timer()-start\n",
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"\n",
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"ReadMarks(1300,'MarksData.txt')\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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 350,
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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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"['4', '36.543', '2', '8', '8']\n",
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"[4.0, 36.543, 2.0, 8.0, 8.0]\n",
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"[4.0, 36.543, 2.0, 8.0, 8.0]\n",
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"0.00058624000303098\n",
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"<class 'list'>\n"
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]
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}
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],
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"source": [
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"import timeit\n",
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"st=timeit.default_timer()\n",
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"li=[4,36.543,2,8,8]\n",
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"print(list(map(str,li)))\n",
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"print([float(i) for i in li])\n",
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"print(s)\n",
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"print((timeit.default_timer()-st))\n",
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"print(type(s))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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",
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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": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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