Task - 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e785e60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  0.,  1.,  1.,  1.,  3.,  4., 10., 28., 30., 32., 47., 57.,\n",
       "        74., 89., 87., 95., 94., 84., 64., 59., 41., 37., 22., 14., 11.,\n",
       "         6.,  3.,  1.,  4.]),\n",
       " array([ 67.87721211,  72.38184151,  76.88647091,  81.39110031,\n",
       "         85.89572971,  90.40035911,  94.90498851,  99.4096179 ,\n",
       "        103.9142473 , 108.4188767 , 112.9235061 , 117.4281355 ,\n",
       "        121.9327649 , 126.4373943 , 130.9420237 , 135.44665309,\n",
       "        139.95128249, 144.45591189, 148.96054129, 153.46517069,\n",
       "        157.96980009, 162.47442949, 166.97905888, 171.48368828,\n",
       "        175.98831768, 180.49294708, 184.99757648, 189.50220588,\n",
       "        194.00683528, 198.51146468, 203.01609407]),\n",
       " <BarContainer object of 30 artists>)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "height = np.random.normal(140, 20, 1000)\n",
    "plt.figure(dpi = 200)\n",
    "plt.title(\"Height Distribution\")\n",
    "plt.xlabel(\"Height (cm)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.hist(height, bins = 30, ec = \"gold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f979e4",
   "metadata": {},
   "source": [
    "Difference between interpreter and compiler:\n",
    "\n",
    "    Compilers and interpreters are two different ways to translate a program from a high-level programming language to machine language.\n",
    "    -> A compiler takes in the entire program and requires a lot of time to analyze the source code, while an interpreter takes a single line of code and very little time to analyze it.\n",
    "    -> Compiled code runs faster than interpreted code, but interpreted code is easier to debug.\n",
    "    -> A compiler displays all errors after compilation, while an interpreter displays errors of each line one by one.\n",
    "    -> A compiler generates a stand-alone program, while an interpreter does not.\n",
    "    -> A compiler is based on the translation linking-loading model, while an interpreter is based on the interpretation method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d68d017a",
   "metadata": {},
   "source": [
    "What is Data Structure, why we need one in programs? \n",
    "\n",
    "    A data structure is a specialised format to organising, processing, retirving and storing datas.\n",
    "    Data structure provides a way to organize and store data efficiently. Different types of data structures are suited to different kinds of applications, and choosing an appropriate data structure can greatly improve the performance of a program. For example, a program that needs to search through a large amount of data may use a data structure like a hash table, which allows for fast search times. Similarly, a program that needs to frequently add and remove items from a list may use a data structure like a linked list, which allows for fast insertions and deletions."
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}