Day 3 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79c6cf0c",
   "metadata": {},
   "source": [
    "## 1.\t\n",
    "\n",
    "#### Using NumPy create an array of random numbers between 0 and 1. The shape of the array must be (3, 4). Use seed to ensure that the results are reproducible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72165647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.77395605, 0.43887844, 0.85859792, 0.69736803],\n",
       "       [0.09417735, 0.97562235, 0.7611397 , 0.78606431],\n",
       "       [0.12811363, 0.45038594, 0.37079802, 0.92676499]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "rng = np.random.default_rng(seed = 42)\n",
    "arr = rng.random(size=(3, 4))\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7414ba",
   "metadata": {},
   "source": [
    "## 2.\t\n",
    "#### Create an array of random integers between 0 and 10. The shape of the array must (3, 4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2629744d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 8, 0, 5],\n",
       "       [4, 6, 0, 1],\n",
       "       [1, 8, 5, 8]], dtype=int64)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.default_rng()\n",
    "rng.integers(10, size=(3, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d08c1e",
   "metadata": {},
   "source": [
    "## 3.\t\n",
    "\n",
    "#### Using NumPy, generate an array of 1000 random numbers from a standard normal distribution with mean 0 and variance 1. Create a hist plot with this array. Use seed to ensure the results are reproducible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a684e8fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Instantianting a generator and setting seed\n",
    "rng = np.random.default_rng(seed = 42)\n",
    "arr2 = rng.standard_normal(size=(1000))\n",
    "\n",
    "# plotting a hist plot\n",
    "plt.hist(arr2, bins=50)\n",
    "plt.title(\"Standard Normal Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62fb7f12",
   "metadata": {},
   "source": [
    "## 4.\t\n",
    "\n",
    "#### arr = [ \"Orange\", \"Apple\", \"Pear\"]\n",
    "\n",
    "#### Using random.choice() from NumPy, generate an array from the list above. The shape of the array must be (3, 4).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0825473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Apple', 'Orange', 'Pear', 'Apple'],\n",
       "       ['Pear', 'Apple', 'Orange', 'Apple'],\n",
       "       ['Apple', 'Apple', 'Apple', 'Apple']], dtype='<U6')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = [\"Orange\", \"Apple\", \"Pear\"]\n",
    "\n",
    "# constructing a random generator\n",
    "rng = np.random.default_rng(seed = 24)\n",
    "\n",
    "arr = rng.choice(lst, size = (3, 4))\n",
    "arr"
   ]
  }
 ],
 "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}