Day 11 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5228e890",
   "metadata": {},
   "source": [
    "## 1. \n",
    "\n",
    "#### Using NumPy, create an array using the list above. Write a code to check how many non-numeric elements are in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cce9b35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "list1 = [\"34\", \"name\", \"45\", \"is\", \"100\"]\n",
    "\n",
    "# Creating an array\n",
    "arr = np.array(list1)\n",
    "\n",
    "non_numeric = list(filter(lambda x: not np.char.isnumeric(x), arr))\n",
    "len(non_numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9f315e",
   "metadata": {},
   "source": [
    "## 2.\t\n",
    "#### Using NumPy, write a code to sum all the numeric elements in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b71ff16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array(list1)\n",
    "\n",
    "# Access the numeric items from the arr array\n",
    "numeric_list = list((filter(lambda x: np.char.isnumeric(x), arr)))\n",
    "\n",
    "# Convert the numeric items to integers\n",
    "numeric_sum =np.array([int(i) for i in numeric_list]).sum()\n",
    "numeric_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8908ee7a",
   "metadata": {},
   "source": [
    "## 3.\t\n",
    "\n",
    "#### Using NumPy, write a code to square all the numeric elements in the array you created in question 1. Return an array of squared elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34b326ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1156,  2025, 10000])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array(list1)\n",
    "\n",
    "# Access the numeric items from the arr\n",
    "numeric_list = list(filter(lambda x: np.char.isnumeric(x), arr))\n",
    "\n",
    "# Convert the numeric items to integers\n",
    "numeric_list =np.array([int(i)**2 for i in numeric_list])\n",
    "numeric_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c116e0",
   "metadata": {},
   "source": [
    "## 4.\n",
    "#### Using NumPy, create an array using the lists below. To visualize the correlation between attendance and final exam score, create a scatter plot using matplotlib. Make the final_ exam_score the x-axis and the attendance the y-axis of your scatter plot. Use the NumPy array you just created as the source of your data. Can you spot any outliers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9546ffd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   2,   3,   4,   5,   6,   7,   8,   9,  10],\n",
       "       [ 85,  92,  70,  95,  80,  60,  90,  75,  65, 100],\n",
       "       [ 80,  89,  65,  56,  66,  50,  85,  70,  55, 100]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_id = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "attendance = [85, 92, 70, 95, 80, 60, 90, 75, 65, 100]\n",
    "final_exam_score = [80, 89, 65, 56, 66, 50, 85, 70, 55, 100]\n",
    "\n",
    "arr = np.array([student_id, attendance, final_exam_score])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3044aca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "# Using slicing to get x-axis and y-axis data\n",
    "plt.scatter(arr[2:3,0:], arr[1:2,0:])\n",
    "plt.xlabel(\"Final Exam Score\")\n",
    "plt.ylabel(\"Attendance Record\")\n",
    "plt.title(\"Attendance and Final Score Analysis\", fontsize=20)\n",
    "plt.show()"
   ]
  }
 ],
 "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
}