Day 7 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c4956cf",
   "metadata": {},
   "source": [
    "## 1.\t\n",
    "\n",
    "#### Using the arange function, create an array of numbers between 0 - 10. The shape of the array must be (2, 5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fdc6b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "array1 = np.arange(0, 10).reshape(2, 5)\n",
    "array1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84f584d",
   "metadata": {},
   "source": [
    "## 2.\t\n",
    "\n",
    "#### Write a code to change the shape of the array (question 1) to (5, 2).  Create a new variable for this array. Now, add this array to the original array in question 1. The resulting array should have shape (2, 5). Save this to a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5b2e5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7],\n",
       "       [8, 9]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshaping array using the shape method\n",
    "array2 = array1.reshape(5, 2)\n",
    "array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b71a7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  6,  9, 12],\n",
       "       [ 6,  9, 12, 15, 18]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transposing and adding the two arrays\n",
    "array3 = array2.transpose() + array1\n",
    "array3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b2435dc",
   "metadata": {},
   "source": [
    "## 3.\t\n",
    "\n",
    "#### Swap the indexes of the array (question 2) using swapaxis() method. Create a new variable for the swapped array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21a7eae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  6],\n",
       "       [ 3,  9],\n",
       "       [ 6, 12],\n",
       "       [ 9, 15],\n",
       "       [12, 18]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swapped_array = np.swapaxes(array3, 0, 1)\n",
    "swapped_array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b1098f",
   "metadata": {},
   "source": [
    "## 4.\t\n",
    "\n",
    "#### Write a code to slice row 1 from the swapped array in question 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "746ade4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 6])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_1 = swapped_array[0, :]\n",
    "row_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "034bbcc8",
   "metadata": {},
   "source": [
    "## 5.\n",
    "\n",
    "#### Each row in the \"goals\" list represent the goals scored by the each player in the last 5 seasons. For example Robin’s goals are [12, 15, 16, 15, 13] and plays for \"Man UTD\" and Leo are [26, 30, 31, 25, 24] and plays for \"Barca\" .Using NumPy, seaborn and matplotlib create a heatmap of the goals, and calculate the gradient of the data. Annot your heatmap. The xticks will be the names of the players and the yticks will be the team of the players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3a81289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "players = [\"Robin\", \"Leo\", \"Pogba\", \"Diego\", \"Ronaldo\"]\n",
    "teams =[\"Man UTD\", \"Barca\", \"Juve\", \"Napo\", \"RMadrid\"]\n",
    "\n",
    "goals =[[12, 15, 16, 15, 13],\n",
    "        [26, 30, 31, 25, 24], \n",
    "        [10, 12, 8, 6, 13],\n",
    "        [18, 19, 17, 20, 21], \n",
    "        [21, 32, 25, 21, 22]\n",
    "       ]\n",
    "\n",
    "# Creating array from list of goals\n",
    "array1 = np.array(goals)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "sns.heatmap(array1, annot= True)\n",
    "ax.set_xticks(np.arange(len(players)), labels=players)\n",
    "ax.set_yticks(np.arange(len(teams)), labels=teams)\n",
    "plt.title(\"Player Analysis Heatmap\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b2bd6ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}