Day 13 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f164573c",
   "metadata": {},
   "source": [
    "## 1.\t\n",
    "\n",
    "#### Using the data above, create an array and transpose it. The creating of an array and transposition should be combined into one code. The code should return a transposed array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9f14e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['John', '21', 'm'],\n",
       "       ['Kelly', '21', 'f'],\n",
       "       ['Jos', '56', 'm'],\n",
       "       ['Peter', '44', 'm'],\n",
       "       ['Robert', '56', 'm'],\n",
       "       ['Piper', '96', 'f']], dtype='<U11')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "names = [\"John\", \"Kelly\", \"Jos\", \"Peter\", \"Robert\", \"Piper\"]\n",
    "age = [21, 21, 56, 44, 56, 96]\n",
    "gender = ['m', 'f', 'm', 'm', 'm', 'f']\n",
    "\n",
    "# creating an array and transposing\n",
    "array1= np.array([names, age, gender]).transpose()\n",
    "array1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3a3416",
   "metadata": {},
   "source": [
    "Another way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d35ac80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['John', '21', 'm'],\n",
       "       ['Kelly', '21', 'f'],\n",
       "       ['Jos', '56', 'm'],\n",
       "       ['Peter', '44', 'm'],\n",
       "       ['Robert', '56', 'm'],\n",
       "       ['Piper', '96', 'f']], dtype='<U11')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [\"John\", \"Kelly\", \"Jos\", \"Peter\", \"Robert\", \"Piper\"]\n",
    "age = [21, 21, 56, 44, 56, 96]\n",
    "gender = ['m', 'f', 'm', 'm', 'm', 'f']\n",
    "\n",
    "# creating an array and transposing\n",
    "array1= np.array([names, age, gender]).T\n",
    "array1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c50255b",
   "metadata": {},
   "source": [
    "## 2.\t\n",
    "\n",
    "#### Using slicing or indexing, write a code to return the last row of the transposed array (question 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f802cc63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Piper', '96', 'f'], dtype='<U11')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing one row of the array\n",
    "last_row_array = array1[-1]\n",
    "last_row_array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c3ca28",
   "metadata": {},
   "source": [
    "## 3.\t\n",
    "\n",
    "#### Using slicing or indexing, write a code to return the first two rows of the transposed array (question 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d7ec307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['John', '21', 'm'],\n",
       "       ['Kelly', '21', 'f']], dtype='<U11')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing two rows of the array\n",
    "slice_two_rows = array1[:2]\n",
    "slice_two_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b07796f2",
   "metadata": {},
   "source": [
    "## 4.\t\n",
    "\n",
    "#### Using slicing, write a code that returns the age of Peter. Return Peter’s age as an integer data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d1f805c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peter is 44 yrs old\n"
     ]
    }
   ],
   "source": [
    "# Accessing Peter's age using slicing\n",
    "peter_age = int(array1[3,1])\n",
    "print(f'Peter is {peter_age} yrs old')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a1eeb86",
   "metadata": {},
   "source": [
    "## 5.\t\n",
    "\n",
    "#### Write code to return an array of Kelly's age and gender using slicing (from transposed array in question 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca45772c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['21', 'f'], dtype='<U11')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kelly_age_gender = array1[1, 1:3]\n",
    "kelly_age_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26481b03",
   "metadata": {},
   "source": [
    "## 6.\t\n",
    "#### One of the uses of slicing is  for Data visualization: Slicing is often used in data visualization to extract specific portions of a dataset for plotting. Write a code to create a subarray of the ages(only ages) in the transposed array using slicing. Using Matplotlib, plot a histogram of the age array. Your graph we should have xlabel, ylabel and a title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "efd27965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['21', '21', '56', '44', '56', '96'], dtype='<U11')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slice age from the array\n",
    "age_array = array1[:,1]\n",
    "age_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aac302d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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",
    "plt.figure(figsize=(12, 8))\n",
    "plt.hist(age_array, rwidth = 2.9, bins= 10)\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Age Distribution\", fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b93e544",
   "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
}