{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ages = [27, 30, 25, 30, 27, 26, 25, 27, 28, 24]\n",
    "values, frequencies = np.unique(ages, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1, 3, 1, 2])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Age'), Text(0, 0.5, 'Frequency')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "axes = sns.barplot(x=values, y=frequencies, color='blue')\n",
    "axes.set_title('Age Distribution')\n",
    "axes.set(xlabel='Age', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}