Results_Barcelona_vs_London.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d35f46e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from PIL import Image\n",
    "import time\n",
    "sns.set_theme(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "876a40d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_london = pd.read_csv('precios_london.csv',index_col = [0])\n",
    "df_bcn = pd.read_csv('Precios_Barcelona.csv',index_col = [0])\n",
    "\n",
    "def Ploting_Comparison(df_london, df_bcn):\n",
    "    cant_london = df_london.shape[0]\n",
    "    cant_barcelona= df_bcn.shape[0]\n",
    "    average_London = df_london.London.mean()\n",
    "    average_Barcelona = df_bcn.Barcelona.mean()\n",
    "    Cities = ['London','Barcelona']\n",
    "    Avg_price = [round(average_London,2), round(average_Barcelona,2)]\n",
    "    df = pd.DataFrame({'Cities': Cities, 'Avg_price': Avg_price})\n",
    "    plt.title('Average of renting a room in London and Barcelona')\n",
    "    ax = sns.barplot(data = df, x=Cities, y = Avg_price) #.set_title('Prices of €/m2 of  London and Barcelona')\n",
    "    ax.bar_label(ax.containers[0], fmt='%.2f € / m2')\n",
    "    plt.savefig('Results.png')\n",
    "    time.sleep(0.5)\n",
    "    im = Image.open('Results.png')\n",
    "    im.show()\n",
    "    print(f'Para la ciudad de London se promediaron {cant_london} y para Barcelona {cant_barcelona} habitaciones')\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "61b0f13c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para la ciudad de London se promediaron 47 y para Barcelona 36 habitaciones\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ploting_Comparison(df_london, df_bcn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd0b40fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "im = Image.open('Results.png')\n",
    "im.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9830039",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}