project.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "innocent-university",
   "metadata": {},
   "source": [
    "# Real Estate Clean up\n",
    "\n",
    "This is a real dataset, and it was downloaded using web scraping techniques. The data contains registers from **Fotocasa** which is one of the most popular real estate websites in Spain. Please, do not do this (web scraping) unless it is for academic purposes.\n",
    "\n",
    "The dataset was downloaded a few years ago by Henry Navarro, and in no case were economic returns obtained from it.\n",
    "\n",
    "It contains thousands of data from real houses published on the web www.fotocasa.com. Your goal is to extract as much information as possible with the knowledge you have so far about data science, for example what is the most expensive house in the entire dataset?\n",
    "\n",
    "Let's start with precisely that question... Good luck!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "multiple-glass",
   "metadata": {},
   "source": [
    "#### Exercise 00. Read the dataset assets/real_estate.csv and try to visualize the table (★☆☆)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "frank-heath",
   "metadata": {},
   "outputs": [
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       "      <th>bathrooms</th>\n",
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       "      <th>accuracy</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>zipCode</th>\n",
       "      <th>customZone</th>\n",
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       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
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       "      <td>153901356</td>\n",
       "      <td>False</td>\n",
       "      <td>infocasa consulting</td>\n",
       "      <td>911360461.0</td>\n",
       "      <td>https://www.fotocasa.es/es/comprar/vivienda/ma...</td>\n",
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       "<p>15335 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  id_realEstates  isNew                    realEstate_name  \\\n",
       "0               1       153771986  False            ferrari 57 inmobiliaria   \n",
       "1               2       153867863  False  tecnocasa fuenlabrada ferrocarril   \n",
       "2               3       153430440  False                 look find boadilla   \n",
       "3               4       152776331  False  tecnocasa fuenlabrada ferrocarril   \n",
       "4               5       153180188  False            ferrari 57 inmobiliaria   \n",
       "...           ...             ...    ...                                ...   \n",
       "15330       15331       153901377  False                infocasa consulting   \n",
       "15331       15332       150394373  False                inmobiliaria pulpon   \n",
       "15332       15333       153901397  False             tecnocasa torrelodones   \n",
       "15333       15334       152607440  False                inmobiliaria pulpon   \n",
       "15334       15335       153901356  False                infocasa consulting   \n",
       "\n",
       "       phone_realEstate                                       url_inmueble  \\\n",
       "0           912177526.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "1           916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "2           916350408.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "3           916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "4           912177526.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "...                 ...                                                ...   \n",
       "15330       911360461.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15331       912788039.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15332       912780348.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15333       912788039.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "15334       911360461.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
       "\n",
       "       rooms  bathrooms  surface   price  ... level4Id level5Id level6Id  \\\n",
       "0        3.0        2.0    103.0  195000  ...        0        0        0   \n",
       "1        3.0        1.0      NaN   89000  ...        0        0        0   \n",
       "2        2.0        2.0     99.0  390000  ...        0        0        0   \n",
       "3        3.0        1.0     86.0   89000  ...        0        0        0   \n",
       "4        2.0        2.0    106.0  172000  ...        0        0        0   \n",
       "...      ...        ...      ...     ...  ...      ...      ...      ...   \n",
       "15330    2.0        1.0     96.0  259470  ...        0        0        0   \n",
       "15331    3.0        1.0    150.0  165000  ...        0        0        0   \n",
       "15332    4.0        2.0    175.0  495000  ...        0        0        0   \n",
       "15333    3.0        2.0    101.0  195000  ...        0        0        0   \n",
       "15334    3.0        2.0    152.0  765000  ...        0        0        0   \n",
       "\n",
       "      level7Id level8Id accuracy          latitude          longitude zipCode  \\\n",
       "0            0        0        0  40,2948276786438  -3,44402412135624     NaN   \n",
       "1            0        0        1          40,28674           -3,79351     NaN   \n",
       "2            0        0        0  40,4115646786438  -3,90662252135624     NaN   \n",
       "3            0        0        0  40,2853785786438  -3,79508142135624     NaN   \n",
       "4            0        0        0  40,2998774864376  -3,45226301356237     NaN   \n",
       "...        ...      ...      ...               ...                ...     ...   \n",
       "15330        0        0        0          40,45416           -3,70286     NaN   \n",
       "15331        0        0        0          40,36652           -3,48951     NaN   \n",
       "15332        0        0        0          40,57444           -3,92124     NaN   \n",
       "15333        0        0        0          40,36967           -3,48105     NaN   \n",
       "15334        0        0        0          40,45773           -3,69068     NaN   \n",
       "\n",
       "      customZone  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "...          ...  \n",
       "15330        NaN  \n",
       "15331        NaN  \n",
       "15332        NaN  \n",
       "15333        NaN  \n",
       "15334        NaN  \n",
       "\n",
       "[15335 rows x 37 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# This CSV file contains semicolons instead of comas as separator\n",
    "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
    "ds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "latin-guest",
   "metadata": {},
   "source": [
    "#### Exercise 01. Which is the most expensive house in the dataset? (★☆☆)\n",
    "\n",
    "Print the address and the price of the selected house. For example:\n",
    "\n",
    "`The house with address General Street Nº5 is the most expensive and its price is 5000000 USD`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "developing-optimum",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "lesser-cosmetic",
   "metadata": {},
   "source": [
    "#### Exercise 02. Which is the cheapest house in the dataset? (★☆☆)\n",
    "\n",
    "Print the address and the price of the selected house. For example:\n",
    "\n",
    "`The house with address Concrete Street Nº1 is the cheapest and its price is 12000 USD`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lovely-oasis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "compliant-fellowship",
   "metadata": {},
   "source": [
    "#### Exercise 03. Which is the biggest and the smallest house in the dataset? (★☆☆)\n",
    "\n",
    "Print both the address and the surface of the selected houses. For example:\n",
    "\n",
    "`The biggest house is located on Yukka Street Nº10 and its surface is 5000 meters`\n",
    "\n",
    "`The smallest house is located on County Road 1 N and its surface is 200 meters`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "every-tiffany",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "danish-spirit",
   "metadata": {},
   "source": [
    "#### Exercise 04. How many populations (level5 column) the dataset contains? (★☆☆)\n",
    "\n",
    "Print the names of the populations with a comma as a separator. For example:\n",
    "\n",
    "`> print(populations)`\n",
    "\n",
    "`population1, population2, population3, ...`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "exciting-accreditation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "crazy-blame",
   "metadata": {},
   "source": [
    "#### Exercise 05. Does the dataset contain NAs? (★☆☆)\n",
    "\n",
    "Print a boolean value (`True` or `False`) followed by the rows/cols that contains NAs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "transparent-poetry",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "italic-hydrogen",
   "metadata": {},
   "source": [
    "#### Exercise 06. Delete the NAs of the dataset, if applicable (★★☆)\n",
    "\n",
    "Print a comparison between the dimensions of the original DataFrame versus the DataFrame after the deletions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "administrative-roads",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "middle-china",
   "metadata": {},
   "source": [
    "#### Exercise 07. Which is the mean of prices in the population (level5 column) of \"Arroyomolinos (Madrid)\"? (★★☆)\n",
    "\n",
    "Print the obtained value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nuclear-belief",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "concerned-radical",
   "metadata": {},
   "source": [
    "#### Exercise 08. Plot the histogram of prices for the population (level5 column) of \"Arroyomolinos (Madrid)\" and explain what you observe (★★☆)\n",
    "\n",
    "Print the histogram of the prices and write in the Markdown cell a brief analysis about the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sudden-message",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Code"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "impressed-combination",
   "metadata": {},
   "source": [
    "**TODO: Markdown**. To write here, double-click on this cell, remove this content and place the text you want to write. Then, execute the cell."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "actual-edinburgh",
   "metadata": {},
   "source": [
    "#### Exercise 09. Are the average prices of \"Valdemorillo\" and \"Galapagar\" the same? (★★☆)\n",
    "\n",
    "Print both average prices and then write a conclusion about them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "numeric-commerce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "lonely-article",
   "metadata": {},
   "source": [
    "#### Exercise 10. Are the average prices per square meter (price/m2) of \"Valdemorillo\" and \"Galapagar\" the same? (★★☆)\n",
    "\n",
    "Print both average prices and then write a conclusion about it.\n",
    "\n",
    "Hint: Create a new column called `pps` (price per square meter) and then analyze the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hourly-globe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "pleasant-invite",
   "metadata": {},
   "source": [
    "#### Exercise 11. Analyze the relation between the surface and the price of the houses (★★☆)\n",
    "\n",
    "Hint: You can make a `scatter plot`, then write a conclusion about it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "common-drilling",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Code"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ahead-liquid",
   "metadata": {},
   "source": [
    "**TODO: Markdown**. To write here, double-click on this cell, remove this content and place the text you want to write. Then, execute the cell."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "coordinate-sunrise",
   "metadata": {},
   "source": [
    "#### Exercise 12. How many real estate agencies does the dataset contain? (★★☆)\n",
    "\n",
    "Print the obtained value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "valid-honolulu",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "binding-ebony",
   "metadata": {},
   "source": [
    "#### Exercise 13. Which is the population (level5 column) that contains the most houses? (★★☆)\n",
    "\n",
    "Print both the population and the number of houses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "static-perry",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "entire-classification",
   "metadata": {},
   "source": [
    "#### Exercise 14. Now let's work with the \"south belt\" of Madrid. Make a subset of the original DataFrame that contains the following populations (level5 column): \"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\" (★★☆)\n",
    "\n",
    "Hint: Filter the original DataFrame using the column `level5` and the function `isin`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "binary-input",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "severe-fisher",
   "metadata": {},
   "source": [
    "#### Exercise 15. Make a bar plot of the median of the prices and explain what you observe (you must use the subset obtained in Exercise 14) (★★★)\n",
    "\n",
    "Print the bar of the median of the prices and write in the Markdown cell a brief analysis about the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lyric-bunch",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Code"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "sublime-newspaper",
   "metadata": {},
   "source": [
    "**TODO: Markdown**. To write here, double-click on this cell, remove this content and place the text you want to write. Then, execute the cell."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "speaking-diamond",
   "metadata": {},
   "source": [
    "#### Exercise 16. Calculate the sample mean and variance of the variables: price, rooms, surface area and bathrooms (you must use the subset obtained in Exercise 14) (★★★)\n",
    "\n",
    "Print both values for each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "random-feeling",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "revolutionary-matrix",
   "metadata": {},
   "source": [
    "#### Exercise 17. What is the most expensive house in each population? You must use the subset obtained in Exercise 14 (★★☆)\n",
    "\n",
    "Print both the address and the price of the selected house of each population. You can print a DataFrame or a single line for each population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fifteen-browse",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "activated-knight",
   "metadata": {},
   "source": [
    "#### Exercise 18. Normalize the variable of prices for each population and plot the 4 histograms in the same plot (you must use the subset obtained in Exercise 14) (★★★)\n",
    "\n",
    "For the normalization method, you can use the one you consider; there is not a single correct answer to this question. Print the plot and write in the Markdown cell a brief analysis about the plot.\n",
    "\n",
    "Hint: You can help yourself by reviewing the *multihist* demo of Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "civic-meditation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "precise-heavy",
   "metadata": {},
   "source": [
    "**TODO: Markdown**. To write here, double-click on this cell, remove this content and place the text you want to write. Then, execute the cell."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "patent-jonathan",
   "metadata": {},
   "source": [
    "#### Exercise 19. What can you say about the price per square meter (price/m2) between the towns of \"Getafe\" and \"Alcorcón\"? You must use the subset obtained in Exercise 14 (★★☆)\n",
    "\n",
    "Hint: Create a new column called `pps` (price per square meter) and then analyze the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "initial-liverpool",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "enhanced-moscow",
   "metadata": {},
   "source": [
    "#### Exercise 20. Make the same plot for 4 different populations (level5 column) and rearrange them on the same graph. You must use the subset obtained in Exercise 14 (★★☆)\n",
    " \n",
    "Hint: Make a scatter plot of each population using subplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "accepting-airfare",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "blocked-effects",
   "metadata": {},
   "source": [
    "#### Exercise 21. Make a plot of the coordinates (latitude and longitude columns) of the south belt of Madrid by color of each population (you must use the subset obtained in Exercise 14) (★★★★)\n",
    "\n",
    "Execute the following cell, and then start coding in the next one. You must implement a simple code that transforms the coordinates columns in a Python dictionary (add more information if needed) and then add it to the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "headed-privacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipyleaflet import Map, basemaps\n",
    "\n",
    "# Map centered on (60 degrees latitude and -2.2 degrees longitude)\n",
    "# Latitude, longitude\n",
    "map = Map(center = (60, -2.2), zoom = 2, min_zoom = 1, max_zoom = 20, \n",
    "    basemap=basemaps.Stamen.Terrain)\n",
    "map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "present-mistress",
   "metadata": {},
   "outputs": [],
   "source": [
    "## HERE: plot the coordinates of the estates\n",
    "\n",
    "## PUT HERE YOUR CODE:\n"
   ]
  }
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