project.es.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "66fac57f",
      "metadata": {},
      "source": [
        "# Prácticas básicas con Python\n",
        "\n",
        "#### Ejercicio 00. Declaración de Variables\n",
        "\n",
        "Define las siguientes variables con valores que tú elijas:\n",
        "\n",
        "- Una variable que almacene tu nombre (texto).\n",
        "- Una variable que almacene tu edad (número entero).\n",
        "- Una variable que indique si te gusta la programación (verdadero o falso).\n",
        "- Una variable que almacene tu calificación promedio (número decimal).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4eca514d",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Declara aquí tus variables.\n",
        "\n",
        "#Ejemplo\n",
        "saludo = \"Hola mundo\" "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21e43590",
      "metadata": {},
      "source": [
        "- Crea una lista con tus cinco números favoritos e imprimelo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "69f9e072",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Lista de números favoritos"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9946452f",
      "metadata": {},
      "source": [
        "- Crea un diccionario que guarde la información de un estudiante e imprimelo:\n",
        "\n",
        "        - Nombre\n",
        "        - Edad\n",
        "        - Calificación final"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d30fb44",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Diccionario del estudiante"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "91eb860d",
      "metadata": {},
      "source": [
        "#### Ejercicio 01. Análisis de datos básico con estructuras nativas de python.\n",
        "Crea una lista con las calificaciones de 5 estudiantes: [8.5, 9.2, 7.8, 8.9, 10].\n",
        "\n",
        "- Calcula el promedio de las calificaciones."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "41b747dc",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Código"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "699ac3a6",
      "metadata": {},
      "source": [
        "- Encuentra la calificación más alta y la más baja."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0224afb4",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Código"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "innocent-university",
      "metadata": {},
      "source": [
        "# Limpieza de bienes raíces con Pandas para un análisis eficiente \n",
        "\n",
        "Este es un conjunto de datos (dataset) reales que fue descargado usando técnicas de web scraping. La data contiene registros de **Fotocasa**, el cual es uno de los sitios más populares de bienes raíces en España. Por favor no hagas esto (web scraping) a no ser que sea para propósitos académicos.\n",
        "\n",
        "El dataset fue descargado hace algunos años por Henry Navarro y en ningún caso se obtuvo beneficio económico de ello.\n",
        "\n",
        "Contiene miles de datos de casas reales publicadas en la web www.fotocasa.com. Tu objetivo es extraer tanta información como sea posible con el conocimiento que tienes hasta ahora de ciencia de datos.\n",
        "\n",
        "¡Comencemos!\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "- Primero leamos y exploraremos el dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "frank-heath",
      "metadata": {},
      "outputs": [
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            "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",
        "# Leer el archivo CSV\n",
        "ds = pd.read_csv('assets/real_estate.csv', sep=';') # Este archivo CSV contiene puntos y comas en lugar de comas como separadores\n",
        "ds # mostramos todo"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "19bc6aa8",
      "metadata": {},
      "source": [
        "- Muestra las primeras filas del archivo CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93434fb6",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Mostrar las primeras filas"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a1095c6b",
      "metadata": {},
      "source": [
        "Perfecto, esto fue una pequeña practica. ¡Ahora empecemos con los verdaderos ejercicios!"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "latin-guest",
      "metadata": {},
      "source": [
        "#### Ejercicio 01. ¿Cuál es la casa más cara en todo el dataset? (★☆☆)\n",
        "\n",
        "Imprime la dirección y el precio de la casa seleccionada. Por ejemplo:\n",
        "\n",
        "`La casa con dirección en Calle del Prado, Nº20 es la más cara y su precio es de 5000000 USD`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "developing-optimum",
      "metadata": {},
      "outputs": [],
      "source": [
        "# TODO"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "lesser-cosmetic",
      "metadata": {},
      "source": [
        "#### Ejercicio 02. ¿Cuál es la casa más barata del dataset? (★☆☆)\n",
        "\n",
        "Este ejercicio es similar al ejercicio anterior, solo que ahora buscamos la casa con el precio más bajo. Recuerda imprimir la dirección y el precio de la casa seleccionada. Por ejemplo:\n",
        "\n",
        "`La casa con dirección en Calle Alcalá, Nº58 es la más barata y su precio es de 12000 USD`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "lovely-oasis",
      "metadata": {},
      "outputs": [],
      "source": [
        "# TODO"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "compliant-fellowship",
      "metadata": {},
      "source": [
        "#### Ejercicio 03. ¿Cuál es la casa más grande y la más pequeña del dataset? (★☆☆)\n",
        "\n",
        "Imprime la dirección y el área de las casas seleccionadas. Por ejemplo:\n",
        "\n",
        "`La casa más grande está ubicada en Calle Gran Vía, Nº38 y su superficie es de 5000 metros`\n",
        "\n",
        "`La casa más pequeña está ubicada en Calle Mayor, Nº12 y su superficie es de 200 metros`\n",
        "\n",
        "Este ejercicio es similar al anterior, pero buscamos las casas más grandes y pequeñas en base a su superficie."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "every-tiffany",
      "metadata": {},
      "outputs": [],
      "source": [
        "# TODO"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "danish-spirit",
      "metadata": {},
      "source": [
        "#### Ejercicio 04. ¿Cuantas poblaciones contiene el dataset? (★☆☆)\n",
        "\n",
        "Cuenta el número de poblaciones únicas en la columna 'level5' e imprime el nombre de las poblaciones separadas por coma. Por ejemplo:\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": [
        "#### Ejercicio 05. ¿El dataset contiene valores nulos (NAs)? (★☆☆)\n",
        "\n",
        "Imprima un booleano (`True` o `False`) para verificar si hay valores nulos seguido de las columnas que contiene el NAs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "transparent-poetry",
      "metadata": {},
      "outputs": [],
      "source": [
        "# TODO"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "italic-hydrogen",
      "metadata": {},
      "source": [
        "#### Ejercicio 06. Elimina los valores nulos (NAs) del dataset, si aplica (★★☆)\n",
        "\n",
        "Despues de eliminar los valores nulos, compara el tamaño del DataFrame antes y después de la eliminación.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "administrative-roads",
      "metadata": {},
      "outputs": [],
      "source": [
        "# TODO"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "middle-china",
      "metadata": {},
      "source": [
        "#### Ejercicio 07. ¿Cuál es la media de precios en la población de \"Arroyomolinos (Madrid)\"? (★★☆)\n",
        "\n",
        "Imprima el valor obtenido de la columna level5."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "nuclear-belief",
      "metadata": {},
      "outputs": [],
      "source": [
        "# TODO"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "concerned-radical",
      "metadata": {},
      "source": [
        "#### Ejercicio 08. Trazar el histograma de los precios para la población de \"Arroyomolinos (Madrid)\" y explica qué observas (★★☆)\n",
        "\n",
        "Imprime el histograma de los precios y escribe en la celda del Markdown un breve análisis del trazado.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "sudden-message",
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Trazar el histograma de precios"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "impressed-combination",
      "metadata": {},
      "source": [
        "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda."
      ]
    }
  ],
  "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": "3.11.3"
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