03_Pandas_Objetos_Basicos.ipynb

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    "# 3 - Pandas, Objetos Básicos\n",
    "\n",
    "\n",
    "* ***Pandas*** es una ***librería de python*** destinada al **análisis de datos**, que proporciona unas ***estructuras de datos flexibles*** y que permiten trabajar con ellos de forma muy eficiente. \n",
    "\n",
    "\n",
    "* Pandas ofrece las siguientes estructuras de datos:\n",
    "\n",
    "    - **Series:** Son arrays unidimensionales con indexación (arrays con índice o etiquetados), similar a los diccionarios. Pueden generarse a partir de diccionarios o de listas.\n",
    "    \n",
    "    - **DataFrame:** Son estructuras de datos similares a las tablas de bases de datos relacionales como SQL.\n",
    "    \n",
    "    - **Panel, Panel4D y PanelND:** Estas estructuras de datos permiten trabajar con más de dos dimensiones.\n",
    "\n",
    "\n",
    "* En este Notebook vamos a ver de manera muy sencilla:\n",
    "\n",
    "    1. ***Importar de pandas***\n",
    "    \n",
    "    2. ***Series***:\n",
    "        + Series con índice por defecto\n",
    "        + Series con índice indicado de manera explicita\n",
    "        + Pasar de un Diccionario a una Serie\n",
    "        + Series Temporales\n",
    "        \n",
    "    3. ***DataFrame***:\n",
    "        + Creacción de un DataFrame\n",
    "        + Lectura de un fichero de texto a DataFrame\n",
    "        + Lectura de un fichero CSV a DataFrame\n",
    "        + Lectura de un fichero de texto desde una URL a DataFrame\n",
    "        + Escritura de un DataFrame en csv\n",
    "        + Escritura de un DataFrame en Excel\n",
    "        + Escritura de un DataFrame en formato JSON\n",
    "\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "\n",
    "# 1. Importar Pandas\n",
    "\n",
    "\n",
    "* Por convenio (de la comunidad de desarrollares) se pone \"**pd**\" como alias de la librería Pandas, importando esta librería de la siguiente manera:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "# 2. Series\n",
    "\n",
    "\n",
    "* Son arrays unidimensionales con indexación (arrays con índice o etiquetados), similar a los diccionarios. \n",
    "\n",
    "\n",
    "* Pueden generarse a partir de diccionarios (si los índices son etiquetados) o de listas (índices por defecto).\n",
    "\n",
    "\n",
    "* Enlace a la Documentación: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html\n",
    "\n",
    "\n",
    "* Las series pueden recibir los siguientes parámetros:\n",
    "\n",
    "    + **data**: Los datos de la serie.\n",
    "    \n",
    "    + **index**: Índice relacionado con cada datos de la serie.\n",
    "    \n",
    "    + **dtype**: Tipo de datos de los valores de la serie.\n",
    "    \n",
    "\n",
    "```python\n",
    "serie = pd.Series(data, index=index, dtype=dtype)\n",
    "```\n",
    "    \n",
    "\n",
    "* A continuación mostramos algunos ejemplos para ver como se pueden crear las series y las estructuras que tienen.\n",
    "\n",
    "\n",
    "### - Series con índice por defecto\n",
    "\n",
    "\n",
    "* Vamos a crear una serie con los nombres de los futbolistas campeones del mundo de la selección española que fueron titulares en la final del campeonato del mundo de fútbol del 2010.\n",
    "\n",
    "\n",
    "* En este caso solo pasamos los valores de la serie, por lo que nos pondrá los índices por defecto (empezando a contar desde cero)."
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Casillas\n",
       "1              Ramos\n",
       "2              Pique\n",
       "3              Puyol\n",
       "4          Capdevila\n",
       "5        Xabi Alonso\n",
       "6           Busquets\n",
       "7     Xavi Hernandez\n",
       "8            Pedrito\n",
       "9            Iniesta\n",
       "10             Villa\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futbolistas = pd.Series(['Casillas', 'Ramos', 'Pique', 'Puyol', 'Capdevila', 'Xabi Alonso', 'Busquets', \n",
    "                         'Xavi Hernandez', 'Pedrito', 'Iniesta', 'Villa'])\n",
    "futbolistas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Series con índice indicado de manera explicita\n",
    "\n",
    "\n",
    "* Creamos la serie con los nombres de los futbolistas como valores y como índice su número de dorsal.\n",
    "\n",
    "\n",
    "* En la serie no puede haber dos valores con el mismo índice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "1           Casillas\n",
       "15             Ramos\n",
       "3              Pique\n",
       "5              Puyol\n",
       "11         Capdevila\n",
       "14       Xabi Alonso\n",
       "16          Busquets\n",
       "8     Xavi Hernandez\n",
       "18           Pedrito\n",
       "6            Iniesta\n",
       "7              Villa\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futbolistas = pd.Series(['Casillas', 'Ramos', 'Pique', 'Puyol', 'Capdevila', 'Xabi Alonso', 'Busquets', \n",
    "                         'Xavi Hernandez', 'Pedrito', 'Iniesta', 'Villa'], \n",
    "                        index=[1, 15, 3, 5, 11, 14, 16, 8, 18, 6, 7])\n",
    "\n",
    "futbolistas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Pasar de un Diccionario a una Serie\n",
    "\n",
    "\n",
    "* Dado que los diccionarios en python son unas estructuras similares a las series (pares clave, valor), podemos construir una serie a partir de un diccionario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1           Casillas\n",
       "15             Ramos\n",
       "3              Pique\n",
       "5              Puyol\n",
       "11         Capdevila\n",
       "14       Xabi Alonso\n",
       "16          Busquets\n",
       "8     Xavi Hernandez\n",
       "18           Pedrito\n",
       "6            Iniesta\n",
       "7              Villa\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_futbolistas = {1: 'Casillas', 15: 'Ramos', 3: 'Pique', 5: 'Puyol', 11: 'Capdevila', 14: 'Xabi Alonso',\n",
    "               16: 'Busquets', 8: 'Xavi Hernandez', 18: 'Pedrito', 6: 'Iniesta', 7: 'Villa'}\n",
    "\n",
    "futbolistas_to_series = pd.Series(dict_futbolistas)\n",
    "\n",
    "futbolistas_to_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Series Temporales\n",
    "\n",
    "\n",
    "* Un caso de uso muy habitual a la hora de trabajar con series, son las series temporales que tienen la peculiaridad de tener:\n",
    "\n",
    "    + ***Índice***: Un instante temporal (timestamp)\n",
    "    + ***Valor***\n",
    "    \n",
    "\n",
    "* Un ejemplo de serie temporal sería el siguiente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1\n",
       "2000-01-02    2\n",
       "2000-01-03    3\n",
       "2000-01-04    4\n",
       "2000-01-05    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "time_serie = pd.Series([1, 2, 3, 4, 5], \n",
    "                       index=pd.DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04', '2000-01-05']))\n",
    "time_serie"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "# 3. DataFrame\n",
    "\n",
    "* Un ***DataFrame*** es una ***estructura de datos similar a una tabla de una base de datos relacionar, una tabla de excel, etc.*** y como tal se pueden hacer muchas ***operaciones como las que se harían con consultas a tablas de bases de datos o en excel***.\n",
    "\n",
    "\n",
    "* Para construir un DataFrame se puede hacer de diferentes formas, como por ejemplo a partir de una lista, de un diccionario, de una Serie, de otro DataFrame, leyendo una tabla excel, de ficheros csv, json, xml, etc. \n",
    "\n",
    "\n",
    "* Enlace a la documentación: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\n",
    "\n",
    "\n",
    "* Para construir un DataFrame puede recibir los siguientes parámetros:\n",
    "    + ***data***: Datos del DataFrame (\"contenido de la tabla\")\n",
    "    + ***index***: Índice de cada una de las filas del DataFrame. Si no se indica nada se pondrán unos índices por defecto.\n",
    "    + ***columns***: Nombre de las columnas del DataFrame (\"nombre de las columnas de la tabla\"). Si no se indica nada se pondrán unos nombres a las columnas por defecto.\n",
    "    \n",
    "```python\n",
    "df = pd.DataFrame(data, index=index, columns=dtype)\n",
    "```\n",
    "\n",
    "\n",
    "* A continuación vamos a ver algunos ejemplos de como crear DataFrames de diferentes maneras. No se van a mostrar todas las formas posibles de crear un DataFrame a partir de una fuente de datos (ya que esto se puede hacer de muchísimas maneras) pero si las más comunes:\n",
    "\n",
    "\n",
    "### - Creacción de un DataFrame\n",
    "\n",
    "\n",
    "* A continuación se muestra como crear de manera sencilla un DataFrame en el que vamos a tener los datos de los futbolístas de la selección Española que gano el mundial de fútbol del 2010:\n",
    "    + Nombre\n",
    "    + Demarcación\n",
    "    + Club al que pertenences\n",
    "    + ***Índice***: Dorsal del jugador\n",
    "    + ***Nombres de las columnas*** que indica el significado del dato."
   ]
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>demarcation</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casillas</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Real Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ramos</td>\n",
       "      <td>Right back</td>\n",
       "      <td>Real Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pique</td>\n",
       "      <td>Centre-back</td>\n",
       "      <td>FC Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Puyol</td>\n",
       "      <td>Centre-back</td>\n",
       "      <td>FC Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Capdevila</td>\n",
       "      <td>Left back</td>\n",
       "      <td>Villareal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Xabi Alonso</td>\n",
       "      <td>Defensive midfield</td>\n",
       "      <td>Real Madrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Busquets</td>\n",
       "      <td>Defensive midfield</td>\n",
       "      <td>FC Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Xavi Hernandez</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>FC Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pedrito</td>\n",
       "      <td>Left winger</td>\n",
       "      <td>FC Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Iniesta</td>\n",
       "      <td>Right winger</td>\n",
       "      <td>FC Barcelona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Villa</td>\n",
       "      <td>Centre forward</td>\n",
       "      <td>FC Barcelona</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name         demarcation          team\n",
       "1         Casillas          Goalkeeper   Real Madrid\n",
       "15           Ramos          Right back   Real Madrid\n",
       "3            Pique         Centre-back  FC Barcelona\n",
       "5            Puyol         Centre-back  FC Barcelona\n",
       "11       Capdevila           Left back     Villareal\n",
       "14     Xabi Alonso  Defensive midfield   Real Madrid\n",
       "16        Busquets  Defensive midfield  FC Barcelona\n",
       "8   Xavi Hernandez          Midfielder  FC Barcelona\n",
       "18         Pedrito         Left winger  FC Barcelona\n",
       "6          Iniesta        Right winger  FC Barcelona\n",
       "7            Villa      Centre forward  FC Barcelona"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_futbolistas = pd.DataFrame(\n",
    "    {\n",
    "        'name': ['Casillas', 'Ramos', 'Pique', 'Puyol', 'Capdevila', 'Xabi Alonso', 'Busquets', 'Xavi Hernandez',\n",
    "                 'Pedrito', 'Iniesta', 'Villa'],\n",
    "        'demarcation': ['Goalkeeper', 'Right back', 'Centre-back', 'Centre-back', 'Left back', 'Defensive midfield',\n",
    "                        'Defensive midfield', 'Midfielder', 'Left winger', 'Right winger', 'Centre forward'],\n",
    "        'team': ['Real Madrid', 'Real Madrid', 'FC Barcelona', 'FC Barcelona', 'Villareal', 'Real Madrid',\n",
    "                 'FC Barcelona', 'FC Barcelona', 'FC Barcelona', 'FC Barcelona', 'FC Barcelona']\n",
    "    }, \n",
    "    index=[1, 15, 3, 5, 11, 14, 16, 8, 18, 6, 7], \n",
    "    columns=['name', 'demarcation', 'team'] \n",
    ")\n",
    "df_futbolistas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Lectura de un fichero de texto a DataFrame\n",
    "\n",
    "\n",
    "* Una de las funciones más habituales es la de ***pasar el contenido de un fichero a un DataFrame***.\n",
    "\n",
    "\n",
    "* Pandas dispone de la función \"*read_table()*\" para leer cualquier fichero de texto plano. Esta función tiene muchísimos parámetros ya que la estructura del fichero de texto puede tener infinidad de formatos y estructuras. Por simplicidad se pasan a mostrar 3 parámetros (quizás los más utilizados):\n",
    "\n",
    "    + ***filepath_or_buffer***: ruta del fichero a leer\n",
    "    + ***sep***: Caracter separado de los datos por columnas\n",
    "    + ***header***: para indicar el nombre de las columnas en modo lista. Si la primera línea del fichero tiene el nombre de las columnas, este parámetro debe de tener el valor '0'.\n",
    "    \n",
    "```python\n",
    "pd.read_table('file_name', sep=',', header=0)\n",
    "```\n",
    "\n",
    "\n",
    "* Enlace a la documentación: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_table.html\n",
    "\n",
    "\n",
    "* Veamos a continuación como leer un fichero de texto plano, que tiene como separador el caracter de la coma y que la primera fila del fichero tiene el nombre de las columnas del DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idPartido</th>\n",
       "      <th>temporada</th>\n",
       "      <th>division</th>\n",
       "      <th>jornada</th>\n",
       "      <th>EquipoLocal</th>\n",
       "      <th>EquipoVisitante</th>\n",
       "      <th>golesLocal</th>\n",
       "      <th>golesVisitante</th>\n",
       "      <th>fecha</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36306</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>Las Palmas</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18/08/2017</td>\n",
       "      <td>1503007200</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36307</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Leganes</td>\n",
       "      <td>Alaves</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18/08/2017</td>\n",
       "      <td>1503007200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36308</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Celta de Vigo</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>19/08/2017</td>\n",
       "      <td>1503093600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36309</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Girona</td>\n",
       "      <td>Atletico de Madrid</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>19/08/2017</td>\n",
       "      <td>1503093600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36310</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>Espanol</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19/08/2017</td>\n",
       "      <td>1503093600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36311</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Levante</td>\n",
       "      <td>Villarreal</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20/08/2017</td>\n",
       "      <td>1503180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36312</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Atletico de Bilbao</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20/08/2017</td>\n",
       "      <td>1503180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36313</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Malaga</td>\n",
       "      <td>Eibar</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20/08/2017</td>\n",
       "      <td>1503180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36314</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Betis</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>20/08/2017</td>\n",
       "      <td>1503180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>36315</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Deportivo</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20/08/2017</td>\n",
       "      <td>1503180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   idPartido temporada  division  jornada         EquipoLocal  \\\n",
       "0      36306   2017-18         1        1            Valencia   \n",
       "1      36307   2017-18         1        1             Leganes   \n",
       "2      36308   2017-18         1        1       Celta de Vigo   \n",
       "3      36309   2017-18         1        1              Girona   \n",
       "4      36310   2017-18         1        1             Sevilla   \n",
       "5      36311   2017-18         1        1             Levante   \n",
       "6      36312   2017-18         1        1  Atletico de Bilbao   \n",
       "7      36313   2017-18         1        1              Malaga   \n",
       "8      36314   2017-18         1        1           Barcelona   \n",
       "9      36315   2017-18         1        1           Deportivo   \n",
       "\n",
       "      EquipoVisitante  golesLocal  golesVisitante       fecha   timestamp  \n",
       "0          Las Palmas           1               0  18/08/2017  1503007200  \n",
       "1              Alaves           1               0  18/08/2017  1503007200  \n",
       "2       Real Sociedad           2               3  19/08/2017  1503093600  \n",
       "3  Atletico de Madrid           2               2  19/08/2017  1503093600  \n",
       "4             Espanol           1               1  19/08/2017  1503093600  \n",
       "5          Villarreal           1               0  20/08/2017  1503180000  \n",
       "6              Getafe           0               0  20/08/2017  1503180000  \n",
       "7               Eibar           0               1  20/08/2017  1503180000  \n",
       "8               Betis           2               0  20/08/2017  1503180000  \n",
       "9         Real Madrid           0               3  20/08/2017  1503180000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_partidos = pd.read_table('./data/Partidos_Futbol_Temporada_2017_18.csv', \n",
    "                            sep=',', \n",
    "                            header=0)\n",
    "\n",
    "df_partidos.head(10) # Mostramos solo los 10 primeros partidos del DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Lectura de un fichero CSV a DataFrame\n",
    "\n",
    "\n",
    "* Dado que existen muchos estandares de ficheros (CSV, JSON, parquet, pickle, etc.), ***Pandas permite leer estos ficheros y pasarlos directamente a un DataFrame con funciones propias***.\n",
    "\n",
    "\n",
    "* En realidad estas funciones que leen ficheros de diferentes formatos es una especialización de la función '*read_table()*', ajustando sus parámetros a la especificación de los estandares de los formatos de los ficheros.\n",
    "\n",
    "\n",
    "* En el siguiente enlace se puede ver los tipos de ficheros que Pandas es capaz de parsear y pasarlo a un DataFrame: https://pandas.pydata.org/pandas-docs/stable/reference/io.html\n",
    "\n",
    "\n",
    "* En particular ***uno de los formatos más habituales para trabajar es el formato CSV***, que consiste en un texto plano cuyas columnas están separadas por comas y los datos de cada posición de la tabla pueden venir o no entre comillas.\n",
    "\n",
    "\n",
    "* Pandas dispone de la función \"***read_csv()***\" para leer directamente los ficheros de texto con ese formato.\n",
    "\n",
    "\n",
    "* Veamos a continuación un ejemplo, en el que solo es necesario indicarle si en el CSV la primera fila contiene el nombre de las columnas o no:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idPartido</th>\n",
       "      <th>temporada</th>\n",
       "      <th>division</th>\n",
       "      <th>jornada</th>\n",
       "      <th>EquipoLocal</th>\n",
       "      <th>EquipoVisitante</th>\n",
       "      <th>golesLocal</th>\n",
       "      <th>golesVisitante</th>\n",
       "      <th>fecha</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>37138</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Granada</td>\n",
       "      <td>Cadiz</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>37139</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Cordoba</td>\n",
       "      <td>Sporting de Gijon</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>37140</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Tenerife</td>\n",
       "      <td>Albacete</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>37141</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Lugo</td>\n",
       "      <td>Almeria</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>37142</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Gimnastic de Tarragona</td>\n",
       "      <td>Rayo Vallecano</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>37143</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Valladolid</td>\n",
       "      <td>Osasuna</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>37144</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Oviedo</td>\n",
       "      <td>Huesca</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>37145</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Alcorcon</td>\n",
       "      <td>Reus</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>37146</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Numancia</td>\n",
       "      <td>Cultural Leonesa</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>37147</td>\n",
       "      <td>2017-18</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Barcelona Atletic</td>\n",
       "      <td>Zaragoza</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>02/06/2018</td>\n",
       "      <td>1527890400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     idPartido temporada  division  jornada             EquipoLocal  \\\n",
       "832      37138   2017-18         2       42                 Granada   \n",
       "833      37139   2017-18         2       42                 Cordoba   \n",
       "834      37140   2017-18         2       42                Tenerife   \n",
       "835      37141   2017-18         2       42                    Lugo   \n",
       "836      37142   2017-18         2       42  Gimnastic de Tarragona   \n",
       "837      37143   2017-18         2       42              Valladolid   \n",
       "838      37144   2017-18         2       42                  Oviedo   \n",
       "839      37145   2017-18         2       42                Alcorcon   \n",
       "840      37146   2017-18         2       42                Numancia   \n",
       "841      37147   2017-18         2       42       Barcelona Atletic   \n",
       "\n",
       "       EquipoVisitante  golesLocal  golesVisitante       fecha   timestamp  \n",
       "832              Cadiz           2               1  02/06/2018  1527890400  \n",
       "833  Sporting de Gijon           3               0  02/06/2018  1527890400  \n",
       "834           Albacete           1               1  02/06/2018  1527890400  \n",
       "835            Almeria           1               1  02/06/2018  1527890400  \n",
       "836     Rayo Vallecano           2               0  02/06/2018  1527890400  \n",
       "837            Osasuna           2               0  02/06/2018  1527890400  \n",
       "838             Huesca           2               1  02/06/2018  1527890400  \n",
       "839               Reus           3               0  02/06/2018  1527890400  \n",
       "840   Cultural Leonesa           2               1  02/06/2018  1527890400  \n",
       "841           Zaragoza           0               2  02/06/2018  1527890400  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_partidos = pd.read_csv('./data/Partidos_Futbol_Temporada_2017_18.csv', \n",
    "                          header=0)\n",
    "\n",
    "df_partidos.tail(10) # Mostramos solo los 10 últimos partidos del DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Lectura de un fichero de texto desde una URL a DataFrame\n",
    "\n",
    "\n",
    "* Pandas permite también leer ficheros obtenidos haciendo una petición http.\n",
    "\n",
    "\n",
    "* En el siguiente ejemplo mostramos como somos capaces de leer un csv desde una dirección web:\n",
    "\n",
    "\n",
    "##### NOTA: El significado de cada una de las columnas lo podeis obtener a partir del siguiente enlace: http://www.football-data.co.uk/notes.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Div</th>\n",
       "      <th>Date</th>\n",
       "      <th>HomeTeam</th>\n",
       "      <th>AwayTeam</th>\n",
       "      <th>FTHG</th>\n",
       "      <th>FTAG</th>\n",
       "      <th>FTR</th>\n",
       "      <th>HTHG</th>\n",
       "      <th>HTAG</th>\n",
       "      <th>HTR</th>\n",
       "      <th>HS</th>\n",
       "      <th>AS</th>\n",
       "      <th>HST</th>\n",
       "      <th>AST</th>\n",
       "      <th>HF</th>\n",
       "      <th>AF</th>\n",
       "      <th>HC</th>\n",
       "      <th>AC</th>\n",
       "      <th>HY</th>\n",
       "      <th>AY</th>\n",
       "      <th>HR</th>\n",
       "      <th>AR</th>\n",
       "      <th>B365H</th>\n",
       "      <th>B365D</th>\n",
       "      <th>B365A</th>\n",
       "      <th>BWH</th>\n",
       "      <th>BWD</th>\n",
       "      <th>BWA</th>\n",
       "      <th>IWH</th>\n",
       "      <th>IWD</th>\n",
       "      <th>IWA</th>\n",
       "      <th>PSH</th>\n",
       "      <th>PSD</th>\n",
       "      <th>PSA</th>\n",
       "      <th>WHH</th>\n",
       "      <th>WHD</th>\n",
       "      <th>WHA</th>\n",
       "      <th>VCH</th>\n",
       "      <th>VCD</th>\n",
       "      <th>VCA</th>\n",
       "      <th>Bb1X2</th>\n",
       "      <th>BbMxH</th>\n",
       "      <th>BbAvH</th>\n",
       "      <th>BbMxD</th>\n",
       "      <th>BbAvD</th>\n",
       "      <th>BbMxA</th>\n",
       "      <th>BbAvA</th>\n",
       "      <th>BbOU</th>\n",
       "      <th>BbMx&gt;2.5</th>\n",
       "      <th>BbAv&gt;2.5</th>\n",
       "      <th>BbMx&lt;2.5</th>\n",
       "      <th>BbAv&lt;2.5</th>\n",
       "      <th>BbAH</th>\n",
       "      <th>BbAHh</th>\n",
       "      <th>BbMxAHH</th>\n",
       "      <th>BbAvAHH</th>\n",
       "      <th>BbMxAHA</th>\n",
       "      <th>BbAvAHA</th>\n",
       "      <th>PSCH</th>\n",
       "      <th>PSCD</th>\n",
       "      <th>PSCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SP1</td>\n",
       "      <td>17/08/2018</td>\n",
       "      <td>Betis</td>\n",
       "      <td>Levante</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.66</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3.70</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.60</td>\n",
       "      <td>4.90</td>\n",
       "      <td>1.69</td>\n",
       "      <td>4.19</td>\n",
       "      <td>5.11</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.75</td>\n",
       "      <td>1.67</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.20</td>\n",
       "      <td>40</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.68</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.25</td>\n",
       "      <td>4.95</td>\n",
       "      <td>38</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.06</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.59</td>\n",
       "      <td>4.42</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SP1</td>\n",
       "      <td>17/08/2018</td>\n",
       "      <td>Girona</td>\n",
       "      <td>Valladolid</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>20</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3.60</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3.60</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.60</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.80</td>\n",
       "      <td>40</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.78</td>\n",
       "      <td>3.83</td>\n",
       "      <td>3.60</td>\n",
       "      <td>5.27</td>\n",
       "      <td>4.79</td>\n",
       "      <td>38</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.71</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.76</td>\n",
       "      <td>3.57</td>\n",
       "      <td>5.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SP1</td>\n",
       "      <td>18/08/2018</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Alaves</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.11</td>\n",
       "      <td>10.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1.11</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1.12</td>\n",
       "      <td>9.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1.11</td>\n",
       "      <td>11.27</td>\n",
       "      <td>25.40</td>\n",
       "      <td>1.08</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>10.5</td>\n",
       "      <td>34.00</td>\n",
       "      <td>40</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.10</td>\n",
       "      <td>11.50</td>\n",
       "      <td>9.82</td>\n",
       "      <td>41.00</td>\n",
       "      <td>25.67</td>\n",
       "      <td>32</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.34</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.18</td>\n",
       "      <td>19</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.10</td>\n",
       "      <td>11.85</td>\n",
       "      <td>32.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SP1</td>\n",
       "      <td>18/08/2018</td>\n",
       "      <td>Celta</td>\n",
       "      <td>Espanol</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.40</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1.90</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4.10</td>\n",
       "      <td>1.93</td>\n",
       "      <td>3.64</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1.91</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.93</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.40</td>\n",
       "      <td>38</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.90</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.53</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.20</td>\n",
       "      <td>36</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.76</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>2.26</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.18</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SP1</td>\n",
       "      <td>18/08/2018</td>\n",
       "      <td>Villarreal</td>\n",
       "      <td>Sociedad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.04</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.90</td>\n",
       "      <td>40</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.93</td>\n",
       "      <td>3.76</td>\n",
       "      <td>37</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.81</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SP1</td>\n",
       "      <td>19/08/2018</td>\n",
       "      <td>Eibar</td>\n",
       "      <td>Huesca</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3.70</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.72</td>\n",
       "      <td>3.90</td>\n",
       "      <td>5.26</td>\n",
       "      <td>1.73</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.75</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.00</td>\n",
       "      <td>40</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3.93</td>\n",
       "      <td>3.77</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.08</td>\n",
       "      <td>37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.91</td>\n",
       "      <td>19</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.91</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.77</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SP1</td>\n",
       "      <td>19/08/2018</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>H</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>7.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.18</td>\n",
       "      <td>7.25</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1.20</td>\n",
       "      <td>6.50</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1.20</td>\n",
       "      <td>7.36</td>\n",
       "      <td>17.47</td>\n",
       "      <td>1.22</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>39</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.21</td>\n",
       "      <td>7.36</td>\n",
       "      <td>6.66</td>\n",
       "      <td>17.47</td>\n",
       "      <td>14.13</td>\n",
       "      <td>33</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.45</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.66</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.19</td>\n",
       "      <td>7.77</td>\n",
       "      <td>17.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SP1</td>\n",
       "      <td>19/08/2018</td>\n",
       "      <td>Vallecano</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.14</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.74</td>\n",
       "      <td>2.13</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.10</td>\n",
       "      <td>40</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.56</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.11</td>\n",
       "      <td>37</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.04</td>\n",
       "      <td>19</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.83</td>\n",
       "      <td>4.57</td>\n",
       "      <td>4.07</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SP1</td>\n",
       "      <td>20/08/2018</td>\n",
       "      <td>Ath Bilbao</td>\n",
       "      <td>Leganes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.30</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.78</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4.40</td>\n",
       "      <td>1.79</td>\n",
       "      <td>3.54</td>\n",
       "      <td>5.46</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.75</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.00</td>\n",
       "      <td>40</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.78</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.43</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.03</td>\n",
       "      <td>36</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.58</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.77</td>\n",
       "      <td>5.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SP1</td>\n",
       "      <td>20/08/2018</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>Ath Madrid</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3.20</td>\n",
       "      <td>2.55</td>\n",
       "      <td>3.12</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.57</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.45</td>\n",
       "      <td>39</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.14</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.51</td>\n",
       "      <td>36</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.59</td>\n",
       "      <td>17</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.12</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Div        Date     HomeTeam    AwayTeam  FTHG  FTAG FTR  HTHG  HTAG HTR  \\\n",
       "0  SP1  17/08/2018        Betis     Levante     0     3   A     0     1   A   \n",
       "1  SP1  17/08/2018       Girona  Valladolid     0     0   D     0     0   D   \n",
       "2  SP1  18/08/2018    Barcelona      Alaves     3     0   H     0     0   D   \n",
       "3  SP1  18/08/2018        Celta     Espanol     1     1   D     0     1   A   \n",
       "4  SP1  18/08/2018   Villarreal    Sociedad     1     2   A     1     1   D   \n",
       "5  SP1  19/08/2018        Eibar      Huesca     1     2   A     0     2   A   \n",
       "6  SP1  19/08/2018  Real Madrid      Getafe     2     0   H     1     0   H   \n",
       "7  SP1  19/08/2018    Vallecano     Sevilla     1     4   A     0     3   A   \n",
       "8  SP1  20/08/2018   Ath Bilbao     Leganes     2     1   H     1     1   D   \n",
       "9  SP1  20/08/2018     Valencia  Ath Madrid     1     1   D     0     1   A   \n",
       "\n",
       "   HS  AS  HST  AST  HF  AF  HC  AC  HY  AY  HR  AR  B365H  B365D  B365A  \\\n",
       "0  22   6    8    4  10  10   5   3   0   2   0   0   1.66   4.00   5.00   \n",
       "1  13   2    1    1  21  20   3   2   1   1   0   0   1.75   3.60   5.00   \n",
       "2  25   3    9    0   6  13   7   1   0   2   0   0   1.11  10.00  21.00   \n",
       "3  12  14    2    5  13  14   8   7   3   2   0   0   1.85   3.50   4.50   \n",
       "4  16   8    7    4  16  10   4   6   2   3   0   0   2.04   3.40   3.80   \n",
       "5  18   8    6    6  12  13   7   0   1   1   0   0   1.66   3.75   5.50   \n",
       "6  10   4    3    1  11  27   3   0   1   7   0   0   1.20   7.00  13.00   \n",
       "7  13  17    2    8   6  15   2   6   1   0   0   0   3.25   3.60   2.14   \n",
       "8  17  12    5    2  12  13   6   2   4   5   0   0   1.75   3.30   5.50   \n",
       "9  13   9    4    3  10  15   4  10   2   3   0   0   3.00   3.20   2.50   \n",
       "\n",
       "    BWH    BWD    BWA   IWH   IWD    IWA   PSH    PSD    PSA   WHH  WHD  \\\n",
       "0  1.70   3.70   5.25  1.75  3.60   4.90  1.69   4.19   5.11  1.67  3.9   \n",
       "1  1.75   3.50   5.25  1.80  3.60   4.50  1.80   3.70   4.99  1.75  3.6   \n",
       "2  1.11  10.00  20.00  1.12  9.00  20.00  1.11  11.27  25.40  1.08  9.0   \n",
       "3  1.91   3.40   4.25  1.90  3.50   4.10  1.93   3.64   4.27  1.91  3.5   \n",
       "4  2.05   3.30   3.90  2.00  3.40   3.80  2.06   3.51   3.91  2.05  3.3   \n",
       "5  1.70   3.70   5.25  1.70  3.75   5.00  1.72   3.90   5.26  1.73  3.6   \n",
       "6  1.18   7.25  16.00  1.20  6.50  15.00  1.20   7.36  17.47  1.22  6.0   \n",
       "7  3.50   3.50   2.10  3.50  3.40   2.10  3.46   3.74   2.13  3.30  3.7   \n",
       "8  1.78   3.50   5.00  1.85  3.50   4.40  1.79   3.54   5.46  1.80  3.4   \n",
       "9  2.85   3.25   2.55  2.85  3.20   2.55  3.12   3.18   2.57  3.00  3.2   \n",
       "\n",
       "     WHA   VCH   VCD    VCA  Bb1X2  BbMxH  BbAvH  BbMxD  BbAvD  BbMxA  BbAvA  \\\n",
       "0   4.75  1.67   4.2   5.20     40   1.75   1.68   4.25   4.00   5.25   4.95   \n",
       "1   4.60  1.80   3.7   4.80     40   1.85   1.78   3.83   3.60   5.27   4.79   \n",
       "2  29.00  1.10  10.5  34.00     40   1.13   1.10  11.50   9.82  41.00  25.67   \n",
       "3   4.00  1.93   3.5   4.40     38   1.97   1.90   3.73   3.53   4.50   4.20   \n",
       "4   3.60  2.05   3.5   3.90     40   2.11   2.03   3.62   3.43   3.93   3.76   \n",
       "5   4.75  1.70   3.8   5.00     40   1.76   1.70   3.93   3.77   5.50   5.08   \n",
       "6  13.00  1.20   7.0  13.00     39   1.24   1.21   7.36   6.66  17.47  14.13   \n",
       "7   2.05  3.40   3.6   2.10     40   3.53   3.38   3.75   3.56   2.20   2.11   \n",
       "8   4.75  1.80   3.4   5.00     40   1.85   1.78   3.64   3.43   5.50   5.03   \n",
       "9   2.40  3.00   3.2   2.45     39   3.12   2.99   3.29   3.14   2.61   2.51   \n",
       "\n",
       "   BbOU  BbMx>2.5  BbAv>2.5  BbMx<2.5  BbAv<2.5  BbAH  BbAHh  BbMxAHH  \\\n",
       "0    38      1.82      1.76      2.15      2.06    20  -0.75     1.89   \n",
       "1    38      2.21      2.13      1.78      1.71    20  -0.75     2.06   \n",
       "2    32      1.39      1.34      3.40      3.18    19  -2.50     1.95   \n",
       "3    36      2.13      2.06      1.84      1.76    18  -0.75     2.26   \n",
       "4    37      2.05      1.99      1.88      1.81    18  -0.25     1.76   \n",
       "5    37      1.95      1.88      1.98      1.91    19  -0.75     1.96   \n",
       "6    33      1.50      1.45      2.75      2.66    19  -1.75     1.85   \n",
       "7    37      1.83      1.76      2.13      2.04    19   0.25     2.08   \n",
       "8    36      2.49      2.35      1.64      1.58    18  -0.75     2.11   \n",
       "9    36      2.45      2.33      1.65      1.59    17   0.25     1.82   \n",
       "\n",
       "   BbAvAHH  BbMxAHA  BbAvAHA  PSCH   PSCD   PSCA  \n",
       "0     1.85     2.07     2.00  1.59   4.42   5.89  \n",
       "1     2.01     1.90     1.85  1.76   3.57   5.62  \n",
       "2     1.91     2.00     1.95  1.10  11.85  32.17  \n",
       "3     2.18     1.74     1.71  2.18   3.26   3.85  \n",
       "4     1.74     2.23     2.14  2.32   3.21   3.53  \n",
       "5     1.91     2.01     1.94  1.77   3.68   5.32  \n",
       "6     1.80     2.15     2.07  1.19   7.77  17.96  \n",
       "7     2.03     1.86     1.83  4.57   4.07   1.78  \n",
       "8     2.04     1.86     1.82  1.69   3.77   5.87  \n",
       "9     1.75     2.23     2.12  3.55   3.28   2.28  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Esta es una opción para que se visualicen todas las columnas del DataFrame\n",
    "pd.options.display.max_columns = None \n",
    "\n",
    "df_partidos_ext = pd.read_csv('https://www.football-data.co.uk/mmz4281/1819/SP1.csv',\n",
    "                              header=0)\n",
    "\n",
    "df_partidos_ext.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Escritura de un DataFrame en csv\n",
    "\n",
    "\n",
    "* Pandas dispone de una serie de funciones para exportar los DataFrames a ficheros de texto en diferentes formatos.\n",
    "\n",
    "\n",
    "* Enlace para ver los formatos en los que se puede exportar un DataFrame: https://pandas.pydata.org/pandas-docs/stable/reference/frame.html#serialization-io-conversion\n",
    "\n",
    "\n",
    "* Ejemplo de como exportar un DataFrame en formato csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_partidos.head(10) # A modo de ejemplo voy a exportar solo información de 10 partidos de fútbol\n",
    "\n",
    "df.to_csv('./data/resultados/ejemplo_export_DataFrame_csv.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Escritura de un DataFrame en Excel\n",
    "\n",
    "\n",
    "* Ejemplo de como exportar un DataFrame en formato excel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('./data/resultados/ejemplo_export_DataFrame_excel.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Escritura de un DataFrame en formato JSON\n",
    "\n",
    "\n",
    "* Ejemplo de como exportar un DataFrame en formato JSON:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_json('./data/resultados/ejemplo_export_DataFrame_JSON.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "*Este Notebook ha sido desarrollado por **Ricardo Moya García** y registrado en Safe Creative como ***Atribución-NoComercial-CompartirIgual***.*\n",
    "\n",
    "<img src=\"./imgs/CC_BY-NC-SA.png\" alt=\"CC BY-NC\">"
   ]
  }
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