ManejoDatosVC.ipynb

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  {
   "source": [
    "Buscar https://www.markdownguide.org/cheat-sheet/\n",
    "\n",
    "Buscar https://www.kaggle.com"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# Manejador de datos\n",
    "En Python, la libreria principal para manejar los datos es Pandas. Con ella podremos manejar Los DataFrames de una manera comoda y rapida.\n",
    "Recordad:\n",
    "- DataFrames: Es un conjunto de tados estructurados por secciones (columnas) y registros (filas)\n",
    "- DataSet: Es una extracción de secciones de un DataFrames (En si, tambien es un DataFrames)\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "source": [
    "## Importación\n",
    "Para importar los datos contenidos en un fichero, haremos uso del metodo read_<tipo>('dirección de la información')\n",
    "El usado en estos apuntes es el \"read_csv()\". Este leerá un archivo de texto plano en la cual la primera linea es la que contituye la cabecera y los seggmentos estan separados por \";\" y los registros por saltos de linea"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      pclass  survived                                             name  \\\n",
       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
       "1          1         1                   Allison, Master. Hudson Trevor   \n",
       "2          1         0                     Allison, Miss. Helen Loraine   \n",
       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
       "...      ...       ...                                              ...   \n",
       "1304       3         0                             Zabour, Miss. Hileni   \n",
       "1305       3         0                            Zabour, Miss. Thamine   \n",
       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
       "1307       3         0                              Zakarian, Mr. Ortin   \n",
       "1308       3         0                               Zimmerman, Mr. Leo   \n",
       "\n",
       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
       "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
       "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
       "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
       "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
       "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
       "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
       "\n",
       "       body                        home.dest  \n",
       "0       NaN                     St Louis, MO  \n",
       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
       "...     ...                              ...  \n",
       "1304  328.0                              NaN  \n",
       "1305    NaN                              NaN  \n",
       "1306  304.0                              NaN  \n",
       "1307    NaN                              NaN  \n",
       "1308    NaN                              NaN  \n",
       "\n",
       "[1309 rows x 14 columns]"
      ],
      "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>name</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>ticket</th>\n      <th>fare</th>\n      <th>cabin</th>\n      <th>embarked</th>\n      <th>boat</th>\n      <th>body</th>\n      <th>home.dest</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allen, Miss. Elisabeth Walton</td>\n      <td>female</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24160</td>\n      <td>211.3375</td>\n      <td>B5</td>\n      <td>S</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>St Louis, MO</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allison, Master. Hudson Trevor</td>\n      <td>male</td>\n      <td>0.9167</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>11</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Miss. Helen Loraine</td>\n      <td>female</td>\n      <td>2.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mr. Hudson Joshua Creighton</td>\n      <td>male</td>\n      <td>30.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>135.0</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n      <td>female</td>\n      <td>25.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1304</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zabour, Miss. Hileni</td>\n      <td>female</td>\n      <td>14.5000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2665</td>\n      <td>14.4542</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>328.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1305</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zabour, Miss. Thamine</td>\n      <td>female</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2665</td>\n      <td>14.4542</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1306</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zakarian, Mr. Mapriededer</td>\n      <td>male</td>\n      <td>26.5000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2656</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>304.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1307</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zakarian, Mr. Ortin</td>\n      <td>male</td>\n      <td>27.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2670</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1308</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zimmerman, Mr. Leo</td>\n      <td>male</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>315082</td>\n      <td>7.8750</td>\n      <td>NaN</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>1309 rows × 14 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "datos = pd.read_csv('Datos/titanic3.csv')\n",
    "datos"
   ]
  },
  {
   "source": [
    "Como hemos visto en la linea anterior, podemos mostrar el contenido del nuestro DataFarme. Sin embargo, si solo queremos hacer una visual de la estructura, es mas idoneo usar el metodo head(). Este nos devolverá los 5 primeros registros."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ],
      "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>name</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>ticket</th>\n      <th>fare</th>\n      <th>cabin</th>\n      <th>embarked</th>\n      <th>boat</th>\n      <th>body</th>\n      <th>home.dest</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allen, Miss. Elisabeth Walton</td>\n      <td>female</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24160</td>\n      <td>211.3375</td>\n      <td>B5</td>\n      <td>S</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>St Louis, MO</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allison, Master. Hudson Trevor</td>\n      <td>male</td>\n      <td>0.9167</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>11</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Miss. Helen Loraine</td>\n      <td>female</td>\n      <td>2.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mr. Hudson Joshua Creighton</td>\n      <td>male</td>\n      <td>30.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>135.0</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n      <td>female</td>\n      <td>25.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "datos.head() # Los 5 primeras filas (row)"
   ]
  },
  {
   "source": [
    "## Analisis estadistico\n",
    "Para hacer un analisis estadistico rapido y sencillo, el metodo describe() nos devolvera un DataFarme con la información de las secciones que se pueden cuantificar. Es decir, las columnas numericas estarán dentro de este analisis, y no las de texto. Esto es importante tenerlo en cuenta, poque si queremos hacer un analisis de una tabla de texto, lo mas idoneo sera binarizar (trasformar las diferentes opciones de valores en valores numericos unicos. Ej: Hombre=1, Mujer=2).\n",
    "Describe nos devolverá:\n",
    "- Count: Recuento de registros no NaN totales. Util para saber si la serie tiene mucha falta de información.\n",
    "- Mean: Media de todos los registros no NaN. Importante tenerlo en cuenta, ya que si queremos hacer un analisis mas exato puede que nos interese rellenar los registros vacios por la media de los que hay por ejemplo.\n",
    "- Std: Desviación estandar. La deviación media que tiene todos los registros con respecto a la media (mean). Será un  +/-.\n",
    "- Min: Nos indicará el valor minimo recogido.\n",
    "- Max: Nos indicará el valor maximo recogido.\n",
    "- 25-50-75%: Los percentiles (En este caso los cuartiles)."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            pclass     survived          age        sibsp        parch  \\\n",
       "count  1309.000000  1309.000000  1046.000000  1309.000000  1309.000000   \n",
       "mean      2.294882     0.381971    29.881135     0.498854     0.385027   \n",
       "std       0.837836     0.486055    14.413500     1.041658     0.865560   \n",
       "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
       "25%       2.000000     0.000000    21.000000     0.000000     0.000000   \n",
       "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
       "75%       3.000000     1.000000    39.000000     1.000000     0.000000   \n",
       "max       3.000000     1.000000    80.000000     8.000000     9.000000   \n",
       "\n",
       "              fare        body  \n",
       "count  1308.000000  121.000000  \n",
       "mean     33.295479  160.809917  \n",
       "std      51.758668   97.696922  \n",
       "min       0.000000    1.000000  \n",
       "25%       7.895800   72.000000  \n",
       "50%      14.454200  155.000000  \n",
       "75%      31.275000  256.000000  \n",
       "max     512.329200  328.000000  "
      ],
      "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>fare</th>\n      <th>body</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1309.000000</td>\n      <td>1309.000000</td>\n      <td>1046.000000</td>\n      <td>1309.000000</td>\n      <td>1309.000000</td>\n      <td>1308.000000</td>\n      <td>121.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2.294882</td>\n      <td>0.381971</td>\n      <td>29.881135</td>\n      <td>0.498854</td>\n      <td>0.385027</td>\n      <td>33.295479</td>\n      <td>160.809917</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.837836</td>\n      <td>0.486055</td>\n      <td>14.413500</td>\n      <td>1.041658</td>\n      <td>0.865560</td>\n      <td>51.758668</td>\n      <td>97.696922</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.166700</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>21.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.895800</td>\n      <td>72.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3.000000</td>\n      <td>0.000000</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14.454200</td>\n      <td>155.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>39.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>31.275000</td>\n      <td>256.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>80.000000</td>\n      <td>8.000000</td>\n      <td>9.000000</td>\n      <td>512.329200</td>\n      <td>328.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "datos.describe() # Muestra una descripción de los campos numericos"
   ]
  },
  {
   "source": [
    "## Definir cabecera\n",
    "Si nuestros datos no tuvieran cabecera, en la importación inticaremos que no tiene cabecera (header=None) e inidcaremos el nombre de los segmentos"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Clase  Supervivientes                                           Nombre  \\\n",
       "0         1               1                    Allen, Miss. Elisabeth Walton   \n",
       "1         1               1                   Allison, Master. Hudson Trevor   \n",
       "2         1               0                     Allison, Miss. Helen Loraine   \n",
       "3         1               0             Allison, Mr. Hudson Joshua Creighton   \n",
       "4         1               0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
       "...     ...             ...                                              ...   \n",
       "1304      3               0                             Zabour, Miss. Hileni   \n",
       "1305      3               0                            Zabour, Miss. Thamine   \n",
       "1306      3               0                        Zakarian, Mr. Mapriededer   \n",
       "1307      3               0                              Zakarian, Mr. Ortin   \n",
       "1308      3               0                               Zimmerman, Mr. Leo   \n",
       "\n",
       "        Sexo     Edad  sibsp  parch  Ticket    precio   Cabina Embarque Barco  \\\n",
       "0     female  29.0000      0      0   24160  211.3375       B5        S     2   \n",
       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S    11   \n",
       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S   NaN   \n",
       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S   NaN   \n",
       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S   NaN   \n",
       "...      ...      ...    ...    ...     ...       ...      ...      ...   ...   \n",
       "1304  female  14.5000      1      0    2665   14.4542      NaN        C   NaN   \n",
       "1305  female      NaN      1      0    2665   14.4542      NaN        C   NaN   \n",
       "1306    male  26.5000      0      0    2656    7.2250      NaN        C   NaN   \n",
       "1307    male  27.0000      0      0    2670    7.2250      NaN        C   NaN   \n",
       "1308    male  29.0000      0      0  315082    7.8750      NaN        S   NaN   \n",
       "\n",
       "      Cuerpo                          Destino  \n",
       "0        NaN                     St Louis, MO  \n",
       "1        NaN  Montreal, PQ / Chesterville, ON  \n",
       "2        NaN  Montreal, PQ / Chesterville, ON  \n",
       "3      135.0  Montreal, PQ / Chesterville, ON  \n",
       "4        NaN  Montreal, PQ / Chesterville, ON  \n",
       "...      ...                              ...  \n",
       "1304   328.0                              NaN  \n",
       "1305     NaN                              NaN  \n",
       "1306   304.0                              NaN  \n",
       "1307     NaN                              NaN  \n",
       "1308     NaN                              NaN  \n",
       "\n",
       "[1309 rows x 14 columns]"
      ],
      "text/html": "<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>Clase</th>\n      <th>Supervivientes</th>\n      <th>Nombre</th>\n      <th>Sexo</th>\n      <th>Edad</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>Ticket</th>\n      <th>precio</th>\n      <th>Cabina</th>\n      <th>Embarque</th>\n      <th>Barco</th>\n      <th>Cuerpo</th>\n      <th>Destino</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allen, Miss. Elisabeth Walton</td>\n      <td>female</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24160</td>\n      <td>211.3375</td>\n      <td>B5</td>\n      <td>S</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>St Louis, MO</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allison, Master. Hudson Trevor</td>\n      <td>male</td>\n      <td>0.9167</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>11</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Miss. Helen Loraine</td>\n      <td>female</td>\n      <td>2.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mr. Hudson Joshua Creighton</td>\n      <td>male</td>\n      <td>30.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>135.0</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n      <td>female</td>\n      <td>25.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1304</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zabour, Miss. Hileni</td>\n      <td>female</td>\n      <td>14.5000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2665</td>\n      <td>14.4542</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>328.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1305</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zabour, Miss. Thamine</td>\n      <td>female</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2665</td>\n      <td>14.4542</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1306</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zakarian, Mr. Mapriededer</td>\n      <td>male</td>\n      <td>26.5000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2656</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>304.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1307</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zakarian, Mr. Ortin</td>\n      <td>male</td>\n      <td>27.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2670</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1308</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zimmerman, Mr. Leo</td>\n      <td>male</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>315082</td>\n      <td>7.8750</td>\n      <td>NaN</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>1309 rows × 14 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "datosSin = pd.read_csv(\"titanic3.csv\",header=None,names=[\n",
    "    'Clase',\n",
    "    'Supervivientes',\n",
    "    'Nombre',\n",
    "    'Sexo',\n",
    "    'Edad',\n",
    "    'sibsp',\n",
    "    'parch',\n",
    "    'Ticket',\n",
    "    'precio',\n",
    "    'Cabina',\n",
    "    'Embarque',\n",
    "    'Barco',\n",
    "    'Cuerpo',\n",
    "    'Destino'\n",
    "    ])\n",
    "datosSin"
   ]
  },
  {
   "source": [
    "## Tipo de valor\n",
    "Si hacemos un type, veremos que tenemos un DataFrame"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "type(datosSin)"
   ]
  },
  {
   "source": [
    "## Creación de una serie\n",
    "Nos puede interesar crear una serie que luego añadamos a un DataFrame. Para ello haremos uso del constructor Series().\n",
    "En ella inicaremos los valores entre corchetes (pues es una colección de datos). Ademas indicaremos:\n",
    "- Indice: index=[]. (No necesario, pues si no se pone el hará una indexación automatica)\n",
    "- Nombre/Cabecera: name=\"Nombre\". No es necesaria pero si recomendable si luego se añadirá a un DataFrame (Devolverá un error si se pretende unir a un DataFrame una serie sin nombre)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2000    345\n",
       "2001    347\n",
       "2002    310\n",
       "2003    220\n",
       "2004    600\n",
       "Name: Población, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "poblacion = pd.Series([345,347,310,220,600],index=[2000,2001,2002,2003,2004], name=\"Población\")\n",
    "poblacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "type(poblacion)"
   ]
  },
  {
   "source": [
    "## Crear una serie directamente en un DataFrame\n",
    "Pandas nos permite crear series en DataFrame de una manera muy sencilla.\n",
    "Para ello, haremos una llamada a una columna que aun no exista e indicaremos el valor que tendrá:\n",
    "- DataFrame\\['nuevaColumna'] = valor"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      pclass  survived                                             name  \\\n",
       "0          1         1                    Allen, Miss. Elisabeth Walton   \n",
       "1          1         1                   Allison, Master. Hudson Trevor   \n",
       "2          1         0                     Allison, Miss. Helen Loraine   \n",
       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
       "...      ...       ...                                              ...   \n",
       "1304       3         0                             Zabour, Miss. Hileni   \n",
       "1305       3         0                            Zabour, Miss. Thamine   \n",
       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
       "1307       3         0                              Zakarian, Mr. Ortin   \n",
       "1308       3         0                               Zimmerman, Mr. Leo   \n",
       "\n",
       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
       "0     female  29.0000      0      0   24160  211.3375       B5        S    2   \n",
       "1       male   0.9167      1      2  113781  151.5500  C22 C26        S   11   \n",
       "2     female   2.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
       "3       male  30.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
       "4     female  25.0000      1      2  113781  151.5500  C22 C26        S  NaN   \n",
       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
       "1304  female  14.5000      1      0    2665   14.4542      NaN        C  NaN   \n",
       "1305  female      NaN      1      0    2665   14.4542      NaN        C  NaN   \n",
       "1306    male  26.5000      0      0    2656    7.2250      NaN        C  NaN   \n",
       "1307    male  27.0000      0      0    2670    7.2250      NaN        C  NaN   \n",
       "1308    male  29.0000      0      0  315082    7.8750      NaN        S  NaN   \n",
       "\n",
       "       body                        home.dest  test  \n",
       "0       NaN                     St Louis, MO     0  \n",
       "1       NaN  Montreal, PQ / Chesterville, ON     0  \n",
       "2       NaN  Montreal, PQ / Chesterville, ON     0  \n",
       "3     135.0  Montreal, PQ / Chesterville, ON     0  \n",
       "4       NaN  Montreal, PQ / Chesterville, ON     0  \n",
       "...     ...                              ...   ...  \n",
       "1304  328.0                              NaN     0  \n",
       "1305    NaN                              NaN     0  \n",
       "1306  304.0                              NaN     0  \n",
       "1307    NaN                              NaN     0  \n",
       "1308    NaN                              NaN     0  \n",
       "\n",
       "[1309 rows x 15 columns]"
      ],
      "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>name</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>ticket</th>\n      <th>fare</th>\n      <th>cabin</th>\n      <th>embarked</th>\n      <th>boat</th>\n      <th>body</th>\n      <th>home.dest</th>\n      <th>test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allen, Miss. Elisabeth Walton</td>\n      <td>female</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24160</td>\n      <td>211.3375</td>\n      <td>B5</td>\n      <td>S</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>St Louis, MO</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allison, Master. Hudson Trevor</td>\n      <td>male</td>\n      <td>0.9167</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>11</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Miss. Helen Loraine</td>\n      <td>female</td>\n      <td>2.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mr. Hudson Joshua Creighton</td>\n      <td>male</td>\n      <td>30.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>135.0</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n      <td>female</td>\n      <td>25.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1304</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zabour, Miss. Hileni</td>\n      <td>female</td>\n      <td>14.5000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2665</td>\n      <td>14.4542</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>328.0</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1305</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zabour, Miss. Thamine</td>\n      <td>female</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2665</td>\n      <td>14.4542</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1306</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zakarian, Mr. Mapriededer</td>\n      <td>male</td>\n      <td>26.5000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2656</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>304.0</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1307</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zakarian, Mr. Ortin</td>\n      <td>male</td>\n      <td>27.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2670</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1308</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zimmerman, Mr. Leo</td>\n      <td>male</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>315082</td>\n      <td>7.8750</td>\n      <td>NaN</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1309 rows × 15 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "datos['test'] = 0\n",
    "datos"
   ]
  },
  {
   "source": [
    "## Crear un DataFrame a partir de un diccionario\n",
    "Podemos crear un DataFrame a partir de un diccionario de una manera similar a la serie. Haremos uso del contructor DataFarme pero en vez de entregarle una lista, le entregarémos un diccionario. \n",
    "\n",
    "Posteriormente podremos indicaremos el nombre de las claves del diccionario que queramos utilizar para formar el DataFarme con el atributo \"column\". De no indicarlo, recogerá todas las claves."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "cumple = {\n",
    "    'nombre':['Sandra','Isma','Guillermo','Graimito','Juan'],\n",
    "    'sexo':['M','H','H','NaN','H'],\n",
    "    'dia':[250,11,49,30,59]\n",
    "}\n",
    "type(cumple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      nombre sexo\n",
       "0     Sandra    M\n",
       "1       Isma    H\n",
       "2  Guillermo    H\n",
       "3   Graimito  NaN\n",
       "4       Juan    H"
      ],
      "text/html": "<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>nombre</th>\n      <th>sexo</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sandra</td>\n      <td>M</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Isma</td>\n      <td>H</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Guillermo</td>\n      <td>H</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Graimito</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Juan</td>\n      <td>H</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "datos1 = pd.DataFrame(cumple,columns=['nombre','sexo'])\n",
    "datos1"
   ]
  },
  {
   "source": [
    "## Seleccionar columna\n",
    "Para seleccionar una columna podremos hacerlo mediante el uso de corchetes y dentro indicar el nombre de la columna entre comillas, o llamarlo como atributo del DataFrame (mas recomendado)\n",
    "- DataFrame\\['columna']\n",
    "- DataFrame.columna\n",
    "\n",
    "Este nos devolverá un objeto tipo Serie con todos sus metodos como el head()"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0       Sandra\n",
       "1         Isma\n",
       "2    Guillermo\n",
       "3     Graimito\n",
       "4         Juan\n",
       "Name: nombre, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "datos1['nombre']\n",
    "datos1.nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "type(datos.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0                      Allen, Miss. Elisabeth Walton\n",
       "1                     Allison, Master. Hudson Trevor\n",
       "2                       Allison, Miss. Helen Loraine\n",
       "3               Allison, Mr. Hudson Joshua Creighton\n",
       "4    Allison, Mrs. Hudson J C (Bessie Waldo Daniels)\n",
       "Name: name, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "datos.name.head()"
   ]
  },
  {
   "source": [
    "### Seleeción multiple\n",
    "Podemos hacer una selección multiple. Para ello hacemos la \"llamada\" a la función de selección con los corchetes y dentro le pasaremos una lista con los nombre de las columnas, por tanto con otros corchetes\n",
    "- DataFrame\\[\\['columna1','columna2']]\n",
    "\n",
    "En este caso, nos devolverá un DataFrame"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                 name      age\n",
       "0                       Allen, Miss. Elisabeth Walton  29.0000\n",
       "1                      Allison, Master. Hudson Trevor   0.9167\n",
       "2                        Allison, Miss. Helen Loraine   2.0000\n",
       "3                Allison, Mr. Hudson Joshua Creighton  30.0000\n",
       "4     Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  25.0000\n",
       "...                                               ...      ...\n",
       "1304                             Zabour, Miss. Hileni  14.5000\n",
       "1305                            Zabour, Miss. Thamine      NaN\n",
       "1306                        Zakarian, Mr. Mapriededer  26.5000\n",
       "1307                              Zakarian, Mr. Ortin  27.0000\n",
       "1308                               Zimmerman, Mr. Leo  29.0000\n",
       "\n",
       "[1309 rows x 2 columns]"
      ],
      "text/html": "<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>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Allen, Miss. Elisabeth Walton</td>\n      <td>29.0000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Allison, Master. Hudson Trevor</td>\n      <td>0.9167</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Allison, Miss. Helen Loraine</td>\n      <td>2.0000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Allison, Mr. Hudson Joshua Creighton</td>\n      <td>30.0000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n      <td>25.0000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1304</th>\n      <td>Zabour, Miss. Hileni</td>\n      <td>14.5000</td>\n    </tr>\n    <tr>\n      <th>1305</th>\n      <td>Zabour, Miss. Thamine</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1306</th>\n      <td>Zakarian, Mr. Mapriededer</td>\n      <td>26.5000</td>\n    </tr>\n    <tr>\n      <th>1307</th>\n      <td>Zakarian, Mr. Ortin</td>\n      <td>27.0000</td>\n    </tr>\n    <tr>\n      <th>1308</th>\n      <td>Zimmerman, Mr. Leo</td>\n      <td>29.0000</td>\n    </tr>\n  </tbody>\n</table>\n<p>1309 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "source": [
    "datos[['name','age']]"
   ]
  },
  {
   "source": [
    "\n",
    "## Filtros\n",
    "Para aplicar un filtro, en vez de poner el nombre de las columnas a recoger entre los corchetes aplicaremos una prueba lógica. Esta se aplicará a todas las filas y las que la cumpla serán devueltas.\n",
    "- DataFrame\\[ DataFrame.columna == <condición> ] (Ejemplo de aplicación)\n",
    "\n",
    "NOTA: Si va a haber mas de una condición, es necesario englobarlos dentro de unos parentersis\n",
    "- DataFrame\\[ (DataFrame.columna == <condición> & DataFrame.columna2 == <condición2>) ] \n",
    "\n",
    "A esta idea podemos desarrollarla mas mediante la utilización de la función map junto a una función lambda. De esta manera a cada vuelta aplicaremos un mapeo a las filas en la que esta devolverá un True o False que se lo ofrecerá la función lambda. Esto nos permitirá implementar de una manera mas sencia un filtrado mas complejo.\n",
    "- DataFrame\\[DataFrame.columna.map(lambda nombre: nombre == <condición>)]"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                   name   age     sex  \\\n",
       "0                         Allen, Miss. Elisabeth Walton  29.0  female   \n",
       "6                     Andrews, Miss. Kornelia Theodosia  63.0  female   \n",
       "8         Appleton, Mrs. Edward Dale (Charlotte Lamson)  53.0  female   \n",
       "11    Astor, Mrs. John Jacob (Madeleine Talmadge Force)  18.0  female   \n",
       "12                        Aubart, Mme. Leontine Pauline  24.0  female   \n",
       "...                                                 ...   ...     ...   \n",
       "1260                            Turja, Miss. Anna Sofia  18.0  female   \n",
       "1261                             Turkula, Mrs. (Hedwig)  63.0  female   \n",
       "1286    Whabee, Mrs. George Joseph (Shawneene Abi-Saab)  38.0  female   \n",
       "1290                   Wilkes, Mrs. James (Ellen Needs)  47.0  female   \n",
       "1300            Yasbeck, Mrs. Antoni (Selini Alexander)  15.0  female   \n",
       "\n",
       "      survived  \n",
       "0            1  \n",
       "6            1  \n",
       "8            1  \n",
       "11           1  \n",
       "12           1  \n",
       "...        ...  \n",
       "1260         1  \n",
       "1261         1  \n",
       "1286         1  \n",
       "1290         1  \n",
       "1300         1  \n",
       "\n",
       "[339 rows x 4 columns]"
      ],
      "text/html": "<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>age</th>\n      <th>sex</th>\n      <th>survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Allen, Miss. Elisabeth Walton</td>\n      <td>29.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Andrews, Miss. Kornelia Theodosia</td>\n      <td>63.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n      <td>53.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Astor, Mrs. John Jacob (Madeleine Talmadge Force)</td>\n      <td>18.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Aubart, Mme. Leontine Pauline</td>\n      <td>24.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1260</th>\n      <td>Turja, Miss. Anna Sofia</td>\n      <td>18.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1261</th>\n      <td>Turkula, Mrs. (Hedwig)</td>\n      <td>63.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1286</th>\n      <td>Whabee, Mrs. George Joseph (Shawneene Abi-Saab)</td>\n      <td>38.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1290</th>\n      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n      <td>47.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1300</th>\n      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n      <td>15.0</td>\n      <td>female</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>339 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "dataset = datos[['name','age','sex','survived']]\n",
    "\n",
    "dataset[(dataset.survived == 1) & (dataset.sex == 'female')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      pclass  survived                                             name  \\\n",
       "3          1         0             Allison, Mr. Hudson Joshua Creighton   \n",
       "4          1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
       "5          1         1                              Anderson, Mr. Harry   \n",
       "7          1         0                           Andrews, Mr. Thomas Jr   \n",
       "8          1         1    Appleton, Mrs. Edward Dale (Charlotte Lamson)   \n",
       "...      ...       ...                                              ...   \n",
       "1302       3         0                                Yousif, Mr. Wazli   \n",
       "1303       3         0                            Yousseff, Mr. Gerious   \n",
       "1306       3         0                        Zakarian, Mr. Mapriededer   \n",
       "1307       3         0                              Zakarian, Mr. Ortin   \n",
       "1308       3         0                               Zimmerman, Mr. Leo   \n",
       "\n",
       "         sex   age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
       "3       male  30.0      1      2  113781  151.5500  C22 C26        S  NaN   \n",
       "4     female  25.0      1      2  113781  151.5500  C22 C26        S  NaN   \n",
       "5       male  48.0      0      0   19952   26.5500      E12        S    3   \n",
       "7       male  39.0      0      0  112050    0.0000      A36        S  NaN   \n",
       "8     female  53.0      2      0   11769   51.4792     C101        S    D   \n",
       "...      ...   ...    ...    ...     ...       ...      ...      ...  ...   \n",
       "1302    male   NaN      0      0    2647    7.2250      NaN        C  NaN   \n",
       "1303    male   NaN      0      0    2627   14.4583      NaN        C  NaN   \n",
       "1306    male  26.5      0      0    2656    7.2250      NaN        C  NaN   \n",
       "1307    male  27.0      0      0    2670    7.2250      NaN        C  NaN   \n",
       "1308    male  29.0      0      0  315082    7.8750      NaN        S  NaN   \n",
       "\n",
       "       body                        home.dest  \n",
       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
       "5       NaN                     New York, NY  \n",
       "7       NaN                      Belfast, NI  \n",
       "8       NaN              Bayside, Queens, NY  \n",
       "...     ...                              ...  \n",
       "1302    NaN                              NaN  \n",
       "1303    NaN                              NaN  \n",
       "1306  304.0                              NaN  \n",
       "1307    NaN                              NaN  \n",
       "1308    NaN                              NaN  \n",
       "\n",
       "[959 rows x 14 columns]"
      ],
      "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>name</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>ticket</th>\n      <th>fare</th>\n      <th>cabin</th>\n      <th>embarked</th>\n      <th>boat</th>\n      <th>body</th>\n      <th>home.dest</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mr. Hudson Joshua Creighton</td>\n      <td>male</td>\n      <td>30.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>135.0</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n      <td>female</td>\n      <td>25.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Montreal, PQ / Chesterville, ON</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Anderson, Mr. Harry</td>\n      <td>male</td>\n      <td>48.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>19952</td>\n      <td>26.5500</td>\n      <td>E12</td>\n      <td>S</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>New York, NY</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Andrews, Mr. Thomas Jr</td>\n      <td>male</td>\n      <td>39.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>112050</td>\n      <td>0.0000</td>\n      <td>A36</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Belfast, NI</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n      <td>female</td>\n      <td>53.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>11769</td>\n      <td>51.4792</td>\n      <td>C101</td>\n      <td>S</td>\n      <td>D</td>\n      <td>NaN</td>\n      <td>Bayside, Queens, NY</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1302</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Yousif, Mr. Wazli</td>\n      <td>male</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2647</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1303</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Yousseff, Mr. Gerious</td>\n      <td>male</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2627</td>\n      <td>14.4583</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1306</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zakarian, Mr. Mapriededer</td>\n      <td>male</td>\n      <td>26.5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2656</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>304.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1307</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zakarian, Mr. Ortin</td>\n      <td>male</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2670</td>\n      <td>7.2250</td>\n      <td>NaN</td>\n      <td>C</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1308</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Zimmerman, Mr. Leo</td>\n      <td>male</td>\n      <td>29.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>315082</td>\n      <td>7.8750</td>\n      <td>NaN</td>\n      <td>S</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>959 rows × 14 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "datos[datos.name.map(lambda name: 'Mr' in name)]"
   ]
  },
  {
   "source": [
    "# Numpy\n",
    "Es la libreria principal de numero que pose Python\n",
    "Con el podremos por ejemplo crear columnas vacias ya que tiene la opción de devolvernos valores vacios (NaN)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       num_critic_for_reviews     duration  director_facebook_likes  \\\n",
       "count             4993.000000  5028.000000              4939.000000   \n",
       "mean               140.194272   107.201074               686.509212   \n",
       "std                121.601675    25.197441              2813.328607   \n",
       "min                  1.000000     7.000000                 0.000000   \n",
       "25%                 50.000000    93.000000                 7.000000   \n",
       "50%                110.000000   103.000000                49.000000   \n",
       "75%                195.000000   118.000000               194.500000   \n",
       "max                813.000000   511.000000             23000.000000   \n",
       "\n",
       "       actor_3_facebook_likes  actor_1_facebook_likes         gross  \\\n",
       "count             5020.000000             5036.000000  4.159000e+03   \n",
       "mean               645.009761             6560.047061  4.846841e+07   \n",
       "std               1665.041728            15020.759120  6.845299e+07   \n",
       "min                  0.000000                0.000000  1.620000e+02   \n",
       "25%                133.000000              614.000000  5.340988e+06   \n",
       "50%                371.500000              988.000000  2.551750e+07   \n",
       "75%                636.000000            11000.000000  6.230944e+07   \n",
       "max              23000.000000           640000.000000  7.605058e+08   \n",
       "\n",
       "       num_voted_users  cast_total_facebook_likes  facenumber_in_poster  \\\n",
       "count     5.043000e+03                5043.000000           5030.000000   \n",
       "mean      8.366816e+04                9699.063851              1.371173   \n",
       "std       1.384853e+05               18163.799124              2.013576   \n",
       "min       5.000000e+00                   0.000000              0.000000   \n",
       "25%       8.593500e+03                1411.000000              0.000000   \n",
       "50%       3.435900e+04                3090.000000              1.000000   \n",
       "75%       9.630900e+04               13756.500000              2.000000   \n",
       "max       1.689764e+06              656730.000000             43.000000   \n",
       "\n",
       "       num_user_for_reviews        budget   title_year  \\\n",
       "count           5022.000000  4.551000e+03  4935.000000   \n",
       "mean             272.770808  3.975262e+07  2002.470517   \n",
       "std              377.982886  2.061149e+08    12.474599   \n",
       "min                1.000000  2.180000e+02  1916.000000   \n",
       "25%               65.000000  6.000000e+06  1999.000000   \n",
       "50%              156.000000  2.000000e+07  2005.000000   \n",
       "75%              326.000000  4.500000e+07  2011.000000   \n",
       "max             5060.000000  1.221550e+10  2016.000000   \n",
       "\n",
       "       actor_2_facebook_likes   imdb_score  aspect_ratio  movie_facebook_likes  \n",
       "count             5030.000000  5043.000000   4714.000000           5043.000000  \n",
       "mean              1651.754473     6.442138      2.220403           7525.964505  \n",
       "std               4042.438863     1.125116      1.385113          19320.445110  \n",
       "min                  0.000000     1.600000      1.180000              0.000000  \n",
       "25%                281.000000     5.800000      1.850000              0.000000  \n",
       "50%                595.000000     6.600000      2.350000            166.000000  \n",
       "75%                918.000000     7.200000      2.350000           3000.000000  \n",
       "max             137000.000000     9.500000     16.000000         349000.000000  "
      ],
      "text/html": "<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>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>num_voted_users</th>\n      <th>cast_total_facebook_likes</th>\n      <th>facenumber_in_poster</th>\n      <th>num_user_for_reviews</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4993.000000</td>\n      <td>5028.000000</td>\n      <td>4939.000000</td>\n      <td>5020.000000</td>\n      <td>5036.000000</td>\n      <td>4.159000e+03</td>\n      <td>5.043000e+03</td>\n      <td>5043.000000</td>\n      <td>5030.000000</td>\n      <td>5022.000000</td>\n      <td>4.551000e+03</td>\n      <td>4935.000000</td>\n      <td>5030.000000</td>\n      <td>5043.000000</td>\n      <td>4714.000000</td>\n      <td>5043.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>140.194272</td>\n      <td>107.201074</td>\n      <td>686.509212</td>\n      <td>645.009761</td>\n      <td>6560.047061</td>\n      <td>4.846841e+07</td>\n      <td>8.366816e+04</td>\n      <td>9699.063851</td>\n      <td>1.371173</td>\n      <td>272.770808</td>\n      <td>3.975262e+07</td>\n      <td>2002.470517</td>\n      <td>1651.754473</td>\n      <td>6.442138</td>\n      <td>2.220403</td>\n      <td>7525.964505</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>121.601675</td>\n      <td>25.197441</td>\n      <td>2813.328607</td>\n      <td>1665.041728</td>\n      <td>15020.759120</td>\n      <td>6.845299e+07</td>\n      <td>1.384853e+05</td>\n      <td>18163.799124</td>\n      <td>2.013576</td>\n      <td>377.982886</td>\n      <td>2.061149e+08</td>\n      <td>12.474599</td>\n      <td>4042.438863</td>\n      <td>1.125116</td>\n      <td>1.385113</td>\n      <td>19320.445110</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.620000e+02</td>\n      <td>5.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>2.180000e+02</td>\n      <td>1916.000000</td>\n      <td>0.000000</td>\n      <td>1.600000</td>\n      <td>1.180000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>50.000000</td>\n      <td>93.000000</td>\n      <td>7.000000</td>\n      <td>133.000000</td>\n      <td>614.000000</td>\n      <td>5.340988e+06</td>\n      <td>8.593500e+03</td>\n      <td>1411.000000</td>\n      <td>0.000000</td>\n      <td>65.000000</td>\n      <td>6.000000e+06</td>\n      <td>1999.000000</td>\n      <td>281.000000</td>\n      <td>5.800000</td>\n      <td>1.850000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>110.000000</td>\n      <td>103.000000</td>\n      <td>49.000000</td>\n      <td>371.500000</td>\n      <td>988.000000</td>\n      <td>2.551750e+07</td>\n      <td>3.435900e+04</td>\n      <td>3090.000000</td>\n      <td>1.000000</td>\n      <td>156.000000</td>\n      <td>2.000000e+07</td>\n      <td>2005.000000</td>\n      <td>595.000000</td>\n      <td>6.600000</td>\n      <td>2.350000</td>\n      <td>166.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>195.000000</td>\n      <td>118.000000</td>\n      <td>194.500000</td>\n      <td>636.000000</td>\n      <td>11000.000000</td>\n      <td>6.230944e+07</td>\n      <td>9.630900e+04</td>\n      <td>13756.500000</td>\n      <td>2.000000</td>\n      <td>326.000000</td>\n      <td>4.500000e+07</td>\n      <td>2011.000000</td>\n      <td>918.000000</td>\n      <td>7.200000</td>\n      <td>2.350000</td>\n      <td>3000.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>813.000000</td>\n      <td>511.000000</td>\n      <td>23000.000000</td>\n      <td>23000.000000</td>\n      <td>640000.000000</td>\n      <td>7.605058e+08</td>\n      <td>1.689764e+06</td>\n      <td>656730.000000</td>\n      <td>43.000000</td>\n      <td>5060.000000</td>\n      <td>1.221550e+10</td>\n      <td>2016.000000</td>\n      <td>137000.000000</td>\n      <td>9.500000</td>\n      <td>16.000000</td>\n      <td>349000.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "movies = pd.read_csv(\"Datos/movie_metadata.csv\")\n",
    "movies.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      color      director_name  num_critic_for_reviews  duration  \\\n",
       "0     Color      James Cameron                   723.0     178.0   \n",
       "1     Color     Gore Verbinski                   302.0     169.0   \n",
       "2     Color         Sam Mendes                   602.0     148.0   \n",
       "3     Color  Christopher Nolan                   813.0     164.0   \n",
       "4       NaN        Doug Walker                     NaN       NaN   \n",
       "...     ...                ...                     ...       ...   \n",
       "5038  Color        Scott Smith                     1.0      87.0   \n",
       "5039  Color                NaN                    43.0      43.0   \n",
       "5040  Color   Benjamin Roberds                    13.0      76.0   \n",
       "5041  Color        Daniel Hsia                    14.0     100.0   \n",
       "5042  Color           Jon Gunn                    43.0      90.0   \n",
       "\n",
       "      director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                         0.0                   855.0  Joel David Moore   \n",
       "1                       563.0                  1000.0     Orlando Bloom   \n",
       "2                         0.0                   161.0      Rory Kinnear   \n",
       "3                     22000.0                 23000.0    Christian Bale   \n",
       "4                       131.0                     NaN        Rob Walker   \n",
       "...                       ...                     ...               ...   \n",
       "5038                      2.0                   318.0     Daphne Zuniga   \n",
       "5039                      NaN                   319.0     Valorie Curry   \n",
       "5040                      0.0                     0.0     Maxwell Moody   \n",
       "5041                      0.0                   489.0     Daniel Henney   \n",
       "5042                     16.0                    16.0  Brian Herzlinger   \n",
       "\n",
       "      actor_1_facebook_likes        gross                           genres  \\\n",
       "0                     1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1                    40000.0  309404152.0         Action|Adventure|Fantasy   \n",
       "2                    11000.0  200074175.0        Action|Adventure|Thriller   \n",
       "3                    27000.0  448130642.0                  Action|Thriller   \n",
       "4                      131.0          NaN                      Documentary   \n",
       "...                      ...          ...                              ...   \n",
       "5038                   637.0          NaN                     Comedy|Drama   \n",
       "5039                   841.0          NaN     Crime|Drama|Mystery|Thriller   \n",
       "5040                     0.0          NaN            Drama|Horror|Thriller   \n",
       "5041                   946.0      10443.0             Comedy|Drama|Romance   \n",
       "5042                    86.0      85222.0                      Documentary   \n",
       "\n",
       "      ... language country  content_rating       budget title_year  \\\n",
       "0     ...  English     USA           PG-13  237000000.0     2009.0   \n",
       "1     ...  English     USA           PG-13  300000000.0     2007.0   \n",
       "2     ...  English      UK           PG-13  245000000.0     2015.0   \n",
       "3     ...  English     USA           PG-13  250000000.0     2012.0   \n",
       "4     ...      NaN     NaN             NaN          NaN        NaN   \n",
       "...   ...      ...     ...             ...          ...        ...   \n",
       "5038  ...  English  Canada             NaN          NaN     2013.0   \n",
       "5039  ...  English     USA           TV-14          NaN        NaN   \n",
       "5040  ...  English     USA             NaN       1400.0     2013.0   \n",
       "5041  ...  English     USA           PG-13          NaN     2012.0   \n",
       "5042  ...  English     USA              PG       1100.0     2004.0   \n",
       "\n",
       "      actor_2_facebook_likes imdb_score aspect_ratio  movie_facebook_likes  \\\n",
       "0                      936.0        7.9         1.78                 33000   \n",
       "1                     5000.0        7.1         2.35                     0   \n",
       "2                      393.0        6.8         2.35                 85000   \n",
       "3                    23000.0        8.5         2.35                164000   \n",
       "4                       12.0        7.1          NaN                     0   \n",
       "...                      ...        ...          ...                   ...   \n",
       "5038                   470.0        7.7          NaN                    84   \n",
       "5039                   593.0        7.5        16.00                 32000   \n",
       "5040                     0.0        6.3          NaN                    16   \n",
       "5041                   719.0        6.3         2.35                   660   \n",
       "5042                    23.0        6.6         1.85                   456   \n",
       "\n",
       "     vacia  \n",
       "0      NaN  \n",
       "1      NaN  \n",
       "2      NaN  \n",
       "3      NaN  \n",
       "4      NaN  \n",
       "...    ...  \n",
       "5038   NaN  \n",
       "5039   NaN  \n",
       "5040   NaN  \n",
       "5041   NaN  \n",
       "5042   NaN  \n",
       "\n",
       "[5043 rows x 29 columns]"
      ],
      "text/html": "<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>color</th>\n      <th>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>genres</th>\n      <th>...</th>\n      <th>language</th>\n      <th>country</th>\n      <th>content_rating</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n      <th>vacia</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Color</td>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.0</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>760505847.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>237000000.0</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>1.78</td>\n      <td>33000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Color</td>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.0</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>309404152.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>300000000.0</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>2.35</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Color</td>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.0</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>200074175.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>...</td>\n      <td>English</td>\n      <td>UK</td>\n      <td>PG-13</td>\n      <td>245000000.0</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>2.35</td>\n      <td>85000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Color</td>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.0</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>448130642.0</td>\n      <td>Action|Thriller</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>250000000.0</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>2.35</td>\n      <td>164000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Doug Walker</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Rob Walker</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>7.1</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5038</th>\n      <td>Color</td>\n      <td>Scott Smith</td>\n      <td>1.0</td>\n      <td>87.0</td>\n      <td>2.0</td>\n      <td>318.0</td>\n      <td>Daphne Zuniga</td>\n      <td>637.0</td>\n      <td>NaN</td>\n      <td>Comedy|Drama</td>\n      <td>...</td>\n      <td>English</td>\n      <td>Canada</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2013.0</td>\n      <td>470.0</td>\n      <td>7.7</td>\n      <td>NaN</td>\n      <td>84</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5039</th>\n      <td>Color</td>\n      <td>NaN</td>\n      <td>43.0</td>\n      <td>43.0</td>\n      <td>NaN</td>\n      <td>319.0</td>\n      <td>Valorie Curry</td>\n      <td>841.0</td>\n      <td>NaN</td>\n      <td>Crime|Drama|Mystery|Thriller</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>TV-14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>593.0</td>\n      <td>7.5</td>\n      <td>16.00</td>\n      <td>32000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5040</th>\n      <td>Color</td>\n      <td>Benjamin Roberds</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Maxwell Moody</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Drama|Horror|Thriller</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>NaN</td>\n      <td>1400.0</td>\n      <td>2013.0</td>\n      <td>0.0</td>\n      <td>6.3</td>\n      <td>NaN</td>\n      <td>16</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5041</th>\n      <td>Color</td>\n      <td>Daniel Hsia</td>\n      <td>14.0</td>\n      <td>100.0</td>\n      <td>0.0</td>\n      <td>489.0</td>\n      <td>Daniel Henney</td>\n      <td>946.0</td>\n      <td>10443.0</td>\n      <td>Comedy|Drama|Romance</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>NaN</td>\n      <td>2012.0</td>\n      <td>719.0</td>\n      <td>6.3</td>\n      <td>2.35</td>\n      <td>660</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5042</th>\n      <td>Color</td>\n      <td>Jon Gunn</td>\n      <td>43.0</td>\n      <td>90.0</td>\n      <td>16.0</td>\n      <td>16.0</td>\n      <td>Brian Herzlinger</td>\n      <td>86.0</td>\n      <td>85222.0</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG</td>\n      <td>1100.0</td>\n      <td>2004.0</td>\n      <td>23.0</td>\n      <td>6.6</td>\n      <td>1.85</td>\n      <td>456</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5043 rows × 29 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "movies['vacia'] = np.nan # Creamos una serie (columna)\n",
    "movies"
   ]
  },
  {
   "source": [
    "## isnull()\n",
    "Este metodo nos permite saber si dentro de una serie hay o no registros vacios (NaN). Esta nos devolverá a cada registro el un True si es NaN o False si no esta vacia. A esta salida le podemos aplicar all(), any() o sum() por ejemplo:\n",
    "- isnull().all(): Nos indicara si todos los registros de la serie/columna esta vacia, es decir, si la columna no tiene registros\n",
    "- isnull().any(): Nos indica si al menos algun registro tiene un valor vacio"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "False\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "test = movies.color.isnull().all() # Toda la columna esta vacía\n",
    "print(test)\n",
    "movies.color.isnull().any() # Algún registro esta vacío"
   ]
  },
  {
   "source": [
    "### Porcentaje de registro vacios\n",
    "Aplicando lo aprendido con isnull, podremos sacar el porcentaje de los registros vacios de una manera sencilla. A la salida de isnull que nos devolverá a cada registro si es o no NaN lo sumamos (True es 1 y False es 0, por lo tanto cuanto mas NaN mas alto será el sumatorio). Esto nos devolverá el numero total de registros vacios, aplicando la división con el numero total de registro ya obtendriamos el porcentaje (aplicando *100)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "color                         0.38\n",
       "director_name                 2.06\n",
       "num_critic_for_reviews        0.99\n",
       "duration                      0.30\n",
       "director_facebook_likes       2.06\n",
       "actor_3_facebook_likes        0.46\n",
       "actor_2_name                  0.26\n",
       "actor_1_facebook_likes        0.14\n",
       "gross                        17.53\n",
       "genres                        0.00\n",
       "actor_1_name                  0.14\n",
       "movie_title                   0.00\n",
       "num_voted_users               0.00\n",
       "cast_total_facebook_likes     0.00\n",
       "actor_3_name                  0.46\n",
       "facenumber_in_poster          0.26\n",
       "plot_keywords                 3.03\n",
       "movie_imdb_link               0.00\n",
       "num_user_for_reviews          0.42\n",
       "language                      0.24\n",
       "country                       0.10\n",
       "content_rating                6.01\n",
       "budget                        9.76\n",
       "title_year                    2.14\n",
       "actor_2_facebook_likes        0.26\n",
       "imdb_score                    0.00\n",
       "aspect_ratio                  6.52\n",
       "movie_facebook_likes          0.00\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# Obtenemos el porcentaje de los campos vacios que tienen las diferentes columnas\n",
    "round(movies.isnull().sum()/len(movies)*100,2)"
   ]
  },
  {
   "source": [
    "## ¿Existe la columna?\n",
    "El atributo columns de un DataFrame almacena una lista con todos los nombre de las series/columnas. Por tanto, si aplicamos a esta el operador \"in\" junto el nombre de la columna que buscamos nos indicará si esa tabla existe o no"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "'vacia' in movies.columns # Existe la columna?"
   ]
  },
  {
   "source": [
    "## dropna() y drop()\n",
    "Metodo importante!. Este nos permite borrar los valores vacios de nuestro DataFarme. Para ello tenemos varios atributos con los que controlar el eliminado:\n",
    "- Axis: Indicaremos que vamos a borrar, si columnas (axis=1) o filas (axis=0, por defecto)\n",
    "- How: La condición de borrado, borrar si algun registro está vacio (how='any') o solo borrar si toda la columna o fila esté vacia (how='all', por defecto)\n",
    "- Thresh: Será a sustitución del how una condicion de borrado mas profunda. En ella indicaremos el numero minimo de columnas que deben de contener valores NO vacios para NO ser borrado. Si indicamos 5, significa que el registro debe de al menos tener 5 registros con valores para no ser borrado\n",
    "- Inplace: Indicaremos si queremos que el borrado se guarde en el DataFarme. Por defecto es False, por lo tanto, si queremos conservar el resultado del borrado deberemos de guardar la salida de la función en una variable. Si es True no hara falta eso ya que el borrado se aplicará directamente al DataFarme al que se llamó.\n",
    "\n",
    "\n",
    "drop() es muy similar, solo que en el debemos decirle nosotros que columnas queremos borrar (entre corchetes, será una lista)\n",
    "- DataFarme.drop(\\['columna1','columna2'],axis=1)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Esta ejecución suele ser la primera. Eliminará las filas que no contenga nada\n",
    "movies.dropna(how=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                            genres  \\\n",
       "0  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1         Action|Adventure|Fantasy   \n",
       "2        Action|Adventure|Thriller   \n",
       "3                  Action|Thriller   \n",
       "4                      Documentary   \n",
       "\n",
       "                                         movie_title  num_voted_users  \\\n",
       "0                                            Avatar            886204   \n",
       "1          Pirates of the Caribbean: At World's End            471220   \n",
       "2                                           Spectre            275868   \n",
       "3                             The Dark Knight Rises           1144337   \n",
       "4  Star Wars: Episode VII - The Force Awakens    ...                8   \n",
       "\n",
       "   cast_total_facebook_likes  \\\n",
       "0                       4834   \n",
       "1                      48350   \n",
       "2                      11700   \n",
       "3                     106759   \n",
       "4                        143   \n",
       "\n",
       "                                     movie_imdb_link  imdb_score  \\\n",
       "0  http://www.imdb.com/title/tt0499549/?ref_=fn_t...         7.9   \n",
       "1  http://www.imdb.com/title/tt0449088/?ref_=fn_t...         7.1   \n",
       "2  http://www.imdb.com/title/tt2379713/?ref_=fn_t...         6.8   \n",
       "3  http://www.imdb.com/title/tt1345836/?ref_=fn_t...         8.5   \n",
       "4  http://www.imdb.com/title/tt5289954/?ref_=fn_t...         7.1   \n",
       "\n",
       "   movie_facebook_likes  \n",
       "0                 33000  \n",
       "1                     0  \n",
       "2                 85000  \n",
       "3                164000  \n",
       "4                     0  "
      ],
      "text/html": "<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>genres</th>\n      <th>movie_title</th>\n      <th>num_voted_users</th>\n      <th>cast_total_facebook_likes</th>\n      <th>movie_imdb_link</th>\n      <th>imdb_score</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>Avatar</td>\n      <td>886204</td>\n      <td>4834</td>\n      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n      <td>7.9</td>\n      <td>33000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Action|Adventure|Fantasy</td>\n      <td>Pirates of the Caribbean: At World's End</td>\n      <td>471220</td>\n      <td>48350</td>\n      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n      <td>7.1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Action|Adventure|Thriller</td>\n      <td>Spectre</td>\n      <td>275868</td>\n      <td>11700</td>\n      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n      <td>6.8</td>\n      <td>85000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Action|Thriller</td>\n      <td>The Dark Knight Rises</td>\n      <td>1144337</td>\n      <td>106759</td>\n      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n      <td>8.5</td>\n      <td>164000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Documentary</td>\n      <td>Star Wars: Episode VII - The Force Awakens    ...</td>\n      <td>8</td>\n      <td>143</td>\n      <td>http://www.imdb.com/title/tt5289954/?ref_=fn_t...</td>\n      <td>7.1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "# dropna elimina los que son NaN. En nuestro caso elimina las columnas (axis=1) y con la condición de que alguna sea NaN (how='any')\n",
    "movies.dropna(axis=1,how='any').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      color      director_name  num_critic_for_reviews  duration  \\\n",
       "0     Color      James Cameron                   723.0     178.0   \n",
       "1     Color     Gore Verbinski                   302.0     169.0   \n",
       "2     Color         Sam Mendes                   602.0     148.0   \n",
       "3     Color  Christopher Nolan                   813.0     164.0   \n",
       "4       NaN        Doug Walker                     NaN       NaN   \n",
       "...     ...                ...                     ...       ...   \n",
       "5038  Color        Scott Smith                     1.0      87.0   \n",
       "5039  Color                NaN                    43.0      43.0   \n",
       "5040  Color   Benjamin Roberds                    13.0      76.0   \n",
       "5041  Color        Daniel Hsia                    14.0     100.0   \n",
       "5042  Color           Jon Gunn                    43.0      90.0   \n",
       "\n",
       "      director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                         0.0                   855.0  Joel David Moore   \n",
       "1                       563.0                  1000.0     Orlando Bloom   \n",
       "2                         0.0                   161.0      Rory Kinnear   \n",
       "3                     22000.0                 23000.0    Christian Bale   \n",
       "4                       131.0                     NaN        Rob Walker   \n",
       "...                       ...                     ...               ...   \n",
       "5038                      2.0                   318.0     Daphne Zuniga   \n",
       "5039                      NaN                   319.0     Valorie Curry   \n",
       "5040                      0.0                     0.0     Maxwell Moody   \n",
       "5041                      0.0                   489.0     Daniel Henney   \n",
       "5042                     16.0                    16.0  Brian Herzlinger   \n",
       "\n",
       "      actor_1_facebook_likes        gross                           genres  \\\n",
       "0                     1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1                    40000.0  309404152.0         Action|Adventure|Fantasy   \n",
       "2                    11000.0  200074175.0        Action|Adventure|Thriller   \n",
       "3                    27000.0  448130642.0                  Action|Thriller   \n",
       "4                      131.0          NaN                      Documentary   \n",
       "...                      ...          ...                              ...   \n",
       "5038                   637.0          NaN                     Comedy|Drama   \n",
       "5039                   841.0          NaN     Crime|Drama|Mystery|Thriller   \n",
       "5040                     0.0          NaN            Drama|Horror|Thriller   \n",
       "5041                   946.0      10443.0             Comedy|Drama|Romance   \n",
       "5042                    86.0      85222.0                      Documentary   \n",
       "\n",
       "      ... num_user_for_reviews language  country  content_rating       budget  \\\n",
       "0     ...               3054.0  English      USA           PG-13  237000000.0   \n",
       "1     ...               1238.0  English      USA           PG-13  300000000.0   \n",
       "2     ...                994.0  English       UK           PG-13  245000000.0   \n",
       "3     ...               2701.0  English      USA           PG-13  250000000.0   \n",
       "4     ...                  NaN      NaN      NaN             NaN          NaN   \n",
       "...   ...                  ...      ...      ...             ...          ...   \n",
       "5038  ...                  6.0  English   Canada             NaN          NaN   \n",
       "5039  ...                359.0  English      USA           TV-14          NaN   \n",
       "5040  ...                  3.0  English      USA             NaN       1400.0   \n",
       "5041  ...                  9.0  English      USA           PG-13          NaN   \n",
       "5042  ...                 84.0  English      USA              PG       1100.0   \n",
       "\n",
       "      title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "0         2009.0                  936.0        7.9          1.78   \n",
       "1         2007.0                 5000.0        7.1          2.35   \n",
       "2         2015.0                  393.0        6.8          2.35   \n",
       "3         2012.0                23000.0        8.5          2.35   \n",
       "4            NaN                   12.0        7.1           NaN   \n",
       "...          ...                    ...        ...           ...   \n",
       "5038      2013.0                  470.0        7.7           NaN   \n",
       "5039         NaN                  593.0        7.5         16.00   \n",
       "5040      2013.0                    0.0        6.3           NaN   \n",
       "5041      2012.0                  719.0        6.3          2.35   \n",
       "5042      2004.0                   23.0        6.6          1.85   \n",
       "\n",
       "     movie_facebook_likes  \n",
       "0                   33000  \n",
       "1                       0  \n",
       "2                   85000  \n",
       "3                  164000  \n",
       "4                       0  \n",
       "...                   ...  \n",
       "5038                   84  \n",
       "5039                32000  \n",
       "5040                   16  \n",
       "5041                  660  \n",
       "5042                  456  \n",
       "\n",
       "[5043 rows x 28 columns]"
      ],
      "text/html": "<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>color</th>\n      <th>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>genres</th>\n      <th>...</th>\n      <th>num_user_for_reviews</th>\n      <th>language</th>\n      <th>country</th>\n      <th>content_rating</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Color</td>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.0</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>760505847.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>...</td>\n      <td>3054.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>237000000.0</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>1.78</td>\n      <td>33000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Color</td>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.0</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>309404152.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>...</td>\n      <td>1238.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>300000000.0</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>2.35</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Color</td>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.0</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>200074175.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>...</td>\n      <td>994.0</td>\n      <td>English</td>\n      <td>UK</td>\n      <td>PG-13</td>\n      <td>245000000.0</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>2.35</td>\n      <td>85000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Color</td>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.0</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>448130642.0</td>\n      <td>Action|Thriller</td>\n      <td>...</td>\n      <td>2701.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>250000000.0</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>2.35</td>\n      <td>164000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Doug Walker</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Rob Walker</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>7.1</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5038</th>\n      <td>Color</td>\n      <td>Scott Smith</td>\n      <td>1.0</td>\n      <td>87.0</td>\n      <td>2.0</td>\n      <td>318.0</td>\n      <td>Daphne Zuniga</td>\n      <td>637.0</td>\n      <td>NaN</td>\n      <td>Comedy|Drama</td>\n      <td>...</td>\n      <td>6.0</td>\n      <td>English</td>\n      <td>Canada</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2013.0</td>\n      <td>470.0</td>\n      <td>7.7</td>\n      <td>NaN</td>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>5039</th>\n      <td>Color</td>\n      <td>NaN</td>\n      <td>43.0</td>\n      <td>43.0</td>\n      <td>NaN</td>\n      <td>319.0</td>\n      <td>Valorie Curry</td>\n      <td>841.0</td>\n      <td>NaN</td>\n      <td>Crime|Drama|Mystery|Thriller</td>\n      <td>...</td>\n      <td>359.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>TV-14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>593.0</td>\n      <td>7.5</td>\n      <td>16.00</td>\n      <td>32000</td>\n    </tr>\n    <tr>\n      <th>5040</th>\n      <td>Color</td>\n      <td>Benjamin Roberds</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Maxwell Moody</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Drama|Horror|Thriller</td>\n      <td>...</td>\n      <td>3.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>NaN</td>\n      <td>1400.0</td>\n      <td>2013.0</td>\n      <td>0.0</td>\n      <td>6.3</td>\n      <td>NaN</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>5041</th>\n      <td>Color</td>\n      <td>Daniel Hsia</td>\n      <td>14.0</td>\n      <td>100.0</td>\n      <td>0.0</td>\n      <td>489.0</td>\n      <td>Daniel Henney</td>\n      <td>946.0</td>\n      <td>10443.0</td>\n      <td>Comedy|Drama|Romance</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>NaN</td>\n      <td>2012.0</td>\n      <td>719.0</td>\n      <td>6.3</td>\n      <td>2.35</td>\n      <td>660</td>\n    </tr>\n    <tr>\n      <th>5042</th>\n      <td>Color</td>\n      <td>Jon Gunn</td>\n      <td>43.0</td>\n      <td>90.0</td>\n      <td>16.0</td>\n      <td>16.0</td>\n      <td>Brian Herzlinger</td>\n      <td>86.0</td>\n      <td>85222.0</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>84.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG</td>\n      <td>1100.0</td>\n      <td>2004.0</td>\n      <td>23.0</td>\n      <td>6.6</td>\n      <td>1.85</td>\n      <td>456</td>\n    </tr>\n  </tbody>\n</table>\n<p>5043 rows × 28 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# En este caso, se borran las columnas que todos sus registros sean NaN (all) y lo guardamos en el DataFrame (inplace=True)\n",
    "movies.dropna(axis=1,how='all',inplace=True)\n",
    "movies"
   ]
  },
  {
   "source": [
    "### Borrado selectivo\n",
    "Podemos combinar la función isnull() con la drop() para poder hacer un borrado mas complejo\n",
    "En este ejemplo nosotros obtenemos las columnas que cumpla la condición de que el porcentaje de registros vacios sea mayor del 5%. Estas columnas se lo entregamos a la función drop para que las borre"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   color      director_name  num_critic_for_reviews  duration  \\\n",
       "0  Color      James Cameron                   723.0     178.0   \n",
       "1  Color     Gore Verbinski                   302.0     169.0   \n",
       "2  Color         Sam Mendes                   602.0     148.0   \n",
       "3  Color  Christopher Nolan                   813.0     164.0   \n",
       "4    NaN        Doug Walker                     NaN       NaN   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                      0.0                   855.0  Joel David Moore   \n",
       "1                    563.0                  1000.0     Orlando Bloom   \n",
       "2                      0.0                   161.0      Rory Kinnear   \n",
       "3                  22000.0                 23000.0    Christian Bale   \n",
       "4                    131.0                     NaN        Rob Walker   \n",
       "\n",
       "   actor_1_facebook_likes                           genres     actor_1_name  \\\n",
       "0                  1000.0  Action|Adventure|Fantasy|Sci-Fi      CCH Pounder   \n",
       "1                 40000.0         Action|Adventure|Fantasy      Johnny Depp   \n",
       "2                 11000.0        Action|Adventure|Thriller  Christoph Waltz   \n",
       "3                 27000.0                  Action|Thriller        Tom Hardy   \n",
       "4                   131.0                      Documentary      Doug Walker   \n",
       "\n",
       "   ... facenumber_in_poster  \\\n",
       "0  ...                  0.0   \n",
       "1  ...                  0.0   \n",
       "2  ...                  1.0   \n",
       "3  ...                  0.0   \n",
       "4  ...                  0.0   \n",
       "\n",
       "                                       plot_keywords  \\\n",
       "0             avatar|future|marine|native|paraplegic   \n",
       "1  goddess|marriage ceremony|marriage proposal|pi...   \n",
       "2                bomb|espionage|sequel|spy|terrorist   \n",
       "3  deception|imprisonment|lawlessness|police offi...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                     movie_imdb_link num_user_for_reviews  \\\n",
       "0  http://www.imdb.com/title/tt0499549/?ref_=fn_t...               3054.0   \n",
       "1  http://www.imdb.com/title/tt0449088/?ref_=fn_t...               1238.0   \n",
       "2  http://www.imdb.com/title/tt2379713/?ref_=fn_t...                994.0   \n",
       "3  http://www.imdb.com/title/tt1345836/?ref_=fn_t...               2701.0   \n",
       "4  http://www.imdb.com/title/tt5289954/?ref_=fn_t...                  NaN   \n",
       "\n",
       "   language country title_year  actor_2_facebook_likes imdb_score  \\\n",
       "0   English     USA     2009.0                   936.0        7.9   \n",
       "1   English     USA     2007.0                  5000.0        7.1   \n",
       "2   English      UK     2015.0                   393.0        6.8   \n",
       "3   English     USA     2012.0                 23000.0        8.5   \n",
       "4       NaN     NaN        NaN                    12.0        7.1   \n",
       "\n",
       "  movie_facebook_likes  \n",
       "0                33000  \n",
       "1                    0  \n",
       "2                85000  \n",
       "3               164000  \n",
       "4                    0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ],
      "text/html": "<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>color</th>\n      <th>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>genres</th>\n      <th>actor_1_name</th>\n      <th>...</th>\n      <th>facenumber_in_poster</th>\n      <th>plot_keywords</th>\n      <th>movie_imdb_link</th>\n      <th>num_user_for_reviews</th>\n      <th>language</th>\n      <th>country</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Color</td>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.0</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>CCH Pounder</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>avatar|future|marine|native|paraplegic</td>\n      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n      <td>3054.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>33000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Color</td>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.0</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>Johnny Depp</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n      <td>1238.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Color</td>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.0</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>Christoph Waltz</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>bomb|espionage|sequel|spy|terrorist</td>\n      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n      <td>994.0</td>\n      <td>English</td>\n      <td>UK</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>85000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Color</td>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.0</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>Action|Thriller</td>\n      <td>Tom Hardy</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>deception|imprisonment|lawlessness|police offi...</td>\n      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n      <td>2701.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>164000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Doug Walker</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Rob Walker</td>\n      <td>131.0</td>\n      <td>Documentary</td>\n      <td>Doug Walker</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>http://www.imdb.com/title/tt5289954/?ref_=fn_t...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>7.1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "# Para un borrado selectivo de unas columnas que cumpla la condición de que sus registros superan el 5% \n",
    "\n",
    "depurado = movies.columns[movies.isnull().sum()/len(movies)*100 > 5]\n",
    "\n",
    "movies.drop(depurado, axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   color      director_name  num_critic_for_reviews  duration  \\\n",
       "0  Color      James Cameron                   723.0     178.0   \n",
       "1  Color     Gore Verbinski                   302.0     169.0   \n",
       "2  Color         Sam Mendes                   602.0     148.0   \n",
       "3  Color  Christopher Nolan                   813.0     164.0   \n",
       "5  Color     Andrew Stanton                   462.0     132.0   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                      0.0                   855.0  Joel David Moore   \n",
       "1                    563.0                  1000.0     Orlando Bloom   \n",
       "2                      0.0                   161.0      Rory Kinnear   \n",
       "3                  22000.0                 23000.0    Christian Bale   \n",
       "5                    475.0                   530.0   Samantha Morton   \n",
       "\n",
       "   actor_1_facebook_likes        gross                           genres  ...  \\\n",
       "0                  1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi  ...   \n",
       "1                 40000.0  309404152.0         Action|Adventure|Fantasy  ...   \n",
       "2                 11000.0  200074175.0        Action|Adventure|Thriller  ...   \n",
       "3                 27000.0  448130642.0                  Action|Thriller  ...   \n",
       "5                   640.0   73058679.0          Action|Adventure|Sci-Fi  ...   \n",
       "\n",
       "  num_user_for_reviews language  country  content_rating       budget  \\\n",
       "0               3054.0  English      USA           PG-13  237000000.0   \n",
       "1               1238.0  English      USA           PG-13  300000000.0   \n",
       "2                994.0  English       UK           PG-13  245000000.0   \n",
       "3               2701.0  English      USA           PG-13  250000000.0   \n",
       "5                738.0  English      USA           PG-13  263700000.0   \n",
       "\n",
       "   title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "0      2009.0                  936.0        7.9          1.78   \n",
       "1      2007.0                 5000.0        7.1          2.35   \n",
       "2      2015.0                  393.0        6.8          2.35   \n",
       "3      2012.0                23000.0        8.5          2.35   \n",
       "5      2012.0                  632.0        6.6          2.35   \n",
       "\n",
       "  movie_facebook_likes  \n",
       "0                33000  \n",
       "1                    0  \n",
       "2                85000  \n",
       "3               164000  \n",
       "5                24000  \n",
       "\n",
       "[5 rows x 28 columns]"
      ],
      "text/html": "<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>color</th>\n      <th>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>genres</th>\n      <th>...</th>\n      <th>num_user_for_reviews</th>\n      <th>language</th>\n      <th>country</th>\n      <th>content_rating</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Color</td>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.0</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>760505847.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>...</td>\n      <td>3054.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>237000000.0</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>1.78</td>\n      <td>33000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Color</td>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.0</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>309404152.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>...</td>\n      <td>1238.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>300000000.0</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>2.35</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Color</td>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.0</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>200074175.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>...</td>\n      <td>994.0</td>\n      <td>English</td>\n      <td>UK</td>\n      <td>PG-13</td>\n      <td>245000000.0</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>2.35</td>\n      <td>85000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Color</td>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.0</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>448130642.0</td>\n      <td>Action|Thriller</td>\n      <td>...</td>\n      <td>2701.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>250000000.0</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>2.35</td>\n      <td>164000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Color</td>\n      <td>Andrew Stanton</td>\n      <td>462.0</td>\n      <td>132.0</td>\n      <td>475.0</td>\n      <td>530.0</td>\n      <td>Samantha Morton</td>\n      <td>640.0</td>\n      <td>73058679.0</td>\n      <td>Action|Adventure|Sci-Fi</td>\n      <td>...</td>\n      <td>738.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>263700000.0</td>\n      <td>2012.0</td>\n      <td>632.0</td>\n      <td>6.6</td>\n      <td>2.35</td>\n      <td>24000</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 28 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "# Eliminará las filas que tengan 3 o mas datos vacios\n",
    "# En nuestro caso decimos que debe tener al menos todas menos 3, es decir, que si tiene 3 o mas datos vacios, esa fila se borrará\n",
    "movies.dropna(thresh=len(movies.columns)-3).head()"
   ]
  },
  {
   "source": [
    "## Fillna\n",
    "Nos permite rellenar aquellos datos que no contenga información (NaN). Por lo general sera un valor por defecto"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      color      director_name  num_critic_for_reviews  duration  \\\n",
       "0     Color      James Cameron                   723.0     178.0   \n",
       "1     Color     Gore Verbinski                   302.0     169.0   \n",
       "2     Color         Sam Mendes                   602.0     148.0   \n",
       "3     Color  Christopher Nolan                   813.0     164.0   \n",
       "4     color        Doug Walker                     NaN       NaN   \n",
       "...     ...                ...                     ...       ...   \n",
       "5038  Color        Scott Smith                     1.0      87.0   \n",
       "5039  Color                NaN                    43.0      43.0   \n",
       "5040  Color   Benjamin Roberds                    13.0      76.0   \n",
       "5041  Color        Daniel Hsia                    14.0     100.0   \n",
       "5042  Color           Jon Gunn                    43.0      90.0   \n",
       "\n",
       "      director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                         0.0                   855.0  Joel David Moore   \n",
       "1                       563.0                  1000.0     Orlando Bloom   \n",
       "2                         0.0                   161.0      Rory Kinnear   \n",
       "3                     22000.0                 23000.0    Christian Bale   \n",
       "4                       131.0                     NaN        Rob Walker   \n",
       "...                       ...                     ...               ...   \n",
       "5038                      2.0                   318.0     Daphne Zuniga   \n",
       "5039                      NaN                   319.0     Valorie Curry   \n",
       "5040                      0.0                     0.0     Maxwell Moody   \n",
       "5041                      0.0                   489.0     Daniel Henney   \n",
       "5042                     16.0                    16.0  Brian Herzlinger   \n",
       "\n",
       "      actor_1_facebook_likes        gross                           genres  \\\n",
       "0                     1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1                    40000.0  309404152.0         Action|Adventure|Fantasy   \n",
       "2                    11000.0  200074175.0        Action|Adventure|Thriller   \n",
       "3                    27000.0  448130642.0                  Action|Thriller   \n",
       "4                      131.0          NaN                      Documentary   \n",
       "...                      ...          ...                              ...   \n",
       "5038                   637.0          NaN                     Comedy|Drama   \n",
       "5039                   841.0          NaN     Crime|Drama|Mystery|Thriller   \n",
       "5040                     0.0          NaN            Drama|Horror|Thriller   \n",
       "5041                   946.0      10443.0             Comedy|Drama|Romance   \n",
       "5042                    86.0      85222.0                      Documentary   \n",
       "\n",
       "      ... num_user_for_reviews language  country  content_rating       budget  \\\n",
       "0     ...               3054.0  English      USA           PG-13  237000000.0   \n",
       "1     ...               1238.0  English      USA           PG-13  300000000.0   \n",
       "2     ...                994.0  English       UK           PG-13  245000000.0   \n",
       "3     ...               2701.0  English      USA           PG-13  250000000.0   \n",
       "4     ...                  NaN      NaN      NaN             NaN          NaN   \n",
       "...   ...                  ...      ...      ...             ...          ...   \n",
       "5038  ...                  6.0  English   Canada             NaN          NaN   \n",
       "5039  ...                359.0  English      USA           TV-14          NaN   \n",
       "5040  ...                  3.0  English      USA             NaN       1400.0   \n",
       "5041  ...                  9.0  English      USA           PG-13          NaN   \n",
       "5042  ...                 84.0  English      USA              PG       1100.0   \n",
       "\n",
       "      title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "0         2009.0                  936.0        7.9          1.78   \n",
       "1         2007.0                 5000.0        7.1          2.35   \n",
       "2         2015.0                  393.0        6.8          2.35   \n",
       "3         2012.0                23000.0        8.5          2.35   \n",
       "4            NaN                   12.0        7.1           NaN   \n",
       "...          ...                    ...        ...           ...   \n",
       "5038      2013.0                  470.0        7.7           NaN   \n",
       "5039         NaN                  593.0        7.5         16.00   \n",
       "5040      2013.0                    0.0        6.3           NaN   \n",
       "5041      2012.0                  719.0        6.3          2.35   \n",
       "5042      2004.0                   23.0        6.6          1.85   \n",
       "\n",
       "     movie_facebook_likes  \n",
       "0                   33000  \n",
       "1                       0  \n",
       "2                   85000  \n",
       "3                  164000  \n",
       "4                       0  \n",
       "...                   ...  \n",
       "5038                   84  \n",
       "5039                32000  \n",
       "5040                   16  \n",
       "5041                  660  \n",
       "5042                  456  \n",
       "\n",
       "[5043 rows x 28 columns]"
      ],
      "text/html": "<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>color</th>\n      <th>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>genres</th>\n      <th>...</th>\n      <th>num_user_for_reviews</th>\n      <th>language</th>\n      <th>country</th>\n      <th>content_rating</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Color</td>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.0</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>760505847.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>...</td>\n      <td>3054.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>237000000.0</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>1.78</td>\n      <td>33000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Color</td>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.0</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>309404152.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>...</td>\n      <td>1238.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>300000000.0</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>2.35</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Color</td>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.0</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>200074175.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>...</td>\n      <td>994.0</td>\n      <td>English</td>\n      <td>UK</td>\n      <td>PG-13</td>\n      <td>245000000.0</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>2.35</td>\n      <td>85000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Color</td>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.0</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>448130642.0</td>\n      <td>Action|Thriller</td>\n      <td>...</td>\n      <td>2701.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>250000000.0</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>2.35</td>\n      <td>164000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>color</td>\n      <td>Doug Walker</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Rob Walker</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>7.1</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5038</th>\n      <td>Color</td>\n      <td>Scott Smith</td>\n      <td>1.0</td>\n      <td>87.0</td>\n      <td>2.0</td>\n      <td>318.0</td>\n      <td>Daphne Zuniga</td>\n      <td>637.0</td>\n      <td>NaN</td>\n      <td>Comedy|Drama</td>\n      <td>...</td>\n      <td>6.0</td>\n      <td>English</td>\n      <td>Canada</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2013.0</td>\n      <td>470.0</td>\n      <td>7.7</td>\n      <td>NaN</td>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>5039</th>\n      <td>Color</td>\n      <td>NaN</td>\n      <td>43.0</td>\n      <td>43.0</td>\n      <td>NaN</td>\n      <td>319.0</td>\n      <td>Valorie Curry</td>\n      <td>841.0</td>\n      <td>NaN</td>\n      <td>Crime|Drama|Mystery|Thriller</td>\n      <td>...</td>\n      <td>359.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>TV-14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>593.0</td>\n      <td>7.5</td>\n      <td>16.00</td>\n      <td>32000</td>\n    </tr>\n    <tr>\n      <th>5040</th>\n      <td>Color</td>\n      <td>Benjamin Roberds</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>Maxwell Moody</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Drama|Horror|Thriller</td>\n      <td>...</td>\n      <td>3.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>NaN</td>\n      <td>1400.0</td>\n      <td>2013.0</td>\n      <td>0.0</td>\n      <td>6.3</td>\n      <td>NaN</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>5041</th>\n      <td>Color</td>\n      <td>Daniel Hsia</td>\n      <td>14.0</td>\n      <td>100.0</td>\n      <td>0.0</td>\n      <td>489.0</td>\n      <td>Daniel Henney</td>\n      <td>946.0</td>\n      <td>10443.0</td>\n      <td>Comedy|Drama|Romance</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>NaN</td>\n      <td>2012.0</td>\n      <td>719.0</td>\n      <td>6.3</td>\n      <td>2.35</td>\n      <td>660</td>\n    </tr>\n    <tr>\n      <th>5042</th>\n      <td>Color</td>\n      <td>Jon Gunn</td>\n      <td>43.0</td>\n      <td>90.0</td>\n      <td>16.0</td>\n      <td>16.0</td>\n      <td>Brian Herzlinger</td>\n      <td>86.0</td>\n      <td>85222.0</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>84.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG</td>\n      <td>1100.0</td>\n      <td>2004.0</td>\n      <td>23.0</td>\n      <td>6.6</td>\n      <td>1.85</td>\n      <td>456</td>\n    </tr>\n  </tbody>\n</table>\n<p>5043 rows × 28 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "# Con fillna podemos llenar los registros vacios con un valor por defectos\n",
    "len(movies.color[movies.color.isnull()]) # Contar los vacios\n",
    "movies.color = movies.color.fillna(\"color\")\n",
    "movies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      color      director_name  num_critic_for_reviews  duration  \\\n",
       "0     Color      James Cameron                   723.0     178.0   \n",
       "1     Color     Gore Verbinski                   302.0     169.0   \n",
       "2     Color         Sam Mendes                   602.0     148.0   \n",
       "3     Color  Christopher Nolan                   813.0     164.0   \n",
       "4     color        Doug Walker                     NaN       NaN   \n",
       "...     ...                ...                     ...       ...   \n",
       "5038  Color        Scott Smith                     1.0      87.0   \n",
       "5039  Color                NaN                    43.0      43.0   \n",
       "5040  Color   Benjamin Roberds                    13.0      76.0   \n",
       "5041  Color        Daniel Hsia                    14.0     100.0   \n",
       "5042  Color           Jon Gunn                    43.0      90.0   \n",
       "\n",
       "      director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                    0.000000                   855.0  Joel David Moore   \n",
       "1                  563.000000                  1000.0     Orlando Bloom   \n",
       "2                    0.000000                   161.0      Rory Kinnear   \n",
       "3                22000.000000                 23000.0    Christian Bale   \n",
       "4                  131.000000                     NaN        Rob Walker   \n",
       "...                       ...                     ...               ...   \n",
       "5038                 2.000000                   318.0     Daphne Zuniga   \n",
       "5039               686.509212                   319.0     Valorie Curry   \n",
       "5040                 0.000000                     0.0     Maxwell Moody   \n",
       "5041                 0.000000                   489.0     Daniel Henney   \n",
       "5042                16.000000                    16.0  Brian Herzlinger   \n",
       "\n",
       "      actor_1_facebook_likes        gross                           genres  \\\n",
       "0                     1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1                    40000.0  309404152.0         Action|Adventure|Fantasy   \n",
       "2                    11000.0  200074175.0        Action|Adventure|Thriller   \n",
       "3                    27000.0  448130642.0                  Action|Thriller   \n",
       "4                      131.0          NaN                      Documentary   \n",
       "...                      ...          ...                              ...   \n",
       "5038                   637.0          NaN                     Comedy|Drama   \n",
       "5039                   841.0          NaN     Crime|Drama|Mystery|Thriller   \n",
       "5040                     0.0          NaN            Drama|Horror|Thriller   \n",
       "5041                   946.0      10443.0             Comedy|Drama|Romance   \n",
       "5042                    86.0      85222.0                      Documentary   \n",
       "\n",
       "      ... num_user_for_reviews language  country  content_rating       budget  \\\n",
       "0     ...               3054.0  English      USA           PG-13  237000000.0   \n",
       "1     ...               1238.0  English      USA           PG-13  300000000.0   \n",
       "2     ...                994.0  English       UK           PG-13  245000000.0   \n",
       "3     ...               2701.0  English      USA           PG-13  250000000.0   \n",
       "4     ...                  NaN      NaN      NaN             NaN          NaN   \n",
       "...   ...                  ...      ...      ...             ...          ...   \n",
       "5038  ...                  6.0  English   Canada             NaN          NaN   \n",
       "5039  ...                359.0  English      USA           TV-14          NaN   \n",
       "5040  ...                  3.0  English      USA             NaN       1400.0   \n",
       "5041  ...                  9.0  English      USA           PG-13          NaN   \n",
       "5042  ...                 84.0  English      USA              PG       1100.0   \n",
       "\n",
       "      title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "0         2009.0                  936.0        7.9          1.78   \n",
       "1         2007.0                 5000.0        7.1          2.35   \n",
       "2         2015.0                  393.0        6.8          2.35   \n",
       "3         2012.0                23000.0        8.5          2.35   \n",
       "4            NaN                   12.0        7.1           NaN   \n",
       "...          ...                    ...        ...           ...   \n",
       "5038      2013.0                  470.0        7.7           NaN   \n",
       "5039         NaN                  593.0        7.5         16.00   \n",
       "5040      2013.0                    0.0        6.3           NaN   \n",
       "5041      2012.0                  719.0        6.3          2.35   \n",
       "5042      2004.0                   23.0        6.6          1.85   \n",
       "\n",
       "     movie_facebook_likes  \n",
       "0                   33000  \n",
       "1                       0  \n",
       "2                   85000  \n",
       "3                  164000  \n",
       "4                       0  \n",
       "...                   ...  \n",
       "5038                   84  \n",
       "5039                32000  \n",
       "5040                   16  \n",
       "5041                  660  \n",
       "5042                  456  \n",
       "\n",
       "[5043 rows x 28 columns]"
      ],
      "text/html": "<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>color</th>\n      <th>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>genres</th>\n      <th>...</th>\n      <th>num_user_for_reviews</th>\n      <th>language</th>\n      <th>country</th>\n      <th>content_rating</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Color</td>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.000000</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>760505847.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>...</td>\n      <td>3054.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>237000000.0</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>1.78</td>\n      <td>33000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Color</td>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.000000</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>309404152.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>...</td>\n      <td>1238.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>300000000.0</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>2.35</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Color</td>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.000000</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>200074175.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>...</td>\n      <td>994.0</td>\n      <td>English</td>\n      <td>UK</td>\n      <td>PG-13</td>\n      <td>245000000.0</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>2.35</td>\n      <td>85000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Color</td>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.000000</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>448130642.0</td>\n      <td>Action|Thriller</td>\n      <td>...</td>\n      <td>2701.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>250000000.0</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>2.35</td>\n      <td>164000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>color</td>\n      <td>Doug Walker</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>131.000000</td>\n      <td>NaN</td>\n      <td>Rob Walker</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>7.1</td>\n      <td>NaN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5038</th>\n      <td>Color</td>\n      <td>Scott Smith</td>\n      <td>1.0</td>\n      <td>87.0</td>\n      <td>2.000000</td>\n      <td>318.0</td>\n      <td>Daphne Zuniga</td>\n      <td>637.0</td>\n      <td>NaN</td>\n      <td>Comedy|Drama</td>\n      <td>...</td>\n      <td>6.0</td>\n      <td>English</td>\n      <td>Canada</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2013.0</td>\n      <td>470.0</td>\n      <td>7.7</td>\n      <td>NaN</td>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>5039</th>\n      <td>Color</td>\n      <td>NaN</td>\n      <td>43.0</td>\n      <td>43.0</td>\n      <td>686.509212</td>\n      <td>319.0</td>\n      <td>Valorie Curry</td>\n      <td>841.0</td>\n      <td>NaN</td>\n      <td>Crime|Drama|Mystery|Thriller</td>\n      <td>...</td>\n      <td>359.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>TV-14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>593.0</td>\n      <td>7.5</td>\n      <td>16.00</td>\n      <td>32000</td>\n    </tr>\n    <tr>\n      <th>5040</th>\n      <td>Color</td>\n      <td>Benjamin Roberds</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Maxwell Moody</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Drama|Horror|Thriller</td>\n      <td>...</td>\n      <td>3.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>NaN</td>\n      <td>1400.0</td>\n      <td>2013.0</td>\n      <td>0.0</td>\n      <td>6.3</td>\n      <td>NaN</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>5041</th>\n      <td>Color</td>\n      <td>Daniel Hsia</td>\n      <td>14.0</td>\n      <td>100.0</td>\n      <td>0.000000</td>\n      <td>489.0</td>\n      <td>Daniel Henney</td>\n      <td>946.0</td>\n      <td>10443.0</td>\n      <td>Comedy|Drama|Romance</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>NaN</td>\n      <td>2012.0</td>\n      <td>719.0</td>\n      <td>6.3</td>\n      <td>2.35</td>\n      <td>660</td>\n    </tr>\n    <tr>\n      <th>5042</th>\n      <td>Color</td>\n      <td>Jon Gunn</td>\n      <td>43.0</td>\n      <td>90.0</td>\n      <td>16.000000</td>\n      <td>16.0</td>\n      <td>Brian Herzlinger</td>\n      <td>86.0</td>\n      <td>85222.0</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>84.0</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG</td>\n      <td>1100.0</td>\n      <td>2004.0</td>\n      <td>23.0</td>\n      <td>6.6</td>\n      <td>1.85</td>\n      <td>456</td>\n    </tr>\n  </tbody>\n</table>\n<p>5043 rows × 28 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "# Pero podemos rellenarlo con un valor medio\n",
    "movies.director_facebook_likes = movies.director_facebook_likes.fillna(movies.director_facebook_likes.mean())\n",
    "\n",
    "movies"
   ]
  },
  {
   "source": [
    "## dtypes\n",
    "Este atributo contiene los tipos de valores que se a asociados a las diferentes columnas de un DataFrame. Object suele ser los tipo str"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "color                         object\n",
       "director_name                 object\n",
       "num_critic_for_reviews       float64\n",
       "duration                     float64\n",
       "director_facebook_likes      float64\n",
       "actor_3_facebook_likes       float64\n",
       "actor_2_name                  object\n",
       "actor_1_facebook_likes       float64\n",
       "gross                        float64\n",
       "genres                        object\n",
       "actor_1_name                  object\n",
       "movie_title                   object\n",
       "num_voted_users                int64\n",
       "cast_total_facebook_likes      int64\n",
       "actor_3_name                  object\n",
       "facenumber_in_poster         float64\n",
       "plot_keywords                 object\n",
       "movie_imdb_link               object\n",
       "num_user_for_reviews         float64\n",
       "language                      object\n",
       "country                       object\n",
       "content_rating                object\n",
       "budget                       float64\n",
       "title_year                   float64\n",
       "actor_2_facebook_likes       float64\n",
       "imdb_score                   float64\n",
       "aspect_ratio                 float64\n",
       "movie_facebook_likes           int64\n",
       "vacia                        float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "movies.dtypes"
   ]
  },
  {
   "source": [
    "## Conversión de tipos\n",
    "Mediante el metodo astype('<tipo>') podemos castear un tipo de valor asociado a una columna a otro. Resulta util si tenemos una columna que por logica solo puede ser numero enteros pero el programa lo ha asociado con un tipo Float"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   color      director_name  num_critic_for_reviews  duration  \\\n",
       "0  Color      James Cameron                   723.0     178.0   \n",
       "1  Color     Gore Verbinski                   302.0     169.0   \n",
       "2  Color         Sam Mendes                   602.0     148.0   \n",
       "3  Color  Christopher Nolan                   813.0     164.0   \n",
       "4    NaN        Doug Walker                     NaN       NaN   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                      0.0                   855.0  Joel David Moore   \n",
       "1                    563.0                  1000.0     Orlando Bloom   \n",
       "2                      0.0                   161.0      Rory Kinnear   \n",
       "3                  22000.0                 23000.0    Christian Bale   \n",
       "4                    131.0                     NaN        Rob Walker   \n",
       "\n",
       "   actor_1_facebook_likes        gross                           genres  ...  \\\n",
       "0                  1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi  ...   \n",
       "1                 40000.0  309404152.0         Action|Adventure|Fantasy  ...   \n",
       "2                 11000.0  200074175.0        Action|Adventure|Thriller  ...   \n",
       "3                 27000.0  448130642.0                  Action|Thriller  ...   \n",
       "4                   131.0          NaN                      Documentary  ...   \n",
       "\n",
       "  language country  content_rating       budget title_year  \\\n",
       "0  English     USA           PG-13  237000000.0     2009.0   \n",
       "1  English     USA           PG-13  300000000.0     2007.0   \n",
       "2  English      UK           PG-13  245000000.0     2015.0   \n",
       "3  English     USA           PG-13  250000000.0     2012.0   \n",
       "4      NaN     NaN             NaN          NaN        NaN   \n",
       "\n",
       "   actor_2_facebook_likes imdb_score aspect_ratio  movie_facebook_likes vacia  \n",
       "0                   936.0        7.9         1.78                 33000   NaN  \n",
       "1                  5000.0        7.1         2.35                     0   NaN  \n",
       "2                   393.0        6.8         2.35                 85000   NaN  \n",
       "3                 23000.0        8.5         2.35                164000   NaN  \n",
       "4                    12.0        7.1          NaN                     0   NaN  \n",
       "\n",
       "[5 rows x 29 columns]"
      ],
      "text/html": "<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>color</th>\n      <th>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>genres</th>\n      <th>...</th>\n      <th>language</th>\n      <th>country</th>\n      <th>content_rating</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n      <th>vacia</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Color</td>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.0</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>760505847.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>237000000.0</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>1.78</td>\n      <td>33000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Color</td>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.0</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>309404152.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>300000000.0</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>2.35</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Color</td>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.0</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>200074175.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>...</td>\n      <td>English</td>\n      <td>UK</td>\n      <td>PG-13</td>\n      <td>245000000.0</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>2.35</td>\n      <td>85000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Color</td>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.0</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>448130642.0</td>\n      <td>Action|Thriller</td>\n      <td>...</td>\n      <td>English</td>\n      <td>USA</td>\n      <td>PG-13</td>\n      <td>250000000.0</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>2.35</td>\n      <td>164000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Doug Walker</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Rob Walker</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Documentary</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>7.1</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 29 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "movies.num_user_for_reviews = movies.num_user_for_reviews.fillna(0)\n",
    "movies.num_user_for_reviews = movies.num_user_for_reviews.astype('int')\n",
    "movies.head()"
   ]
  },
  {
   "source": [
    "## unique()\n",
    "Asociado a una columna nos muestra todos los diferentes valores que aparece"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array(['Color', 'color', ' Black and White'], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "# Nos muestra las diferences valores que aparece en una columna\n",
    "movies.color.unique()"
   ]
  },
  {
   "source": [
    "## get_dummies\n",
    "Este metodo de la libreria nos permite obtener de una columna sus datos separados por sus valores. Es decir, obtendremos de una Serie un DataFrame en la cual las columnas serán los diferentes valores que contenia la serie y su contenido estará constituido por 0 y 1, siendo 0 ausencia del valor indicada por la columna y 1 presencia de ese valor. Por tanto, en cada fila solo habra un 1.\n",
    "\n",
    "Esta funcionalidad sera muy interesante para la binarización ya que nos permitirá aplicar estudios estadisticos a columnas a las que no se les podia aplicar por ser tipo str."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        Black and White        Color        color\n",
       "count       5043.000000  5043.000000  5043.000000\n",
       "mean           0.041444     0.954789     0.003768\n",
       "std            0.199334     0.207788     0.061271\n",
       "min            0.000000     0.000000     0.000000\n",
       "25%            0.000000     1.000000     0.000000\n",
       "50%            0.000000     1.000000     0.000000\n",
       "75%            0.000000     1.000000     0.000000\n",
       "max            1.000000     1.000000     1.000000"
      ],
      "text/html": "<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>Black and White</th>\n      <th>Color</th>\n      <th>color</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>5043.000000</td>\n      <td>5043.000000</td>\n      <td>5043.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.041444</td>\n      <td>0.954789</td>\n      <td>0.003768</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.199334</td>\n      <td>0.207788</td>\n      <td>0.061271</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "# get_dummies es una codificacion para coger datos torpes. Pone todos los datos y lo pone en lista\n",
    "colores = pd.get_dummies(movies.color)\n",
    "colores"
   ]
  },
  {
   "source": [
    "## Join()\n",
    "Metodo que nos permite unir 2 DataFrame"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          director_name  num_critic_for_reviews  duration  \\\n",
       "0         James Cameron                   723.0     178.0   \n",
       "1        Gore Verbinski                   302.0     169.0   \n",
       "2            Sam Mendes                   602.0     148.0   \n",
       "3     Christopher Nolan                   813.0     164.0   \n",
       "4           Doug Walker                     NaN       NaN   \n",
       "...                 ...                     ...       ...   \n",
       "5038        Scott Smith                     1.0      87.0   \n",
       "5039                NaN                    43.0      43.0   \n",
       "5040   Benjamin Roberds                    13.0      76.0   \n",
       "5041        Daniel Hsia                    14.0     100.0   \n",
       "5042           Jon Gunn                    43.0      90.0   \n",
       "\n",
       "      director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                    0.000000                   855.0  Joel David Moore   \n",
       "1                  563.000000                  1000.0     Orlando Bloom   \n",
       "2                    0.000000                   161.0      Rory Kinnear   \n",
       "3                22000.000000                 23000.0    Christian Bale   \n",
       "4                  131.000000                     NaN        Rob Walker   \n",
       "...                       ...                     ...               ...   \n",
       "5038                 2.000000                   318.0     Daphne Zuniga   \n",
       "5039               686.509212                   319.0     Valorie Curry   \n",
       "5040                 0.000000                     0.0     Maxwell Moody   \n",
       "5041                 0.000000                   489.0     Daniel Henney   \n",
       "5042                16.000000                    16.0  Brian Herzlinger   \n",
       "\n",
       "      actor_1_facebook_likes        gross                           genres  \\\n",
       "0                     1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1                    40000.0  309404152.0         Action|Adventure|Fantasy   \n",
       "2                    11000.0  200074175.0        Action|Adventure|Thriller   \n",
       "3                    27000.0  448130642.0                  Action|Thriller   \n",
       "4                      131.0          NaN                      Documentary   \n",
       "...                      ...          ...                              ...   \n",
       "5038                   637.0          NaN                     Comedy|Drama   \n",
       "5039                   841.0          NaN     Crime|Drama|Mystery|Thriller   \n",
       "5040                     0.0          NaN            Drama|Horror|Thriller   \n",
       "5041                   946.0      10443.0             Comedy|Drama|Romance   \n",
       "5042                    86.0      85222.0                      Documentary   \n",
       "\n",
       "         actor_1_name  ... content_rating       budget  title_year  \\\n",
       "0         CCH Pounder  ...          PG-13  237000000.0      2009.0   \n",
       "1         Johnny Depp  ...          PG-13  300000000.0      2007.0   \n",
       "2     Christoph Waltz  ...          PG-13  245000000.0      2015.0   \n",
       "3           Tom Hardy  ...          PG-13  250000000.0      2012.0   \n",
       "4         Doug Walker  ...            NaN          NaN         NaN   \n",
       "...               ...  ...            ...          ...         ...   \n",
       "5038      Eric Mabius  ...            NaN          NaN      2013.0   \n",
       "5039      Natalie Zea  ...          TV-14          NaN         NaN   \n",
       "5040      Eva Boehnke  ...            NaN       1400.0      2013.0   \n",
       "5041        Alan Ruck  ...          PG-13          NaN      2012.0   \n",
       "5042      John August  ...             PG       1100.0      2004.0   \n",
       "\n",
       "     actor_2_facebook_likes  imdb_score aspect_ratio movie_facebook_likes  \\\n",
       "0                     936.0         7.9         1.78                33000   \n",
       "1                    5000.0         7.1         2.35                    0   \n",
       "2                     393.0         6.8         2.35                85000   \n",
       "3                   23000.0         8.5         2.35               164000   \n",
       "4                      12.0         7.1          NaN                    0   \n",
       "...                     ...         ...          ...                  ...   \n",
       "5038                  470.0         7.7          NaN                   84   \n",
       "5039                  593.0         7.5        16.00                32000   \n",
       "5040                    0.0         6.3          NaN                   16   \n",
       "5041                  719.0         6.3         2.35                  660   \n",
       "5042                   23.0         6.6         1.85                  456   \n",
       "\n",
       "       Black and White Color color  \n",
       "0                    0     1     0  \n",
       "1                    0     1     0  \n",
       "2                    0     1     0  \n",
       "3                    0     1     0  \n",
       "4                    0     0     1  \n",
       "...                ...   ...   ...  \n",
       "5038                 0     1     0  \n",
       "5039                 0     1     0  \n",
       "5040                 0     1     0  \n",
       "5041                 0     1     0  \n",
       "5042                 0     1     0  \n",
       "\n",
       "[5043 rows x 30 columns]"
      ],
      "text/html": "<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>director_name</th>\n      <th>num_critic_for_reviews</th>\n      <th>duration</th>\n      <th>director_facebook_likes</th>\n      <th>actor_3_facebook_likes</th>\n      <th>actor_2_name</th>\n      <th>actor_1_facebook_likes</th>\n      <th>gross</th>\n      <th>genres</th>\n      <th>actor_1_name</th>\n      <th>...</th>\n      <th>content_rating</th>\n      <th>budget</th>\n      <th>title_year</th>\n      <th>actor_2_facebook_likes</th>\n      <th>imdb_score</th>\n      <th>aspect_ratio</th>\n      <th>movie_facebook_likes</th>\n      <th>Black and White</th>\n      <th>Color</th>\n      <th>color</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>James Cameron</td>\n      <td>723.0</td>\n      <td>178.0</td>\n      <td>0.000000</td>\n      <td>855.0</td>\n      <td>Joel David Moore</td>\n      <td>1000.0</td>\n      <td>760505847.0</td>\n      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n      <td>CCH Pounder</td>\n      <td>...</td>\n      <td>PG-13</td>\n      <td>237000000.0</td>\n      <td>2009.0</td>\n      <td>936.0</td>\n      <td>7.9</td>\n      <td>1.78</td>\n      <td>33000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Gore Verbinski</td>\n      <td>302.0</td>\n      <td>169.0</td>\n      <td>563.000000</td>\n      <td>1000.0</td>\n      <td>Orlando Bloom</td>\n      <td>40000.0</td>\n      <td>309404152.0</td>\n      <td>Action|Adventure|Fantasy</td>\n      <td>Johnny Depp</td>\n      <td>...</td>\n      <td>PG-13</td>\n      <td>300000000.0</td>\n      <td>2007.0</td>\n      <td>5000.0</td>\n      <td>7.1</td>\n      <td>2.35</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Sam Mendes</td>\n      <td>602.0</td>\n      <td>148.0</td>\n      <td>0.000000</td>\n      <td>161.0</td>\n      <td>Rory Kinnear</td>\n      <td>11000.0</td>\n      <td>200074175.0</td>\n      <td>Action|Adventure|Thriller</td>\n      <td>Christoph Waltz</td>\n      <td>...</td>\n      <td>PG-13</td>\n      <td>245000000.0</td>\n      <td>2015.0</td>\n      <td>393.0</td>\n      <td>6.8</td>\n      <td>2.35</td>\n      <td>85000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Christopher Nolan</td>\n      <td>813.0</td>\n      <td>164.0</td>\n      <td>22000.000000</td>\n      <td>23000.0</td>\n      <td>Christian Bale</td>\n      <td>27000.0</td>\n      <td>448130642.0</td>\n      <td>Action|Thriller</td>\n      <td>Tom Hardy</td>\n      <td>...</td>\n      <td>PG-13</td>\n      <td>250000000.0</td>\n      <td>2012.0</td>\n      <td>23000.0</td>\n      <td>8.5</td>\n      <td>2.35</td>\n      <td>164000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Doug Walker</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>131.000000</td>\n      <td>NaN</td>\n      <td>Rob Walker</td>\n      <td>131.0</td>\n      <td>NaN</td>\n      <td>Documentary</td>\n      <td>Doug Walker</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>7.1</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5038</th>\n      <td>Scott Smith</td>\n      <td>1.0</td>\n      <td>87.0</td>\n      <td>2.000000</td>\n      <td>318.0</td>\n      <td>Daphne Zuniga</td>\n      <td>637.0</td>\n      <td>NaN</td>\n      <td>Comedy|Drama</td>\n      <td>Eric Mabius</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2013.0</td>\n      <td>470.0</td>\n      <td>7.7</td>\n      <td>NaN</td>\n      <td>84</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5039</th>\n      <td>NaN</td>\n      <td>43.0</td>\n      <td>43.0</td>\n      <td>686.509212</td>\n      <td>319.0</td>\n      <td>Valorie Curry</td>\n      <td>841.0</td>\n      <td>NaN</td>\n      <td>Crime|Drama|Mystery|Thriller</td>\n      <td>Natalie Zea</td>\n      <td>...</td>\n      <td>TV-14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>593.0</td>\n      <td>7.5</td>\n      <td>16.00</td>\n      <td>32000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5040</th>\n      <td>Benjamin Roberds</td>\n      <td>13.0</td>\n      <td>76.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>Maxwell Moody</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>Drama|Horror|Thriller</td>\n      <td>Eva Boehnke</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>1400.0</td>\n      <td>2013.0</td>\n      <td>0.0</td>\n      <td>6.3</td>\n      <td>NaN</td>\n      <td>16</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5041</th>\n      <td>Daniel Hsia</td>\n      <td>14.0</td>\n      <td>100.0</td>\n      <td>0.000000</td>\n      <td>489.0</td>\n      <td>Daniel Henney</td>\n      <td>946.0</td>\n      <td>10443.0</td>\n      <td>Comedy|Drama|Romance</td>\n      <td>Alan Ruck</td>\n      <td>...</td>\n      <td>PG-13</td>\n      <td>NaN</td>\n      <td>2012.0</td>\n      <td>719.0</td>\n      <td>6.3</td>\n      <td>2.35</td>\n      <td>660</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5042</th>\n      <td>Jon Gunn</td>\n      <td>43.0</td>\n      <td>90.0</td>\n      <td>16.000000</td>\n      <td>16.0</td>\n      <td>Brian Herzlinger</td>\n      <td>86.0</td>\n      <td>85222.0</td>\n      <td>Documentary</td>\n      <td>John August</td>\n      <td>...</td>\n      <td>PG</td>\n      <td>1100.0</td>\n      <td>2004.0</td>\n      <td>23.0</td>\n      <td>6.6</td>\n      <td>1.85</td>\n      <td>456</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5043 rows × 30 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "# Borramos la columna color y unimos la columnas obtenidas del get_dummies\n",
    "movies.drop(['color'],inplace=True, axis=1)\n",
    "movies = movies.join(colores)\n",
    "movies\n"
   ]
  }
 ]
}