04_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./imagenes/python_logo.jpeg)\n",
    "# Librería Pandas.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[*Pandas*](https://pandas.pydata.org/pandas-docs/stable/) es sin duda el paquete más importante de *Python* utilizado en la *Ciencia de Datos*. No solo ofrece muchos métodos y funciones que facilitan el trabajo con los datos, sino que además se ha optimizado para la velocidad, lo que le brinda una ventaja significativa en comparación al trabajo con datos numéricos en *Python*.\n",
    "\n",
    "*Pandas* es una librería que provee estructuras de datos rápidas, flexibles y expresivas; diseñadas para trabajar con *rotulados* y/o *relacionales*. Conceptualmente se puede pensar como *arrays* de *NumPy* donde las filas y columnas están *rótuladas*. Las estructuras de datos de *Pandas* tienen forma similar a hojas de cálculo en *Python*.\n",
    "\n",
    "Asi como *NumPy*, *Pandas* es una muy buena herramienta para trabajar con números, vectores, álgebra lineal, etc. *Pandas* es adecuado para trabajar con:\n",
    "\n",
    "* Datos tabulares y heterogéneos (flotantes, string, enteros, etc.)\n",
    "* Series temporales.\n",
    "* Los mismos datos que se pueden manipular con arreglos de *NumPy*.\n",
    "\n",
    "*Pandas* no forma parte de la instalación estándar de *Python*, así que debe instalarse por separado. Para instalar *Pandas* debe ejecutarse el siguiente comando desde una cónsola o terminal:\n",
    "```bash\n",
    "pip install pandas\n",
    "```\n",
    "Además, para trabajar con archivos de Excel, también deberá ejecutarse:\n",
    "```bash\n",
    "pip install xlrd\n",
    "```\n",
    "\n",
    "La siguiente *hoja-resumen* condensa los principales comandos de *Pandas* que revisaremos en este *Notebook* y que luego podrá servir para consulta rápida.\n",
    "\n",
    "![](./imagenes/cheat_sheet_pandas.png)\n",
    "\n",
    "*Pandas* tiene una documentación muy completa y diversos [tutoriales](http://pandas.pydata.org/pandas-docs/stable/tutorials.html).\n",
    "\n",
    "En *Pandas* existen tres tipos básicos de objetos todos ellos basados a su vez en *NumPy*:\n",
    "\n",
    "* *Series* (listas, 1D),\n",
    "* *DataFrame* (tablas, 2D) y\n",
    "* *Panels* (tablas 3D).\n",
    "\n",
    "Nosotros vamos a ver el uso básico de los dos primeros tipos de objetos, para un mayor detalle puedes consultar el [manual](http://pandas.pydata.org/pandas-docs/stable/dsintro.html).\n",
    "\n",
    "En este notebook vamos a usar la abreviación:\n",
    "\n",
    "* **df**: para cualquier objeto `DataFrame` *Pandas*.\n",
    "* **s**: para cualquier objeto de `Series` *Pandas*.\n",
    "\n",
    "Para comenzar iniciamos importando *Pandas* según la convención:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importador de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_data  = \"datos/capacitaciones.csv\"\n",
    "json_data = \"datos/capacitaciones.json\"\n",
    "xlsx_data = \"datos/capacitaciones.xlsx\"\n",
    "\n",
    "df = pd.read_csv(csv_data, encoding = \"ISO-8859-1\") # De un archivo CSV.\n",
    "df = pd.read_json(json_data)                        # De un archivo JSON.\n",
    "df = pd.read_excel(xlsx_data)                       # De un archivo XLSX."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existen otros tipos de *importadores* de datos, tales como:\n",
    "\n",
    "* `pd.read_table(filename)`: importa desde un archivo de texto delimitado (como TSV).\n",
    "* `pd.read_sql(query, connection_object)`: importa desde una BaseDeDatos/Tabla SQL.\n",
    "* `pd.read_html(url)`: importa desde una URL html, una cadena o un archivo y extrae tablas a una lista.\n",
    "* `pd.read_clipboard()`: toma los datos desde el contenido del portapapeles.\n",
    "* `pd.DataFrame(dict)`: importa desde un diccionario *Python*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exportador de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los *exportadores* de datos guardan en disco los datos del *df*:\n",
    "\n",
    "* `df.to_csv(filename)`: escribe los datos en un archivo CSV.\n",
    "* `df.to_excel(filename)`: escribe los datos en un archivo Excel.\n",
    "* `df.to_sql(table_name, connection_object)`: escribe los datos en una tabla SQL.\n",
    "* `df.to_json(filename)`: escribe los datos en un archivo con formato JSON;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear objetos de prueba."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear datos de prueba es útil para probar segmentos de código:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
       "      <td>0.459418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "2  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "3  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "4  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "5  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "6  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "7  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "8  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "9  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Genera un dataframe con 5 columnas y 10 filas.\n",
    "df = pd.DataFrame(pd.np.random.rand(10, 5))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    7\n",
       "2    3\n",
       "3    9\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crea una series a partir de una lista.\n",
    "my_list = [2, 7, 3, 9, 5]\n",
    "s = pd.Series(my_list)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
       "      <td>0.459418</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2         3         4\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Añade un índice de fecha al dataframe.\n",
    "df.index = pd.date_range('1900/1/30', periods = df.shape[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizar / inspeccionar datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
       "      <td>0.459418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2         3         4\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5) # Muestra las primeras 5 filas del DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2         3         4\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5) # Muestra las últimas 5 filas del DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # Muestra el número de filas y columnas del DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 10 entries, 1900-01-30 to 1900-02-08\n",
      "Freq: D\n",
      "Data columns (total 5 columns):\n",
      "0    10 non-null float64\n",
      "1    10 non-null float64\n",
      "2    10 non-null float64\n",
      "3    10 non-null float64\n",
      "4    10 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 480.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info() # Muestra el índice, tipo de datos y memoria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.404542</td>\n",
       "      <td>0.611446</td>\n",
       "      <td>0.602829</td>\n",
       "      <td>0.593818</td>\n",
       "      <td>0.549798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.299602</td>\n",
       "      <td>0.290064</td>\n",
       "      <td>0.275752</td>\n",
       "      <td>0.335810</td>\n",
       "      <td>0.258476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.158581</td>\n",
       "      <td>0.346469</td>\n",
       "      <td>0.519294</td>\n",
       "      <td>0.504266</td>\n",
       "      <td>0.440327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.292600</td>\n",
       "      <td>0.607703</td>\n",
       "      <td>0.632097</td>\n",
       "      <td>0.619816</td>\n",
       "      <td>0.484734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.628783</td>\n",
       "      <td>0.895393</td>\n",
       "      <td>0.773017</td>\n",
       "      <td>0.884151</td>\n",
       "      <td>0.765904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0          1          2          3          4\n",
       "count  10.000000  10.000000  10.000000  10.000000  10.000000\n",
       "mean    0.404542   0.611446   0.602829   0.593818   0.549798\n",
       "std     0.299602   0.290064   0.275752   0.335810   0.258476\n",
       "min     0.086453   0.221798   0.143109   0.006667   0.118458\n",
       "25%     0.158581   0.346469   0.519294   0.504266   0.440327\n",
       "50%     0.292600   0.607703   0.632097   0.619816   0.484734\n",
       "75%     0.628783   0.895393   0.773017   0.884151   0.765904\n",
       "max     0.976877   0.954365   0.944503   0.955938   0.946960"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # Muestra estadísticas resumidas de columnas numéricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    1\n",
       "5    1\n",
       "3    1\n",
       "2    1\n",
       "9    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts(dropna = False) # Muestra valores y recuentos únicos en la serie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selección."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1900-01-30    0.780768\n",
       "1900-01-31    0.893211\n",
       "1900-02-01    0.143109\n",
       "1900-02-02    0.636465\n",
       "1900-02-03    0.944503\n",
       "1900-02-04    0.613187\n",
       "1900-02-05    0.151554\n",
       "1900-02-06    0.627729\n",
       "1900-02-07    0.749764\n",
       "1900-02-08    0.487996\n",
       "Freq: D, Name: 2, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2] # Devuelve la columna con la etiqueta 2 como una Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-01-30</th>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.321628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.459418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.440059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.576196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   2         4\n",
       "1900-01-30  0.780768  0.118458\n",
       "1900-01-31  0.893211  0.321628\n",
       "1900-02-01  0.143109  0.459418\n",
       "1900-02-02  0.636465  0.440059\n",
       "1900-02-03  0.944503  0.854934\n",
       "1900-02-04  0.613187  0.510050\n",
       "1900-02-05  0.151554  0.576196\n",
       "1900-02-06  0.627729  0.946960\n",
       "1900-02-07  0.749764  0.829140\n",
       "1900-02-08  0.487996  0.441132"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[2, 4]] # Devuelve columnas 2 y 4 como un nuevo DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[0] # Selección por posición: selecciona el elemento 0 de la Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc[3] # Selección por índice de la Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.323111\n",
       "1    0.746657\n",
       "2    0.780768\n",
       "3    0.955938\n",
       "4    0.118458\n",
       "Name: 1900-01-30 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, :] # Primera fila del DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3231114297946387"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, 0] # Primer elemento de la primera columna."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limpieza de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
       "      <td>0.459418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   a         b         c         x         y\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = [\"a\", \"b\", \"c\", \"x\", \"y\"] # Renombrar columnas del DataFrame.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1900-02-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1900-02-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                a      b      c      x      y\n",
       "1900-01-30  False  False  False  False  False\n",
       "1900-01-31  False  False  False  False  False\n",
       "1900-02-01  False  False  False  False  False\n",
       "1900-02-02  False  False  False  False  False\n",
       "1900-02-03  False  False  False  False  False\n",
       "1900-02-04  False  False  False  False  False\n",
       "1900-02-05  False  False  False  False  False\n",
       "1900-02-06  False  False  False  False  False\n",
       "1900-02-07  False  False  False  False  False\n",
       "1900-02-08  False  False  False  False  False"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull() # Comprueba valores nulos, devuelve un Boolean Arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1900-02-03</th>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>1900-02-04</th>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>1900-02-05</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>1900-02-06</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>1900-02-07</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "               a     b     c     x     y\n",
       "1900-01-30  True  True  True  True  True\n",
       "1900-01-31  True  True  True  True  True\n",
       "1900-02-01  True  True  True  True  True\n",
       "1900-02-02  True  True  True  True  True\n",
       "1900-02-03  True  True  True  True  True\n",
       "1900-02-04  True  True  True  True  True\n",
       "1900-02-05  True  True  True  True  True\n",
       "1900-02-06  True  True  True  True  True\n",
       "1900-02-07  True  True  True  True  True\n",
       "1900-02-08  True  True  True  True  True"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notnull() # El opuesto a df.isnull()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.955938</td>\n",
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       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
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       "      <th>1900-02-02</th>\n",
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       "      <td>0.006667</td>\n",
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       "      <td>0.618441</td>\n",
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       "      <th>1900-02-04</th>\n",
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       "      <td>0.976877</td>\n",
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       "      <td>0.624012</td>\n",
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       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
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      ],
      "text/plain": [
       "                   a         b         c         x         y\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis = 0) # Elimina todas las filas que contienen valores nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
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       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
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       "      <th>1900-02-06</th>\n",
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       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
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      ],
      "text/plain": [
       "                   a         b         c         x         y\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis = 1) # Elimina todas las columnas que contienen valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
       "      <td>0.459418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   a         b         c         x         y\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Elimina todas las filas que tienen menos de 3 valores no nulos.\n",
    "df.dropna(axis = 1, thresh = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
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       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.139962</td>\n",
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       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
       "      <td>0.459418</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   a         b         c         x         y\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0) # Remplaza todos los valores nulos por 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    7\n",
       "2    3\n",
       "3    9\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(s.mean()) # Remplaza todos los valores nulos por la media."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    7.0\n",
       "2    3.0\n",
       "3    9.0\n",
       "4    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(float) # Convierte el tipo de datos a float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2\n",
       "1      7\n",
       "2    100\n",
       "3      9\n",
       "4      5\n",
       "dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace(3, '100') # Remplaza todos los valores iguales a 3 con '100'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2\n",
       "1    700\n",
       "2      3\n",
       "3    900\n",
       "4      5\n",
       "dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.replace([7, 9], ['700', '900']) # Remplaza todos los 7 por '700' y 9 por '900'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   1         3         5         7         9\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = (0, 1, 2, 3, 4)\n",
    "df.rename(columns = lambda x: 2*x + 1) # Cambio de nombre de columnas en masa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
       "      <td>0.459418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0       one         2         3         4\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={1:'one'}) # Renombrar seleccionando columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.440059</td>\n",
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       "    <tr>\n",
       "      <th>0.944503</th>\n",
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       "      <td>0.618441</td>\n",
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       "    <tr>\n",
       "      <th>0.613187</th>\n",
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       "      <td>0.510050</td>\n",
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       "    <tr>\n",
       "      <th>0.151554</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
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       "    <tr>\n",
       "      <th>0.627729</th>\n",
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       "    <tr>\n",
       "      <th>0.749764</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
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       "      <td>0.829140</td>\n",
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       "      <th>0.487996</th>\n",
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      "text/plain": [
       "                 0         1         3         4\n",
       "2                                               \n",
       "0.780768  0.323111  0.746657  0.955938  0.118458\n",
       "0.893211  0.123266  0.310638  0.902347  0.321628\n",
       "0.143109  0.139962  0.826734  0.621192  0.459418\n",
       "0.636465  0.664837  0.321543  0.006667  0.440059\n",
       "0.944503  0.630373  0.918279  0.618441  0.854934\n",
       "0.613187  0.262089  0.468748  0.829566  0.510050\n",
       "0.151554  0.976877  0.954365  0.506109  0.576196\n",
       "0.627729  0.214438  0.421249  0.916895  0.946960\n",
       "0.749764  0.624012  0.221798  0.077372  0.829140\n",
       "0.487996  0.086453  0.924445  0.503651  0.441132"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(2) # Cambiar el índice por una columna (2) del Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.780768</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
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       "      <th>1.893211</th>\n",
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       "      <th>1.151554</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
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       "      <th>1.627729</th>\n",
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       "      <td>0.946960</td>\n",
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       "    <tr>\n",
       "      <th>1.749764</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
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       "    <tr>\n",
       "      <th>1.487996</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                 0         1         3         4\n",
       "2                                               \n",
       "1.780768  0.323111  0.746657  0.955938  0.118458\n",
       "1.893211  0.123266  0.310638  0.902347  0.321628\n",
       "1.143109  0.139962  0.826734  0.621192  0.459418\n",
       "1.636465  0.664837  0.321543  0.006667  0.440059\n",
       "1.944503  0.630373  0.918279  0.618441  0.854934\n",
       "1.613187  0.262089  0.468748  0.829566  0.510050\n",
       "1.151554  0.976877  0.954365  0.506109  0.576196\n",
       "1.627729  0.214438  0.421249  0.916895  0.946960\n",
       "1.749764  0.624012  0.221798  0.077372  0.829140\n",
       "1.487996  0.086453  0.924445  0.503651  0.441132"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(2).rename(index = lambda x: x + 1) # Cambia el índice en lote."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtro, orden y agrupamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
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       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
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       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2         3         4\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[2] > 0.5] # Filtra las filas donde la columna 2 es mayor que 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1         2         3         4\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[2] > 0.5) & (df[2] < 0.7)] # Filtra las filas donde 0.7 > columna 2 > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.924445</td>\n",
       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-01</th>\n",
       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.621192</td>\n",
       "      <td>0.459418</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
       "      <td>0.468748</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>0.829566</td>\n",
       "      <td>0.510050</td>\n",
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       "    <tr>\n",
       "      <th>1900-01-31</th>\n",
       "      <td>0.123266</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.893211</td>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-06</th>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.916895</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
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      "text/plain": [
       "                   0         1         2         3         4\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(3) # Ordena por los valores de la columna 3 en orden ascendente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1900-01-30</th>\n",
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       "      <td>0.214438</td>\n",
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       "      <td>0.944503</td>\n",
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       "      <td>0.854934</td>\n",
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       "      <th>1900-02-05</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.151554</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.576196</td>\n",
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       "      <th>1900-02-08</th>\n",
       "      <td>0.086453</td>\n",
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       "      <td>0.487996</td>\n",
       "      <td>0.503651</td>\n",
       "      <td>0.441132</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
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       "      <td>0.077372</td>\n",
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       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
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       "      <td>0.006667</td>\n",
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       "                   0         1         2         3         4\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(3, ascending = False) # Ordena por la columna 3 en orden descendente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.139962</td>\n",
       "      <td>0.826734</td>\n",
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       "      <td>0.976877</td>\n",
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       "      <td>0.506109</td>\n",
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       "      <th>1900-02-08</th>\n",
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       "      <th>1900-02-04</th>\n",
       "      <td>0.262089</td>\n",
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       "      <td>0.829566</td>\n",
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       "      <th>1900-02-06</th>\n",
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       "      <th>1900-02-02</th>\n",
       "      <td>0.664837</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>0.636465</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.440059</td>\n",
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       "    <tr>\n",
       "      <th>1900-02-07</th>\n",
       "      <td>0.624012</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.749764</td>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
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       "    <tr>\n",
       "      <th>1900-01-30</th>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.746657</td>\n",
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       "      <th>1900-01-31</th>\n",
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       "      <th>1900-02-03</th>\n",
       "      <td>0.630373</td>\n",
       "      <td>0.918279</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.618441</td>\n",
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      "text/plain": [
       "                   0         1         2         3         4\n",
       "1900-02-01  0.139962  0.826734  0.143109  0.621192  0.459418\n",
       "1900-02-05  0.976877  0.954365  0.151554  0.506109  0.576196\n",
       "1900-02-08  0.086453  0.924445  0.487996  0.503651  0.441132\n",
       "1900-02-04  0.262089  0.468748  0.613187  0.829566  0.510050\n",
       "1900-02-06  0.214438  0.421249  0.627729  0.916895  0.946960\n",
       "1900-02-02  0.664837  0.321543  0.636465  0.006667  0.440059\n",
       "1900-02-07  0.624012  0.221798  0.749764  0.077372  0.829140\n",
       "1900-01-30  0.323111  0.746657  0.780768  0.955938  0.118458\n",
       "1900-01-31  0.123266  0.310638  0.893211  0.902347  0.321628\n",
       "1900-02-03  0.630373  0.918279  0.944503  0.618441  0.854934"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ordena los valores por la columna 2 de forma ascendente\n",
    "# y luego por la columna 3 en orden descendente.\n",
    "df.sort_values([2, 3], ascending = [True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f0a41968b00>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(3) # Devuelve un objeto groupby para los valores de una columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f0a41968e80>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Devuelve un objeto groupby para valores de múltiples columnas,\n",
    "# en este caso columnas 3 y 4.\n",
    "df.groupby([3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.749764</th>\n",
       "      <td>0.077372</td>\n",
       "      <td>0.829140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.780768</th>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.893211</th>\n",
       "      <td>0.902347</td>\n",
       "      <td>0.321628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.944503</th>\n",
       "      <td>0.618441</td>\n",
       "      <td>0.854934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 3         4\n",
       "2                           \n",
       "0.143109  0.621192  0.459418\n",
       "0.151554  0.506109  0.576196\n",
       "0.487996  0.503651  0.441132\n",
       "0.613187  0.829566  0.510050\n",
       "0.627729  0.916895  0.946960\n",
       "0.636465  0.006667  0.440059\n",
       "0.749764  0.077372  0.829140\n",
       "0.780768  0.955938  0.118458\n",
       "0.893211  0.902347  0.321628\n",
       "0.944503  0.618441  0.854934"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crea una tabla dinámica que agrupa por la columna 2\n",
    "# y calcula la media de las columnas 3 y 4.\n",
    "df.pivot_table(index = 2, values = [3, 4], aggfunc = pd.np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0.916895</th>\n",
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       "      <td>0.421249</td>\n",
       "      <td>0.627729</td>\n",
       "      <td>0.946960</td>\n",
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       "    <tr>\n",
       "      <th>0.955938</th>\n",
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       "      <td>0.746657</td>\n",
       "      <td>0.780768</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0         1         2         4\n",
       "3                                               \n",
       "0.006667  0.664837  0.321543  0.636465  0.440059\n",
       "0.077372  0.624012  0.221798  0.749764  0.829140\n",
       "0.503651  0.086453  0.924445  0.487996  0.441132\n",
       "0.506109  0.976877  0.954365  0.151554  0.576196\n",
       "0.618441  0.630373  0.918279  0.944503  0.854934\n",
       "0.621192  0.139962  0.826734  0.143109  0.459418\n",
       "0.829566  0.262089  0.468748  0.613187  0.510050\n",
       "0.902347  0.123266  0.310638  0.893211  0.321628\n",
       "0.916895  0.214438  0.421249  0.627729  0.946960\n",
       "0.955938  0.323111  0.746657  0.780768  0.118458"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encuentra el promedio en todas las columnas\n",
    "# para cada grupo de la columna 3 único.\n",
    "df.groupby(3).agg(pd.np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.404542\n",
       "1    0.611446\n",
       "2    0.602829\n",
       "3    0.593818\n",
       "4    0.549798\n",
       "dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(pd.np.mean) # Aplica la función pd.np.mean() en cada columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1900-01-30    0.955938\n",
       "1900-01-31    0.902347\n",
       "1900-02-01    0.826734\n",
       "1900-02-02    0.664837\n",
       "1900-02-03    0.944503\n",
       "1900-02-04    0.829566\n",
       "1900-02-05    0.976877\n",
       "1900-02-06    0.946960\n",
       "1900-02-07    0.829140\n",
       "1900-02-08    0.924445\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(pd.np.max, axis = 1) # Aplica la función pd.np.max() en cada fila."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unir / Combinar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ejemplificar la unión y conbinación de dataframes, supongamos que tenemos los siguientes dos dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>d</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "0  1  a  w\n",
       "1  2  b  x\n",
       "2  3  c  y\n",
       "3  4  d  z"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1 = {\"A\": [1, 2, 3, 4],\n",
    "          \"B\": [\"a\", \"b\", \"c\", \"d\"],\n",
    "          \"C\": [\"w\", \"x\", \"y\", \"z\"]}\n",
    "df1 = pd.DataFrame(data_1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
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       "      <th>C</th>\n",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>False</td>\n",
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      "text/plain": [
       "   A      B  C\n",
       "0  6   True  1\n",
       "1  7  False  2\n",
       "2  3   True  3\n",
       "3  4  False  4"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2 = {\"A\": [6, 7, 3, 4],\n",
    "          \"B\": [True, False, True, False],\n",
    "          \"C\": [1, 2, 3, 4]}\n",
    "df2 = pd.DataFrame(data_2)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>x</td>\n",
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       "      <th>3</th>\n",
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       "      <td>d</td>\n",
       "      <td>z</td>\n",
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       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A      B  C\n",
       "0  1      a  w\n",
       "1  2      b  x\n",
       "2  3      c  y\n",
       "3  4      d  z\n",
       "0  6   True  1\n",
       "1  7  False  2\n",
       "2  3   True  3\n",
       "3  4  False  4"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupa las filas en df1 al final de df2 (las columnas deben ser idénticas).\n",
    "df1.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>d</td>\n",
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      "text/plain": [
       "   A  B  C  A      B  C\n",
       "0  1  a  w  6   True  1\n",
       "1  2  b  x  7  False  2\n",
       "2  3  c  y  3   True  3\n",
       "3  4  d  z  4  False  4"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrega las columnas en df1 al final de df2 (las filas deben ser idénticas)\n",
    "pd.concat([df1, df2], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>d</td>\n",
       "      <td>z</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A B_x C_x    B_y  C_y\n",
       "0  3   c   y   True    3\n",
       "1  4   d   z  False    4"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Une las columnas en df1 con las columnas en df2 donde las filas para A\n",
    "# tienen valores idénticos. También puede utilizarse: left, right, outer, inner.\n",
    "pd.merge(df1, df2, on = [\"A\"], how = 'inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estadísticas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.404542</td>\n",
       "      <td>0.611446</td>\n",
       "      <td>0.602829</td>\n",
       "      <td>0.593818</td>\n",
       "      <td>0.549798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.299602</td>\n",
       "      <td>0.290064</td>\n",
       "      <td>0.275752</td>\n",
       "      <td>0.335810</td>\n",
       "      <td>0.258476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.086453</td>\n",
       "      <td>0.221798</td>\n",
       "      <td>0.143109</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.118458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.158581</td>\n",
       "      <td>0.346469</td>\n",
       "      <td>0.519294</td>\n",
       "      <td>0.504266</td>\n",
       "      <td>0.440327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.292600</td>\n",
       "      <td>0.607703</td>\n",
       "      <td>0.632097</td>\n",
       "      <td>0.619816</td>\n",
       "      <td>0.484734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.628783</td>\n",
       "      <td>0.895393</td>\n",
       "      <td>0.773017</td>\n",
       "      <td>0.884151</td>\n",
       "      <td>0.765904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.976877</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.944503</td>\n",
       "      <td>0.955938</td>\n",
       "      <td>0.946960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0          1          2          3          4\n",
       "count  10.000000  10.000000  10.000000  10.000000  10.000000\n",
       "mean    0.404542   0.611446   0.602829   0.593818   0.549798\n",
       "std     0.299602   0.290064   0.275752   0.335810   0.258476\n",
       "min     0.086453   0.221798   0.143109   0.006667   0.118458\n",
       "25%     0.158581   0.346469   0.519294   0.504266   0.440327\n",
       "50%     0.292600   0.607703   0.632097   0.619816   0.484734\n",
       "75%     0.628783   0.895393   0.773017   0.884151   0.765904\n",
       "max     0.976877   0.954365   0.944503   0.955938   0.946960"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # Resumen de estadísticas para columnas numéricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.404542\n",
       "1    0.611446\n",
       "2    0.602829\n",
       "3    0.593818\n",
       "4    0.549798\n",
       "dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis = 0) # Devuelve la media de todas las columnas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1900-01-30    0.584987\n",
       "1900-01-31    0.510218\n",
       "1900-02-01    0.438083\n",
       "1900-02-02    0.413914\n",
       "1900-02-03    0.793306\n",
       "1900-02-04    0.536728\n",
       "1900-02-05    0.633020\n",
       "1900-02-06    0.625454\n",
       "1900-02-07    0.500417\n",
       "1900-02-08    0.488735\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis = 1) # Devuelve la media de todas las filas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.099910</td>\n",
       "      <td>-0.113422</td>\n",
       "      <td>-0.552906</td>\n",
       "      <td>0.293707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.099910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.446381</td>\n",
       "      <td>0.214068</td>\n",
       "      <td>-0.129492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.113422</td>\n",
       "      <td>-0.446381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.137561</td>\n",
       "      <td>0.078250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.552906</td>\n",
       "      <td>0.214068</td>\n",
       "      <td>0.137561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.223966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.293707</td>\n",
       "      <td>-0.129492</td>\n",
       "      <td>0.078250</td>\n",
       "      <td>-0.223966</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  1.000000  0.099910 -0.113422 -0.552906  0.293707\n",
       "1  0.099910  1.000000 -0.446381  0.214068 -0.129492\n",
       "2 -0.113422 -0.446381  1.000000  0.137561  0.078250\n",
       "3 -0.552906  0.214068  0.137561  1.000000 -0.223966\n",
       "4  0.293707 -0.129492  0.078250 -0.223966  1.000000"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr() # Devuelve la correlación entre las columnas en un DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    10\n",
       "2    10\n",
       "3    10\n",
       "4    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count(axis = 0) # Devuelve el número de valores no nulos en cada columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.976877\n",
       "1    0.954365\n",
       "2    0.944503\n",
       "3    0.955938\n",
       "4    0.946960\n",
       "dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max(axis = 0) # Devuelve el valor más alto en cada columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.086453\n",
       "1    0.221798\n",
       "2    0.143109\n",
       "3    0.006667\n",
       "4    0.118458\n",
       "dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min(axis = 0) # Devuelve el valor más bajo en cada columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.292600\n",
       "1    0.607703\n",
       "2    0.632097\n",
       "3    0.619816\n",
       "4    0.484734\n",
       "dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median(axis = 0) # Devuelve la mediana de cada columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.299602\n",
       "1    0.290064\n",
       "2    0.275752\n",
       "3    0.335810\n",
       "4    0.258476\n",
       "dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std(axis = 0) # Devuelve la desviación estándar de cada columna."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Referencias adicionales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ver operaciones adicionales de *NumPy* podemos consultar los siguientes repositorios:\n",
    "* [Pandas Operations.](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Pandas_Operations.ipynb)\n",
    "* [Advanced Pandas Operations.](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Advanced%20Pandas%20Operations.ipynb)\n",
    "* [Tools Pandas.](https://github.com/ageron/handson-ml2/blob/master/tools_pandas.ipynb)"
   ]
  }
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