pandas_12_22_es_alumno.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción a Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas es una poderosa y popular librería para trabajar con datos en Python. Algunos puntos clave a cubrir al introducir Pandas incluyen:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Pandas proporciona una serie de estructuras de datos para trabajar con datos tabulares, incluyendo el DataFrame, que es similar a una hoja de cálculo de Excel, y la Serie, que es un array unidimensional de datos.\n",
    "\n",
    "2. Pandas permite cargar, manipular y analizar fácilmente datos de una variedad de fuentes, incluyendo archivos CSV y Excel, bases de datos SQL e incluso el portapapeles.\n",
    "\n",
    "3. Pandas incluye una amplia gama de funciones y métodos para limpiar, transformar y resumir datos, así como para visualizar y trazar datos.\n",
    "\n",
    "4. Pandas se integra perfectamente con otras librerías populares de Python, como Numpy, Matplotlib y Scikit-learn, para permitir capacidades aún más potentes de análisis de datos y aprendizaje automático.\n",
    "\n",
    "5. Por último, es importante señalar que Pandas es particularmente adecuado para trabajar con conjuntos de datos grandes y complejos, y puede ser mucho más eficiente y conveniente que trabajar con datos en bruto en otros formatos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estructuras de datos de Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay dos estructuras de datos principales en Pandas: Series y DataFrame.\n",
    "\n",
    "Una Serie es un array unidimensional etiquetado que puede contener cualquier tipo de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es similar a una columna en una hoja de cálculo de Excel o a un campo en una tabla de una base de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una Serie se crea pasando una lista o array de datos y un índice opcional al constructor Serie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "data = [1, 2, 3, 4]\n",
    "index = ['a', 'b', 'c', 'd']\n",
    "\n",
    "s = pd.Series(data, index=index)\n",
    "s\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un DataFrame es una estructura de datos bidimensional etiquetada con columnas. Es similar a una hoja de cálculo o una tabla SQL. Un DataFrame se puede crear a partir de una variedad de fuentes, incluidas las listas y los diccionarios de Series, los diccionarios de listas y las matrices NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Altura</th>\n",
       "      <th>Peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Altura  Peso\n",
       "a       3     4\n",
       "b       1     5\n",
       "c       2     6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "        'Altura': pd.Series([1,2,3], index=['b', 'c', 'a']), \n",
    "        'Peso': pd.Series([4,5,6], index=['a', 'b', 'c'])\n",
    "    }\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay varias formas de crear una Serie de Pandas:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Desde una lista o array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Pedro\n",
       "1     Juan\n",
       "2      Ana\n",
       "3    Maria\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = ['Pedro', 'Juan', 'Ana', 'Maria']\n",
    "\n",
    "serie = pd.Series(data)\n",
    "serie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Desde un dictionario:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pedro    12\n",
       "Juan     14\n",
       "Ana      13\n",
       "Maria    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = { 'Pedro': 12, 'Juan': 14, 'Ana': 13, 'Maria': 12}\n",
    "\n",
    "serie = pd.Series(data)\n",
    "serie\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pedro    [6, 7.5, 8]\n",
       "Juan      [9, 9, 10]\n",
       "Ana        [5, 6, 7]\n",
       "Maria      [4, 7, 5]\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = { 'Pedro': [ 6,7.5,8], 'Juan': [ 9,9,10], 'Ana': [5,6,7], 'Maria': [4,7,5] }\n",
    "\n",
    "serie = pd.Series(data)\n",
    "serie\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Con un valor escalar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie = pd.Series(0, index=[0,1,2,3,4,5])\n",
    "serie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    0\n",
       "c    0\n",
       "d    0\n",
       "e    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie = pd.Series(0, index=['a','b','c','d','e'])\n",
    "serie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    10\n",
       "2    10\n",
       "3    10\n",
       "4    10\n",
       "5    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie = pd.Series(10, index=range(6))\n",
    "serie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Desde un NumPy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10    32\n",
      "11    69\n",
      "12     7\n",
      "13    10\n",
      "14    13\n",
      "15    52\n",
      "16    66\n",
      "17    34\n",
      "18    35\n",
      "19    79\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data = np.random.randint(0,100, 10)\n",
    "\n",
    "serie = pd.Series(data)\n",
    "print(serie)\n",
    "serie[0]\n",
    "\n",
    "serie = pd.Series(data, index=range(10,20))\n",
    "print(serie)\n",
    "serie[16]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Pedro</th>\n",
       "      <th>Juan</th>\n",
       "      <th>Ana</th>\n",
       "      <th>Maria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pedro  Juan  Ana  Maria\n",
       "0    6.0     9    5      4\n",
       "1    7.5     9    6      7\n",
       "2    8.0    10    7      5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crea un DataFrame desde un diccionario de listas\n",
    "data = { 'Pedro': [ 6,7.5,8], 'Juan': [ 9,9,10], 'Ana': [5,6,7], 'Maria': [4,7,5] }\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       altura  peso  edad\n",
      "Pedro     160    70    19\n",
      "Juan      170    65    18\n",
      "Ana       155    75    19\n",
      "María     160    65    20\n",
      "       altura  peso  edad\n",
      "Ana       155    75    19\n",
      "Juan      170    70    18\n",
      "María     160    65    20\n",
      "Pedro     160    65    19\n"
     ]
    }
   ],
   "source": [
    "# Crea un DataFrame desde un diccionario de listas especificando el índice\n",
    "nombres = ['Pedro', 'Juan', 'Ana', 'María']\n",
    "\n",
    "data = {\n",
    "    'altura': [160, 170, 155, 160],\n",
    "    'peso': [ 70, 65, 75, 65],\n",
    "    'edad': [ 19, 18, 19, 20]\n",
    "}\n",
    "\n",
    "df = pd. DataFrame(data, index=nombres)\n",
    "#print(df)\n",
    "#print('\\n')\n",
    "#print(df.loc['Ana'])\n",
    "\n",
    "\n",
    "# Crea un DataFrame desde un diccionario de pd.Series\n",
    "data = {\n",
    "    'altura': pd.Series([160, 170, 155, 160], index=['Pedro', 'Juan', 'Ana', 'María']),\n",
    "    'peso': pd.Series([ 70, 65, 75, 65], index=['Pedro', 'Juan', 'Ana', 'María']),\n",
    "    'edad': pd.Series([ 19, 18, 19, 20], index=['Pedro', 'Juan', 'Ana', 'María'])\n",
    "}\n",
    "\n",
    "data_2 = {\n",
    "    'altura': pd.Series([160, 170, 155, 160], index=['Pedro', 'Juan', 'Ana', 'María']),\n",
    "    'peso': pd.Series([ 70, 65, 75, 65], index=['Juan','Pedro' ,'Ana', 'María']),\n",
    "    'edad': pd.Series([ 19, 18, 19, 20], index=['Pedro', 'Juan', 'Ana', 'María'])\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df_2 = pd.DataFrame(data_2)\n",
    "\n",
    "print(df)\n",
    "print(df_2)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay muchas formas de crear un DataFrame de Pandas. Algunos métodos comunes incluyen:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Desde un NumPy array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>9</td>\n",
       "      <td>591</td>\n",
       "      <td>814</td>\n",
       "      <td>310</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>265</td>\n",
       "      <td>433</td>\n",
       "      <td>628</td>\n",
       "      <td>114</td>\n",
       "      <td>504</td>\n",
       "      <td>844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>589</td>\n",
       "      <td>667</td>\n",
       "      <td>892</td>\n",
       "      <td>635</td>\n",
       "      <td>643</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>859</td>\n",
       "      <td>364</td>\n",
       "      <td>179</td>\n",
       "      <td>713</td>\n",
       "      <td>785</td>\n",
       "      <td>681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>932</td>\n",
       "      <td>488</td>\n",
       "      <td>174</td>\n",
       "      <td>918</td>\n",
       "      <td>856</td>\n",
       "      <td>914</td>\n",
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       "     0    1    2    3    4    5\n",
       "0  842    9  591  814  310  283\n",
       "1  265  433  628  114  504  844\n",
       "2  589  667  892  635  643   20\n",
       "3  859  364  179  713  785  681\n",
       "4  932  488  174  918  856  914"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "array = np.random.randint(0,1000, (5,6))\n",
    "\n",
    "df = pd.DataFrame(array)\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Desde un dictionario de listas o NumPy arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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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       "    }\n",
       "\n",
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       "        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>Pedro</th>\n",
       "      <th>Juan</th>\n",
       "      <th>Ana</th>\n",
       "      <th>Maria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pedro  Juan  Ana  Maria\n",
       "0    6.0     9    5      4\n",
       "1    7.5     9    6      7\n",
       "2    8.0    10    7      5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = { 'Pedro': [ 6,7.5,8], 'Juan': [ 9,9,10], 'Ana': [5,6,7], 'Maria': [4,7,5] }\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Desde una lista de dictionarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>nombre</th>\n",
       "      <th>edad</th>\n",
       "      <th>altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>18</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ana</td>\n",
       "      <td>19</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Juan</td>\n",
       "      <td>17</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maria</td>\n",
       "      <td>16</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nombre  edad  altura\n",
       "0  Pedro    18     180\n",
       "1    Ana    19     165\n",
       "2   Juan    17     175\n",
       "3  Maria    16     170"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    { 'nombre': 'Pedro', 'edad': 18, 'altura': 180},\n",
    "    { 'nombre': 'Ana', 'edad': 19, 'altura': 165},\n",
    "    { 'nombre': 'Juan', 'edad': 17, 'altura': 175},\n",
    "    { 'nombre': 'Maria', 'edad': 16, 'altura': 170},\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Desde una lista de Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b    c\n",
       "0    1    2    3\n",
       "1   10   20   30\n",
       "2    A    B    C\n",
       "3  1.0  2.5  3.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    pd.Series([1,2,3], index=['a', 'b', 'c']),\n",
    "    pd.Series([10,20,30], index=['a', 'b', 'c']),\n",
    "    pd.Series(['A','B','C'], index=['a', 'b', 'c']),\n",
    "    pd.Series([1.0,2.5,3.0], index=['a', 'b', 'c']),\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>edad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>75</td>\n",
       "      <td>170</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>70</td>\n",
       "      <td>170</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juan</th>\n",
       "      <td>65</td>\n",
       "      <td>170</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maria</th>\n",
       "      <td>70</td>\n",
       "      <td>170</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       peso  altura  edad\n",
       "Pedro    75     170    21\n",
       "Ana      70     170    30\n",
       "Juan     65     170    20\n",
       "Maria    70     170    19"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nombres = ['Pedro', 'Ana', 'Juan', 'Maria']\n",
    "data = [\n",
    "    pd.Series([75,170,21], index=['peso', 'altura', 'edad']),\n",
    "    pd.Series([70,170,30], index=['peso', 'altura', 'edad']),\n",
    "    pd.Series([65,170,20], index=['peso', 'altura', 'edad']),\n",
    "    pd.Series([70,170,19], index=['peso', 'altura', 'edad']),\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(data, index=nombres)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Desde un CSV file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crea un DataFrame desde un CSV file\n",
    "df = pd.read_csv('employees.csv', encoding='latin1')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Guarda el DataFrame en un CSV file\n",
    "\n",
    "# Econdings más comunes\n",
    "# ASCII: US-ASCII, ISO 646, ANSI_X3.4-1968, …\n",
    "# ISO-8859-1: Latin-1, iso88591, …\n",
    "# UTF-8: utf8, UTF_8, …\n",
    "\n",
    "df.to_csv('employees_2.csv', encoding='latin1', index=False, sep='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEID</th>\n",
       "      <th>Full Name</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Business Unit</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Age</th>\n",
       "      <th>Hire Date</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Exit Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>E01578</td>\n",
       "      <td>Anthony Hong</td>\n",
       "      <td>Sr. Manger</td>\n",
       "      <td>IT</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>37</td>\n",
       "      <td>11/29/2010</td>\n",
       "      <td>$146,961</td>\n",
       "      <td>11%</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>E03430</td>\n",
       "      <td>Leo Herrera</td>\n",
       "      <td>Sr. Business Partner</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Male</td>\n",
       "      <td>Latino</td>\n",
       "      <td>48</td>\n",
       "      <td>4/22/1998</td>\n",
       "      <td>$85,369</td>\n",
       "      <td>0%</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>11/27/2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>E03058</td>\n",
       "      <td>Robert Wright</td>\n",
       "      <td>Technical Architect</td>\n",
       "      <td>IT</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>30</td>\n",
       "      <td>6/14/2015</td>\n",
       "      <td>$67,489</td>\n",
       "      <td>0%</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>E04762</td>\n",
       "      <td>Audrey Richardson</td>\n",
       "      <td>Director</td>\n",
       "      <td>IT</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>46</td>\n",
       "      <td>10/6/2018</td>\n",
       "      <td>$166,259</td>\n",
       "      <td>17%</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>E01148</td>\n",
       "      <td>Scarlett Kumar</td>\n",
       "      <td>Systems Analyst</td>\n",
       "      <td>IT</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>55</td>\n",
       "      <td>1/7/2009</td>\n",
       "      <td>$47,032</td>\n",
       "      <td>0%</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>E03094</td>\n",
       "      <td>Wesley Young</td>\n",
       "      <td>Sr. Analyst</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>33</td>\n",
       "      <td>9/18/2016</td>\n",
       "      <td>$98,427</td>\n",
       "      <td>0%</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>E01909</td>\n",
       "      <td>Lillian Khan</td>\n",
       "      <td>Analyst</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>44</td>\n",
       "      <td>5/31/2010</td>\n",
       "      <td>$47,387</td>\n",
       "      <td>0%</td>\n",
       "      <td>China</td>\n",
       "      <td>Chengdu</td>\n",
       "      <td>1/8/2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>E04398</td>\n",
       "      <td>Oliver Yang</td>\n",
       "      <td>Director</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>31</td>\n",
       "      <td>6/10/2019</td>\n",
       "      <td>$176,710</td>\n",
       "      <td>15%</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>E02521</td>\n",
       "      <td>Lily Nguyen</td>\n",
       "      <td>Sr. Analyst</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>33</td>\n",
       "      <td>1/28/2012</td>\n",
       "      <td>$95,960</td>\n",
       "      <td>0%</td>\n",
       "      <td>China</td>\n",
       "      <td>Chengdu</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>E03545</td>\n",
       "      <td>Sofia Cheng</td>\n",
       "      <td>Vice President</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>63</td>\n",
       "      <td>7/26/2020</td>\n",
       "      <td>$216,195</td>\n",
       "      <td>31%</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       EEID          Full Name             Job Title       Department  \\\n",
       "990  E01578       Anthony Hong            Sr. Manger               IT   \n",
       "991  E03430        Leo Herrera  Sr. Business Partner  Human Resources   \n",
       "992  E03058      Robert Wright   Technical Architect               IT   \n",
       "993  E04762  Audrey Richardson              Director               IT   \n",
       "994  E01148     Scarlett Kumar       Systems Analyst               IT   \n",
       "995  E03094       Wesley Young           Sr. Analyst        Marketing   \n",
       "996  E01909       Lillian Khan               Analyst          Finance   \n",
       "997  E04398        Oliver Yang              Director        Marketing   \n",
       "998  E02521        Lily Nguyen           Sr. Analyst          Finance   \n",
       "999  E03545        Sofia Cheng        Vice President       Accounting   \n",
       "\n",
       "              Business Unit  Gender  Ethnicity  Age   Hire Date Annual Salary  \\\n",
       "990  Research & Development    Male      Asian   37  11/29/2010     $146,961    \n",
       "991  Research & Development    Male     Latino   48   4/22/1998      $85,369    \n",
       "992           Manufacturing    Male  Caucasian   30   6/14/2015      $67,489    \n",
       "993           Manufacturing  Female  Caucasian   46   10/6/2018     $166,259    \n",
       "994               Corporate  Female      Asian   55    1/7/2009      $47,032    \n",
       "995     Speciality Products    Male  Caucasian   33   9/18/2016      $98,427    \n",
       "996     Speciality Products  Female      Asian   44   5/31/2010      $47,387    \n",
       "997     Speciality Products    Male      Asian   31   6/10/2019     $176,710    \n",
       "998     Speciality Products  Female      Asian   33   1/28/2012      $95,960    \n",
       "999               Corporate  Female      Asian   63   7/26/2020     $216,195    \n",
       "\n",
       "    Bonus %        Country      City   Exit Date  \n",
       "990    11%   United States  Columbus         NaN  \n",
       "991     0%          Brazil    Manaus  11/27/2004  \n",
       "992     0%   United States   Chicago         NaN  \n",
       "993    17%   United States   Chicago         NaN  \n",
       "994     0%   United States  Columbus         NaN  \n",
       "995     0%   United States  Columbus         NaN  \n",
       "996     0%           China   Chengdu    1/8/2018  \n",
       "997    15%   United States     Miami         NaN  \n",
       "998     0%           China   Chengdu         NaN  \n",
       "999    31%   United States     Miami         NaN  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('employees_2.csv', encoding='latin1', sep='\\t')\n",
    "\n",
    "\n",
    "# Ver las 5 primeras filas\n",
    "df.head()\n",
    "\n",
    "# ver las 10 primeras filas\n",
    "df.head(10)\n",
    "\n",
    "\n",
    "# Ver las 5 últimas filas\n",
    "df.tail()\n",
    "\n",
    "# Ver las 10 últimas filas\n",
    "df.tail(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Desde un excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>EEID</th>\n",
       "      <th>Full Name</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Department</th>\n",
       "      <th>Business Unit</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Age</th>\n",
       "      <th>Hire Date</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Exit Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E02387</td>\n",
       "      <td>Emily Davis</td>\n",
       "      <td>Sr. Manger</td>\n",
       "      <td>IT</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Female</td>\n",
       "      <td>Black</td>\n",
       "      <td>55</td>\n",
       "      <td>2016-04-08</td>\n",
       "      <td>141604</td>\n",
       "      <td>0.15</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>2021-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E04105</td>\n",
       "      <td>Theodore Dinh</td>\n",
       "      <td>Technical Architect</td>\n",
       "      <td>IT</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>59</td>\n",
       "      <td>1997-11-29</td>\n",
       "      <td>99975</td>\n",
       "      <td>0.00</td>\n",
       "      <td>China</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E02572</td>\n",
       "      <td>Luna Sanders</td>\n",
       "      <td>Director</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>50</td>\n",
       "      <td>2006-10-26</td>\n",
       "      <td>163099</td>\n",
       "      <td>0.20</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E02832</td>\n",
       "      <td>Penelope Jordan</td>\n",
       "      <td>Computer Systems Manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>84913</td>\n",
       "      <td>0.07</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E01639</td>\n",
       "      <td>Austin Vo</td>\n",
       "      <td>Sr. Analyst</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>55</td>\n",
       "      <td>1995-11-20</td>\n",
       "      <td>95409</td>\n",
       "      <td>0.00</td>\n",
       "      <td>United States</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>E03094</td>\n",
       "      <td>Wesley Young</td>\n",
       "      <td>Sr. Analyst</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>33</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>98427</td>\n",
       "      <td>0.00</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>E01909</td>\n",
       "      <td>Lillian Khan</td>\n",
       "      <td>Analyst</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>44</td>\n",
       "      <td>2010-05-31</td>\n",
       "      <td>47387</td>\n",
       "      <td>0.00</td>\n",
       "      <td>China</td>\n",
       "      <td>Chengdu</td>\n",
       "      <td>2018-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>E04398</td>\n",
       "      <td>Oliver Yang</td>\n",
       "      <td>Director</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>176710</td>\n",
       "      <td>0.15</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>E02521</td>\n",
       "      <td>Lily Nguyen</td>\n",
       "      <td>Sr. Analyst</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>33</td>\n",
       "      <td>2012-01-28</td>\n",
       "      <td>95960</td>\n",
       "      <td>0.00</td>\n",
       "      <td>China</td>\n",
       "      <td>Chengdu</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>E03545</td>\n",
       "      <td>Sofia Cheng</td>\n",
       "      <td>Vice President</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>63</td>\n",
       "      <td>2020-07-26</td>\n",
       "      <td>216195</td>\n",
       "      <td>0.31</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       EEID        Full Name                 Job Title  Department  \\\n",
       "0    E02387      Emily Davis                Sr. Manger          IT   \n",
       "1    E04105    Theodore Dinh       Technical Architect          IT   \n",
       "2    E02572     Luna Sanders                  Director     Finance   \n",
       "3    E02832  Penelope Jordan  Computer Systems Manager          IT   \n",
       "4    E01639        Austin Vo               Sr. Analyst     Finance   \n",
       "..      ...              ...                       ...         ...   \n",
       "995  E03094     Wesley Young               Sr. Analyst   Marketing   \n",
       "996  E01909     Lillian Khan                   Analyst     Finance   \n",
       "997  E04398      Oliver Yang                  Director   Marketing   \n",
       "998  E02521      Lily Nguyen               Sr. Analyst     Finance   \n",
       "999  E03545      Sofia Cheng            Vice President  Accounting   \n",
       "\n",
       "              Business Unit  Gender  Ethnicity  Age  Hire Date  Annual Salary  \\\n",
       "0    Research & Development  Female      Black   55 2016-04-08         141604   \n",
       "1             Manufacturing    Male      Asian   59 1997-11-29          99975   \n",
       "2       Speciality Products  Female  Caucasian   50 2006-10-26         163099   \n",
       "3             Manufacturing  Female  Caucasian   26 2019-09-27          84913   \n",
       "4             Manufacturing    Male      Asian   55 1995-11-20          95409   \n",
       "..                      ...     ...        ...  ...        ...            ...   \n",
       "995     Speciality Products    Male  Caucasian   33 2016-09-18          98427   \n",
       "996     Speciality Products  Female      Asian   44 2010-05-31          47387   \n",
       "997     Speciality Products    Male      Asian   31 2019-06-10         176710   \n",
       "998     Speciality Products  Female      Asian   33 2012-01-28          95960   \n",
       "999               Corporate  Female      Asian   63 2020-07-26         216195   \n",
       "\n",
       "     Bonus %        Country       City  Exit Date  \n",
       "0       0.15  United States    Seattle 2021-10-16  \n",
       "1       0.00          China  Chongqing        NaT  \n",
       "2       0.20  United States    Chicago        NaT  \n",
       "3       0.07  United States    Chicago        NaT  \n",
       "4       0.00  United States    Phoenix        NaT  \n",
       "..       ...            ...        ...        ...  \n",
       "995     0.00  United States   Columbus        NaT  \n",
       "996     0.00          China    Chengdu 2018-01-08  \n",
       "997     0.15  United States      Miami        NaT  \n",
       "998     0.00          China    Chengdu        NaT  \n",
       "999     0.31  United States      Miami        NaT  \n",
       "\n",
       "[1000 rows x 14 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('employees.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Desde una SQL database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sqlite3\n",
    "\n",
    "# Preprar la conexión\n",
    "conn = sqlite3.connect('chinook.db')\n",
    "\n",
    "#df = pd.read_sql_query('SELECT * FROM customers', conn )\n",
    "#df\n",
    "\n",
    "\n",
    "df = pd.read_sql_query('SELECT FirstName, LastName FROM customers', conn )\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing y selección"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Indexación basada en etiquetas: Consiste en seleccionar los datos basándose en las etiquetas de índice de un DataFrame o Serie. Por ejemplo, puede utilizar el atributo .loc para seleccionar datos basándose en las etiquetas de fila y columna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "a  1  4  7\n",
       "b  2  5  8\n",
       "c  3  6  9"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}, index=['a', 'b', 'c'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecciona el numero 5\n",
    "'''\n",
    "df.loc[etiquetas_filas, etiquetas_columnas]\n",
    "'''\n",
    "\n",
    "valor = df.loc['b','B']\n",
    "valor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2\n",
       "B    5\n",
       "C    8\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleciona la fila 2\n",
    "'''\n",
    "df.loc[etiquetas_filas]\n",
    "'''\n",
    "df.loc['b']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "Name: A, dtype: int64\n",
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "Name: A, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Seleccciona la primera columna\n",
    "print(df.loc[:,'A']) \n",
    "# Otra forma\n",
    "print(df['A']) # <- Esta es más correcta para selecctionar toda la columna\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B  C\n",
      "a  1  4  7\n",
      "b  2  5  8\n",
      "   A  B  C\n",
      "a  1  4  7\n",
      "c  3  6  9\n"
     ]
    }
   ],
   "source": [
    "# Seleccione las filas 1 y 2\n",
    "valores = df.loc['a':'b']\n",
    "print(valores)\n",
    "\n",
    "# Seleccione las filas 1 y 3\n",
    "valores = df.loc[ ['a','c'] ]\n",
    "print(valores)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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",
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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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   B  C\n",
       "a  4  7\n",
       "b  5  8\n",
       "c  6  9"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecciona las dos últimas columnas\n",
    "df[['B','C']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   B  C\n",
       "a  4  7\n",
       "b  5  8"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecciona las dos primeras filas y las dos últimas columnas\n",
    "df.loc[['a','b'],['B', 'C']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Indexación basada en números enteros: Esto implica la selección de datos basada en la posición basada en enteros de las filas y columnas en un DataFrame o Serie. Por ejemplo, puede utilizar el atributo .iloc para seleccionar datos basados en índices enteros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B  C\n",
      "a  1  4  7\n",
      "b  2  5  8\n",
      "c  3  6  9\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}, index=['a', 'b', 'c'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleciona el numero 5\n",
    "df.iloc[1,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2\n",
       "B    5\n",
       "C    8\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecciona la fila 2\n",
    "df.iloc[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    7\n",
       "b    8\n",
       "c    9\n",
       "Name: C, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccione la tercera columna\n",
    "# Antes\n",
    "df['C']\n",
    "\n",
    "# Otra forma con iloc\n",
    "df.iloc[:,2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
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       "      <th>b</th>\n",
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       "   A  B  C\n",
       "a  1  4  7\n",
       "b  2  5  8"
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     "execution_count": 107,
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   "source": [
    "# Selecciona las filas 1 y 2\n",
    "df.iloc[[0,1]]\n",
    "\n",
    "# otra forma\n",
    "df.iloc[0:2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
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       "   B  C\n",
       "a  4  7\n",
       "b  5  8\n",
       "c  6  9"
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     "execution_count": 108,
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   ],
   "source": [
    "#Slicing Start:Stop:Step\n",
    "\n",
    "# selecciona las dos últimas columnas\n",
    "df.iloc[:,1:3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
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       "      <th>b</th>\n",
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       "   B  C\n",
       "a  4  7\n",
       "b  5  8"
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     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecciona las dos primeras filas y las dos últimas columnas\n",
    "df.iloc[:2, 1:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Indexación booleana: Consiste en seleccionar datos basándose en una máscara o condición booleana. Por ejemplo, puede utilizar la indexación booleana para seleccionar filas que cumplan determinados criterios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>160</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maria</td>\n",
       "      <td>165</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jose</td>\n",
       "      <td>170</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jorge</td>\n",
       "      <td>175</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ana</td>\n",
       "      <td>180</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Juan</td>\n",
       "      <td>185</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Luis</td>\n",
       "      <td>190</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Laura</td>\n",
       "      <td>195</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Carlos</td>\n",
       "      <td>200</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Marcos</td>\n",
       "      <td>205</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Altura  Edad\n",
       "0   Pedro     160    12\n",
       "1   Maria     165    34\n",
       "2    Jose     170    32\n",
       "3   Jorge     175    45\n",
       "4     Ana     180    21\n",
       "5    Juan     185    23\n",
       "6    Luis     190    45\n",
       "7   Laura     195    32\n",
       "8  Carlos     200    34\n",
       "9  Marcos     205    56"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({ 'Name': ['Pedro', 'Maria', 'Jose', 'Jorge', 'Ana', 'Juan', 'Luis', 'Laura', 'Carlos', 'Marcos'],\n",
    "                    'Altura': [160, 165, 170, 175, 180, 185, 190, 195, 200, 205],\n",
    "                    'Edad': [12, 34, 32, 45, 21, 23, 45, 32, 34, 56],\n",
    "                })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "9     True\n",
       "Name: Altura, dtype: bool"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecciona las filas donde la altura es mayor a 180\n",
    "gente_alta = df['Altura'] > 180\n",
    "gente_alta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Laura</td>\n",
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       "      <th>8</th>\n",
       "      <td>Carlos</td>\n",
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       "      <th>9</th>\n",
       "      <td>Marcos</td>\n",
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       "     Name  Altura  Edad\n",
       "5    Juan     185    23\n",
       "6    Luis     190    45\n",
       "7   Laura     195    32\n",
       "8  Carlos     200    34\n",
       "9  Marcos     205    56"
      ]
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     "execution_count": 113,
     "metadata": {},
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   ],
   "source": [
    "df[gente_alta]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Luis</td>\n",
       "      <td>190</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Laura</td>\n",
       "      <td>195</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Carlos</td>\n",
       "      <td>200</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Marcos</td>\n",
       "      <td>205</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "     Name  Altura  Edad\n",
       "6    Luis     190    45\n",
       "7   Laura     195    32\n",
       "8  Carlos     200    34\n",
       "9  Marcos     205    56"
      ]
     },
     "execution_count": 116,
     "metadata": {},
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    }
   ],
   "source": [
    "# Seleccion las filas donde la altura es mayor a 180 y la edad es mayor a 30\n",
    "gente_alta = df['Altura'] > 180\n",
    "mayor_30 = df['Edad'] > 30\n",
    "\n",
    "df[ gente_alta & mayor_30 ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Jorge</td>\n",
       "      <td>45</td>\n",
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      "text/plain": [
       "    Name  Edad\n",
       "0  Pedro    12\n",
       "1  Maria    34\n",
       "2   Jose    32\n",
       "3  Jorge    45"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# El nombre y edad de la gente que mida menos de 180\n",
    "menor_de_180 = df['Altura'] < 180\n",
    "nuevo_df = df[menor_de_180]\n",
    "\n",
    "nuevo_df[['Name', 'Edad']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>Jose</td>\n",
       "      <td>32</td>\n",
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       "      <th>3</th>\n",
       "      <td>Jorge</td>\n",
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      "text/plain": [
       "    Name  Edad\n",
       "0  Pedro    12\n",
       "1  Maria    34\n",
       "2   Jose    32\n",
       "3  Jorge    45"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El nombre y edad de la gente que mida menos de 180 sin crear un nuevo df\n",
    "menor_de_180 = df['Altura'] < 180\n",
    "df[menor_de_180][['Name', 'Edad']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Del mismo modo, podemos utilizar la indexación basada en etiquetas y la indexación basada en enteros para asignar valores a un DataFrame o a una Serie:"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "a  1  4  7\n",
       "b  2  5  8\n",
       "c  3  6  9"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}, index=['a', 'b', 'c'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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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       "  <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>a</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "    A  B  C\n",
       "a  10  4  7\n",
       "b   2  5  8\n",
       "c   3  6  9"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Establece el valor de la celda en la fila 'a' y la columna 'A' en 10\n",
    "df.loc['a','A'] = 10\n",
    "\n",
    "df\n"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    A   B   C\n",
       "a  10   4   7\n",
       "b  10  11  12\n",
       "c   3   6   9"
      ]
     },
     "execution_count": 135,
     "metadata": {},
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    }
   ],
   "source": [
    "# Establece la segunda fila a [10, 11, 12]\n",
    "df.loc['b'] = [10,11,12]\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "    A   B   C\n",
       "a  10   4  13\n",
       "b  10  11  14\n",
       "c   3   6  15"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Establece la tercera columna en [13, 14, 15]\n",
    "df['C'] = [13,14,15]\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>110</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>120</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "     A   B   C\n",
       "a  100   4  13\n",
       "b  110  11  14\n",
       "c  120   6  15"
      ]
     },
     "execution_count": 137,
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   ],
   "source": [
    "# Establece la primera columna en [100, 110, 120]\n",
    "df['A'] = [100,110,120] \n",
    "df\n",
    "\n",
    "# otra forma. Aquí siempre va a modificar la primera columna\n",
    "df.iloc[:,0] = [100,110,120]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "     A  B   C\n",
       "a  100  4  13\n",
       "b  110  0   0\n",
       "c  120  0   0"
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     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Establece las dos últimas columnas y las dos últimas filas en 0\n",
    "df.loc[['b','c'],['B','C']] = 0\n",
    "df\n",
    "\n",
    "# La alternativa\n",
    "df.iloc[1:,1:] = 0\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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",
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       "\n",
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       "    <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>a</th>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>110</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>120</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     A   B   C\n",
       "a  100   4  13\n",
       "b  110  10  20\n",
       "c  120  30  40"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Establece las dos últimas columnas y las dos últimas filas en a [[10,20], [30,40]]\n",
    "\n",
    "df.iloc[1:,1:] = [[10,20], [30,40]]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>110</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>120</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C\n",
       "a  100   4  13\n",
       "b  110  10  20\n",
       "c  120  30  40\n",
       "d   10  20  30"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# podemos añadir un dato al dataframe con la siguiente sintaxis:\n",
    "df.loc['d'] = [10,20,30 ]\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>110</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>120</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C\n",
       "a  100   4  13\n",
       "b  110  10  20\n",
       "c  120  30  40"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminar una fila del DataFrame\n",
    "df.drop('d', inplace=True)\n",
    "df\n",
    "\n",
    "#inplace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejercicios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>Animal</th>\n",
       "      <th>Size</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dog</td>\n",
       "      <td>Small</td>\n",
       "      <td>Brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dog</td>\n",
       "      <td>Large</td>\n",
       "      <td>Brown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Animal   Size  Color\n",
       "0    Dog  Small  Brown\n",
       "2    Dog  Large  Brown"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, selecciona las filas donde la columna Animal sea igual a Dog\n",
    "df = pd.DataFrame({'Animal': ['Dog', 'Cat', 'Dog', 'Fish', 'Fish'],\n",
    "                   'Size': ['Small', 'Small', 'Large', 'Small', 'Large'],\n",
    "                   'Color': ['Brown', 'Black', 'Brown', 'Gray', 'White']})\n",
    "\n",
    "animal_dog = df['Animal'] == 'Dog'\n",
    "df[animal_dog]\n",
    "\n",
    "# Lo normal\n",
    "df[df['Animal'] == 'Dog']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dave</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eve</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age\n",
       "2  Charlie   35\n",
       "3     Dave   40\n",
       "4      Eve   45"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, selecciona las filas donde la columna Age sea mayor a 30\n",
    "df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve'],\n",
    "                   'Age': [25, 30, 35, 40, 45]})\n",
    "\n",
    "df[ df['Age'] > 30  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eve</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name  Height\n",
       "4  Eve     3.5"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, selecciona las filas donde la columna Height sea menor o igual a 5\n",
    "df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve'],\n",
    "                   'Height': [5.5, 6.0, 6.5, 7.0, 3.5]})\n",
    "\n",
    "df[ df['Height'] <= 5 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eve</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Score\n",
       "0  Alice     85\n",
       "1    Bob     90\n",
       "4    Eve     80"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, selecciona las filas donde la columna Score sea mayor o igual a 80 y menor o igual a 90\n",
    "df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve'],\n",
    "                   'Score': [85, 90, 95, 70, 80]})\n",
    "\n",
    "gte_80 = df['Score'] >= 80\n",
    "lte_90 = df['Score'] <= 90\n",
    "\n",
    "df[ gte_80 & lte_90 ]\n",
    "\n",
    "# otra forma\n",
    "df[(df['Score'] >= 80) & (df['Score'] <= 90)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>Name</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Gender\n",
       "1      Bob   Male\n",
       "2  Charlie   Male"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, selecciona todas las filas cuyo Gender no sea de Female\n",
    "df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve'],\n",
    "                   'Gender': ['Female', 'Male', 'Male', 'Female', 'Female']})\n",
    "\n",
    "df[ df['Gender'] != 'Female']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limpieza y preparación de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tratamiento de datos no disponibles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para identificar los valores que faltan, puede utilizar la función isnull, que devuelve una máscara booleana que indica si falta o no cada valor. Por ejemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A      B\n",
       "0  True   True\n",
       "1  True  False\n",
       "2  True   True\n",
       "3  True  False\n",
       "4  True   True"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, None, 8, None, 10]})\n",
    "\n",
    "# Buscar valores nulos\n",
    "df.isnull()\n",
    "\n",
    "# Buscar valores no nulos\n",
    "df.notnull()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También puede utilizar la función notnull, que es lo contrario de isnull y devuelve una máscara booleana que indica si cada valor no falta."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez identificados los valores que faltan, puede tratarlos de varias maneras. Una opción es simplemente eliminar las filas o columnas que contienen valores perdidos utilizando la función dropna. Por ejemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A     B\n",
      "0  1   6.0\n",
      "1  2   NaN\n",
      "2  3   8.0\n",
      "3  4   NaN\n",
      "4  5  10.0\n"
     ]
    },
    {
     "data": {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
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       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A     B\n",
       "0  1   6.0\n",
       "2  3   8.0\n",
       "4  5  10.0"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, None, 8, None, 10]})\n",
    "print(df)\n",
    "\n",
    "\n",
    "# Elimina las filas con valores nulos\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A     B\n",
      "0  1   6.0\n",
      "1  2   NaN\n",
      "2  3   8.0\n",
      "3  4   NaN\n",
      "4  5  10.0\n"
     ]
    },
    {
     "data": {
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       "      <th>A</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   A\n",
       "0  1\n",
       "1  2\n",
       "2  3\n",
       "3  4\n",
       "4  5"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, None, 8, None, 10]})\n",
    "print(df)\n",
    "\n",
    "# Elimina la columnas con valores nulos\n",
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra opción es rellenar los valores que faltan con un valor específico utilizando la función fillna. Por ejemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A     B\n",
       "0  1   6.0\n",
       "1  2   0.0\n",
       "2  3   8.0\n",
       "3  4   0.0\n",
       "4  5  10.0"
      ]
     },
     "execution_count": 193,
     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, None, 8, None, 10]})\n",
    "df\n",
    "\n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "   A     B\n",
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       "2  3   8.0\n",
       "3  4   8.0\n",
       "4  5  10.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, None, 8, None, 10]})\n",
    "df\n",
    "# Rellena los valores nulos con la media de la columna\n",
    "df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1\n",
       "B    1\n",
       "C    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, cuenta el número de valores faltantes por columna\n",
    "df = pd.DataFrame({'A': [1, 2, 3, 4, None],\n",
    "                   'B': [None, 2, 3, 4, 5],\n",
    "                   'C': [1, 2, None, 4, None]})\n",
    "\n",
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  3.5  1.0\n",
       "1  2.0  2.0  2.0\n",
       "2  3.0  3.0  3.0\n",
       "3  4.0  4.0  4.0\n",
       "4  2.5  5.0  5.0"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, rellena los valores faltantes de cada columna con la media de la columna\n",
    "df = pd.DataFrame({'A': [1, 2, 3, 4, None],\n",
    "                   'B': [None, 2, 3, 4, 5],\n",
    "                   'C': [1, 2, None, 4, 5]})\n",
    "\n",
    "\n",
    "df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manipulación de tipos de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los tipos de datos en Pandas se refieren al tipo de datos que se almacenan en una columna de un DataFrame. Algunos tipos de datos comunes en Pandas incluyen enteros, flotantes, cadenas y fechas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. La función astype() permite convertir explícitamente un tipo de datos en otro. Por ejemplo, puede utilizarla para convertir una columna de enteros en una columna de flotantes, o una columna de cadenas en una columna de fechas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    object\n",
       "B    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, convierte los valores de la segunda columna a enteros\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3,4,5,6],\n",
    "    'B': ['1','2','3','4','5','6']\n",
    "})\n",
    "\n",
    "df['A'] = df['A'].astype('str')\n",
    "\n",
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. La función to_numeric es similar a astype, pero está diseñada específicamente para convertir datos a tipos numéricos (por ejemplo, enteros o valores de coma flotante). Esta función es especialmente útil si tiene datos almacenados en forma de cadena, pero desea realizar operaciones numéricas con ellos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A      int64\n",
      "B    float32\n",
      "dtype: object\n",
      "   A    B\n",
      "0  1  1.0\n",
      "1  2  2.0\n",
      "2  3  3.0\n",
      "3  4  4.0\n",
      "4  5  5.0\n",
      "5  6  6.0\n"
     ]
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, convierte los valores de la segunda columna a números reales\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3,4,5,6],\n",
    "    'B': ['1','2','3','4','5','6']\n",
    "})\n",
    "\n",
    "df.dtypes\n",
    "\n",
    "df['B'] = pd.to_numeric(df['B'], downcast='float')\n",
    "print(df.dtypes)\n",
    "\n",
    "print(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. La función to_datetime se utiliza para convertir datos en una variedad de formatos al tipo de datos datetime. Esto puede ser particularmente útil si tienes datos que se almacenan como una cadena, pero representan una fecha u hora."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    datetime64[ns]\n",
       "B            object\n",
       "C            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dado el siguiente DataFrame, convierte los valores de la segunda columna a números objetos DateTime\n",
    "df = pd.DataFrame({'A': ['2022-01-01', '2022-02-01', '2022-03-01'], 'B': ['4', '5', '6'], 'C': ['7', '8', '9']})\n",
    "\n",
    "df['A'] = pd.to_datetime(df['A'])\n",
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminación de duplicados: Pandas proporciona la función drop_duplicates para identificar y eliminar filas duplicadas de un DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Name  Age  Gender\n",
      "0    Alice   24  Female\n",
      "1      Bob   50    Male\n",
      "2  Charlie   36    Male\n",
      "3    Alice   24  Female\n",
      "4      Bob   50    Male\n",
      "5  Charlie   38    Male\n",
      "\n",
      "\n",
      "      Name  Age  Gender\n",
      "0    Alice   24  Female\n",
      "1      Bob   50    Male\n",
      "2  Charlie   36    Male\n",
      "5  Charlie   38    Male\n"
     ]
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, elimina los valores duplicados\n",
    "df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob', 'Charlie'],\n",
    "                   'Age': [24, 50, 36, 24, 50, 38],\n",
    "                   'Gender': ['Female', 'Male', 'Male', 'Female', 'Male', 'Male']})\n",
    "\n",
    "print(df)\n",
    "print('\\n')\n",
    "print(df.drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Name  Age  Gender\n",
      "0      Alice   24  Female\n",
      "1        Bob   50    Male\n",
      "2    Charlie   36    Male\n",
      "3      Alice   34  Female\n",
      "4        Bob   60    Male\n",
      "5  Charlie_2   46  Female\n",
      "\n",
      "\n",
      "        Name  Age  Gender\n",
      "2    Charlie   36    Male\n",
      "5  Charlie_2   46  Female\n"
     ]
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, elimina los valores duplicados basándose en la columna Name\n",
    "df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob', 'Charlie'],\n",
    "                   'Age': [24, 50, 36, 34, 60, 46],\n",
    "                   'Gender': ['Female', 'Male', 'Male', 'Female', 'Male', 'Female']})\n",
    "\n",
    "print(df)\n",
    "print('\\n')\n",
    "print(df.drop_duplicates(subset=['Name'], keep=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordenando datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ordenar datos en un Pandas DataFrame, puede utilizar la función sort_values(). Esta función permite especificar una o más columnas para su ordenación, y proporciona una serie de opciones para controlar el orden de ordenación y el manejo de valores nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alice</td>\n",
       "      <td>34</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob</td>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Gender\n",
       "0    Alice   24  Female\n",
       "1      Bob   50    Male\n",
       "2  Charlie   36    Male\n",
       "3    Alice   34  Female\n",
       "4      Bob   60    Male\n",
       "5  Charlie   46  Female"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dado el siguiente DataFrame, ordena los valores por la columna Age en orden ascendente\n",
    "df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob', 'Charlie'],\n",
    "                   'Age': [24, 50, 36, 34, 60, 46],\n",
    "                   'Gender': ['Female', 'Male', 'Male', 'Female', 'Male', 'Female']})\n",
    "\n",
    "\n",
    "df.sort_values('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob</td>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alice</td>\n",
       "      <td>34</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Gender\n",
       "4      Bob   60    Male\n",
       "1      Bob   50    Male\n",
       "5  Charlie   46  Female\n",
       "2  Charlie   36    Male\n",
       "3    Alice   34  Female\n",
       "0    Alice   24  Female"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ahora ordena por edad en orden descendente\n",
    "df.sort_values('Age', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También puede ordenar por varias columnas pasando una lista de nombres de columnas al parámetro by. Por ejemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob</td>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alice</td>\n",
       "      <td>34</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Gender\n",
       "2  Charlie   36    Male\n",
       "1      Bob   50    Male\n",
       "4      Bob   60    Male\n",
       "0    Alice   24  Female\n",
       "3    Alice   34  Female\n",
       "5  Charlie   46  Female"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ordena el dataframe por la columna Gender en orden descendente y por la columna Age en orden ascendente\n",
    "df.sort_values(['Gender', 'Age'], ascending=[False, True])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fusión y unión de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas proporciona varias funciones para combinar y unir datos, incluyendo concat, merge y join. Estas funciones permiten combinar datos de múltiples fuentes en un único DataFrame, o dividir un único DataFrame en múltiples partes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. concat() es una función para concatenar datos de múltiples fuentes a lo largo de un eje determinado. Puede utilizar concat() para combinar datos de múltiples DataFrames o de una lista de objetos Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bob</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dave</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age\n",
       "0    Alice   24\n",
       "1      Bob   50\n",
       "2  Charlie   36\n",
       "3      Bob   50\n",
       "4  Charlie   36\n",
       "5     Dave   30"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Dados los siguientes DataFrames, concatena los DataFrames en un único DataFrame\n",
    "df1 = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'],\n",
    "                    'Age': [24, 50, 36]})\n",
    "df2 = pd.DataFrame({'Name': ['Bob', 'Charlie', 'Dave'],\n",
    "                    'Age': [50, 36, 30]})\n",
    "\n",
    "concatenados = pd.concat([df1, df2],ignore_index=True)\n",
    "\n",
    "concatenados\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. merge() es una función para combinar datos de dos o más fuentes basándose en columnas comunes. Puede utilizar merge() para combinar datos de diferentes DataFrames basándose en una o más columnas compartidas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Name  Age\n",
      "0    Alice   24\n",
      "1      Bob   50\n",
      "2  Charlie   36\n",
      "      Name  Salary\n",
      "0      Bob   50000\n",
      "1  Charlie   60000\n",
      "2     Dave   40000\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>36.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name   Age   Salary\n",
       "0    Alice  24.0      NaN\n",
       "1      Bob  50.0  50000.0\n",
       "2  Charlie  36.0  60000.0\n",
       "3     Dave   NaN  40000.0"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dados los siguientes DataFrames, concatena los DataFrames en un único DataFrame basado en la columna Name\n",
    "df1 = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'],\n",
    "                    'Age': [24, 50, 36]})\n",
    "df2 = pd.DataFrame({'Name': ['Bob', 'Charlie', 'Dave'],\n",
    "                    'Salary': [50000, 60000, 40000]})\n",
    "\n",
    "print(df1)\n",
    "print(df2)\n",
    "\n",
    "pd.merge(df1, df2, on='Name', how='outer')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. join() es una función para unir datos de un DataFrame a otro basándose en sus valores de índice. Puede utilizar join() para combinar datos de dos DataFrames basándose en sus etiquetas de fila (valores de índice). Por ejemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Name  Age\n",
      "A    Alice   24\n",
      "B      Bob   50\n",
      "C  Charlie   36\n",
      "   Salary\n",
      "A   50000\n",
      "B   60000\n",
      "C   40000\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Alice</td>\n",
       "      <td>24</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Bob</td>\n",
       "      <td>50</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>36</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Salary\n",
       "A    Alice   24   50000\n",
       "B      Bob   50   60000\n",
       "C  Charlie   36   40000"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dados los siguientes DataFrames, concatena los DataFrames en un único DataFrame basado los indices\n",
    "df1 = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'],\n",
    "                    'Age': [24, 50, 36]},\n",
    "                   index=['A', 'B', 'C'])\n",
    "\n",
    "df2 = pd.DataFrame({'Salary': [50000, 60000, 40000]},\n",
    "                   index=['A', 'B', 'C'])\n",
    "\n",
    "\n",
    "print(df1)\n",
    "print(df2)\n",
    "\n",
    "df1.join(df2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Name  Age\n",
      "A    Alice   24\n",
      "B      Bob   50\n",
      "C  Charlie   36\n",
      "D     Dave   13\n",
      "   Salary\n",
      "A   50000\n",
      "B   60000\n",
      "C   40000\n"
     ]
    },
    {
     "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>Salary</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>Alice</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>60000.0</td>\n",
       "      <td>Bob</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Dave</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Salary     Name  Age\n",
       "A  50000.0    Alice   24\n",
       "B  60000.0      Bob   50\n",
       "C  40000.0  Charlie   36\n",
       "D      NaN     Dave   13"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datos los siguientes DataFrames, concatena los DataFrames en un único DataFrame basado los indices\n",
    "df1 = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'Dave'],\n",
    "                    'Age': [24, 50, 36, 13]},\n",
    "                   index=['A', 'B', 'C', 'D'])\n",
    "df2 = pd.DataFrame({'Salary': [50000, 60000, 40000]},\n",
    "                   index=['A', 'B', 'C'])\n",
    "\n",
    "\n",
    "\n",
    "print(df1)\n",
    "print(df2)\n",
    "\n",
    "df2.join(df1, how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cargar datos desde un archivo\n",
    "### cargar un archivo csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "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>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amir Johnson</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>90.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jordan Mickey</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>55.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>235.0</td>\n",
       "      <td>LSU</td>\n",
       "      <td>1170960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kelly Olynyk</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>41.0</td>\n",
       "      <td>C</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>Gonzaga</td>\n",
       "      <td>2165160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Terry Rozier</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>12.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>1824360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Marcus Smart</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>36.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>3431040.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "5   Amir Johnson  Boston Celtics    90.0       PF  29.0    6-9   240.0   \n",
       "6  Jordan Mickey  Boston Celtics    55.0       PF  21.0    6-8   235.0   \n",
       "7   Kelly Olynyk  Boston Celtics    41.0        C  25.0    7-0   238.0   \n",
       "8   Terry Rozier  Boston Celtics    12.0       PG  22.0    6-2   190.0   \n",
       "9   Marcus Smart  Boston Celtics    36.0       PG  22.0    6-4   220.0   \n",
       "\n",
       "             College      Salary  \n",
       "0              Texas   7730337.0  \n",
       "1          Marquette   6796117.0  \n",
       "2  Boston University         NaN  \n",
       "3      Georgia State   1148640.0  \n",
       "4                NaN   5000000.0  \n",
       "5                NaN  12000000.0  \n",
       "6                LSU   1170960.0  \n",
       "7            Gonzaga   2165160.0  \n",
       "8         Louisville   1824360.0  \n",
       "9     Oklahoma State   3431040.0  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('nba_players_2.csv')\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lectura de datos de un dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Team', 'Number', 'Position', 'Age', 'Height', 'Weight',\n",
       "       'College', 'Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Muestra las cabeceras del dataframe df\n",
    "'''\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name   Age\n",
       "0    Avery Bradley  25.0\n",
       "1      Jae Crowder  25.0\n",
       "2     John Holland  27.0\n",
       "3      R.J. Hunter  22.0\n",
       "4    Jonas Jerebko  29.0\n",
       "..             ...   ...\n",
       "452     Trey Lyles  20.0\n",
       "453   Shelvin Mack  26.0\n",
       "454      Raul Neto  24.0\n",
       "455   Tibor Pleiss  26.0\n",
       "456    Jeff Withey  26.0\n",
       "\n",
       "[457 rows x 2 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Muestra las columnas Name y Age del dataframe df\n",
    "'''\n",
    "df[ ['Name', 'Age'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Muestra las primeras 5 filas del dataframe df\n",
    "'''\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amir Johnson</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>90.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jordan Mickey</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>55.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>235.0</td>\n",
       "      <td>LSU</td>\n",
       "      <td>1170960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kelly Olynyk</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>41.0</td>\n",
       "      <td>C</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>Gonzaga</td>\n",
       "      <td>2165160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Terry Rozier</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>12.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>1824360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Marcus Smart</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>36.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Oklahoma State</td>\n",
       "      <td>3431040.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "5   Amir Johnson  Boston Celtics    90.0       PF  29.0    6-9   240.0   \n",
       "6  Jordan Mickey  Boston Celtics    55.0       PF  21.0    6-8   235.0   \n",
       "7   Kelly Olynyk  Boston Celtics    41.0        C  25.0    7-0   238.0   \n",
       "8   Terry Rozier  Boston Celtics    12.0       PG  22.0    6-2   190.0   \n",
       "9   Marcus Smart  Boston Celtics    36.0       PG  22.0    6-4   220.0   \n",
       "\n",
       "             College      Salary  \n",
       "0              Texas   7730337.0  \n",
       "1          Marquette   6796117.0  \n",
       "2  Boston University         NaN  \n",
       "3      Georgia State   1148640.0  \n",
       "4                NaN   5000000.0  \n",
       "5                NaN  12000000.0  \n",
       "6                LSU   1170960.0  \n",
       "7            Gonzaga   2165160.0  \n",
       "8         Louisville   1824360.0  \n",
       "9     Oklahoma State   3431040.0  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Muestra 10 filas del dataframe df\n",
    "'''\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mostrar las filas en función de una condición"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
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       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Pablo Prigioni</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>9.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>185.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>947726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Vince Carter</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>15.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>220.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>4088019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Tim Duncan</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Wake Forest</td>\n",
       "      <td>5250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>Andre Miller</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>24.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Utah</td>\n",
       "      <td>250750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Kevin Garnett</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>21.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8500000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name                    Team  Number Position   Age Height  \\\n",
       "102  Pablo Prigioni    Los Angeles Clippers     9.0       PG  39.0    6-3   \n",
       "261    Vince Carter       Memphis Grizzlies    15.0       SG  39.0    6-6   \n",
       "298      Tim Duncan       San Antonio Spurs    21.0        C  40.0   6-11   \n",
       "304    Andre Miller       San Antonio Spurs    24.0       PG  40.0    6-3   \n",
       "400   Kevin Garnett  Minnesota Timberwolves    21.0       PF  40.0   6-11   \n",
       "\n",
       "     Weight         College     Salary  \n",
       "102   185.0             NaN   947726.0  \n",
       "261   220.0  North Carolina  4088019.0  \n",
       "298   250.0     Wake Forest  5250000.0  \n",
       "304   200.0            Utah   250750.0  \n",
       "400   240.0             NaN  8500000.0  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Muestra los jugadores que tengan una edad igual o mayor a 39\n",
    "'''\n",
    "igual_o_mayor_39 = df['Age'] >= 39\n",
    "\n",
    "\n",
    "df[igual_o_mayor_39]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Nemanja Bjelica</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>88.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3950001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Gorgui Dieng</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>5.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>241.0</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>1474440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Kevin Garnett</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>21.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Tyus Jones</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>195.0</td>\n",
       "      <td>Duke</td>\n",
       "      <td>1282080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Zach LaVine</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>2148360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Shabazz Muhammad</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>15.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>223.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>2056920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Adreian Payne</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>33.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>237.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>1938840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Nikola Pekovic</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>14.0</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>307.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Tayshaun Prince</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>12.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>212.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Ricky Rubio</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>9.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>194.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Damjan Rudez</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>230.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1149500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Greg Smith</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Fresno State</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Karl-Anthony Towns</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>32.0</td>\n",
       "      <td>C</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>5703600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Andrew Wiggins</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>199.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>5758680.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name                    Team  Number Position   Age Height  \\\n",
       "398     Nemanja Bjelica  Minnesota Timberwolves    88.0       PF  28.0   6-10   \n",
       "399        Gorgui Dieng  Minnesota Timberwolves     5.0        C  26.0   6-11   \n",
       "400       Kevin Garnett  Minnesota Timberwolves    21.0       PF  40.0   6-11   \n",
       "401          Tyus Jones  Minnesota Timberwolves     1.0       PG  20.0    6-2   \n",
       "402         Zach LaVine  Minnesota Timberwolves     8.0       PG  21.0    6-5   \n",
       "403    Shabazz Muhammad  Minnesota Timberwolves    15.0       SF  23.0    6-6   \n",
       "404       Adreian Payne  Minnesota Timberwolves    33.0       PF  25.0   6-10   \n",
       "405      Nikola Pekovic  Minnesota Timberwolves    14.0        C  30.0   6-11   \n",
       "406     Tayshaun Prince  Minnesota Timberwolves    12.0       SF  36.0    6-9   \n",
       "407         Ricky Rubio  Minnesota Timberwolves     9.0       PG  25.0    6-4   \n",
       "408        Damjan Rudez  Minnesota Timberwolves    10.0       SF  29.0    6-9   \n",
       "409          Greg Smith  Minnesota Timberwolves     4.0       PF  25.0   6-10   \n",
       "410  Karl-Anthony Towns  Minnesota Timberwolves    32.0        C  20.0    7-0   \n",
       "411      Andrew Wiggins  Minnesota Timberwolves    22.0       SG  21.0    6-8   \n",
       "\n",
       "     Weight         College      Salary  \n",
       "398   240.0             NaN   3950001.0  \n",
       "399   241.0      Louisville   1474440.0  \n",
       "400   240.0             NaN   8500000.0  \n",
       "401   195.0            Duke   1282080.0  \n",
       "402   189.0            UCLA   2148360.0  \n",
       "403   223.0            UCLA   2056920.0  \n",
       "404   237.0  Michigan State   1938840.0  \n",
       "405   307.0             NaN  12100000.0  \n",
       "406   212.0        Kentucky    947276.0  \n",
       "407   194.0             NaN  12700000.0  \n",
       "408   230.0             NaN   1149500.0  \n",
       "409   250.0    Fresno State         NaN  \n",
       "410   244.0        Kentucky   5703600.0  \n",
       "411   199.0          Kansas   5758680.0  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Muestra los jugadores de los Minnesota Timberwolves\n",
    "'''\n",
    "\n",
    "df[ df['Team'] ==  'Minnesota Timberwolves']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Operaciones con los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "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>Number</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>457.000000</td>\n",
       "      <td>457.000000</td>\n",
       "      <td>457.000000</td>\n",
       "      <td>4.460000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.678337</td>\n",
       "      <td>26.938731</td>\n",
       "      <td>221.522976</td>\n",
       "      <td>4.842684e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.966090</td>\n",
       "      <td>4.404016</td>\n",
       "      <td>26.368343</td>\n",
       "      <td>5.229238e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>3.088800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.044792e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>2.839073e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>6.500000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>2.500000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Number         Age      Weight        Salary\n",
       "count  457.000000  457.000000  457.000000  4.460000e+02\n",
       "mean    17.678337   26.938731  221.522976  4.842684e+06\n",
       "std     15.966090    4.404016   26.368343  5.229238e+06\n",
       "min      0.000000   19.000000  161.000000  3.088800e+04\n",
       "25%      5.000000   24.000000  200.000000  1.044792e+06\n",
       "50%     13.000000   26.000000  220.000000  2.839073e+06\n",
       "75%     25.000000   30.000000  240.000000  6.500000e+06\n",
       "max     99.000000   40.000000  307.000000  2.500000e+07"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas puede hacer un resumen del dataframe\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "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>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>2250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>4171680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Aaron Harrison</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>525093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Adreian Payne</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>33.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>237.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>1938840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Wilson Chandler</td>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>21.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>225.0</td>\n",
       "      <td>DePaul</td>\n",
       "      <td>10449438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Xavier Munford</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>14.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Zach LaVine</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>2148360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>Zach Randolph</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>50.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>9638555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Zaza Pachulia</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>27.0</td>\n",
       "      <td>C</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>275.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name                    Team  Number Position   Age Height  \\\n",
       "152     Aaron Brooks           Chicago Bulls     0.0       PG  31.0    6-0   \n",
       "356     Aaron Gordon           Orlando Magic     0.0       PF  20.0    6-9   \n",
       "328   Aaron Harrison       Charlotte Hornets     9.0       SG  21.0    6-6   \n",
       "404    Adreian Payne  Minnesota Timberwolves    33.0       PF  25.0   6-10   \n",
       "312       Al Horford           Atlanta Hawks    15.0        C  30.0   6-10   \n",
       "..               ...                     ...     ...      ...   ...    ...   \n",
       "386  Wilson Chandler          Denver Nuggets    21.0       SF  29.0    6-8   \n",
       "270   Xavier Munford       Memphis Grizzlies    14.0       PG  24.0    6-3   \n",
       "402      Zach LaVine  Minnesota Timberwolves     8.0       PG  21.0    6-5   \n",
       "271    Zach Randolph       Memphis Grizzlies    50.0       PF  34.0    6-9   \n",
       "237    Zaza Pachulia        Dallas Mavericks    27.0        C  32.0   6-11   \n",
       "\n",
       "     Weight         College      Salary  \n",
       "152   161.0          Oregon   2250000.0  \n",
       "356   220.0         Arizona   4171680.0  \n",
       "328   210.0        Kentucky    525093.0  \n",
       "404   237.0  Michigan State   1938840.0  \n",
       "312   245.0         Florida  12000000.0  \n",
       "..      ...             ...         ...  \n",
       "386   225.0          DePaul  10449438.0  \n",
       "270   180.0    Rhode Island         NaN  \n",
       "402   189.0            UCLA   2148360.0  \n",
       "271   260.0  Michigan State   9638555.0  \n",
       "237   275.0             NaN   5200000.0  \n",
       "\n",
       "[457 rows x 9 columns]"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Muestra el dataframe ordenado por el atributo Name\n",
    "'''\n",
    "df.sort_values('Name')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Dennis Schroder</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>17.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>172.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1763400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>Jeff Teague</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>186.0</td>\n",
       "      <td>Wake Forest</td>\n",
       "      <td>8000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Kent Bazemore</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>201.0</td>\n",
       "      <td>Old Dominion</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Kirk Hinrich</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>12.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>2854940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Marcus Thornton</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>15.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>205.0</td>\n",
       "      <td>LSU</td>\n",
       "      <td>200600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Markieff Morris</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>5.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>8000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>Nene Hilario</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>42.0</td>\n",
       "      <td>C</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Otto Porter Jr.</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>198.0</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>4662960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>Ramon Sessions</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>7.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2170465.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name                Team  Number Position   Age Height  \\\n",
       "312       Al Horford       Atlanta Hawks    15.0        C  30.0   6-10   \n",
       "318  Dennis Schroder       Atlanta Hawks    17.0       PG  22.0    6-1   \n",
       "323      Jeff Teague       Atlanta Hawks     0.0       PG  27.0    6-2   \n",
       "309    Kent Bazemore       Atlanta Hawks    24.0       SF  26.0    6-5   \n",
       "311     Kirk Hinrich       Atlanta Hawks    12.0       SG  35.0    6-4   \n",
       "..               ...                 ...     ...      ...   ...    ...   \n",
       "381  Marcus Thornton  Washington Wizards    15.0       SF  29.0    6-4   \n",
       "376  Markieff Morris  Washington Wizards     5.0       PF  26.0   6-10   \n",
       "375     Nene Hilario  Washington Wizards    42.0        C  33.0   6-11   \n",
       "378  Otto Porter Jr.  Washington Wizards    22.0       SF  23.0    6-8   \n",
       "379   Ramon Sessions  Washington Wizards     7.0       PG  30.0    6-3   \n",
       "\n",
       "     Weight       College      Salary  \n",
       "312   245.0       Florida  12000000.0  \n",
       "318   172.0           NaN   1763400.0  \n",
       "323   186.0   Wake Forest   8000000.0  \n",
       "309   201.0  Old Dominion   2000000.0  \n",
       "311   190.0        Kansas   2854940.0  \n",
       "..      ...           ...         ...  \n",
       "381   205.0           LSU    200600.0  \n",
       "376   245.0        Kansas   8000000.0  \n",
       "375   250.0           NaN  13000000.0  \n",
       "378   198.0    Georgetown   4662960.0  \n",
       "379   190.0        Nevada   2170465.0  \n",
       "\n",
       "[457 rows x 9 columns]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Muestra el dataframe ordenado por el atributo Team y Name\n",
    "'''\n",
    "\n",
    "df.sort_values( ['Team', 'Name'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>Walter Tavares</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>22.0</td>\n",
       "      <td>C</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Tim Hardaway Jr.</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1304520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Tiago Splitter</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>11.0</td>\n",
       "      <td>C</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>245.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9756250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Thabo Sefolosha</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>25.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Paul Millsap</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>246.0</td>\n",
       "      <td>Louisiana Tech</td>\n",
       "      <td>18671659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>JJ Hickson</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>242.0</td>\n",
       "      <td>North Carolina State</td>\n",
       "      <td>273038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>Garrett Temple</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>17.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>195.0</td>\n",
       "      <td>LSU</td>\n",
       "      <td>1100602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>Drew Gooden</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>90.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>3300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>Bradley Beal</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>3.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>207.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>5694674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>Alan Anderson</td>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>6.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name                Team  Number Position   Age Height  \\\n",
       "322    Walter Tavares       Atlanta Hawks    22.0        C  24.0    7-3   \n",
       "310  Tim Hardaway Jr.       Atlanta Hawks    10.0       SG  24.0    6-6   \n",
       "321    Tiago Splitter       Atlanta Hawks    11.0        C  31.0   6-11   \n",
       "320   Thabo Sefolosha       Atlanta Hawks    25.0       SF  32.0    6-7   \n",
       "315      Paul Millsap       Atlanta Hawks     4.0       PF  31.0    6-8   \n",
       "..                ...                 ...     ...      ...   ...    ...   \n",
       "374        JJ Hickson  Washington Wizards    21.0        C  27.0    6-9   \n",
       "380    Garrett Temple  Washington Wizards    17.0       SG  30.0    6-6   \n",
       "372       Drew Gooden  Washington Wizards    90.0       PF  34.0   6-10   \n",
       "369      Bradley Beal  Washington Wizards     3.0       SG  22.0    6-5   \n",
       "368     Alan Anderson  Washington Wizards     6.0       SG  33.0    6-6   \n",
       "\n",
       "     Weight               College      Salary  \n",
       "322   260.0                   NaN   1000000.0  \n",
       "310   205.0              Michigan   1304520.0  \n",
       "321   245.0                   NaN   9756250.0  \n",
       "320   220.0                   NaN   4000000.0  \n",
       "315   246.0        Louisiana Tech  18671659.0  \n",
       "..      ...                   ...         ...  \n",
       "374   242.0  North Carolina State    273038.0  \n",
       "380   195.0                   LSU   1100602.0  \n",
       "372   250.0                Kansas   3300000.0  \n",
       "369   207.0               Florida   5694674.0  \n",
       "368   220.0        Michigan State   4000000.0  \n",
       "\n",
       "[457 rows x 9 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Ordena alfabetiticamente los equipos y dentro de cada equipo ordena alfabeticamente por nombre (descendente)\n",
    "'''\n",
    "\n",
    "df.sort_values(['Team', 'Name'], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modificar el dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Año nacimiento</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2239800.0</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2     John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4    Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "452     Trey Lyles       Utah Jazz    41.0       PF  20.0   6-10   234.0   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "454      Raul Neto       Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
       "455   Tibor Pleiss       Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "\n",
       "               College     Salary  Año nacimiento  \n",
       "0                Texas  7730337.0            1998  \n",
       "1            Marquette  6796117.0            1998  \n",
       "2    Boston University        NaN            1996  \n",
       "3        Georgia State  1148640.0            2001  \n",
       "4                  NaN  5000000.0            1994  \n",
       "..                 ...        ...             ...  \n",
       "452           Kentucky  2239800.0            2003  \n",
       "453             Butler  2433333.0            1997  \n",
       "454                NaN   900000.0            1999  \n",
       "455                NaN  2900000.0            1997  \n",
       "456             Kansas   947276.0            1997  \n",
       "\n",
       "[457 rows x 10 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Añade una columna al dataframe df con el año de nacimiento del jugador\n",
    "'''\n",
    "df['Año nacimiento'] = 2023 - df['Age']\n",
    "df['Año nacimiento'] = pd.to_numeric(df['Año nacimiento'], downcast='integer')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "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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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2239800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2     John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4    Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "452     Trey Lyles       Utah Jazz    41.0       PF  20.0   6-10   234.0   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "454      Raul Neto       Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
       "455   Tibor Pleiss       Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "\n",
       "               College     Salary  \n",
       "0                Texas  7730337.0  \n",
       "1            Marquette  6796117.0  \n",
       "2    Boston University        NaN  \n",
       "3        Georgia State  1148640.0  \n",
       "4                  NaN  5000000.0  \n",
       "..                 ...        ...  \n",
       "452           Kentucky  2239800.0  \n",
       "453             Butler  2433333.0  \n",
       "454                NaN   900000.0  \n",
       "455                NaN  2900000.0  \n",
       "456             Kansas   947276.0  \n",
       "\n",
       "[457 rows x 9 columns]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Elimina la columna Anno nacimiento del dataframe df\n",
    "'''\n",
    "df.drop('Año nacimiento', axis=1, inplace=True)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reordenar columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>College</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
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       "      <th>0</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Marquette</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boston University</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>27.0</td>\n",
       "      <td>John Holland</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Georgia State</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>22.0</td>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>1148640.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2239800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Butler</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               College            Team   Age           Name  Number Position  \\\n",
       "0                Texas  Boston Celtics  25.0  Avery Bradley     0.0       PG   \n",
       "1            Marquette  Boston Celtics  25.0    Jae Crowder    99.0       SF   \n",
       "2    Boston University  Boston Celtics  27.0   John Holland    30.0       SG   \n",
       "3        Georgia State  Boston Celtics  22.0    R.J. Hunter    28.0       SG   \n",
       "4                  NaN  Boston Celtics  29.0  Jonas Jerebko     8.0       PF   \n",
       "..                 ...             ...   ...            ...     ...      ...   \n",
       "452           Kentucky       Utah Jazz  20.0     Trey Lyles    41.0       PF   \n",
       "453             Butler       Utah Jazz  26.0   Shelvin Mack     8.0       PG   \n",
       "454                NaN       Utah Jazz  24.0      Raul Neto    25.0       PG   \n",
       "455                NaN       Utah Jazz  26.0   Tibor Pleiss    21.0        C   \n",
       "456             Kansas       Utah Jazz  26.0    Jeff Withey    24.0        C   \n",
       "\n",
       "    Height  Weight     Salary  \n",
       "0      6-2   180.0  7730337.0  \n",
       "1      6-6   235.0  6796117.0  \n",
       "2      6-5   205.0        NaN  \n",
       "3      6-5   185.0  1148640.0  \n",
       "4     6-10   231.0  5000000.0  \n",
       "..     ...     ...        ...  \n",
       "452   6-10   234.0  2239800.0  \n",
       "453    6-3   203.0  2433333.0  \n",
       "454    6-1   179.0   900000.0  \n",
       "455    7-3   256.0  2900000.0  \n",
       "456    7-0   231.0   947276.0  \n",
       "\n",
       "[457 rows x 9 columns]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Reordena las columnas del dataframe df (elige tú el orden que quieras)\n",
    "'''\n",
    "\n",
    "df.columns\n",
    "\n",
    "df2 = df[['College','Team', 'Age' , 'Name', 'Number', 'Position', 'Height', 'Weight', 'Salary']]\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Guardar los datos en un archivo csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>College</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Marquette</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boston University</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>27.0</td>\n",
       "      <td>John Holland</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Georgia State</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>22.0</td>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2239800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Butler</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               College            Team   Age           Name  Number Position  \\\n",
       "0                Texas  Boston Celtics  25.0  Avery Bradley     0.0       PG   \n",
       "1            Marquette  Boston Celtics  25.0    Jae Crowder    99.0       SF   \n",
       "2    Boston University  Boston Celtics  27.0   John Holland    30.0       SG   \n",
       "3        Georgia State  Boston Celtics  22.0    R.J. Hunter    28.0       SG   \n",
       "4                  NaN  Boston Celtics  29.0  Jonas Jerebko     8.0       PF   \n",
       "..                 ...             ...   ...            ...     ...      ...   \n",
       "452           Kentucky       Utah Jazz  20.0     Trey Lyles    41.0       PF   \n",
       "453             Butler       Utah Jazz  26.0   Shelvin Mack     8.0       PG   \n",
       "454                NaN       Utah Jazz  24.0      Raul Neto    25.0       PG   \n",
       "455                NaN       Utah Jazz  26.0   Tibor Pleiss    21.0        C   \n",
       "456             Kansas       Utah Jazz  26.0    Jeff Withey    24.0        C   \n",
       "\n",
       "    Height  Weight     Salary  \n",
       "0      6-2   180.0  7730337.0  \n",
       "1      6-6   235.0  6796117.0  \n",
       "2      6-5   205.0        NaN  \n",
       "3      6-5   185.0  1148640.0  \n",
       "4     6-10   231.0  5000000.0  \n",
       "..     ...     ...        ...  \n",
       "452   6-10   234.0  2239800.0  \n",
       "453    6-3   203.0  2433333.0  \n",
       "454    6-1   179.0   900000.0  \n",
       "455    7-3   256.0  2900000.0  \n",
       "456    7-0   231.0   947276.0  \n",
       "\n",
       "[457 rows x 9 columns]"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.to_csv('nba_copia.csv', index=False, sep=';')\n",
    "\n",
    "\n",
    "df3 = pd.read_csv('nba_copia.csv', sep=';')\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtrando datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Kris Humphries</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name           Team  Number Position   Age Height  Weight  \\\n",
       "313  Kris Humphries  Atlanta Hawks    43.0       PF  31.0    6-9   235.0   \n",
       "\n",
       "       College     Salary  \n",
       "313  Minnesota  1000000.0  "
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Carga el dataframe de jugadores en df\n",
    "filtra los jugadores que tengan de la universidad de Minnesota\n",
    "'''\n",
    "df[ df['College'] == 'Minnesota' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
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       "      <th>Age</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Kirk Hinrich</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>12.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>2854940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Kris Humphries</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Kyle Korver</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>26.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>212.0</td>\n",
       "      <td>Creighton</td>\n",
       "      <td>5746479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Paul Millsap</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>246.0</td>\n",
       "      <td>Louisiana Tech</td>\n",
       "      <td>18671659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Thabo Sefolosha</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>25.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Tiago Splitter</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>11.0</td>\n",
       "      <td>C</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>245.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9756250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name           Team  Number Position   Age Height  Weight  \\\n",
       "311     Kirk Hinrich  Atlanta Hawks    12.0       SG  35.0    6-4   190.0   \n",
       "313   Kris Humphries  Atlanta Hawks    43.0       PF  31.0    6-9   235.0   \n",
       "314      Kyle Korver  Atlanta Hawks    26.0       SG  35.0    6-7   212.0   \n",
       "315     Paul Millsap  Atlanta Hawks     4.0       PF  31.0    6-8   246.0   \n",
       "320  Thabo Sefolosha  Atlanta Hawks    25.0       SF  32.0    6-7   220.0   \n",
       "321   Tiago Splitter  Atlanta Hawks    11.0        C  31.0   6-11   245.0   \n",
       "\n",
       "            College      Salary  \n",
       "311          Kansas   2854940.0  \n",
       "313       Minnesota   1000000.0  \n",
       "314       Creighton   5746479.0  \n",
       "315  Louisiana Tech  18671659.0  \n",
       "320             NaN   4000000.0  \n",
       "321             NaN   9756250.0  "
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Filtra de los Atlanta Hawks los jugadores con mas de 30 años\n",
    "'''\n",
    "\n",
    "df[(df['Team'] == 'Atlanta Hawks') & (df['Age'] > 30) ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kirk Hinrich</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>12.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6-4</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>2854940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kris Humphries</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1000000.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kyle Korver</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>26.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>212.0</td>\n",
       "      <td>Creighton</td>\n",
       "      <td>5746479.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Paul Millsap</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>246.0</td>\n",
       "      <td>Louisiana Tech</td>\n",
       "      <td>18671659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thabo Sefolosha</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>25.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-7</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tiago Splitter</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>11.0</td>\n",
       "      <td>C</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>245.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9756250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name           Team  Number Position   Age Height  Weight  \\\n",
       "0     Kirk Hinrich  Atlanta Hawks    12.0       SG  35.0    6-4   190.0   \n",
       "1   Kris Humphries  Atlanta Hawks    43.0       PF  31.0    6-9   235.0   \n",
       "2      Kyle Korver  Atlanta Hawks    26.0       SG  35.0    6-7   212.0   \n",
       "3     Paul Millsap  Atlanta Hawks     4.0       PF  31.0    6-8   246.0   \n",
       "4  Thabo Sefolosha  Atlanta Hawks    25.0       SF  32.0    6-7   220.0   \n",
       "5   Tiago Splitter  Atlanta Hawks    11.0        C  31.0   6-11   245.0   \n",
       "\n",
       "          College      Salary  \n",
       "0          Kansas   2854940.0  \n",
       "1       Minnesota   1000000.0  \n",
       "2       Creighton   5746479.0  \n",
       "3  Louisiana Tech  18671659.0  \n",
       "4             NaN   4000000.0  \n",
       "5             NaN   9756250.0  "
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Otro ejemplo de filtrado\n",
    "'''\n",
    "atlanta_30 = df[(df['Team'] == 'Atlanta Hawks') & (df['Age'] > 30) ]\n",
    "atlanta_30.reset_index(drop=True, inplace=True)\n",
    "\n",
    "atlanta_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
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       "      <th>40</th>\n",
       "      <td>Kristaps Porzingis</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>6.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
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       "      <th>322</th>\n",
       "      <td>Walter Tavares</td>\n",
       "      <td>Atlanta Hawks</td>\n",
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       "      <td>C</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>2900000.0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Boban Marjanovic</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>40.0</td>\n",
       "      <td>C</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>290.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Roy Hibbert</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>17.0</td>\n",
       "      <td>C</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7-2</td>\n",
       "      <td>270.0</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>15592217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>J.J. Barea</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>5.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Northeastern</td>\n",
       "      <td>4290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5-9</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>6912869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Ty Lawson</td>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>10.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5-11</td>\n",
       "      <td>195.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>211744.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shane Larkin</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5-11</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Miami (FL)</td>\n",
       "      <td>1500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Phil Pressey</td>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5-11</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>55722.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name                Team  Number Position   Age Height  \\\n",
       "40   Kristaps Porzingis     New York Knicks     6.0       PF  20.0    7-3   \n",
       "322      Walter Tavares       Atlanta Hawks    22.0        C  24.0    7-3   \n",
       "455        Tibor Pleiss           Utah Jazz    21.0        C  26.0    7-3   \n",
       "302    Boban Marjanovic   San Antonio Spurs    40.0        C  27.0    7-3   \n",
       "111         Roy Hibbert  Los Angeles Lakers    17.0        C  29.0    7-2   \n",
       "..                  ...                 ...     ...      ...   ...    ...   \n",
       "228          J.J. Barea    Dallas Mavericks     5.0       PG  31.0    6-0   \n",
       "11        Isaiah Thomas      Boston Celtics     4.0       PG  27.0    5-9   \n",
       "203           Ty Lawson      Indiana Pacers    10.0       PG  28.0   5-11   \n",
       "22         Shane Larkin       Brooklyn Nets     0.0       PG  23.0   5-11   \n",
       "130        Phil Pressey        Phoenix Suns    25.0       PG  25.0   5-11   \n",
       "\n",
       "     Weight         College      Salary  \n",
       "40    240.0             NaN   4131720.0  \n",
       "322   260.0             NaN   1000000.0  \n",
       "455   256.0             NaN   2900000.0  \n",
       "302   290.0             NaN   1200000.0  \n",
       "111   270.0      Georgetown  15592217.0  \n",
       "..      ...             ...         ...  \n",
       "228   185.0    Northeastern   4290000.0  \n",
       "11    185.0      Washington   6912869.0  \n",
       "203   195.0  North Carolina    211744.0  \n",
       "22    175.0      Miami (FL)   1500000.0  \n",
       "130   175.0        Missouri     55722.0  \n",
       "\n",
       "[457 rows x 9 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Ordena los jugadores por altura del mas alto al mas bajo\n",
    "'''\n",
    "df.sort_values('Height', ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expresiones regulares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Las expresiones regulares son una forma de escribir un patrón de caracteres que se puede usar para buscar una cadena de texto.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Busca los jugadores que se llamen James\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Busca los jugadores que se llamen James como primer nombre\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Busca los jugadores que se llamen James como primer nombre (ignora mayusculas)\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cambios condicionales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Busca los jugadores del Boston Celtics y cambialo por Boston Celtics CocaCola\n",
    "'''\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones de agregación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Agrupa los jugadores por universidad y suma su salario\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Agrupa los jugadores por universidad y suma su salario y ordenalos por salario\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Agrupamos los jugadores por universidad y contabilizamos cuantos jugadores hay en cada una\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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