2_Introducción a Pandas.ipynb

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    "<a href=\"https://colab.research.google.com/drive/1Ohbu1EgL06hQwXLKyAWvDEJeOnufZ0qD\">Abre este Jupyter en Google Colab</a>"
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   "metadata": {},
   "source": [
    "# Introducción a Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pandas](https://pandas.pydata.org/about/index.html) es una librería que proporciona estructuras de datos y herramientas de análisis de datos de alto rendimiento y fáciles de usar. \n",
    "* La estructura de datos principal es el DataFrame, que puede considerarse como una tabla 2D en memoria (como una hoja de cálculo, con nombres de columna y etiquetas de fila). \n",
    "* Muchas funciones disponibles en Excel están disponibles mediante programación, como crear tablas dinámicas, calcular columnas basadas en otras columnas, trazar gráficos, etc.\n",
    "* Proporciona un alto rendimiento para manipular (unir, dividir, modificar…) grandes conjuntos de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (2.2.0)\n",
      "Requirement already satisfied: numpy<2,>=1.23.2 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pandas) (1.26.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pandas) (2023.4)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Requirement already satisfied: pyarrow in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (15.0.0)\n",
      "Requirement already satisfied: numpy<2,>=1.16.6 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pyarrow) (1.26.3)\n"
     ]
    }
   ],
   "source": [
    "# Instalación de Pandas\n",
    "!pip install pandas\n",
    "!pip install pyarrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estructuras de datos en Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La librería Pandas, de manera genérica, contiene las siguientes estructuras de datos:\n",
    "* **Series**: Array de una dimensión\n",
    "* **DataFrame**: Se corresponde con una tabla de 2 dimensiones\n",
    "* **Panel**: Similar a un diccionario de DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación del objeto Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     2\n",
      "1     4\n",
      "2     6\n",
      "3     8\n",
      "4    10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Creacion de un objeto Series\n",
    "s = pd.Series([2, 4, 6, 8, 10])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Santiago    187\n",
      "Pedro       178\n",
      "Julia       170\n",
      "Ana         165\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Creación de un objeto Series inicializándolo con un diccionario de Python\n",
    "altura = {\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}\n",
    "s = pd.Series(altura)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pedro    178\n",
      "Julia    170\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Creación de un objeto Series inicializándolo con algunos \n",
    "# de los elementos de un diccionario de Python\n",
    "altura = {\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}\n",
    "s = pd.Series(altura, index = [\"Pedro\", \"Julia\"])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test1    34\n",
      "test2    34\n",
      "test3    34\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Creación de un objeto Series inicializandolo con un escalar\n",
    "s = pd.Series(34, [\"test1\", \"test2\", \"test3\"])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acceso a los elementos de un objeto Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cada elemento en un objeto Series tiene un identificador único que se denomina **_index label_**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num1    2\n",
      "num2    4\n",
      "num3    6\n",
      "num4    8\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Creación de un objeto Series\n",
    "s = pd.Series([2, 4, 6, 8], index=[\"num1\", \"num2\", \"num3\", \"num4\"])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accediendo al tercer elemento del objeto\n",
    "s[\"num3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\santiago\\AppData\\Local\\Temp\\ipykernel_13908\\360602738.py:2: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  s[2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tambien se puede acceder al elemento por posición\n",
    "s[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc es la forma estándar de acceder a un elemento de un objeto Series por atributo\n",
    "s.loc[\"num3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc es la forma estándar de acceder a un elemento de un objeto Series por posición\n",
    "s.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num3    6\n",
       "num4    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accediendo al segundo y tercer elemento por posición\n",
    "s.iloc[2:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operaciones aritméticas con Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     2\n",
      "1     4\n",
      "2     6\n",
      "3     8\n",
      "4    10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Creacion de un objeto Series\n",
    "s = pd.Series([2, 4, 6, 8, 10])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Los objeto Series son similares y compatibles con los Arrays de Numpy\n",
    "import numpy as np\n",
    "# Ufunc de Numpy para sumar los elementos de un Array\n",
    "np.sum(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     8\n",
       "2    12\n",
       "3    16\n",
       "4    20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# El resto de operaciones aritméticas de Numpy sobre Arrays también son posibles\n",
    "# Más información al respecto en la Introducción a Numpy\n",
    "s * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representación gráfica de un objeto Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.4\n",
       "1     5.1\n",
       "2     6.1\n",
       "3     6.2\n",
       "4     6.1\n",
       "5     6.1\n",
       "6     5.7\n",
       "7     5.2\n",
       "8     4.7\n",
       "9     4.1\n",
       "10    3.9\n",
       "Name: Temperaturas, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un objeto Series denominado Temperaturas\n",
    "temperaturas = [4.4, 5.1, 6.1, 6.2, 6.1, 6.1, 5.7, 5.2, 4.7, 4.1, 3.9]\n",
    "s = pd.Series(temperaturas, name=\"Temperaturas\")\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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LVt4QBQB4YWsGDuZVCk5EZN1YRqhHdpxqWxUZE+QGN0dbwWmIeu/28YGYP8oPBqOMJRuOory2UXQkIqvFMkI9wqtoyFJIkoTn5sdgmLcTztU24cENx9BqMIqORWSVWEao22oaWnCgfTmbZYQsgYOtCmtvHw0ntQoH8irx0vZM0ZGIrBLLCHXbrsxyGIwywrycEOzhKDoOUb8Y4umEf/xhOADg3eRcbDtRKjgRkfVhGaFu4yEaslTXxfrg7kkhAIBHPz+OMxX1ghMRWReWEeqWplYDfmwfjMcyQpbor7MjMDbYFbVNrbjvoyO40MyBekSDhWWEumXf6fOobzbAy1mNEf4uouMQ9TsbpQJv3hYHDydbZJTW4m+b0rkhGtEgYRmhbrl4iGZapDcUHIxHFspbY4c3FsZBIQH/PVqETw8Vio5EZBVYRqhLRqPcsb/IDB6iIQsXP8Qdj82MAAA8/fUJpBXVCE5EZPlYRqhLaWdrUKZvgoOtEvFD3EXHIRpw900JxfQobzS3GnH/x0dQ3dAsOhKRRWMZoS5dPEQzZZgnB+ORVZAkCf9cMAJB7g4oqrqA5Z8fh9HI80eIBgrLCHWJl/SSNdLa2+DtxDioVQr8kFGOt3/MER2JyGKxjNAVFZxvQGZZLZQKCddGeImOQzSoon21WDUvBgDwclIW9mZXCE5EZJlYRuiKtp9s241ybLArXBw4GI+sz81jAnDr2AAYZeChT4+hpOaC6EhEFodlhK7ol0M0OsFJiMR55sZoRPtqUFnfjCUfH0VzKwfqEfUnlhG6rKr6Zhw60zYYj5f0kjWzs1FibeJoaOxUOFpQjee/PyU6EpFFYRmhy/ohoxxGGYjQOSPAzUF0HCKhAt0d8MotIwEA61PO4JvjxWIDEVkQlhG6LF5FQ9TZtEhvLJk6BADwxMY0nKttEpyIyDKwjNAlNbYYsDv7HACWEaJfWz49HLF+WtQ2tuK5706KjkNkEVhG6JJSTlegodkAncYOsX5a0XGITIZSIeH5+bFQSMCm1GL8lMPLfYn6imWELuniIZqEKC9IEgfjEf1arL8Wi+KDAQB/25SOxhaD2EBEZo5lhH6nbTBeOQBe0kt0OctnDIOXsxp5FfV4NzlXdBwis8YyQr+TWlSNc7VNcFKrcFWom+g4RCZJY2eDp26IAgC89WMO8irqBSciMl8sI/Q7HYPxwj2hVnEwHtHlzIn1wdXDPNHcasRTm9MhyxymR9QbLCP0OxfLCDc6I7oySZKwam40bFUK7MmuwDc/l4iORGSWWEaok7yKeuSU10GlkHBNOAfjEXUlyN0RD04dCgBY9e1J1FxoEZyIyPywjFAnSe2D8caHukFrbyM4DZF5uHdKKEI9HXGutglrtmeKjkNkdlhGqJNfDtHwKhqi7lKrlPj7vBgAwP/tz8fxwmqxgYjMDMsIdThf14Qj+VUAgASeL0LUIxOGeOCmUX6Q5bat4lsNnOxL1F0sI9RhZ/tgvGhfDfxc7EXHITI7T8yJhMZOhRPFevzf/nzRcYjMBssIdeBgPKK+8XBS4/HZkQCANduzUFrTKDgRkXlgGSEAwIVmA/ZwMB5Rn906NgCjAl1Q19SKVd9ykB5Rd7CMEABgb04FGluM8HOxR5SPRnQcIrOlUEh4bl4slAoJ36WVYFdmuehIRCaPZYQA/HJJ7/Qobw7GI+qjKF8NFk8MBgA8tZmD9Ii6wjJCMBhl7OwYjMdDNET9YVnCMPho7VBYeQFv/pAjOg6RSWMZIRwrqML5+mZo7FQYF8LBeET9wVGtwjM3RgMA3t19GjnltYITEZkulhHquIpmaoQXbJT8K0HUX2ZEeSMh0gstBhlPbuQgPaLL4TcP8ZJeogEiSRKeuTEa9jZKHMirxFdHz4qORGSSWEasXE55HXIr6mGjlDBlmKfoOEQWx9/VAUsTwgAAz31/CtUNzYITEZkelhErd3FVJH6IB5ztOBiPaCDcNSkEw7ydUFnfjBe2ZIiOQ2RyWEas3K8v6SWigWGjVOC5+bEAgE8PFeLwmUrBiYhMC8uIFTtX24Rj7dNFp0eyjBANpLHBbrhlTAAA4MmN6WjhID2iDiwjVmznqTLIMjDcXwud1k50HCKL9/jsCLg62CCzrBYf7M0THYfIZPSojDzzzDOQJKnTLSIi4rLPX79+/e+eb2fHLz1T0XEVDVdFiAaFq6MtnriubZDeqzuyUVTVIDgRkWno8cpIdHQ0SkpKOm579+694vM1Gk2n5+fnc6y2KWhobsXenAoAwPRolhGiwfKH0f4YF+KGCy0GPPM1B+kRAYCqxy9QqaDT6br9fEmSevR8Ghy7syrQ1GpEgJs9wr2dRcchshqSJOG5eTGY/doe7DhVhu0nSjEjmv9GknXr8cpIdnY2fH19ERoaisTERBQUFFzx+XV1dQgKCkJAQADmzp2LEydOdPkeTU1N0Ov1nW7Uv345RKPjYDyiQRbm7Yx7rw4FADzz9QnUN7UKTkQkVo/KyPjx47F+/Xps3boVa9euRV5eHiZPnoza2kvPXAgPD8cHH3yAzZs346OPPoLRaMSECRNQVFR0xfdZvXo1tFptxy0gIKAnMakLrQYjfsjgrqtEIj14bRj8Xe1RXNOI13Zmi45DJJQk92FYQnV1NYKCgvDyyy/jrrvu6vL5LS0tiIyMxMKFC7Fq1arLPq+pqQlNTU0df9br9QgICEBNTQ00Gk1v41K7A7nnccu6/XBxsMHhJxOg4jwaIiF2ZZTjzvWHoFRI+PbBSYj04b9vZFn0ej20Wm2X3999+hZycXHBsGHDkJPTvfHYNjY2GDVqVJfPV6vV0Gg0nW7Ufy4eork23ItFhEigqRFemB2jg8Eo48mNaTAaOUiPrFOfvonq6upw+vRp+Pj4dOv5BoMBaWlp3X4+9T9ZlpF0iodoiEzFUzdEwdFWiaMF1fjscKHoOERC9KiMPProo0hOTsaZM2eQkpKC+fPnQ6lUYuHChQCARYsWYcWKFR3Pf/bZZ7F9+3bk5ubi6NGjuP3225Gfn4+77767f38L6rbs8jrkn2+ArUqBqzkYj0g4H609HpkRDgB4YUsGKuqaungFkeXpURkpKirCwoULER4ejptvvhnu7u7Yv38/PD3bvtQKCgpQUlLS8fyqqircc889iIyMxHXXXQe9Xo+UlBRERUX1729B3XbxEM3EIe5wVPf4ym4iGgCL4oMQ7atBzYUWPP/9KdFxiAZdn05gHSzdPQGGujb3rZ9wvLAaz8+PxW3jA0XHIaJ2qYXVmP/2T5BlYMM94zFhiIfoSER9NignsJJ5KdM34nj7YLyESC+xYYiok5EBLrh9fBAA4G+b0tHUahCciGjwsIxYkR3tJ66ODHCBl4YzgohMzaMzw+HhpEbuuXq8tztXdByiQcMyYkU6dl3lVTREJklrb4OV17cN0nvjhxzkn68XnIhocLCMWIm6plak5JwHAMxgGSEyWTeO8MWkoR5oajXiqc0nYAan9RH1GcuIldiddQ7NBiOC3R0w1MtJdBwiugxJkrBqXgxsVQokZ53D92mloiMRDTiWESvx60M0HIxHZNpCPBzx52uGAAD+95sTqG1sEZyIaGCxjFiBFoMRP2SUAwCmR3FUOZE5uG/KEIR4OKK8tglrtmeJjkM0oFhGrMChM5WoudACN0dbjA5yFR2HiLrBzkaJVXNjAAD/2XcGaUU1ghMRDRyWESvQMRgvwgtKBQ/REJmLSWEemDvSF0YZeHJTGgwcpEcWimXEwsmyzEt6iczYk3Mi4Wynws9FNfj4QL7oOEQDgmXEwmWU1qKo6gLUKgUmh3F7aSJz4+Vsh7/MigAAvLQ1E+X6RsGJiPofy4iFu7gqMjnMAw62HIxHZI5uGxeIEQEuqG1qxarvOEiPLA/LiIXjIRoi86dUSHhuXgwUEvDN8WLszjonOhJRv2IZsWAlNReQdrYGkgRcG8EyQmTOYvy0+NOEEADAys3paGzhID2yHCwjFmxH+6pIXKArPJ3VgtMQUV8tnzEMOo0d8s834O0fT4uOQ9RvWEYs2HYeoiGyKE5qFZ6+IQoA8M6Pp3H6XJ3gRET9g2XEQukbW7A/t20wHssIkeWYFaPD1HBPNBuMWLkpnYP0yCKwjFio5MxzaDHICPV0xBBPDsYjshSSJOHZuTFQqxRIOX0em1OLRUci6jOWEQvFq2iILFeAmwMemhYGAPj7dydR08BBemTeWEYsUIvBiF2ZbYPxZrCMEFmkeyaHYqiXEyrqmvHitgzRcYj6hGXEAh3IrURtYys8nGwxMoCD8Ygska1KgefmtQ3S23CgAEfyqwQnIuo9lhELlHSyFAAwLcKbg/GILNj4UHf8YbQ/AODJjWloNRgFJyLqHZYRC8PBeETWZcXsCLg42CCjtBbrU86IjkPUKywjFuZEsR7FNY2wt1FiEgfjEVk8dyc1VsxuG6T3clIWiqsvCE5E1HMsIxbm14Px7GyUgtMQ0WBYMDoAY4Jc0dBswP9+c0J0HKIeYxmxMDxEQ2R9FAoJf58fA5VCwrYTZR2jIIjMBcuIBSmqasDJEj0UEjAtkmWEyJpE6DS4a3LbIL2nvz6BhuZWwYmIuo9lxIJc/K+hMUFucHO0FZyGiAbb0mlh8HOxx9nqC3h9Z47oOETdxjJiQZJO8RANkTVzsFXhf2+MBgD8a08uMktrBSci6h6WEQtRc6EFB3IrAbCMEFmzhChvzIjyRqtRxt82pcFo5CA9Mn0sIxbix8xytBplhHk5IdjDUXQcIhLo6Ruj4WCrxKEzVfjySJHoOERdYhmxENt5FQ0RtfNzscfDCcMAAM9vOYXK+mbBiYiujGXEAjS1GpCceQ4AywgRtfnTxGBE6JxR3dCC1d+fEh2H6IpYRizA/txK1DW1wstZjRH+LqLjEJEJsFEq8Nz8WEgS8MWRIhzIPS86EtFlsYxYgI7BeJHeUHAwHhG1Gx3kioXjAgEAf9uUjuZWDtIj08QyYuZkWcaOk+UAgBk8RENEv/HXmRFwd7RFdnkd/rU3V3QcoktiGTFzaWdrUKpvhIOtEvFD3EXHISITo3Wwwd+ujwQAvL4zG4WVDYITEf0ey4iZuziLZsowTw7GI6JLmjfSD/Gh7mhsMeKpzemQZe49QqaFZcTMcTAeEXVFktoG6dkqFdiVeQ7bTpSKjkTUCcuIGSs434CM0looFRKujfASHYeITNgQTyfcNyUUAPDM1ydR18RBemQ6WEbM2Pb2q2jGBrvCxYGD8Yjoyv48dSiC3B1Qqm/EK0lZouMQdWAZMWO/HKLRCU5CRObAzkaJZ+fGAAA+/CkP6WdrBCciasMyYqaq6ptx6EzbYDxe0ktE3TVlmCeuH+4Doww8uSkdBg7SIxPAMmKmfsgoh1EGInTOCHBzEB2HiMzIyuuj4KxW4XhhNT45WCA6DhHLiLniVTRE1FveGjs8OjMcAPDi1gycq20SnIisHcuIGWpsMWB3NgfjEVHv3X5VEGL9tKhtbMVz350UHYesHMuIGUo5XYGGZgN0GjvE+mlFxyEiM6RUSHh+fiwUErAptRg/5VSIjkRWjGXEDF08RJMQ5QVJ4mA8IuqdWH8tFsUHA2gbpNfYYhAbiKwWy4iZMRpl7Dh1cTAeL+klor5ZPmMYvJzVyKuoxzvJp0XHISvFMmJmUouqca62Cc5qFa4K5WA8IuobjZ0NnrohCgDw9q7TyKuoF5yIrBHLiJnpGIwX7glbFf/nI6K+mxPrg6uHeaLZYMTKTRykR4OP32ZmpKnVgC+PFAEAZsXwEA0R9Q9JkrBqbjRsVQrszanA18eLRUciK8MyYka+PV6Cc7VN8Naoeb4IEfWrIHdHPDh1KABg1benUHOhRXAisiYsI2ZClmW8vzcPALAoPpiHaIio3907JRShno6oqGvCP7dlio5DVoTfaGZif24lTpboYWejQOL4QNFxiMgCqVVK/H1e2yC9jw7kI7WwWmwgshosI2bi4qrI/8T5w8XBVnAaIrJUE4Z44KZRfpBl4MmNaWg1GEVHIivAMmIGzlTUY2dG21U0iyeFCE5DRJbuiTmR0NipcKJYj//syxcdh6wAy4gZWJ9yBrIMTA33xBBPJ9FxiMjCeTip8fjsSADAmu2ZKK1pFJyILB3LiImrudCCzw8XAgDumhQqOA0RWYtbxwZgVKAL6psNePbbE6LjkIVjGTFxnx0qQEOzAeHezpg4lDuuEtHgUCgkPDcvFkqFhO/TSrEro1x0JLJgLCMmrNVgxL9T2o7XLp4UzKF4RDSoonw1WDwxGADw1NfpuNDMQXo0MFhGTNjWE6U4W30B7o62mDvST3QcIrJCyxKGwUdrh8LKC3hzV7boOGShelRGnnnmGUiS1OkWERFxxdd88cUXiIiIgJ2dHWJjY/H999/3KbA1uXg5b+JVQbCzUQpOQ0TWyFGtwjM3RgMA1u3ORXZZreBEZIl6vDISHR2NkpKSjtvevXsv+9yUlBQsXLgQd911F44dO4Z58+Zh3rx5SE9P71Noa3C0oArHCqphq1Tgj1cFiY5DRFZsRpQ3EiK90GKQ8SQH6dEA6HEZUalU0Ol0HTcPD4/LPve1117DrFmz8NhjjyEyMhKrVq1CXFwc3nzzzT6FtgYftK+K3DjSF57OasFpiMiaSZKEZ26Mhr2NEgfzKvHfo2dFRyIL0+Mykp2dDV9fX4SGhiIxMREFBQWXfe6+ffuQkJDQ6b6ZM2di3759V3yPpqYm6PX6Tjdrcrb6AraklwIAFk/kJmdEJJ6/qwOWJoQBAJ7//hSq6psFJyJL0qMyMn78eKxfvx5bt27F2rVrkZeXh8mTJ6O29tLHEEtLS+Ht7d3pPm9vb5SWll7xfVavXg2tVttxCwgI6ElMs/eflDMwGGVMGOKOKF+N6DhERACAuyaFYJi3Eyrrm/Hi1gzRcciC9KiMzJ49GwsWLMDw4cMxc+ZMfP/996iursbnn3/er6FWrFiBmpqajlthYWG//nxTVt/Uig0H21ab7uLW70RkQmyUCjw3PxYA8OmhQhw+Uyk4EVmKPl3a6+LigmHDhiEnJ+eSj+t0OpSVlXW6r6ysDDqd7oo/V61WQ6PRdLpZiy+PFKG2sRUhHo6YGu4lOg4RUSdjg91wy5i21eonN6ajhYP0qB/0qYzU1dXh9OnT8PHxueTj8fHx2LlzZ6f7kpKSEB8f35e3tVhGo4wPf2o7cfXOicFQKLjJGRGZnsdnR8DVwQaZZbUdJ9sT9UWPysijjz6K5ORknDlzBikpKZg/fz6USiUWLlwIAFi0aBFWrFjR8fylS5di69atWLNmDTIyMvDMM8/g8OHDeOCBB/r3t7AQOzPKceZ8AzR2KvxPnL/oOEREl+TqaIsnrmsbpPfqjmwUVTUITkTmrkdlpKioCAsXLkR4eDhuvvlmuLu7Y//+/fD09AQAFBQUoKSkpOP5EyZMwIYNG7Bu3TqMGDECX375JTZt2oSYmJj+/S0sxPt7cwEAC8cHwlGtEpyGiOjy/jDaH+NC3HChxYBnvj4pOg6ZOUk2g91r9Ho9tFotampqLPb8kRPFNZjz+l4oFRL2/GUqfF3sRUciIrqi7LJazH5tD1qNMtb9cTRmRF/5fECyPt39/uZsGhPxwd4zAIDrYn1YRIjILIR5O+Peq0MBAM98fQL1Ta2CE5G5YhkxAeW1jfjmeDEAXs5LROblwWvDEOBmj+KaRry2k4P0qHdYRkzAR/vy0WwwYnSQK0YGuIiOQ0TUbfa2Sjx7Y9t5gO/vzcOpEuvaMZv6B8uIYI0tBnx0gJucEZH5mhrhhetidTAYZTyxMQ1Go8mfikgmhmVEsE3HzqKyvhl+LvaYEeXd9QuIiEzQU9dHw9FWiWMF1fj0kPXsmk39g2VEIFmW8UH7Jmd/mhAMlZL/cxCRedJp7fDIjHAAwAtbTqGirklwIjIn/PYTaE92BbLK6uBoq8Qt46xrGCARWZ5F8UGI9tVA39iK5787JToOmRGWEYHeb99GecGYAGjsbASnISLqG1X7ID1JAr46dhYppytERyIzwTIiSE55LZKzzkGS2ubQEBFZgpEBLrh9fBAA4G+b0tHUahCciMwBy4ggH/x0BgAwPdIbQe6OYsMQEfWjR2eGw8NJjdxz9ViXnCs6DpkBlhEBquqb8dXRIgC8nJeILI/W3gYrr28bpPfGrhycqagXnIhMHcuIABsOFqCxxYgYPw3GhbiJjkNE1O9uHOGLSUM90NxqxMrN6TCDMWgkEMvIIGtuNeLfKWcAAIsnhkCSJLGBiIgGgCRJWDUvBrYqBfZkV+C7tJKuX0RWi2VkkH2XVozy2iZ4Oatx/XBf0XGIiAZMiIcj/nzNEADAs9+chL6xRXAiMlUsI4NIluWOy3kXxQfBVsWPn4gs231ThiDEwxHltU14eXuW6DhkovhtOIgO5lUi/aweapUCt7Vf+kZEZMnsbJRYNbdtkN5/9p3Bz0XVYgORSWIZGUQXV0VuivOHm6Ot4DRERINjUpgH5o70hVEGntyYDgMH6dFvsIwMkoLzDUg6VQYAuGtSsNgwRESD7Mk5kXC2UyHtbA0+2p8vOg6ZGJaRQfJhSh5kGZgyzBNDvZxFxyEiGlReznb4y6wIAMBL2zJRpm8UnIhMCcvIINA3tuDz9pHa3OSMiKzVbeMCMSLABXVNrVj17UnRcciEsIwMgs8PFaK+2YAwLydMDvMQHYeISAilQsJz82KgkIBvfy5BctY50ZHIRLCMDLBWgxEfts+hWTyJm5wRkXWL8dPiTxPaVoif2pyOxhYO0iOWkQG3/WQZzlZfgJujLeaP8hMdh4hIuOUzhkGnsUP++Qa8vStHdBwyASwjA+zi5byJ4wNhZ6MUnIaISDwntQpP3xAFAFibfBo55XWCE5FoLCMDKLWwGkfyq2CjlPDHq7jJGRHRRbNidJga7okWg4yVmzhIz9qxjAygD9pXRW4Y4QsvjZ3gNEREpkOSJDw7NwZqlQL7cs9jU+pZ0ZFIIJaRAVJScwHft0+p5OW8RES/F+DmgIemhQEA/v7tKdQ0cJCetWIZGSD/TslHq1HGVaFuiPbVio5DRGSS7pkciqFeTjhf34wXt2WIjkOCsIwMgIbmVnxysAAAcNekUMFpiIhMl61KgefmtQ3S23CgAEfyqwQnIhFYRgbAf48UoeZCC4LcHXBthJfoOEREJm18qDv+MNofAPDkxjS0GoyCE9FgYxnpZ0ajjA/aNzm7c0IwlApuckZE1JUVsyPg4mCDjNLajo0iyXqwjPSzXZnlyKuoh7OdCgvGBIiOQ0RkFtyd1Fgxu22Q3is7snC2+oLgRDSYWEb62cVNzhaOC4SjWiU4DRGR+VgwOgBjglzR0GzA/359QnQcGkQsI/3oZLEeKafPQ6mQcMeEYNFxiIjMikIh4e/zY6BSSNh+sgxJJ8tER6JBwjLSjz78qW1VZFaMDn4u9oLTEBGZnwidBndNbtub6ZmvT6ChuVVwIhoMLCP95FxtEzanFgPgJmdERH2xdFoY/Fzscbb6Al7bmS06Dg0ClpF+8tH+fDQbjBgV6IK4QFfRcYiIzJaDrQr/e2M0AOD9PXkoON8gOBENNJaRftDYYsBH+/MBAIsnclWEiKivEqK8MTnMA61GmasjVoBlpB98nVqM8/XN8NXaYXaMTnQcIiKL8MiMcADAxmNFyCmvE5yGBhLLSB/JsowP2k9cvWNCMFRKfqRERP1hZIALEiK9YZTB1RELx2/OPvop5zwySmvhYKvEreMCRcchIrIoy6cPAwB8c7wYp0r0gtPQQGEZ6aP39+YCABaM9ofW3kZwGiIiyxLlq8GcWB8AwCtJWYLT0EBhGemD0+fqsCvzHCQJuJMnrhIRDYiHp4dBIQHbT5YhrahGdBwaACwjfXBxk7NpEd4I9nAUnIaIyDIN9XLG3JF+AIA1SZmC09BAYBnppeqGZvz3yFkA3OSMiGigLZ0WBqVCwo+Z53Akv1J0HOpnLCO9tOFgAS60GBDlo8FVoW6i4xARWbRgD0csGO0PAFizneeOWBqWkV5oMRjxn5T2Tc4mhUCSJMGJiIgs34PTwmCrVCDl9Hmk5FSIjkP9iGWkF75PK0GpvhEeTmrcMMJHdBwiIqvg52KPW8cFAADWJGVBlmXBiai/sIz0kCzLeH9v24mri+KDoFYpBSciIrIeS6YOhVqlwJH8KvyYdU50HOonLCM9dDi/Cj8X1cBWpUDieG5yRkQ0mLw1dlgUHwQAeHk7V0csBctID72/p21V5KZRfnB3UgtOQ0Rkfe6bMgQOtkqkna3B9pNlouNQP2AZ6YHCygZsP1kKoO3EVSIiGnzuTmrcOTEYQNvqiNHI1RFzxzLSA+tTzsAoA5PDPDDM21l0HCIiq3Xv5CFwtlMhs6wW36WViI5DfcQy0k21jS347FAhAG5yRkQkmtbBBndPCgUAvLIjC60Go+BE1BcsI930+eEi1DW1YoinI64O8xQdh4jI6i2eFAwXBxvknqvHptRi0XGoD1hGusFglLE+pe3E1cWTQqBQcJMzIiLRnO1scN+UIQCA13ZmoYWrI2aLZaQbkk6WorDyAlwcbHDTKH/RcYiIqN2i+CB4OKlRWHkBXxwuEh2HeollpBsubnKWOD4Q9rbc5IyIyFQ42Krw52vaVkfe+CEbjS0GwYmoN1hGuvBzUTUOnamCjVLCovhg0XGIiOg3bhsfCJ3GDiU1jfjkYIHoONQLLCNd+KB9VeT64b7w1tgJTkNERL9lZ6PEg9OGAgDe2nUaF5q5OmJuWEauoLSmEd/+3Hb9Oi/nJSIyXQtGB8Df1R4VdU34z74zouNQD7GMXMF/9p1Bq1HGuBA3xPhpRcchIqLLsFUpsHRaGADgneTTqGtqFZyIeqJPZeSFF16AJElYtmzZZZ+zfv16SJLU6WZnZ/qHOy40G7Ch/djj4olcFSEiMnXzR/kh1MMRVQ0t+LD9EDuZh16XkUOHDuHdd9/F8OHDu3yuRqNBSUlJxy0/P7+3bzto/nu0CNUNLQh0c8D0KG/RcYiIqAsqpQJLE9pWR9btyUVNQ4vgRNRdvSojdXV1SExMxHvvvQdXV9cuny9JEnQ6XcfN29u0v9yNRhkf/NTWqv80IRhKbnJGRGQWbhjui3BvZ9Q2tuK9Pbmi41A39aqMLFmyBHPmzEFCQkK3nl9XV4egoCAEBARg7ty5OHHixBWf39TUBL1e3+k2mJKzziH3XD2c1SrcPDZgUN+biIh6T6GQ8PD0YQCAD3/Kw/m6JsGJqDt6XEY+/fRTHD16FKtXr+7W88PDw/HBBx9g8+bN+Oijj2A0GjFhwgQUFV1+p7zVq1dDq9V23AICBrcQXNzk7JaxAXBSqwb1vYmIqG9mRnsjxk+D+mYD3t3N1RFz0KMyUlhYiKVLl+Ljjz/u9kmo8fHxWLRoEUaOHIkpU6bgq6++gqenJ959993LvmbFihWoqanpuBUWFvYkZp9klOqxN6cCCgm4Y0LwoL0vERH1D0mS8Mj0cADAv1POoFzfKDgRdaVHZeTIkSMoLy9HXFwcVCoVVCoVkpOT8frrr0OlUsFg6HqjGRsbG4waNQo5OTmXfY5arYZGo+l0Gywf7j0DAJgVo0OAm8OgvS8REfWfa8I9ERfogqZWI97adfnvGzINPSoj06ZNQ1paGlJTUztuY8aMQWJiIlJTU6FUdj23xWAwIC0tDT4+Pr0OPVAq6pqwMfUsAG5yRkRkziRJwqMz2lZHPjlYiLPVFwQnoivp0QkRzs7OiImJ6XSfo6Mj3N3dO+5ftGgR/Pz8Os4pefbZZ3HVVVdh6NChqK6uxksvvYT8/Hzcfffd/fQr9J+P9xegudWIEQEuiAvs+iohIiIyXROGeuCqUDfsz63Emz9kY/VNXW9FQWL0+w6sBQUFKCkp6fhzVVUV7rnnHkRGRuK6666DXq9HSkoKoqKi+vut+6Sp1YD/29+2/8niicGQJF7OS0Rk7h5pXx35/HAR8s/XC05DlyPJsiyLDtEVvV4PrVaLmpqaATt/5IvDhXjsy5/ho7XD7r9MhY2SO+UTEVmCOz44iOSsc7gpzg8v3zxSdByr0t3vb37jApBlueNy3kXxwSwiREQWZHn7viObjp1FTnmt4DR0KfzWBbDv9HlklNbC3kaJ28YFio5DRET9aESAC6ZHecMoA6/syBYdhy6BZQS/bHL2h9H+0DrYCE5DRET97eLqyHc/l+BUyeDu6k1ds/oykldRj50Z5QCAOycGiw1DREQDItJHgznD27aUeDkpS3Aa+i2rLyMftg/EmxbhhVBPJ8FpiIhooDycEAaFBCSdLMPxwmrRcehXrLqM1DS04IvDbTNyuMkZEZFlG+rljHmj/AAAa7g6YlKsuox8cqgAF1oMiNA5I36Iu+g4REQ0wJZOC4NKIWF31jkcOlMpOg61s9oy0mow4t8pZwAAiyeFcJMzIiIrEOTuiAVj/AEA/9yWCTPYassqWG0ZUSokvL5wFP4w2h83jvAVHYeIiAbJA9eGwVapwIG8SqScPi86DsGKy4gkSRgb7IZ/LhgBO5uuB/wREZFl8HOxx23j2/aUWrOdqyOmwGrLCBERWa8/XzMEapUCRwuq8WPmOdFxrB7LCBERWR0vjR3umBAMAFiTxNUR0VhGiIjIKv2/q0PhaKtE+lk9tp0oEx3HqrGMEBGRVXJ3UuPOiW17TL2clAmDkasjorCMEBGR1bpnciic7VTIKqvDtz8Xi45jtVhGiIjIamkdbHDv5FAAwKs7stFqMApOZJ1YRoiIyKrdOSkErg42yKuox8ZjZ0XHsUosI0REZNWc1CrcN2UIAOC1ndlobuXqyGBjGSEiIqu3KD4YHk5qFFVdwOeHC0XHsTosI0REZPXsbZV4YGrb6sibP+SgscUgOJF1YRkhIiICcOu4QPho7VCqb8SGAwWi41gVlhEiIiIAdjZKPHhtGADg7R9z0NDcKjiR9WAZISIiardgjD8C3RxQUdeM/+zLFx3HarCMEBERtbNRKvDQtLbVkXeST6O2sUVwIuvAMkJERPQr80b6ItTTEdUNLfhg7xnRcawCywgREdGvqJQKPJwwDADwrz25qG5oFpzI8rGMEBER/cacWB9E6JxR29SK9/bkio5j8VhGiIiIfkOhkPDw9LbVkQ9/OoOKuibBiSwbywgREdElzIjyRqyfFg3NBrzz42nRcSwaywgREdElSJKER2a0rY783/58lOkbBSeyXCwjRERElzFlmCdGB7miqdWIt3bliI5jsVhGiIiILuPXqyOfHCxAUVWD4ESWiWWEiIjoCiYM8cCEIe5oMch48weujgwElhEiIqIuXFwd+eJIEc5U1AtOY3lYRoiIiLowOsgN14R7wmCU8drObNFxLA7LCBERUTc8Mj0cALAp9Syyy2oFp7EsLCNERETdEOuvxYwob8gy8OoOro70J5YRIiKiblo+YxgkCfgurQQnimtEx7EYLCNERETdFKHT4PrhvgCAV5KyBKexHCwjREREPbAsIQwKCdhxqhyphdWi41gElhEiIqIeGOLphPmj/AEAa7ZnCk5jGVhGiIiIemjptDCoFBL2ZFfgYF6l6Dhmj2WEiIiohwLdHXDz2AAAwD+3Z0KWZcGJzBvLCBERUS88MHUobJUKHMyrxE8550XHMWssI0RERL3g62KP28YHAuDqSF+xjBAREfXSn6cOgZ2NAqmF1fgho1x0HLPFMkJERNRLXs52uCM+GADwclIWjEaujvQGywgREVEf/L8pQ+Boq8SJYj22nSgVHccssYwQERH1gZujLe6aFAKgbXXEwNWRHmMZISIi6qO7JodCY6dCdnkdvv25WHQcs8MyQkRE1Edaexvce3UoAGDN9iwUVTUITmReWEaIiIj6wZ8mhsDTWY2CygbMfGU3/rPvDE9o7SaWESIion7gpFbhs3uvwthgV9Q3G/DU5hO4+d19yCmvEx3N5LGMEBER9ZNQTyd8dm88Vs2NhqOtEofzq3Dda3vw1q4ctBiMouOZLJYRIiKifqRQSPhjfDC2L5+CKcM80Www4qVtmbjxzZ+QfrZGdDyTxDJCREQ0APxc7LH+zrF45ZYRcHGwwakSPea+9RNe2JKBxhaD6HgmhWWEiIhogEiShPmj/LFj+RRcP9wHBqOMd5JPY/Zre3Agl8P1LmIZISIiGmAeTmq8eVsc1v1xNLw1auRV1OOWdfvxt01pqG1sER1POJYRIiKiQTIjWoftD0/BwnEBAICP9hdgxiu78UNGmeBkYrGMEBERDSKtvQ1W3zQcG+4ZjyB3B5TUNGLx+sNY+ukxnK9rEh1PCJYRIiIiASYM8cDWpVfj3qtDoZCAzanFmP7KbmxOPQtZtq7N0lhGiIiIBLG3VeKJ6yKx8c8TEaFzRmV9M5Z+moq7/30YJTUXRMcbNH0qIy+88AIkScKyZcuu+LwvvvgCERERsLOzQ2xsLL7//vu+vC0REZFFGRHggq8fmITl04fBRilhZ0Y5pr+8Gx8fyLeKLeV7XUYOHTqEd999F8OHD7/i81JSUrBw4ULcddddOHbsGObNm4d58+YhPT29t29NRERkcWxVCjw0LQzfPzQZowJdUNfUiic3pmPhe/uRV1EvOt6A6lUZqaurQ2JiIt577z24urpe8bmvvfYaZs2ahcceewyRkZFYtWoV4uLi8Oabb/YqMBERkSUL83bGl/dNwNM3RMHeRokDeZWY9epuvJt8Gq0WuqV8r8rIkiVLMGfOHCQkJHT53H379v3ueTNnzsS+ffsu+5qmpibo9fpONyIiImuhVEi4c2IItj98NSaHeaCp1YjVWzIw/+0UnCy2vO/EHpeRTz/9FEePHsXq1au79fzS0lJ4e3t3us/b2xulpaWXfc3q1auh1Wo7bgEBAT2NSUREZPYC3Bzwn8Xj8NIfhkNjp0La2Rrc+OZerNmeiaZWy9lSvkdlpLCwEEuXLsXHH38MOzu7gcqEFStWoKampuNWWFg4YO9FRERkyiRJwoIxAdjxyBTMjtGh1SjjjR9ycN1re3Akv1J0vH7RozJy5MgRlJeXIy4uDiqVCiqVCsnJyXj99dehUqlgMPy+pel0OpSVdd5ZrqysDDqd7rLvo1arodFoOt2IiIismZezHdbePhprE+Pg4aTG6XP1+MM7+/DM1ydQ39QqOl6f9KiMTJs2DWlpaUhNTe24jRkzBomJiUhNTYVSqfzda+Lj47Fz585O9yUlJSE+Pr5vyYmIiKzQ7Fgf7Fw+BQtG+0OWgfUpZzDjld1IzjonOlqvqXryZGdnZ8TExHS6z9HREe7u7h33L1q0CH5+fh3nlCxduhRTpkzBmjVrMGfOHHz66ac4fPgw1q1b10+/AhERkXXROtjgpQUjcONIX6z4Kg1FVRdwxwcH8T9x/lh5fSRcHGxFR+yRft+BtaCgACUlJR1/njBhAjZs2IB169ZhxIgR+PLLL7Fp06bflRoiIiLqmclhnti27GosnhgCSQL+e7QICS8n4/u0ErPaUl6SzSCtXq+HVqtFTU0Nzx8hIiK6hCP5VXj8vz8ju7wOADAz2hur5sbASzNwF5x0pbvf35xNQ0REZAFGB7ni24cm4aFpYVApJGw7UYZpLyfjs0MFJr9KwjJCRERkIdQqJZZPH4ZvHpyE4f5a1Da24q//TUPivw6g4HyD6HiXxTJCRERkYSJ9NPjq/gl48rpI2NkokHL6PGa8mox/7cmFwQQH77GMEBERWSCVUoF7rg7FtmVXIz7UHY0tRvz9u1O4aW0KMktrRcfrhGWEiIjIggW5O2LDPePxwk2xcFarcLywGte/sQevJGWhudU0Bu+xjBAREVk4SZJw67hAJC2fgoRIb7QYZLy2MxvXv7EHxwqqRMdjGSEiIrIWOq0d3ls0Gm/eNgrujrbIKqvDTWtTsOrbk2hoFrelPMsIERGRFZEkCdcP98WO5VNw0yg/yDLw/t48fPdzSdcvHiA92g6eiIiILIOroy1evmUkbhzpi82pxfifOH9hWVhGiIiIrNg14V64JtxLaAYepiEiIiKhWEaIiIhIKJYRIiIiEoplhIiIiIRiGSEiIiKhWEaIiIhIKJYRIiIiEoplhIiIiIRiGSEiIiKhWEaIiIhIKJYRIiIiEoplhIiIiIRiGSEiIiKhzGJqryzLAAC9Xi84CREREXXXxe/ti9/jl2MWZaS2thYAEBAQIDgJERER9VRtbS20Wu1lH5fkruqKCTAajSguLoazszMkSeq3n6vX6xEQEIDCwkJoNJp++7nUGT/nwcPPenDwcx4c/JwHx0B+zrIso7a2Fr6+vlAoLn9miFmsjCgUCvj7+w/Yz9doNPyLPgj4OQ8eftaDg5/z4ODnPDgG6nO+0orIRTyBlYiIiIRiGSEiIiKhrLqMqNVqPP3001Cr1aKjWDR+zoOHn/Xg4Oc8OPg5Dw5T+JzN4gRWIiIislxWvTJCRERE4rGMEBERkVAsI0RERCQUywgREREJZdVl5K233kJwcDDs7Owwfvx4HDx4UHQki7J69WqMHTsWzs7O8PLywrx585CZmSk6lsV74YUXIEkSli1bJjqKxTl79ixuv/12uLu7w97eHrGxsTh8+LDoWBbHYDBg5cqVCAkJgb29PYYMGYJVq1Z1Od+Ermz37t244YYb4OvrC0mSsGnTpk6Py7KMp556Cj4+PrC3t0dCQgKys7MHJZvVlpHPPvsMy5cvx9NPP42jR49ixIgRmDlzJsrLy0VHsxjJyclYsmQJ9u/fj6SkJLS0tGDGjBmor68XHc1iHTp0CO+++y6GDx8uOorFqaqqwsSJE2FjY4MtW7bg5MmTWLNmDVxdXUVHszgvvvgi1q5dizfffBOnTp3Ciy++iH/84x944403REcza/X19RgxYgTeeuutSz7+j3/8A6+//jreeecdHDhwAI6Ojpg5cyYaGxsHPpxspcaNGycvWbKk488Gg0H29fWVV69eLTCVZSsvL5cByMnJyaKjWKTa2lo5LCxMTkpKkqdMmSIvXbpUdCSL8te//lWeNGmS6BhWYc6cOfLixYs73XfTTTfJiYmJghJZHgDyxo0bO/5sNBplnU4nv/TSSx33VVdXy2q1Wv7kk08GPI9Vrow0NzfjyJEjSEhI6LhPoVAgISEB+/btE5jMstXU1AAA3NzcBCexTEuWLMGcOXM6/b2m/vP1119jzJgxWLBgAby8vDBq1Ci89957omNZpAkTJmDnzp3IysoCABw/fhx79+7F7NmzBSezXHl5eSgtLe3074dWq8X48eMH5XvRLAbl9beKigoYDAZ4e3t3ut/b2xsZGRmCUlk2o9GIZcuWYeLEiYiJiREdx+J8+umnOHr0KA4dOiQ6isXKzc3F2rVrsXz5cjzxxBM4dOgQHnroIdja2uKOO+4QHc+iPP7449Dr9YiIiIBSqYTBYMBzzz2HxMRE0dEsVmlpKQBc8nvx4mMDySrLCA2+JUuWID09HXv37hUdxeIUFhZi6dKlSEpKgp2dneg4FstoNGLMmDF4/vnnAQCjRo1Ceno63nnnHZaRfvb555/j448/xoYNGxAdHY3U1FQsW7YMvr6+/KwtlFUepvHw8IBSqURZWVmn+8vKyqDT6QSlslwPPPAAvv32W+zatQv+/v6i41icI0eOoLy8HHFxcVCpVFCpVEhOTsbrr78OlUoFg8EgOqJF8PHxQVRUVKf7IiMjUVBQICiR5Xrsscfw+OOP49Zbb0VsbCz++Mc/4uGHH8bq1atFR7NYF7/7RH0vWmUZsbW1xejRo7Fz586O+4xGI3bu3In4+HiBySyLLMt44IEHsHHjRvzwww8ICQkRHckiTZs2DWlpaUhNTe24jRkzBomJiUhNTYVSqRQd0SJMnDjxd5emZ2VlISgoSFAiy9XQ0ACFovPXk1KphNFoFJTI8oWEhECn03X6XtTr9Thw4MCgfC9a7WGa5cuX44477sCYMWMwbtw4vPrqq6ivr8edd94pOprFWLJkCTZs2IDNmzfD2dm547ijVquFvb294HSWw9nZ+Xfn4Tg6OsLd3Z3n5/Sjhx9+GBMmTMDzzz+Pm2++GQcPHsS6deuwbt060dEszg033IDnnnsOgYGBiI6OxrFjx/Dyyy9j8eLFoqOZtbq6OuTk5HT8OS8vD6mpqXBzc0NgYCCWLVuGv//97wgLC0NISAhWrlwJX19fzJs3b+DDDfj1OibsjTfekAMDA2VbW1t53Lhx8v79+0VHsigALnn78MMPRUezeLy0d2B88803ckxMjKxWq+WIiAh53bp1oiNZJL1eLy9dulQODAyU7ezs5NDQUPnJJ5+Um5qaREcza7t27brkv8l33HGHLMttl/euXLlS9vb2ltVqtTxt2jQ5MzNzULJJsswt7YiIiEgcqzxnhIiIiEwHywgREREJxTJCREREQrGMEBERkVAsI0RERCQUywgREREJxTJCREREQrGMEBERkVAsI0RERCQUywgREREJxTJCREREQrGMEBERkVD/H/rPTV0Er5Y5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representación gráfica del objeto Series\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "s.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creación de un objeto DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con un diccionario de objetios Series\n",
    "personas = {\n",
    "    \"peso\": pd.Series([84, 90, 56, 64], [\"Santiago\",\"Pedro\", \"Ana\", \"Julia\"]),\n",
    "    \"altura\": pd.Series({\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}),\n",
    "    \"hijos\": pd.Series([2, 3], [\"Pedro\", \"Julia\"])\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(personas)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Puede forzarse al DataFrame a que presente unas columnas determinadas y en un orden determinado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Ana</th>\n",
       "      <td>165</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>170</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>187</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          altura  peso\n",
       "Ana          165    56\n",
       "Julia        170    64\n",
       "Santiago     187    84"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con algunos elementos de un diccionario\n",
    "# de objetos Series\n",
    "personas = {\n",
    "    \"peso\": pd.Series([84, 90, 56, 64], [\"Santiago\",\"Pedro\", \"Ana\", \"Julia\"]),\n",
    "    \"altura\": pd.Series({\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}),\n",
    "    \"hijos\": pd.Series([2, 3], [\"Pedro\", \"Julia\"])\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(\n",
    "        personas,\n",
    "        columns = [\"altura\", \"peso\"],\n",
    "        index = [\"Ana\", \"Julia\", \"Santiago\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>hijos</th>\n",
       "      <th>peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>185</td>\n",
       "      <td>4</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juan</th>\n",
       "      <td>190</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       altura  hijos  peso\n",
       "Pedro     185      4    76\n",
       "Ana       170      0    65\n",
       "Juan      190      1    89"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con una lista de listas de Python\n",
    "# Importante: Deben especificarse las columnas e indices por separado\n",
    "valores = [\n",
    "    [185, 4, 76],\n",
    "    [170, 0, 65],\n",
    "    [190, 1, 89]\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(\n",
    "        valores,\n",
    "        columns = [\"altura\", \"hijos\", \"peso\"],\n",
    "        index = [\"Pedro\", \"Ana\", \"Juan\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Santiago</th>\n",
       "      <td>187</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>178</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>165</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          altura  peso\n",
       "Santiago     187    87\n",
       "Pedro        178    78\n",
       "Julia        170    70\n",
       "Ana          165    65"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con un diccionario de Python\n",
    "personas = {\n",
    "    \"altura\": {\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}, \n",
    "    \"peso\": {\"Santiago\": 87, \"Pedro\": 78, \"Julia\": 70, \"Ana\": 65}}\n",
    "\n",
    "df = pd.DataFrame(personas)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acceso a los elementos de un DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con un diccionario de objetios Series\n",
    "personas = {\n",
    "    \"peso\": pd.Series([84, 90, 56, 64], [\"Santiago\",\"Pedro\", \"Ana\", \"Julia\"]),\n",
    "    \"altura\": pd.Series({\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}),\n",
    "    \"hijos\": pd.Series([2, 3], [\"Pedro\", \"Julia\"])\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(personas)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acceso a los elementos de las columnas del DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana         56\n",
       "Julia       64\n",
       "Pedro       90\n",
       "Santiago    84\n",
       "Name: peso, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"peso\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura\n",
       "Ana         56     165\n",
       "Julia       64     170\n",
       "Pedro       90     178\n",
       "Santiago    84     187"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"peso\", \"altura\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana         False\n",
       "Julia       False\n",
       "Pedro        True\n",
       "Santiago     True\n",
       "Name: peso, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pueden combinarse los metodos anteriores con expresiones booleanas\n",
    "df[\"peso\"] > 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pueden combinarse los metodos anteriores con expresiones booleanas\n",
    "df[df[\"peso\"] > 80]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acceso a los elementos de las filas del DataFrame"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe thead th {\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>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrar el DataFrame\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "peso       90.0\n",
       "altura    178.0\n",
       "hijos       2.0\n",
       "Name: Pedro, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Pedro\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "peso       90.0\n",
       "altura    178.0\n",
       "hijos       2.0\n",
       "Name: Pedro, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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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       "<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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       peso  altura  hijos\n",
       "Julia    64     170    3.0\n",
       "Pedro    90     178    2.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consulta avanzada de los elementos de un DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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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       "</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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrar el DataFrame\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"altura >= 170 and peso > 60\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copiar un DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con un diccionario de objetios Series\n",
    "personas = {\n",
    "    \"peso\": pd.Series([84, 90, 56, 64], [\"Santiago\",\"Pedro\", \"Ana\", \"Julia\"]),\n",
    "    \"altura\": pd.Series({\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}),\n",
    "    \"hijos\": pd.Series([2, 3], [\"Pedro\", \"Julia\"])\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(personas)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copia del DataFrame df en df_copy\n",
    "# Importante: Al modificar un elemento de df_copy no se modifica df\n",
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modificación de un DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con un diccionario de objetios Series\n",
    "personas = {\n",
    "    \"peso\": pd.Series([84, 90, 56, 64], [\"Santiago\",\"Pedro\", \"Ana\", \"Julia\"]),\n",
    "    \"altura\": pd.Series({\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}),\n",
    "    \"hijos\": pd.Series([2, 3], [\"Pedro\", \"Julia\"])\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(personas)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>hijos</th>\n",
       "      <th>cumpleaños</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos  cumpleaños\n",
       "Ana         56     165    NaN        1990\n",
       "Julia       64     170    3.0        1987\n",
       "Pedro       90     178    2.0        1980\n",
       "Santiago    84     187    NaN        1994"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Añadir una nueva columna al DataFrame\n",
    "df[\"cumpleaños\"] = [1990, 1987, 1980, 1994]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
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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>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>hijos</th>\n",
       "      <th>cumpleaños</th>\n",
       "      <th>años</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos  cumpleaños  años\n",
       "Ana         56     165    NaN        1990    30\n",
       "Julia       64     170    3.0        1987    33\n",
       "Pedro       90     178    2.0        1980    40\n",
       "Santiago    84     187    NaN        1994    26"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Añadir una nueva columna calculada al DataFrame\n",
    "df[\"años\"] = 2020 - df[\"cumpleaños\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>hijos</th>\n",
       "      <th>cumpleaños</th>\n",
       "      <th>años</th>\n",
       "      <th>mascotas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos  cumpleaños  años  mascotas\n",
       "Ana         56     165    NaN        1990    30         1\n",
       "Julia       64     170    3.0        1987    33         3\n",
       "Pedro       90     178    2.0        1980    40         0\n",
       "Santiago    84     187    NaN        1994    26         0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Añadir una nueva columna creando un DataFrame nuevo\n",
    "df_mod = df.assign(mascotas = [1, 3, 0, 0])\n",
    "df_mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>hijos</th>\n",
       "      <th>cumpleaños</th>\n",
       "      <th>años</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos  cumpleaños  años\n",
       "Ana         56     165    NaN        1990    30\n",
       "Julia       64     170    3.0        1987    33\n",
       "Pedro       90     178    2.0        1980    40\n",
       "Santiago    84     187    NaN        1994    26"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>altura</th>\n",
       "      <th>hijos</th>\n",
       "      <th>cumpleaños</th>\n",
       "      <th>años</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          altura  hijos  cumpleaños  años\n",
       "Ana          165    NaN        1990    30\n",
       "Julia        170    3.0        1987    33\n",
       "Pedro        178    2.0        1980    40\n",
       "Santiago     187    NaN        1994    26"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminar una columna existente del DataFrame\n",
    "del df[\"peso\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>altura</th>\n",
       "      <th>cumpleaños</th>\n",
       "      <th>años</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>165</td>\n",
       "      <td>1990</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>170</td>\n",
       "      <td>1987</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>178</td>\n",
       "      <td>1980</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>187</td>\n",
       "      <td>1994</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          altura  cumpleaños  años\n",
       "Ana          165        1990    30\n",
       "Julia        170        1987    33\n",
       "Pedro        178        1980    40\n",
       "Santiago     187        1994    26"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminar una columna existente devolviendo una copia del DataFrame resultante\n",
    "df_mod = df.drop([\"hijos\"], axis=1)\n",
    "df_mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>hijos</th>\n",
       "      <th>cumpleaños</th>\n",
       "      <th>años</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          altura  hijos  cumpleaños  años\n",
       "Ana          165    NaN        1990    30\n",
       "Julia        170    3.0        1987    33\n",
       "Pedro        178    2.0        1980    40\n",
       "Santiago     187    NaN        1994    26"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluación de expresiones sobre un DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con un diccionario de objetios Series\n",
    "personas = {\n",
    "    \"peso\": pd.Series([84, 90, 56, 64], [\"Santiago\",\"Pedro\", \"Ana\", \"Julia\"]),\n",
    "    \"altura\": pd.Series({\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}),\n",
    "    \"hijos\": pd.Series([2, 3], [\"Pedro\", \"Julia\"])\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(personas)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana         82.5\n",
       "Julia       85.0\n",
       "Pedro       89.0\n",
       "Santiago    93.5\n",
       "Name: altura, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluar una función sobre una columna del DataFrame\n",
    "df.eval(\"altura / 2\")"
   ]
  },
  {
   "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>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>hijos</th>\n",
       "      <th>media_altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos  media_altura\n",
       "Ana         56     165    NaN          82.5\n",
       "Julia       64     170    3.0          85.0\n",
       "Pedro       90     178    2.0          89.0\n",
       "Santiago    84     187    NaN          93.5"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Asignar el valor resultante como una nueva columna\n",
    "df.eval(\"media_altura = altura / 2\", inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana         False\n",
       "Julia       False\n",
       "Pedro       False\n",
       "Santiago     True\n",
       "Name: altura, dtype: bool"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluar una función utilizando una variable local\n",
    "max_altura = 180\n",
    "df.eval(\"altura > @max_altura\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ana         58\n",
       "Julia       66\n",
       "Pedro       92\n",
       "Santiago    86\n",
       "Name: peso, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aplicar una función externa a una columna del DataFrame\n",
    "def func(x):\n",
    "    return x + 2\n",
    "\n",
    "df[\"peso\"].apply(func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>hijos</th>\n",
       "      <th>media_altura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos  media_altura\n",
       "Ana         56     165    NaN          82.5\n",
       "Julia       64     170    3.0          85.0\n",
       "Pedro       90     178    2.0          89.0\n",
       "Santiago    84     187    NaN          93.5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guardar y Cargar el DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creación de un DataFrame inicializándolo con un diccionario de objetios Series\n",
    "personas = {\n",
    "    \"peso\": pd.Series([84, 90, 56, 64], [\"Santiago\",\"Pedro\", \"Ana\", \"Julia\"]),\n",
    "    \"altura\": pd.Series({\"Santiago\": 187, \"Pedro\": 178, \"Julia\": 170, \"Ana\": 165}),\n",
    "    \"hijos\": pd.Series([2, 3], [\"Pedro\", \"Julia\"])\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(personas)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardar el DataFrame como CSV, HTML y JSON\n",
    "df.to_csv(\"df_personas.csv\")\n",
    "df.to_html(\"df_personas.html\")\n",
    "df.to_json(\"df_personas.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cargar el DataFrame en Jupyter\n",
    "df2 = pd.read_csv(\"df_personas.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Unnamed: 0</th>\n",
       "      <th>peso</th>\n",
       "      <th>altura</th>\n",
       "      <th>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ana</td>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Julia</td>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedro</td>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0  peso  altura  hijos\n",
       "0        Ana    56     165    NaN\n",
       "1      Julia    64     170    3.0\n",
       "2      Pedro    90     178    2.0\n",
       "3   Santiago    84     187    NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>hijos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ana</th>\n",
       "      <td>56</td>\n",
       "      <td>165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julia</th>\n",
       "      <td>64</td>\n",
       "      <td>170</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro</th>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santiago</th>\n",
       "      <td>84</td>\n",
       "      <td>187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peso  altura  hijos\n",
       "Ana         56     165    NaN\n",
       "Julia       64     170    3.0\n",
       "Pedro       90     178    2.0\n",
       "Santiago    84     187    NaN"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cargar el DataFrame con la primera columna correctamente asignada\n",
    "df2 = pd.read_csv(\"df_personas.csv\", index_col=0)\n",
    "df2"
   ]
  }
 ],
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