Clase_9 (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gA098fNUqvb_"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/ITBA-Python/Certificacion-Profesional-Python/blob/main/Clase_9 (1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UShQ7MbxF39r"
   },
   "source": [
    "<img src=\"https://raw.githubusercontent.com/ITBA-Python/Certificacion-Profesional-Python/main/assets/clase2/Headers%20c%C3%A1talogo_Mesa%20de%20trabajo%201%20copia%2044.jpg\" width=\"800\">\n",
    "\n",
    "\n",
    "\n",
    "# Certificación profesional de Python\n",
    "\n",
    "# Clase \n",
    "\n",
    "El objetivo de la clase es seguir sumando experiencia de trabajo con programación aprendiendo acerca de librerías comunes en python:\n",
    "\n",
    "*   numpy\n",
    "*   pandas \n",
    "*   matplotlib\n",
    "\n",
    "La clase incluye teoría y práctica sobre cada tema aprendido.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/ITBA-Python/Certificacion-Profesional-Python/main/assets/clase2/LOGOTIPO_sin_fondo_positivo_sin_bajada.png\" width=\"400\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IOV0vIY_qvcC"
   },
   "source": [
    "# Librerias en python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Por qué usar librerías?\n",
    "Las librerías (o bibliotecas) son un componente esencial para que los desarrolladores puedan programar de forma sencilla, evitando la duplicidad de código y minimizando errores. También permiten una mayor agilidad, al reducir el tiempo de desarrollo, así como los costes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cómo empezar a usar una biblioteca/librería nueva?\n",
    "1. Buscar un ejemplo de lo que quiero hacer.\n",
    "2. Con el nombre de la función que será de utilidad, leer la documentación.\n",
    "3. Explorar en la documentación si hay una forma mejor de hacer lo que busco (suelen sugerirse funciones similares en las páginas de documentación)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalar las librerías que vamos a usar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Retrieving notices: ...working... done\n"
     ]
    }
   ],
   "source": [
    "# Instalar un paquete de conda en el kernel de Jupyter que estoy usando (también se puede correr con 'conda install' en la terminal, adentro del venv del curso\n",
    "import sys\n",
    "!conda install --yes --prefix {sys.prefix} numpy pandas matplotlib scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy es un paquete fundamental para hacer computación científica en python.\n",
    "Es una librería que proporciona un array multidimensional (equivalente a una lista, pero más óptima), varios objetos derivados y una variedad de rutinas para operaciones rápidas en arrays, incluyendo operaciones matemáticas, lógicas, manipulación de dimensiones, clasificación, selección, I/O, transformadas discretas de Fourier, álgebra lineal básica, operaciones estadísticas básicas , simulación aleatoria y mucho más."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cualquier duda, busquen en la documentación. [Acá](https://numpy.org/doc/stable/user/quickstart.html) les dejo una guía rápida para ver algunas cosas básicas (y no tan básicas)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear un array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3, 4, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También puedo crear un array a través de una estructura que itera los valores dentro de un rango (como la función `range` que usamos en la clase 3):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(6)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cambiarle la dimensión a un array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [2 3]\n",
      " [4 5]]\n"
     ]
    }
   ],
   "source": [
    "b = a.reshape(3, 2)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seleccionar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El slicing funciona igual que una lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0:2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1 , 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
    "print(a[a < 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3  4  5  6  7  8  9 10]\n"
     ]
    }
   ],
   "source": [
    "print(a[(a > 2) & (a < 11)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operaciones matemáticas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Las operaciones matemáticas básicas son iguales que lo que vimos antes. Otras son nuevas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12, 1, 6.5, 78\n"
     ]
    }
   ],
   "source": [
    "print(f'{a.max()}, {a.min()}, {a.mean()}, {a.sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4], [1 5 9]\n"
     ]
    }
   ],
   "source": [
    "print(f'{a.min(axis=0)}, {a.min(axis=1)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operar con arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12 11 10  9]\n",
      " [ 8  7  6  5]\n",
      " [ 4  3  2  1]]\n"
     ]
    }
   ],
   "source": [
    "reversed_arr = np.flip(a)\n",
    "print(reversed_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]\n",
      " [12 11 10  9]\n",
      " [ 8  7  6  5]\n",
      " [ 4  3  2  1]]\n"
     ]
    }
   ],
   "source": [
    "concat_arrays = np.concatenate((a, reversed_arr))\n",
    "print(concat_arrays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4]\n",
      " [2 5]\n",
      " [3 6]]\n"
     ]
    }
   ],
   "source": [
    "concat_on_two_d = np.c_[np.array([1,2,3]), np.array([4,5,6])]\n",
    "print(concat_on_two_d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas es una librería de python que se utiliza para trabajar con data sets.\n",
    "Tiene funciones para analizar, limpiar, explorar y manipular data.\n",
    "\n",
    "Vamos a ver algunas funciones, siempre pueden buscar más información en la documentación oficial de pandas ([aquí](https://pandas.pydata.org/docs/reference/index.html#api)) o pueden buscar una cheat sheet para tener algunas cosas a mano (por ejemplo [esta](https://www.datacamp.com/cheat-sheet/pandas-cheat-sheet-for-data-science-in-python))\n",
    "\n",
    "En genereal (aunque no exclusivamente) se trabaja con dos tipos de estructuras de datos para manipular la data:\n",
    "\n",
    "*    Series: se puede pensar como una única columna.\n",
    "*    DataFrame: tiene forma de tabla (filas, columnas, índices)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Una serie puede crearse a partir de una lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### También puede crearse a partir de otras funciones de la misma librería: \n",
    "#### Pandas maneja muy bien las fechas, tiene muchas funciones para trabajar con ellas. Un ejemplo de esto es que podemos generar un rango de fechas fácilmente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-11-23', '2022-11-24', '2022-11-25', '2022-11-26',\n",
       "               '2022-11-27', '2022-11-28'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fechas = pd.date_range(\"20221123\", periods=6)\n",
    "fechas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Un dataframe puede generarse pasandole los valores, los índices y las columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-11-23</th>\n",
       "      <td>-0.012106</td>\n",
       "      <td>-0.728156</td>\n",
       "      <td>0.702452</td>\n",
       "      <td>0.136681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-24</th>\n",
       "      <td>1.473751</td>\n",
       "      <td>-0.603671</td>\n",
       "      <td>-0.932677</td>\n",
       "      <td>-0.681707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>-1.762968</td>\n",
       "      <td>-0.757496</td>\n",
       "      <td>-0.879576</td>\n",
       "      <td>0.662284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-26</th>\n",
       "      <td>-1.151010</td>\n",
       "      <td>0.557225</td>\n",
       "      <td>-0.335070</td>\n",
       "      <td>0.576282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-27</th>\n",
       "      <td>-0.586370</td>\n",
       "      <td>-2.536841</td>\n",
       "      <td>-1.786970</td>\n",
       "      <td>-0.071097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-28</th>\n",
       "      <td>-0.670917</td>\n",
       "      <td>1.504470</td>\n",
       "      <td>0.257540</td>\n",
       "      <td>-0.080328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2022-11-23 -0.012106 -0.728156  0.702452  0.136681\n",
       "2022-11-24  1.473751 -0.603671 -0.932677 -0.681707\n",
       "2022-11-25 -1.762968 -0.757496 -0.879576  0.662284\n",
       "2022-11-26 -1.151010  0.557225 -0.335070  0.576282\n",
       "2022-11-27 -0.586370 -2.536841 -1.786970 -0.071097\n",
       "2022-11-28 -0.670917  1.504470  0.257540 -0.080328"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=np.random.randn(6, 4), index=fechas, columns=list(\"ABCD\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Otra forma de crearlo puede ser a través de un diccionario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-11-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-11-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-11-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-11-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2022-11-23  1.0  3   test  foo\n",
       "1  1.0 2022-11-24  1.0  3  train  foo\n",
       "2  1.0 2022-11-25  1.0  3   test  foo\n",
       "3  1.0 2022-11-26  1.0  3  train  foo"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(\n",
    "    {\n",
    "        \"A\": 1.0,\n",
    "        \"B\": fechas[:4],\n",
    "        \"C\": pd.Series(1, index=list(range(4)), dtype=\"float32\"),\n",
    "        \"D\": np.array([3] * 4, dtype=\"int32\"),\n",
    "        \"E\": pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "        \"F\": \"foo\",\n",
    "    }\n",
    ")\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Otras opciones pueden ser `pd.from_records` o `pd.from_dict`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funciones que pueden ser útiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   pd.describe(): Genera estadísticas descriptivas. También se puede correr cada operación por separado (por ejemplo, pd.mean() o pd.max() ).\n",
    "*   pd.sort_values(by): Ordena los valores según el índice o columna que le indique (puede ser ascendente  descendente).>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seleccionar una parte del data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-11-23   -0.012106\n",
       "2022-11-24    1.473751\n",
       "2022-11-25   -1.762968\n",
       "2022-11-26   -1.151010\n",
       "2022-11-27   -0.586370\n",
       "2022-11-28   -0.670917\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'] # equivalente a df.A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slice (o un pedazo de una tabla)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecciono algunos índices y algunas columnas según sus nombres mediante `loc[idx, col]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-11-23</th>\n",
       "      <td>-0.012106</td>\n",
       "      <td>0.702452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-24</th>\n",
       "      <td>1.473751</td>\n",
       "      <td>-0.932677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>-1.762968</td>\n",
       "      <td>-0.879576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2022-11-23 -0.012106  0.702452\n",
       "2022-11-24  1.473751 -0.932677\n",
       "2022-11-25 -1.762968 -0.879576"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"20221123\":\"20221125\", [\"A\", \"C\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También puedo usar `loc` para obtener un único valor, indicando solo un índice y una columna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecciono algunos índices y algunas columnas según sus índices mediante `iloc[idx, col]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "        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>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-11-24</th>\n",
       "      <td>1.473751</td>\n",
       "      <td>-0.932677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>-1.762968</td>\n",
       "      <td>-0.879576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-27</th>\n",
       "      <td>-0.586370</td>\n",
       "      <td>-1.786970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2022-11-24  1.473751 -0.932677\n",
       "2022-11-25 -1.762968 -0.879576\n",
       "2022-11-27 -0.586370 -1.786970"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1, 2, 4], [0, 2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seleccionar según una condición"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-11-24</th>\n",
       "      <td>1.473751</td>\n",
       "      <td>-0.603671</td>\n",
       "      <td>-0.932677</td>\n",
       "      <td>-0.681707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2022-11-24  1.473751 -0.603671 -0.932677 -0.681707"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"A\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-11-24</th>\n",
       "      <td>1.473751</td>\n",
       "      <td>-0.603671</td>\n",
       "      <td>-0.932677</td>\n",
       "      <td>-0.681707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2022-11-24  1.473751 -0.603671 -0.932677 -0.681707"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"A\"] > 0) & (df['B'] < 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unir dos tablas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-11-23</th>\n",
       "      <td>-0.012106</td>\n",
       "      <td>-0.728156</td>\n",
       "      <td>0.702452</td>\n",
       "      <td>0.136681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-24</th>\n",
       "      <td>1.473751</td>\n",
       "      <td>-0.603671</td>\n",
       "      <td>-0.932677</td>\n",
       "      <td>-0.681707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>-1.762968</td>\n",
       "      <td>-0.757496</td>\n",
       "      <td>-0.879576</td>\n",
       "      <td>0.662284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-26</th>\n",
       "      <td>-1.151010</td>\n",
       "      <td>0.557225</td>\n",
       "      <td>-0.335070</td>\n",
       "      <td>0.576282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-27</th>\n",
       "      <td>-0.586370</td>\n",
       "      <td>-2.536841</td>\n",
       "      <td>-1.786970</td>\n",
       "      <td>-0.071097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-28</th>\n",
       "      <td>-0.670917</td>\n",
       "      <td>1.504470</td>\n",
       "      <td>0.257540</td>\n",
       "      <td>-0.080328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2022-11-23 -0.012106 -0.728156  0.702452  0.136681\n",
       "2022-11-24  1.473751 -0.603671 -0.932677 -0.681707\n",
       "2022-11-25 -1.762968 -0.757496 -0.879576  0.662284\n",
       "2022-11-26 -1.151010  0.557225 -0.335070  0.576282\n",
       "2022-11-27 -0.586370 -2.536841 -1.786970 -0.071097\n",
       "2022-11-28 -0.670917  1.504470  0.257540 -0.080328"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-11-23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-11-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-11-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-11-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2022-11-23  1.0  3   test  foo\n",
       "1  1.0 2022-11-24  1.0  3  train  foo\n",
       "2  1.0 2022-11-25  1.0  3   test  foo\n",
       "3  1.0 2022-11-26  1.0  3  train  foo"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>D_left</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-11-23</th>\n",
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       "      <td>foo</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-24</th>\n",
       "      <td>1.473751</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-25</th>\n",
       "      <td>-1.762968</td>\n",
       "      <td>-0.757496</td>\n",
       "      <td>-0.879576</td>\n",
       "      <td>0.662284</td>\n",
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       "      <td>foo</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-26</th>\n",
       "      <td>-1.151010</td>\n",
       "      <td>0.557225</td>\n",
       "      <td>-0.335070</td>\n",
       "      <td>0.576282</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-27</th>\n",
       "      <td>-0.586370</td>\n",
       "      <td>-2.536841</td>\n",
       "      <td>-1.786970</td>\n",
       "      <td>-0.071097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-28</th>\n",
       "      <td>-0.670917</td>\n",
       "      <td>1.504470</td>\n",
       "      <td>0.257540</td>\n",
       "      <td>-0.080328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A_left         B    C_left    D_left  A_right  C_right  D_right  \\\n",
       "2022-11-23 -0.012106 -0.728156  0.702452  0.136681      1.0      1.0      3.0   \n",
       "2022-11-24  1.473751 -0.603671 -0.932677 -0.681707      1.0      1.0      3.0   \n",
       "2022-11-25 -1.762968 -0.757496 -0.879576  0.662284      1.0      1.0      3.0   \n",
       "2022-11-26 -1.151010  0.557225 -0.335070  0.576282      1.0      1.0      3.0   \n",
       "2022-11-27 -0.586370 -2.536841 -1.786970 -0.071097      NaN      NaN      NaN   \n",
       "2022-11-28 -0.670917  1.504470  0.257540 -0.080328      NaN      NaN      NaN   \n",
       "\n",
       "                E    F  \n",
       "2022-11-23   test  foo  \n",
       "2022-11-24  train  foo  \n",
       "2022-11-25   test  foo  \n",
       "2022-11-26  train  foo  \n",
       "2022-11-27    NaN  NaN  \n",
       "2022-11-28    NaN  NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined = df.join(df2.set_index('B'), how='left', lsuffix='_left', rsuffix='_right')\n",
    "df_joined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se puede usar `merge` que tiene una funcionalidad similar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agrupar la data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.749791</td>\n",
       "      <td>1.468095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.021207</td>\n",
       "      <td>-1.529563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.286077</td>\n",
       "      <td>-0.396420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.747305</td>\n",
       "      <td>0.635923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.286476</td>\n",
       "      <td>1.233228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.289581</td>\n",
       "      <td>0.060645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.058261</td>\n",
       "      <td>-0.263501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.997759</td>\n",
       "      <td>0.086012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -0.749791  1.468095\n",
       "1  bar    one -1.021207 -1.529563\n",
       "2  foo    two  0.286077 -0.396420\n",
       "3  bar  three -0.747305  0.635923\n",
       "4  foo    two -0.286476  1.233228\n",
       "5  bar    two -0.289581  0.060645\n",
       "6  foo    one -1.058261 -0.263501\n",
       "7  foo  three -0.997759  0.086012"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_df = pd.DataFrame(\n",
    "    {\n",
    "        \"A\": [\"foo\", \"bar\", \"foo\", \"bar\", \"foo\", \"bar\", \"foo\", \"foo\"],\n",
    "        \"B\": [\"one\", \"one\", \"two\", \"three\", \"two\", \"two\", \"one\", \"three\"],\n",
    "        \"C\": np.random.randn(8),\n",
    "        \"D\": np.random.randn(8),\n",
    "    }\n",
    ")\n",
    "group_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A\n",
       "bar    3\n",
       "foo    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_df.groupby('A').size()"
   ]
  },
  {
   "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",
       "        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></th>\n",
       "      <th>A_left</th>\n",
       "      <th>B</th>\n",
       "      <th>C_left</th>\n",
       "      <th>D_left</th>\n",
       "      <th>A_right</th>\n",
       "      <th>C_right</th>\n",
       "      <th>D_right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <th>foo</th>\n",
       "      <td>-1.775075</td>\n",
       "      <td>-1.485652</td>\n",
       "      <td>-0.177123</td>\n",
       "      <td>0.798964</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <th>foo</th>\n",
       "      <td>0.322741</td>\n",
       "      <td>-0.046445</td>\n",
       "      <td>-1.267746</td>\n",
       "      <td>-0.105425</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A_left         B    C_left    D_left  A_right  C_right  D_right\n",
       "E     F                                                                     \n",
       "test  foo -1.775075 -1.485652 -0.177123  0.798964      2.0      2.0      6.0\n",
       "train foo  0.322741 -0.046445 -1.267746 -0.105425      2.0      2.0      6.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined.groupby(['E', 'F']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trabajar con CSVs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Leer un csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('iris.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal.length</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5.1</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.6</th>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.7</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.3</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.2</th>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal.width  petal.length  petal.width    variety\n",
       "sepal.length                                                   \n",
       "5.1                   3.5           1.4          0.2     Setosa\n",
       "4.9                   3.0           1.4          0.2     Setosa\n",
       "4.7                   3.2           1.3          0.2     Setosa\n",
       "4.6                   3.1           1.5          0.2     Setosa\n",
       "5.0                   3.6           1.4          0.2     Setosa\n",
       "...                   ...           ...          ...        ...\n",
       "6.7                   3.0           5.2          2.3  Virginica\n",
       "6.3                   2.5           5.0          1.9  Virginica\n",
       "6.5                   3.0           5.2          2.0  Virginica\n",
       "6.2                   3.4           5.4          2.3  Virginica\n",
       "5.9                   3.0           5.1          1.8  Virginica\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Guardar en un csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('prueba.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hay muchas funcionalidades en pandas, las más sencillas pueden encontrarlas [acà](https://pandas.pydata.org/docs/user_guide/10min.html), lo más complejo pueden googlearlo según lo que quieran hacer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Esta es una librería que nos permite realizar gráficos simples de manera muy sencilla. A su vez, también nos permite, con un poco más de esfuerzo, realizar gráficos muy complejos.\n",
    "\n",
    "#### Está basada en arrays de NumPy y nos permite hacer gráficos de líneas, barras, dispersión, histogramas y muchísimas cosas más.\n",
    "\n",
    "#### Pyplot es un módulo de Matplotlib que proporciona una interfaz similar a MATLAB. Pyplot proporciona funciones que interactúan con la figura, es decir, crea una figura, decora el gráfico con etiquetas y crea un área de trazado en una figura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mi primer gráfico')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 2, 3, 4]\n",
    "y = [1, 4, 9, 16]\n",
    "plt.plot(x, y)\n",
    "plt.title('Mi primer gráfico')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9e07e53048>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, color='blue', linewidth=3, linestyle='--')\n",
    "plt.scatter(x,y, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A veces vamos a necesitar hacer cosas un poco más complejas (varios gráficos en uno, dos gráficos uno al lado del otro, y muchas cosas más), para eso va a ser necesario que aprendamos lo que es figure y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9e07ddb048>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x194.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 2.7))\n",
    "\n",
    "x = np.linspace(0, 2, 100)  \n",
    "ax.plot(x, x, label='lineal') \n",
    "ax.plot(x, x**2, label='cuadrática')\n",
    "ax.plot(x, x**3, label='cubica')\n",
    "ax.set_xlabel('eje x')\n",
    "ax.set_ylabel('eje y')\n",
    "ax.set_title('Un título')\n",
    "ax.legend() # necesario para que se muestren los lables de la data que estoy graficando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9e07d190b8>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,2))\n",
    "\n",
    "ax1.plot([1, 2, 3, 4], [1, 2, 3, 4])\n",
    "ax1.plot([1, 2, 3, 4], [4, 3, 2, 1])\n",
    " \n",
    "ax2.plot([1, 2, 3, 4], [1, 1, 1, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prueben que sucede si, en la creación de la figura, añaden el parámetro `sharex='all'`o `sharey='all'`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilice la librería sklearn para cargar un dataset con información acerca de tipos de hojas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "iris_df = pd.DataFrame(\n",
    "    data= np.c_[iris['data'], iris['target']],\n",
    "    columns= iris['feature_names'] + ['species']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Visualice la estrucura del pandas.Dataframe.\n",
    "\n",
    "2. Seleccione dos columnas y realice un gráfico de dispersión de ambas (una como x, ora como y).\n",
    "\n",
    "3. Elija una columna, cuente el número de veces que un valor se repite en dicha columna y realice un gráfico de barras. Para ello\n",
    "    * Agrupe el dataframe según la columna de interés (groupby) y calcule el tamaño con la función `.size()` (después de hacer el groupby). Esto le dará como resultado un `pd.Series`.\n",
    "    * Use la función `bar` dentro de pyplot realizar el gráfico de barras. Asigne los valores de `series.index` y `serie.values` a las variables `x` y `height` respectivamente.\n",
    "\n",
    "4. Utilice los mismos valores del punto anterior para realizar un histograma.\n",
    "    * AYUDA: Pandas tiene incorporadas algunas funciones de matplotlib. Investiguen que sucede cuando hacen el nombre de la serie `.plot.hist()`"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Clase 8.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
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 },
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}