S5_clase2.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7b62585",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import lxml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2791b5a1",
   "metadata": {},
   "source": [
    "# XML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901712bc",
   "metadata": {},
   "source": [
    "El lenguaje de marcado extensible (XML) permite definir y almacenar datos de forma compartible. XML admite el intercambio de información entre sistemas de computación, como sitios web, bases de datos y aplicaciones de terceros."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e715bd42",
   "metadata": {},
   "source": [
    "Etiquetas XML\n",
    "\n",
    "Los símbolos de marcado, denominados etiquetas en XML, se utilizan para definir los datos. Por ejemplo, para representar los datos de una librería, puede crear etiquetas como < libro >, < título > y < autor >. El documento XML de un solo libro tendría el siguiente contenido:\n",
    "\n",
    "< libro >\n",
    "\n",
    "< título >Introducción a Amazon Web Services</título>\n",
    "\n",
    "< autor >Mark Wilkins< /autor >\n",
    "\n",
    "< /libro >\n",
    "\n",
    "Las etiquetas ofrecen una sofisticada codificación de datos para integrar los flujos de información en diferentes sistemas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fc4b9bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59b473ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'https://raw.githubusercontent.com/jamc88/Analisis-de-datos/main/books.xml'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91648b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "perf = pd.read_xml(path) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e6d0d238",
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       "      <th>Title</th>\n",
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       "      <th>0</th>\n",
       "      <td>bk101</td>\n",
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       "      <td>XML Developer's Guide</td>\n",
       "      <td>Computer</td>\n",
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       "      <td>An in-depth look at creating applications\\n   ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>bk102</td>\n",
       "      <td>Garcia, Debra</td>\n",
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       "      <td>5.95</td>\n",
       "      <td>2000-12-16</td>\n",
       "      <td>A former architect battles corporate zombies,\\...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Jake VanderPlas</td>\n",
       "      <td>Python Data Science Handbook</td>\n",
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       "      <td>2016-08-16</td>\n",
       "      <td>Get complete instructions for manipulating, \\n...</td>\n",
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       "      <td>2018-10-16</td>\n",
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       "      id               Author   \n",
       "0  bk101  Garghentini, Davide  \\\n",
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       "2  bk101      Jake VanderPlas   \n",
       "3  bk104   Gillian McAllister   \n",
       "4  bk102     William McKinney   \n",
       "\n",
       "                                              Title     Genre  Price   \n",
       "0                             XML Developer's Guide  Computer  44.95  \\\n",
       "1                                     Midnight Rain   Fantasy   5.95   \n",
       "2                      Python Data Science Handbook  Computer  42.05   \n",
       "3  Wrong Place Wrong Time: A Reese's Book Club Pick   Mistery  15.45   \n",
       "4                          Python for Data Analysis   Fantasy  45.20   \n",
       "\n",
       "  PublishDate                                        Description  \n",
       "0  2000-10-01  An in-depth look at creating applications\\n   ...  \n",
       "1  2000-12-16  A former architect battles corporate zombies,\\...  \n",
       "2  2016-08-16  Get complete instructions for manipulating, \\n...  \n",
       "3  2023-04-10  New York Times bestselling author Gillian McAl...  \n",
       "4  2018-10-16  Written by Wes McKinney, the creator of the Py...  "
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    "perf"
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84e185b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f1c95074",
   "metadata": {},
   "source": [
    "# Limpieza y preparación de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f59bf9a",
   "metadata": {},
   "source": [
    "Mientras se realiza análisis y modelado de datos, se emplea una considerable cantidad de tiempo en la preparación de los mismos: carga, limpieza, transformación y reordenación. A menudo estas tareas le ocupan al analista más del 80 % de su tiempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "816cae9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/jamc88/TSMCI-Analisis-de-datos-con-Python/main/Datos/metro_NA.csv',index_col=['fecha']) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224a072e",
   "metadata": {},
   "source": [
    "Datos: https://datos.cdmx.gob.mx/dataset/ingresos-del-sistema-de-transporte-colectivo-metro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2cbbd80c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>324825.0</td>\n",
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       "      <th>2022-12-31</th>\n",
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       "<p>17532 rows × 3 columns</p>\n",
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      "text/plain": [
       "            tipo_ingreso     linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01           NaN       NaN       NaN\n",
       "2022-01-01           NaN       NaN  180225.0\n",
       "2022-01-01       Boletos   Línea 4       NaN\n",
       "2022-01-01           NaN   Línea 5  134725.0\n",
       "...                  ...       ...       ...\n",
       "2022-12-31  QR/Validador   Línea 8       NaN\n",
       "2022-12-31           NaN   Línea 9    7590.0\n",
       "2022-12-31  QR/Validador       NaN       0.0\n",
       "2022-12-31           NaN       NaN       NaN\n",
       "2022-12-31  QR/Validador  Línea 12       0.0\n",
       "\n",
       "[17532 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3bd8ea24",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1.735900e+04</td>\n",
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       "            ingreso\n",
       "count  1.735900e+04\n",
       "mean   2.771655e+05\n",
       "std    4.117393e+05\n",
       "min    0.000000e+00\n",
       "25%    0.000000e+00\n",
       "50%    8.805000e+03\n",
       "75%    4.468910e+05\n",
       "max    2.619488e+06"
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a791bdb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tipo_ingreso     39\n",
       "linea           102\n",
       "ingreso         173\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8884d2",
   "metadata": {},
   "source": [
    "**dropna** elimina por defecto cualquier fila que contiene un valor faltante:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c610feab",
   "metadata": {},
   "outputs": [
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       "      <th>2022-12-31</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 4</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 5</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
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       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "            tipo_ingreso     linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01       Boletos   Línea A  163450.0\n",
       "2022-01-01      Recargas   Línea 1  439332.5\n",
       "2022-01-01      Recargas   Línea 2  369354.8\n",
       "2022-01-01      Recargas   Línea 3  347269.8\n",
       "...                  ...       ...       ...\n",
       "2022-12-31  QR/Validador   Línea 1       0.0\n",
       "2022-12-31  QR/Validador   Línea 2       0.0\n",
       "2022-12-31  QR/Validador   Línea 4       0.0\n",
       "2022-12-31  QR/Validador   Línea 5       0.0\n",
       "2022-12-31  QR/Validador  Línea 12       0.0\n",
       "\n",
       "[17240 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d08c08",
   "metadata": {},
   "source": [
    "Pasar **how='all'** quitará solamente las filas que sean todas nulas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eda3aed7",
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2022-01-01</th>\n",
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       "      <td>324825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
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       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Línea 5</td>\n",
       "      <td>134725.0</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2022-12-31</th>\n",
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       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
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       "      <td>Línea 9</td>\n",
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       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
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       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
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       "  </tbody>\n",
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       "<p>17527 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "            tipo_ingreso     linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01           NaN       NaN  180225.0\n",
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       "2022-01-01           NaN   Línea 5  134725.0\n",
       "2022-01-01       Boletos       NaN       NaN\n",
       "...                  ...       ...       ...\n",
       "2022-12-31  QR/Validador       NaN       NaN\n",
       "2022-12-31  QR/Validador   Línea 8       NaN\n",
       "2022-12-31           NaN   Línea 9    7590.0\n",
       "2022-12-31  QR/Validador       NaN       0.0\n",
       "2022-12-31  QR/Validador  Línea 12       0.0\n",
       "\n",
       "[17527 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(how='all') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0add37cf",
   "metadata": {},
   "source": [
    "Supongamos que queremos mantener solo filas que contengan como máximo un cierto número de observaciones faltantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1e5263b6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2022-12-31</th>\n",
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       "<p>17515 rows × 3 columns</p>\n",
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      ],
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       "            tipo_ingreso     linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01       Boletos   Línea 4       NaN\n",
       "2022-01-01           NaN   Línea 5  134725.0\n",
       "2022-01-01       Boletos       NaN   88100.0\n",
       "2022-01-01       Boletos   Línea 8       NaN\n",
       "...                  ...       ...       ...\n",
       "2022-12-31  QR/Validador   Línea 6       NaN\n",
       "2022-12-31  QR/Validador   Línea 8       NaN\n",
       "2022-12-31           NaN   Línea 9    7590.0\n",
       "2022-12-31  QR/Validador       NaN       0.0\n",
       "2022-12-31  QR/Validador  Línea 12       0.0\n",
       "\n",
       "[17515 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   "source": [
    "df.dropna(thresh=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f801660",
   "metadata": {},
   "source": [
    "En lugar de filtrar datos ausentes (y posiblemente arrastrar con ellos otros datos), quizá sea más conveniente rellenar los “huecos” de distintas maneras. Para la mayoría de los casos se debe emplear el método **fillna**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70217e2b",
   "metadata": {},
   "outputs": [
    {
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       "<p>17532 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            tipo_ingreso     linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01             0         0       0.0\n",
       "2022-01-01             0         0  180225.0\n",
       "2022-01-01       Boletos   Línea 4       0.0\n",
       "2022-01-01             0   Línea 5  134725.0\n",
       "...                  ...       ...       ...\n",
       "2022-12-31  QR/Validador   Línea 8       0.0\n",
       "2022-12-31             0   Línea 9    7590.0\n",
       "2022-12-31  QR/Validador         0       0.0\n",
       "2022-12-31             0         0       0.0\n",
       "2022-12-31  QR/Validador  Línea 12       0.0\n",
       "\n",
       "[17532 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d484d09d",
   "metadata": {},
   "source": [
    "Llamando a fillna con un diccionario se puede utilizar un valor de relleno distinto para cada columna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "546007ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>17532 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            tipo_ingreso     linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01          otro         x       0.0\n",
       "2022-01-01          otro         x  180225.0\n",
       "2022-01-01       Boletos   Línea 4       0.0\n",
       "2022-01-01          otro   Línea 5  134725.0\n",
       "...                  ...       ...       ...\n",
       "2022-12-31  QR/Validador   Línea 8       0.0\n",
       "2022-12-31          otro   Línea 9    7590.0\n",
       "2022-12-31  QR/Validador         x       0.0\n",
       "2022-12-31          otro         x       0.0\n",
       "2022-12-31  QR/Validador  Línea 12       0.0\n",
       "\n",
       "[17532 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({'tipo_ingreso': 'otro', 'linea': 'x', 'ingreso':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "27ce5d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "L=[20,18,19,24,22,20,19,55,60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ec10bd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.555555555555557"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b90c8958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42149cfd",
   "metadata": {},
   "source": [
    "El método \"forward fill\" (\"ffill\"),hace que los valores no nulos se copien \"hacia adelante\" siempre que se encuentren valores nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46313abf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>tipo_ingreso</th>\n",
       "      <th>linea</th>\n",
       "      <th>ingreso</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 1</td>\n",
       "      <td>324825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180225.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tipo_ingreso    linea   ingreso\n",
       "fecha                                     \n",
       "2022-01-01      Boletos  Línea 1  324825.0\n",
       "2022-01-01          NaN      NaN       NaN\n",
       "2022-01-01          NaN      NaN  180225.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f786b394",
   "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>tipo_ingreso</th>\n",
       "      <th>linea</th>\n",
       "      <th>ingreso</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 1</td>\n",
       "      <td>324825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 1</td>\n",
       "      <td>324825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 1</td>\n",
       "      <td>180225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 4</td>\n",
       "      <td>180225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 5</td>\n",
       "      <td>134725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 9</td>\n",
       "      <td>7590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17532 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            tipo_ingreso     linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01       Boletos   Línea 1  180225.0\n",
       "2022-01-01       Boletos   Línea 4  180225.0\n",
       "2022-01-01       Boletos   Línea 5  134725.0\n",
       "...                  ...       ...       ...\n",
       "2022-12-31  QR/Validador   Línea 8       0.0\n",
       "2022-12-31  QR/Validador   Línea 9    7590.0\n",
       "2022-12-31  QR/Validador   Línea 9       0.0\n",
       "2022-12-31  QR/Validador   Línea 9       0.0\n",
       "2022-12-31  QR/Validador  Línea 12       0.0\n",
       "\n",
       "[17532 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a3c2ae5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fecha\n",
       "2022-01-01    324825.0\n",
       "2022-01-01      8805.0\n",
       "2022-01-01    180225.0\n",
       "2022-01-01      8805.0\n",
       "2022-01-01    134725.0\n",
       "                ...   \n",
       "2022-12-31      8805.0\n",
       "2022-12-31      7590.0\n",
       "2022-12-31         0.0\n",
       "2022-12-31      8805.0\n",
       "2022-12-31         0.0\n",
       "Name: ingreso, Length: 17532, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ingreso'].fillna(df['ingreso'].median())"
   ]
  },
  {
   "attachments": {
    "fillna.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d2249e6b",
   "metadata": {},
   "source": [
    "![fillna.png](attachment:fillna.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bd45fbd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ad9b2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb442bf7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95cdf74c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e117cd89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/jamc88/TSMCI-Analisis-de-datos-con-Python/main/Datos/ingresosstc_2023_07.csv',index_col=['fecha']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cc8469b9",
   "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>tipo_ingreso</th>\n",
       "      <th>linea</th>\n",
       "      <th>ingreso</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 1</td>\n",
       "      <td>648825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 2</td>\n",
       "      <td>657120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 3</td>\n",
       "      <td>466803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 4</td>\n",
       "      <td>60120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 5</td>\n",
       "      <td>242280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 6</td>\n",
       "      <td>138525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 7</td>\n",
       "      <td>138690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 8</td>\n",
       "      <td>328383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 9</td>\n",
       "      <td>176400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea A</td>\n",
       "      <td>275370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea B</td>\n",
       "      <td>472665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 1</td>\n",
       "      <td>185792.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 2</td>\n",
       "      <td>230394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 3</td>\n",
       "      <td>172118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 4</td>\n",
       "      <td>19707.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 5</td>\n",
       "      <td>71305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 6</td>\n",
       "      <td>39235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 7</td>\n",
       "      <td>60717.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 8</td>\n",
       "      <td>110213.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tipo_ingreso     linea   ingreso\n",
       "fecha                                      \n",
       "2012-01-01      Boletos   Línea 1  648825.0\n",
       "2012-01-01      Boletos   Línea 2  657120.0\n",
       "2012-01-01      Boletos   Línea 3  466803.0\n",
       "2012-01-01      Boletos   Línea 4   60120.0\n",
       "2012-01-01      Boletos   Línea 5  242280.0\n",
       "2012-01-01      Boletos   Línea 6  138525.0\n",
       "2012-01-01      Boletos   Línea 7  138690.0\n",
       "2012-01-01      Boletos   Línea 8  328383.0\n",
       "2012-01-01      Boletos   Línea 9  176400.0\n",
       "2012-01-01      Boletos   Línea A  275370.0\n",
       "2012-01-01      Boletos   Línea B  472665.0\n",
       "2012-01-01      Boletos  Línea 12       NaN\n",
       "2012-01-01     Recargas   Línea 1  185792.5\n",
       "2012-01-01     Recargas   Línea 2  230394.0\n",
       "2012-01-01     Recargas   Línea 3  172118.0\n",
       "2012-01-01     Recargas   Línea 4   19707.0\n",
       "2012-01-01     Recargas   Línea 5   71305.0\n",
       "2012-01-01     Recargas   Línea 6   39235.0\n",
       "2012-01-01     Recargas   Línea 7   60717.5\n",
       "2012-01-01     Recargas   Línea 8  110213.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4ebd1ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "A22 = df.loc['2022-01-01':'2022-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "62bb58f3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2022-01-01</th>\n",
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       "            tipo_ingreso     linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01       Boletos   Línea 1  324825.0\n",
       "2022-01-01       Boletos   Línea 2  302150.0\n",
       "2022-01-01       Boletos   Línea 3  180225.0\n",
       "2022-01-01       Boletos   Línea 4   44330.0\n",
       "2022-01-01       Boletos   Línea 5  134725.0\n",
       "...                  ...       ...       ...\n",
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       "2022-12-31  QR/Validador   Línea 9    7590.0\n",
       "2022-12-31  QR/Validador   Línea A       0.0\n",
       "2022-12-31  QR/Validador   Línea B       0.0\n",
       "2022-12-31  QR/Validador  Línea 12       0.0\n",
       "\n",
       "[17532 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
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   ],
   "source": [
    "A22"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2256871",
   "metadata": {},
   "source": [
    "Opción 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "30472a31",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtro = (A22['linea']=='Línea 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fee1d11a",
   "metadata": {},
   "outputs": [
    {
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       "             tipo_ingreso    linea   ingreso\n",
       "fecha                                       \n",
       "2022-01-01        Boletos  Línea 1  324825.0\n",
       "2022-01-01       Recargas  Línea 1  439332.5\n",
       "2022-01-01       Tarjetas  Línea 1    6105.0\n",
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       "2022-01-02        Boletos  Línea 1  676425.0\n",
       "...                   ...      ...       ...\n",
       "2022-12-30   QR/Validador  Línea 1       0.0\n",
       "2022-12-31        Boletos  Línea 1  189585.0\n",
       "2022-12-31       Recargas  Línea 1  286964.5\n",
       "2022-12-31       Tarjetas  Línea 1    3075.0\n",
       "2022-12-31   QR/Validador  Línea 1       0.0\n",
       "\n",
       "[1461 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A22[filtro]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f4152167",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                                           linea       ingreso\n",
       "tipo_ingreso                                                                  \n",
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       "QR/Validador   Línea 1Línea 1Línea 1Línea 1Línea 1Línea 1Líne...  0.000000e+00\n",
       "Recargas       Línea 1Línea 1Línea 1Línea 1Línea 1Línea 1Líne...  3.028067e+08\n",
       "Tarjetas       Línea 1Línea 1Línea 1Línea 1Línea 1Línea 1Líne...  1.964235e+06"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "A22[filtro].groupby(['tipo_ingreso']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c62514",
   "metadata": {},
   "source": [
    "Opción 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5aa74d98",
   "metadata": {},
   "outputs": [
    {
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       "      <th>QR/Validador</th>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">Línea 3</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>2.307642e+08</td>\n",
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       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <th>QR/Validador</th>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
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       "      <td>5.158978e+08</td>\n",
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       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>2.421660e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Línea 4</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>4.588458e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validador</th>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>5.726947e+07</td>\n",
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       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>2.244600e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Línea 5</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>9.737268e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validador</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recargas</th>\n",
       "      <td>1.522707e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>8.673150e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Línea 6</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>4.672255e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
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       "    <tr>\n",
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       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
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       "      <td>9.165376e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>3.909600e+05</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Línea 7</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>1.072815e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validador</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recargas</th>\n",
       "      <td>2.630049e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>1.083240e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Línea 8</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>1.644456e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validador</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recargas</th>\n",
       "      <td>3.486327e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>1.491855e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Línea 9</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>1.160982e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
       "      <td>3.949220e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validador</th>\n",
       "      <td>4.111050e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recargas</th>\n",
       "      <td>2.696052e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>1.419810e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Línea A</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>1.406302e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validador</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recargas</th>\n",
       "      <td>2.253472e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>9.696300e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Línea B</th>\n",
       "      <th>Boletos</th>\n",
       "      <td>2.180905e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validación</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QR/Validador</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recargas</th>\n",
       "      <td>3.562333e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarjetas</th>\n",
       "      <td>1.568115e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             ingreso\n",
       "linea    tipo_ingreso               \n",
       "Línea 1  Boletos        1.935986e+08\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       3.028067e+08\n",
       "         Tarjetas       1.964235e+06\n",
       "Línea 12 Boletos        0.000000e+00\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       0.000000e+00\n",
       "         Tarjetas       0.000000e+00\n",
       "Línea 2  Boletos        3.045251e+08\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       5.458772e+08\n",
       "         Tarjetas       2.844420e+06\n",
       "Línea 3  Boletos        2.307642e+08\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       5.158978e+08\n",
       "         Tarjetas       2.421660e+06\n",
       "Línea 4  Boletos        4.588458e+07\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       5.726947e+07\n",
       "         Tarjetas       2.244600e+05\n",
       "Línea 5  Boletos        9.737268e+07\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       1.522707e+08\n",
       "         Tarjetas       8.673150e+05\n",
       "Línea 6  Boletos        4.672255e+07\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       9.165376e+07\n",
       "         Tarjetas       3.909600e+05\n",
       "Línea 7  Boletos        1.072815e+08\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       2.630049e+08\n",
       "         Tarjetas       1.083240e+06\n",
       "Línea 8  Boletos        1.644456e+08\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       3.486327e+08\n",
       "         Tarjetas       1.491855e+06\n",
       "Línea 9  Boletos        1.160982e+08\n",
       "         QR/Validación  3.949220e+06\n",
       "         QR/Validador   4.111050e+05\n",
       "         Recargas       2.696052e+08\n",
       "         Tarjetas       1.419810e+06\n",
       "Línea A  Boletos        1.406302e+08\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       2.253472e+08\n",
       "         Tarjetas       9.696300e+05\n",
       "Línea B  Boletos        2.180905e+08\n",
       "         QR/Validación  0.000000e+00\n",
       "         QR/Validador   0.000000e+00\n",
       "         Recargas       3.562333e+08\n",
       "         Tarjetas       1.568115e+06"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A22.groupby(['linea','tipo_ingreso']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "040f07ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tipo_ingreso      0\n",
       "linea             0\n",
       "ingreso         152\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A22.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "818bd7b7",
   "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>tipo_ingreso</th>\n",
       "      <th>linea</th>\n",
       "      <th>ingreso</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>Tarjetas</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>QR/Validación</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>Boletos</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>Recargas</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>Tarjetas</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>QR/Validador</td>\n",
       "      <td>Línea 12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1461 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tipo_ingreso     linea  ingreso\n",
       "fecha                                       \n",
       "2022-01-01        Boletos  Línea 12      0.0\n",
       "2022-01-01       Recargas  Línea 12      0.0\n",
       "2022-01-01       Tarjetas  Línea 12      0.0\n",
       "2022-01-01  QR/Validación  Línea 12      0.0\n",
       "2022-01-02        Boletos  Línea 12      0.0\n",
       "...                   ...       ...      ...\n",
       "2022-12-30   QR/Validador  Línea 12      0.0\n",
       "2022-12-31        Boletos  Línea 12      0.0\n",
       "2022-12-31       Recargas  Línea 12      0.0\n",
       "2022-12-31       Tarjetas  Línea 12      0.0\n",
       "2022-12-31   QR/Validador  Línea 12      0.0\n",
       "\n",
       "[1461 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtro = (A22['linea']=='Línea 12')\n",
    "A22[filtro]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2259b001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tipo_ingreso      0\n",
       "linea             0\n",
       "ingreso         152\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A22[filtro].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "53aa3808",
   "metadata": {},
   "outputs": [],
   "source": [
    "A22 = A22.fillna({'ingreso':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c5dc4813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tipo_ingreso    0\n",
       "linea           0\n",
       "ingreso         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A22.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "92691d6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lineas = A22.groupby(['linea']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4507af8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lineas=lineas.sort_values(by=['ingreso'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "76e15928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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799aYMWNMz53WeHt7222wWLZsmQurebLcunVLW7duNYy3bNlSmTJlcmFFTwYPDw917drVZsyRBnOj18LOnTvL29s7xXmT6+rVq6pbt66mTZvmlPxhYWGqVauWDhw44JT8qcWZ14fR0dFq166dPv/8c9NzS1JQUJBq1KjhtA/MmOXRo0eGU+2NmurSssSawO01gRqxd1eNihUrJjtfYipVqmQY27dvn+n7OcreY56Sx9tVzp49a/eOA86o3Whi7t27d7Vp0ybT9wMAAEDa4JHaBQAAAAAAgLTr2LFjatWqleGU0uLFi6tgwYLKkSOHvL29dfXqVZ04cUJHjx61m/fBgwfq37+/NmzYIIvF4lCNffr0UVBQUKLHeXp6qnz58sqTJ49y5sypO3fu6NKlSzpy5IjNJtzz58+rbdu2mjdvnkP1JUdcXJzatm1rePt4X19flS9fXjlz5lT27NkVGxurK1eu6ODBg0lqDD127FiSG3AzZsyoihUrKnv27MqaNavi4+N19epVnT59WkeOHLF77smTJ9WoUSOFhoYqICAg0b3+rX///jp9+nSix2XJkkXFihVTgQIF5OvrKx8fH92/f1937txRZGSkjh8/7pRm42vXrqlhw4ZJmrqbJUsWVapUSfnz55ePj4/u3r2rkydPau/evYqKirJ77sqVK9W5c2ctXrw4Rc+T+fPn68cff0z0OE9PTxUpUkRFihRRlixZ5Ovrq7i4ON2+fVu3b9/WhQsXdPz4cadNtnWWt99+O0kN7BkyZFDlypVVuHBh+fn56cGDBzpz5oz27NljsyF1//796tGjhwYOHOiMsiVJ77zzjr744oskHVu0aFEVLlxYgYGBypw5s27duqWrV69qz549iU6RHTZsmDJnzqzXX3/djLLTrJIlS2rXrl02Y7/++qu2bt2q2rVru7iq9G/t2rV2Jz+3adPGhdU8WXr37m3zNWDRokX66quvkj3dLioqSj///LPhXq7yV7NlUqaoenl5qWLFisqZM6cCAwPl4eGhq1ev6vz58zpw4IDdKdPXr19Xo0aNtHPnThUsWNDEryB1OPP6MD4+Xi+++GKSPsD01/ekaNGiypw5sx49eqRz585p7969iU7vDA8PV8OGDbVlyxZlz5490b1Sw+bNmw3/5qlTp46Lq3GcUdOt9Ock5ZR8H+z9DVC8ePFk50tM0aJFU1RLarH3mJcsWdKFlSSPvbotFotTvrd169Y1jK1Zs0YNGzY0fU8AAACkPhpVAQAAAACATY8ePVKnTp0STPPLnTu3hgwZorZt2xq++X/69Gl9+eWX+vrrrw2b2zZt2qQff/xR/fv3T3GNP/zwQ6KNpKVLl9Y777yjtm3b2rwFdFxcnP744w/9+OOPmj9//mOxHTt2aPz48SmuL7k+++wzbd++PcH6888/r759+6phw4bKkCGDzXMPHjxotynx5s2batmypd3GTV9fX/Xp00ddu3ZV5cqV5enpafO4iIgILV68WOPGjTO8DfqJEyfUtWtXw1uoGlmyZInWrFljGA8ICPj7VvYVKlRItIHz+vXrOnDggNatW6dffvnF4UlkcXFxevHFF3XixAm7xzVp0kSDBw9Wo0aN5O7uniB+//59LV26VGPHjrX7RvvSpUv1v//9TyNGjEhWnXfv3tWgQYMM4xaLRW3btlW/fv1Uv359ZcyY0W6+qKgoHT58WNu2bdMvv/yikJCQRBttU9PixYv15Zdf2j2mbNmyev/999WuXTv5+PgkiEdHR+u3337TuHHjFBoa+lhs2bJlTmu0mTRpUqJNqlWqVNHAgQPVpEkT5c6d2+YxMTEx2rJliyZNmqQVK1YY5nr77bdVqVKldNmEk1SNGjUybFSV/nyNXb9+vUqXLu3CqtK/bdu22Y0bTUtD4sqWLauqVasm+Lm9f/++Fi9erF69eiUr37Jly2w2EpYrV87023UbiYqKUps2bew2qXp6eqpz587q3bu3atasaXjNc/36da1cuVKjR482bN68fv262rZtq9DQ0EQnxjZu3DhB8+/s2bNtNvvnyZNH7dq1s5vPFkcmEjrz+nDkyJGJTpYuWbKkhg4dqvbt29tsko6Li1NISIg+//xzrV692jDPiRMn1LlzZ61Zs8bmtVFq+/fv+n8qX768Cysxx8KFCw1j9erVS3a+Bw8e6OrVq4Zxe02lKWUv55kzZ0zfzxEXL17U5s2bDeMpecxdZcGCBYax8uXLy9/f3/Q9ixQpIl9fX5uTXO09FwEAAJDOWQEAAAAAQLpRoEABqySb/2bMmGF6zn//Gzp0qPXevXtJzr1z505rQECAYb7SpUunqGar1WqNiIiw+vr6GuZ2c3OzDh8+3BoVFZXknGvWrLHmy5fvsTwWi8XUx3zGjBlJfrwLFSpkDQ4OTvYetjRv3tzuXv369bPevHkzWTnv3btnffXVV+3m/fHHH5OVs2HDhoa5WrZsab1161ay8v1beHi49Z133rH6+/tbP/nkk2SfP378eLtfb+bMma0LFixIcr6YmBjrsGHD7P6cubm5Wbdv356sOmfPnm2YL1euXNZNmzYl90t/zN27d63Tpk2zlitXzlqkSBGHcpnt2rVr1sDAQMOv32KxWIcPH26NjY1NUr74+Hjrl19+afX09Ezya0NISEiKat+8ebPV3d3dMG+ePHmsq1evTnbeTZs2WfPkyWOYt1ixYtaHDx8mO6+917PTp08nO5+z/PHHH4m+3mbIkMH6ySefOPwak9pOnz5t9+s08/vy9NNPG+6TP39+0/Z5UoSEhBg+Xp06dUpw/DfffGPz2KeffjrZezdt2tRmri+++CLBsT179jT1uucvr7zyit2fzeeee8564cKFZOWMjo62jhw50u7r8Ycffpiieo2uj5955pkU5UtMalwfhoaGWt3c3Oz+vhw2bJg1Ojo6yTkXLlxozZw5s936P/vsM4drd4Y2bdoYPg63b99O7fKSZfXq1Xa/B8m9PrdardbDhw/bzRkZGWn617FkyRLD/XLlymX6fo5477337D4+J06cSO0SbTpy5IhTXkOTokaNGjb3zJQpkzUuLs5p+wIAACD1uAkAAAAAACARHh4emjZtmsaNGydfX98kn1e1alWtWbNGHh62b+py+PDhFE9M+eCDD2xOYJH+nBb5ww8/aMSIEfLy8kpyzqZNm2rDhg2PTb2yWq0pqs9RxYsX16ZNm0y57eH06dMNJ1x5eXnp559/1tSpUxUQEJCsvL6+vvrmm2/sTn987733bN4+3ZYbN25o48aNNmPVq1fXsmXLlCVLlmTV+G9FihTRhAkTFBERoQ4dOiTr3AsXLtidbBoQEKCQkBB17NgxyTk9PDw0atQoTZ061XA6bHx8vF5++WW7tzn+t6VLl9pc9/T01C+//GL3dptJ4efnpz59+ujAgQN2pzClhuHDh+vmzZs2YxaLRdOmTdOIESOSPM3NYrHojTfe0NKlSx+bMmz2a8OjR4/Up08fw1upN2jQQPv371ezZs2Snbtu3boKDQ01nBh64sQJTZ48Odl504tnnnlG1apVs3vMo0eP9NFHH+mpp55Snz59tGLFCj148MBFFaZPBw4cMIwxndZxnTt3tjkJdNOmTYZTRG2JiIhQUFBQgnUPDw917drVoRqTav369fruu+8M459//rlWrFihPHnyJCuvp6enPvroIy1YsMDwWvOzzz7TuXPnkpU3LTPr+tBqteqVV14xvLawWCz68ccfNWrUKMMJ+7Z06NBB69evt3u9NmLECJ0/fz65JTud0eTtAgUKKHPmzC6uJuWsVquGDRtmGM+YMaPat2+f7LyXL182jGXIkMEpUzdz5cplGLt69Wqyro2d6dKlS/rqq68M43Xq1HHKxFkzfPDBB4bXtBaLRd27d3fa3kaTiu/evatjx445bV8AAACkHhpVAQAAAABAosaNG6c+ffqk6Nxq1arpjTfeMIzPmTMn2TlPnz6tefPmGcY/+eQT9e3bN9l5JalQoUJat25dordCd6YsWbJo/fr1Dt0m9i/379/X+++/bzNmsVg0c+ZMvfjiiw7t8fbbb+vVV1+1Gbt+/bq+/fbbJOUJDQ1VbGyszVhyGyUS4+fnpxIlSiTrnE8//dSwOdrNzU2LFi1S5cqVU1RP37597TYV7N+/X0uWLElyvi1btthc79Chg6pWrZrs+uxx1W2jk+LChQuaOnWqYfz9999X7969U5S7VatWmjRpUgorS9xXX32l48eP24xVrVpVK1euVLZs2VKc/6mnntKKFSsMm23Gjx//RDdmjhw5MknH3b9/XzNmzFDbtm0VEBCgGjVq6M0339RPP/2k8PBwJ1eZfty+fVuRkZGG8ZIlS7qumCdUQECA2rRpk2DdarVq5syZSc4za9Ysm81cLVu2VI4cORwpMUni4+P15ptvGsbHjh2rQYMGObRHhw4dNGbMGJux6OhojRs3zqH8aYWZ14fLli3T3r17DeMfffRRiq/9q1SpooULFxp+AOfBgwdp7nty9epVXbhwwWasVKlSLq7GMVOnTtXu3bsN4y+99FKKmkpv3LhhGAsMDEx2vqTImjWrYSw+Pt7u7yFXeueddwz/RpCkd99914XVJN3atWu1bNkyw3ibNm1UvHhxp+1v70Mt9n6GAQAAkH7RqAoAAAAAAOxq0qSJww0E7733nuGkq23btiU73zfffGM4dbBcuXIaOnRosnP+U6lSpfTBBx84lMMR48ePN6UJQZK+/vprXb161WbsnXfeUefOnU3ZZ+LEiYZvZH7//fdJmj5pNKnJ09NTjRo1cqg+R926dUs//vijYbxv374O1/jxxx/bbe767LPPkpQnNjZW169ftxlr3rx5impLL77++mvFxMTYjBUtWlTDhw93KP+AAQNUu3Zth3LYcv/+fX366ac2Y5kyZdLKlSvl5+fn8D5FixY1nJx648YNLVq0yOE90qrmzZtr4MCByTonOjpaO3bs0JdffqmuXbuqWLFiCgwMVLNmzfTRRx/p119/NXyuPekiIiLsxpM7GRO2GTXWz549O8lTnY2aWlPatJ9cixYt0qFDh2zG2rdvr/fee8+UfQYPHqz69evbjM2ZMyfJ093TMjOvD8ePH28YK126tD766COH8jdp0sRuo+v06dPtNj66mr0pxU899ZQLK3HM2bNnNXjwYMN45syZU/ycu3XrlmEsU6ZMKcqZmMTyGk3Qd6WlS5fq559/NozXrFlTzz33nAsrSprbt2+rX79+hnEPDw998sknTq3B3nMrOZPDAQAAkH7QqAoAAAAAAOz67LPPDCciJVWOHDnUuHFjm7FDhw4ZNpbZEh8fb/fNwMmTJxs2xSbH4MGDVahQIYfzJFe5cuVSPA323+Lj4zVlyhSbsZw5c+rjjz82ZR9J8vLyMnzj+9SpU9q8eXOiOYwaarNlyyY3t9T9z1gLFy7Uw4cPbcYCAgIMJ7klh6enp7788kvD+K5duxQWFpZonuvXrxs2MOXMmTPF9aV1VqvV7oTmiRMnysvLy6E9LBaLvvzyS9N/HufPn2/YsPPhhx8qd+7cpu3VvXt3FS5c2GZs9uzZpu2TFn3xxRdq2bKlQzlu3bqltWvXatSoUWrdurVy5MihSpUqadiwYdq3b585haYDly5dshu3d7tmJF2TJk1sNv2ePXtW69evT/T8TZs22ZwEnD17drVo0cKUGhPz9ddf21z38fHRhAkTTNvHYrEYXtfcu3fP7tTA9MDM68Njx45p+/bthvEvv/zSlGvpsWPHKkuWLDZjjx490vz58x3ewyxnzpwxjJnVHOxs0dHR6tixo+7evWt4zIgRI1J8LWiv2duMD9PYklijamo3oJ85c8Zus6ebm5u+/vprh/+WdobevXvr/PnzhvFXX31VZcuWdWoN9p5b9p6TAAAASL9oVAUAAAAAAIYaNGigihUrmpKrYcOGNtejo6OT1Hz3l507d+rixYs2YyVLllSDBg1SVN+/eXt7m9YQkBx9+vQx7c3M4OBgnT171mbs7bffNn36Ubdu3QwbEn7//fdEzzdqIrx165bhBF1XWbhwoWGsb9++Dt2S/Z+aNGmiatWqGcYXLFiQaA57zZjXrl1LUV3pwdatWw1v21uiRAm1atXKlH2qVKli2uvMX6ZPn25zPUuWLHZvmZ0S7u7uGjBggM3Y5s2bU73pw5nc3d21bNkyvfzyy6bltFqt2rdvn8aMGaNKlSqpYsWKmjp1qqKjo03bIy2yd4tjSSm6rTQScnd3V48ePWzGjCal/tOMGTNsrnfr1k2enp6OlJYkx48fN/ygSs+ePVWgQAFT92vQoIHKlCljM5aU65C0zMzrQ3vXNFWrVjVtin327NnVv39/w3hSrmlcxeh6WUo/jaqDBg3Sjh07DOO1a9d26JrC3u81Z72eJJY3OR92NNujR4/UoUMHu5Nm3333XVWpUsWFVSXNhAkT7DbvFylSxJQP4SXG3nPL3nMSAAAA6ReNqgAAAAAAwFCnTp1My1WuXDnD2Llz55KcJzg42DDWtWvXZNWUmG7durl0Ao67u7u6detmWr4VK1bYXLdYLHrxxRdN2+cvnp6eqlevns3Yhg0bEj3fqNnz0aNHqdpg8vDhQ23ZssUwbub3TPpz4qWRdevWJXp+QECA3N3dbcbS+0Q5e4KCggxjXbp0MXUvM19rrl27pm3bttmMtWvXTt7e3qbt9Rd7HxwwquVJ4enpqe+++06rVq1S0aJFTc+/f/9+vfTSSypWrJjdZrD07tGjR3bjzvi5/a/q1auXzfWlS5fqzp07hufdv39fixYtshnr3bu3GaUlauXKlYaxzp07O2VPo9e3pFyHpFVmXx/au5Zw5TXN9u3b08yHIyIiIgxj6WFC9JQpU/TNN98YxjNlyqTZs2c7NBHeXlOosxpVE5vsm1ofCrFarerVq5d27dpleEzFihU1cuRIF1aVNL/++quGDh1qGPfw8NDs2bPl6+vr9FrsPbfsTXsFAABA+kWjKgAAAAAAMFSnTh3TchUvXtwwdvv27STnsXer0ueeey5ZNSWmQIECqlChgqk57SlZsqRpkzklac2aNTbXK1WqZPoUs7/UrVvX5vr+/fsVHx9v91x7t5d86623dPnyZYdqS6mdO3cqKirKZqxUqVKm/4y8+OKLho0EO3fuTLRBzGKxqHTp0jZjS5YseWKb5zZu3GgY69ixo6l7vfDCC6bcFlmS1q5dK6vVajPWrl07U/b4t4oVKxo2IOzdu9cpe6Y1LVq00OHDh/Xjjz8aTmB0xLlz59SpUye1bdvW7rS19MroNfEv9iY7I3lKlCihWrVqJVh/8OCB3dfzRYsW2WwCrFy5st0PD5nJ6DokW7Zshh9scZTRdcjFixd19epVp+zpbGZeH8bExCg0NNQwbvYHmcqVK2d4fRcTE2P3ut6V7DV9Z8yY0YWVJN+aNWs0cOBAu8dMnz5dRYoUcWgfe9fxzvpgn9EHr/6S2N8WzvLxxx/bnQjs7++vxYsXp7nfhfv27VPnzp3tPm5jx45V7dq1XVKPl5eX4fX03bt3XVIDAAAAXItGVQAAAAAAYJOvr6+pzTuZM2c2jCWnUfXgwYM21729vQ2b8xxRsWJF03MaMfPWkLdu3VJ4eLjNWPny5U3b599y585tc/3+/ft2p1VJfz7WgYGBNmMnT55UtWrVtHDhQpe/Kb1//37DmK3mIUdlz57dcNJjXFycwsLCEs1h77a9Xbp00TvvvKObN2+muMa06MCBAzbXM2fOrBIlSpi6l5k57d2m11nPVTc3N+XMmdNm7OjRo07ZMy3y9PRU3759dejQIW3ZskVvvPGG8uXLZ+oeK1asUM2aNZ+4W9gm1nyTmrdjfhIZTUCdMWOG4TlGMVdNU5VkOG2wbNmyTmtsM7oOkdLv65uZ14dHjx41nEJZuHBhw98NjrDX9GbvGsuVHjx4YBjz8fFxYSXJs2vXLrVv316xsbGGx7z77rtq3769w3vZm5pqb39HJPa7xFmTXO35/vvvNWrUKMO4m5ub5syZ43BjsNnOnDmjFi1a2J1i3KFDBw0ePNiFVRk/v+7fv+/SOgAAAOAaNKoCAAAAAACbsmfPbmoTgb1GVXtTjP4pNjbWsNmndOnSpk04/CdXTlQ1sylt3759hjFnNPT+JWvWrIaxxBpV3dzc1LdvX7vnd+rUSUWKFNHw4cNd1txw5MgRw5izGpkrVapkGLNXz1/69etn+PyNi4vTF198obx586pHjx5auXJlolNa07pr167p+vXrNmMVKlRwSkOUWd97o+eqn5+f8ufPb8oethg9VxN7nj6pateurS+//FLnzp3T4cOH9c0336hbt24qUaKEwz8/x48fV/PmzZ+o6WCJNW6l99eUtKZTp042H/OtW7fqxIkTCdZPnjypTZs2JVj39vZWly5dnFLjv50+fVqRkZE2Y2n1OiStMvP6MD1e07iCvaa4tNqoeuzYMT377LN2Gw+7dOmiTz/91JT9UqNRNbG8rp5YumjRIr366qt2j/n222/VunVrF1WUNFeuXFGTJk106dIlw2Pq16+vOXPmuLCqPxk9v+w1jwMAACD9olEVAAAAAADYZDTZMqXsvZFoNNnp3y5fvmx4i2yjCZSOKlasmFPy2mKvuSK5Tp06ZRgbMmSILBaLU/61bNnScN9r164lWvfgwYOVJUsWu8ecOXNGI0eOVMWKFfXUU0+pa9eu+uGHHxQWFuaUaasXLlwwjDlr4qW9Bml79fylTJky6ty5s91jHj16pDlz5qhNmzYKDAxUo0aN9MknnygkJCTdTTGy95g46xbXZuU1eq7eu3dPbm5uTnuu7ty50+a+SXmePulKlSqlV199VXPmzNHRo0d1584dbdy4URMnTlS3bt1S1EB89OhR9ejRw/B3WHrj6+trN57UD6AgaTJnzqwXXnjBZszW5NSZM2fa/Fl77rnnTL++M2LvOuTbb7912mubvbsBpNfXNzOvD9PjNY0rREVFGcbS2u3bJens2bNq3Lix4Yd0JKlVq1aaNWuWaR/WyZAhg2Hs4cOHpuzxb4k1K3p7eztlX1t+//13de3a1e7fGmPHjtXLL7/sspqS4tatW2ratKnhnTYkqWrVqlq5cqVLH8+/GO0ZFxfHdHYAAIAnEI2qAAAAAADApkyZMqV2CQnYezPW3sRWRzgrry3+/v6m5Uorb/z/U1LexM6RI4fmz58vN7ek/WerCxcu6KefftLLL7+ssmXLKjAwUK1bt9bEiRO1b98+U5rCLl++bBjLli2bw/ltyZ49u2HMXj3/NGXKFJUqVSpJxz58+FDr16/Xxx9/rIYNG8rf319Vq1bVkCFDtHr16jTfuGrvMTGzwcfsvPHx8Un+frqKs5pN0jM/Pz/Vq1dPb731lubMmaOzZ8/q9OnT+vrrr1W7du0kNwEtX75cixYtcnK1rpErVy678bT2c/0k6NWrl831OXPmPNY4FR8fr1mzZiUrhzOk1+uQtMjM68P0ek3jbPaaMJP6gTpXuXjxoho1amR3QnDDhg21aNEiU+82Ye+DZM6aGJ5Y3oCAAKfs+28bNmzQCy+8YLdx8oMPPtB7773nknqS6u7du2revLkOHDhgeEzZsmW1Zs2aVPvb32gCu7u7u90pvgAAAEifaFQFAAAAAADphr0GgyehUdXMvW7evGlaLrPYm1b1T82aNdOSJUtS9Ibp7du39euvv2rQoEGqVKmSChUqpA8++MChW8vau6Wqmc0j/2TvZ8FePf/OsW7dOtWoUSPZ+8fFxWn37t0aP368nn32WWXPnl0vvviifv31V6fd3tUR9hoZ0vJrw507d9Lc45nU5+l/XcGCBfXaa69py5Yt2r9/v55//vkknffJJ588EVNV8+XLZzd+8eJFF1Xy39GwYUMVKFAgwXpERITWrVv39/8PDg7W+fPnExyXO3duNWvWzKk1/lN6vg5Ja8z8PZZer2mcLWPGjIaxtNTgfO3aNTVp0kQnT540PKZWrVpauXKl3ebblLD3AZ3bt2+butdfEpvO7awPI/1TaGioWrdubffnYODAgRo9erTTa0mOhw8fqnXr1tqxY4fhMcWKFdO6detcNmnbFqPHNbHJ7QAAAEifaFQFAAAAAADphr0pNs56M8uVb5K5u7ublistvan+l+Q0Z7Vt21a7du1SixYtHNrz7NmzGjt2rEqXLq02bdpo//79yc5hr7HFWU2Q9ppFjCYP2ZI3b15t3LhRw4cPl5+fX4rrefjwoRYsWKDWrVurRIkSmj17tuLi4lKcz2xp7XuUVOn9eYo/lStXTkuWLNGyZcsSne526NAhrV692kWVOY+/v7/d58DRo0ddWM1/g8ViUc+ePW3GZs6c+ff/njFjhs1jevToYep1RmJ4fTOPmd+3tPb7MjnXNM5k73o/rfws/3UL98OHDxseU7lyZf3+++9O+fvF3sTdyMhIp9ym3d7EXV9fX6ffqn7v3r1q3ry53Q9E9evXT5MmTXJqHckVFRWltm3basOGDYbHFCxYUMHBwYlOSHc2o+eXveZxAAAApF80qgIAAAAAgHTD3u3/nHVr8rR+y3MjaW1KY0oUL15cq1at0qZNm9SpUyeH34xeuXKlKlWqpCFDhpj2+Dir6cXMvF5eXhoxYoTCw8M1bNiwRCchJubUqVPq2bOnatSooVOnTplU5X/Tk/A8xf9p27at1qxZk2hzxZPQqCpJ5cuXN4yFhYW5sJL/jp49e8pisSRYX758uSIjIxUZGally5bZPLdXr15Oru5xvL6lP+nhmsZZ7DXppoW/Bf66hfu+ffsMjylbtqzWrl3rtMm49q4frVarrly5Yvqe9nLamjBtprCwMDVt2lSRkZGGx3Tp0kXff/+9zdfl1BITE6OOHTtq7dq1hsfkzZtXwcHBDv9N4KioqCjDD7658q4mAAAAcB2P1C4AAAAAAAAgqXx8fAxjid0aMqWcldfZ7N3us2HDhipVqpQLq/lTSvesW7eu6tatq8jISP3yyy8KDg5WSEiIzp07l+xcVqtV48ePV2hoqFavXm33Z+ov9hpk79y545SGAHs/dym9lWvOnDk1atQojRw5Uhs2bNDatWsVHBysPXv2pGg66u7du1W5cmX99ttvql27dopqMkti3yNnMOM2t/a+l35+fobTE53JFbfRfZJVq1ZNn376qd544w3DY4KDg11YkfNUrlxZmzZtshk7d+6cLl68qDx58ri4qidb4cKF9fTTTyeYkvfo0SPNnz//7//9bzVr1lTJkiVdUuNf7L2+Va1aVTVq1HBhNX+qXr26y/dMa1Lj96UzrmnM9tRTTxnG7E31dIX79++rRYsWdm/hXrx4cQUFBTn1d3iuXLnk4+NjOAHz9OnTdh/HlDh9+rRhrFChQqbu9U/Hjx9X48aNdf36dcNjnn/+ec2ePVtubmlnJlRcXJy6dOmilStXGh6TI0cOBQcHq3Dhwi6szDZ7z63UbqIFAACAc9CoCgAAAAAA0g17b77SqPo4e7f8bN26td566y3XFWOSLFmyqHv37urevbsk6cyZM9q6dau2bt2qLVu26MCBA4qPj09Sro0bN6pHjx5atGhRosf6+fkZxlKjCdJePUnh5uamBg0aqEGDBpL+nNIVGhr69+O4bds2u7c4/Xedbdu2VWhoqFMbBhKTKVMmw1hafm2w9zx1d3fX119/7fAecL2XX35Z48aN04ULF2zGw8LC9OjRozTToJVSNWvW1OTJkw3jISEh6tq1qwsr+m/o3bu3zds5z5gxw+45rmbv9a1OnTpp7lbZ/xVP2jWNWQoWLGgYi4iIcF0h//Lo0SM999xz2rx5s+ExhQoVUnBwsHLmzOn0egoXLmw4MTs8PFz16tUzdb+TJ08axooUKWLqXn85deqUGjZsaLeJskWLFvr555/l7u7ulBpSIj4+Xj179tTixYsNjwkMDFRQUJBKlCjhwsqMGV0nSfafkwAAAEi/0s7HvAAAAAAAABKRK1cuw1sr2nsj0xHh4eFOyetsuXPnNozduHHDhZU4T8GCBdWlSxd9/fXX2rt3r65fv64FCxaoW7duSbpd5OLFi7V8+fJEj8uVK5dhzN6kJUfYy2uvnpTIlCmTGjdurI8//lhr1qzRzZs3tWXLFn3wwQdJmsB37do1vf3226bWlFz2mjOc9fNuRt6MGTMa/qzeuXMnRZNukfo8PT3VokULu8c467XDlZo2bWq3SWfFihUurOa/o3379jab+3bs2GFz4qKPj486derkitIe81+4DkmPnvRrmpSy1xRnr5nOmaKjo9WuXTutX7/e8JinnnpKwcHBpk8yNVKxYkXD2KFDh0zfz17OChUqmL7fuXPn1LBhQ7vf80aNGmnJkiXy8vIyff+Uslqt6t+/v+bNm2d4jL+/v9asWaNy5cq5sDL77D3OBQoUcGElAAAAcBUaVQEAAAAAQLrh6emp/Pnz24yFhYUpNjbW9D337dtnek5XsPfm3pPQIGVLQECAOnbsqDlz5ujy5cuaNm1aolM+R4wYkWhee2/+HzhwILllJsn+/fsNY3nz5nXKnn/x8PBQ7dq1NXr0aB05ckRbt25VmzZt7J6zYsUK7d2716l12WPve3Tw4EGn7GlWXqPnqtVqfWKfq/8FNWvWtBt/Er63gYGBqlWrlmF81apVunfvngsr+m/w9fVVhw4dknx8u3bt5O/v78SKbPsvXoekB/+1a5qksncb9PPnz7uwkj/FxMSoQ4cOWr16teExuXPn1vr161060b5SpUqGsT179pi6V3R0tN1GVXu1pMTFixfVsGFDnT171vCYp59+WitXrkxzE9Fff/11TZ8+3TDu5+en33//XVWrVnVhVYmzN63Y3nMSAAAA6ReNqgAAAAAAIF0xmgITFRWlw4cPm75fem1ULVu2rGFs9+7dLqwkdfj4+KhPnz4KCwtT27ZtDY/bv3+/Tp8+bTeXvamizmrOtJe3dOnSTtnTSK1atbR8+XLNmTPH7vTEpEyndZbs2bMrW7ZsNmOp0XiTHP/15+qTKkeOHHbj9+/fd1ElztWlSxfD2IMHD+w2ziDlevfu7ZRjzVSyZEl5eHjYjJnd0Iak+69f0xjJli2b8uXLZzPmjL8v7ImLi1OXLl20cuVKw2OyZ8+uoKAgFStWzIWVye6HE3bu3KmYmBjT9goNDTXM5+fnZ/f6KbmuXr2qRo0a2b1DR82aNbVq1SplzJjRtH3NMGjQIH377beG8YwZM2rVqlV2v3epJSwszDBWuXJlF1YCAAAAV6FRFQAAAAAApCv2ptTZe0M3Jc6ePWtaM5qrFStWTAEBATZje/bs0d27d11cUerw8fHR/Pnz7b6Rb++WqpL9W4uGhoamuDYj169fV3h4uM2Yh4dHqjV1dOvWTcOGDTOMJ/Y4OptRE/vt27d19OhRU/e6c+eOaTlr1KhhGPvjjz9M2QOuZ7FY7MbTWqNLSnXt2lW+vr6G8S+++ELR0dEurOi/oV69eipatGiix+XPn18NGzZ0QUUJZcyY0bCR7OrVqy5v/sOfSpYsaXjL8jNnzujq1aum77l9+3bDWPny5U3fL6WqVatmcz0iIkKRkZEuqSE+Pl69evXS4sWLDY8JDAzUunXrUuV6sHr16sqUKZPN2P3797Vt2zbT9goODjaMPfPMM/L09DRlnxs3bqhx48Z2r+uqVKmi1atXy8/Pz5Q9zfLBBx9o4sSJhvEMGTJo+fLlevrpp11YVdIZfZgsU6ZMKlGihIurAQAAgCvQqAoAAAAAANIVew0X8+bNM3WvuXPnymq1mprTVSwWixo1amQzFhcXZ/fN3yeNt7e3Bg4caBi3d4tP6c/GBW9vb5uxw4cPmz6xc8GCBYqPjzesJTVvN/rWW28ZTshL7HF0Nntvwi9cuNDUvZYuXarY2FhTcjVu3NgwtnbtWlP2gOtdvHjRbjxr1qwuqsS5MmfOrJ49exrGz549qy+++MKFFf132Hvc/9KjRw+5uaXe20Dp+fXNaIK4mRMjU4Onp6fhh76sVqvmz59v6n5hYWGG10n2akkN9m6L7qzp7P9ktVr18ssva+7cuYbH+Pv7a+3atXY/ROVMHh4eatCggWF8yZIlpu1lL1fTpk1N2eP27dtq2rSpDh48aHhM+fLltXbtWvn7+5uyp1k++eQTjR071jDu5eWlJUuWqEmTJi6sKuni4+MNJ6pWqVIlVX93AQAAwHm4ygMAAAAAAOlKjRo1lDt3bpuxo0ePasOGDabsEx0dne5vWdyuXTvD2Oeff+7CSlKfvUaI69ev2z3Xx8dH9erVM4zbayhIiTlz5hjGUvvN5oCAAMMJR4k9js5mryHq559/NnUvM5viy5QpYzjxd//+/QoKCjJtL7jOoUOHDGNeXl566qmnXFiNcw0bNkw+Pj6G8VGjRun48eMurOhxzpgQmRb07Nkz0UaeXr16uaYYA/auQyZPnmxaw78zGH1A5cGDBy6uxHz2mvxceU1Tq1atNDWh0t61oisaVQcOHKgff/zRMJ4pUyatXr1aVapUcXot9nTq1MkwtnDhQlOauffv32/4e9TNzU3t27d3eI979+6pefPm2rNnj+ExpUuXVlBQkAIDAx3ez0wTJkzQxx9/bBj38PDQggUL1KJFCxdWlTzh4eGGr6f27jYAAACA9I1GVQAAAAAAkK64ubnpxRdfNIy/+eabiouLc3ifzz//XKdOnXI4T2pq27atAgICbMY2b978n7qtuNFtSiUZTi/9pw4dOhjGpk2bphs3bqSorn9bv369QkNDDeP2mgNcxeixTMrj6Ey1a9dWnjx5bMaOHj2qVatWmbLP3r17tX79elNy/aVPnz6GsVGjRpm6F5zParXavW1z5cqVn6hJYXny5NFbb71lGL9//77at2+v+/fvu66o/+/DDz/UlClTXL6vK+TLl089e/ZUhQoVbP7r2rWrihQpkqo11qpVSyVLlrQZO3PmjOlNkWbKkiWLzfVr1665thAnsHdNs3PnToWEhJiyz40bNzR16lTDeFq4pvmn2rVry9fX12Zs8+bNTt17yJAh+vrrrw3jGTNm1KpVq9LEBNo2bdoYNhhfvnzZlKm8kydPNozVr1/f8HovqR4+fKhWrVpp+/bthscUK1ZMwcHByp49u0N7me2bb77Ru+++axh3d3fXvHnz1LZtW9cVlQL2nlNmTcwFAABA2vPk/NcwAAAAAADwn/Haa68ZNvns379fEyZMcCj/sWPHNHr0aIdypAUZM2ZU//79DeP9+vXTzZs3XVhR6omIiDCMZcuWLdHzO3bsaDgx8ObNm/rwww9TXNtfYmNj9cYbbxjGq1WrptKlSzu8j6OMHsukPI7O5Obmpm7duhnGBw0apOjoaIf3GThwoOlNuf3791fGjBltxjZs2GC3YQOJ++2332S1Wl2238yZM3Xx4kXDeMOGDV1Wi6t89NFHKl68uGH84MGDatOmjR49euSSem7fvq0OHTpo9OjRLv3eu9r06dO1b98+m//SQhOoxWLRwIEDDeODBw9Osx8KMpref/HixXQ/VbV48eKqVauWYXzgwIGmTLt9//33Da8zM2TIYPeDZ6nB29vb8PXZrDs22DJ8+HCNHz/eMJ4hQwatXLnS7nR/V/L19VXPnj0N4yNHjnToeuvo0aN2J/G+9tprKc4tSVFRUWrbtq3d72mhQoW0fv165cqVy6G9zDZjxgy7fyu4ublpxowZ6tixowurShmjD0xmypRJdevWdW0xAAAAcBkaVQEAAAAAQLpTpEgRde7c2TD+/vvva9asWSnKffbsWTVu3DhVJr85w9ChQw2nqp48eVIdO3Y05Rad9iT1tstnzpxxWg3Lli0zjJUpUybR87NkyWK36feHH35wuIlh1KhROnz4sGF8yJAhScpz4cIFp31Pd+3aZdiompTH0dneeOMNeXh42IwdP37c4emk33//vVOmqmXNmlWDBw82jL/zzjtat26d6fv+U2xsrG7duuXUPVJLnz59VKVKFdOm6tpz9uxZDRo0yO4xXbp0cXodrubj46NZs2YZPv8kKTg4WM2aNXP6RMoNGzaoYsWKdqfawnX69u2rokWL2ozduHFDbdq00Z07d5xaQ1KvQ/6pWLFiNtetVqtTmxZdxd5ExkOHDmns2LEO5V+/fr2mTZtmGO/bt2+au526JD377LM21y9fvqwjR46Yvt9nn32mkSNHGsa9vb21bNkyNWrUyPS9HTFo0CC5u7vbjIWHh6f4A3/x8fEaMGCAYaN0sWLFHJoUGhsbq44dO2rt2rWGx+TPn18hISF66qmnUryPM8yfP1/9+vUz/PCFxWLR1KlT1b17dxdXljJGr6ONGjWSl5eXi6sBAACAq9CoCgAAAAAA0qWxY8caTiC0Wq3q27evPvnkk2Q17AUHB+uZZ555rBHPYrE4XGtqCgwMtPtmcXBwsBo0aGB3+l9KhYaGqn379mrSpEmSjm/Xrp0aNWqkNWvWmFrHnj179OOPP9qMWSwWNWjQIEl5hgwZYnhL2Pj4eL3wwgs6cOBAimqcOXOm3UaFihUr6vnnn09SrkWLFqlw4cL6/PPPdffu3RTVY0tsbKzdyXhpoYniqaeeUr9+/Qzjo0aNsjuly57ff//d7tfvqCFDhqhQoUI2Y3FxcWrdurV++OEH0/e9d++eJk6cqMKFCys4ONj0/GnF3r171apVK5UvX16zZ892ymTPI0eOqG7duoqMjDQ8pmHDhmmiqdsZatasaffW1ZK0ceNGValSRb///rvp+0dERKhbt26qX7++Uz/4gOTx8vKyOxX60KFDqlWrllOaAI8cOaK+ffuqVKlSyT63SpUqhrEvv/zSkbLShLZt26py5cqG8REjRqT49+XevXvVoUMHw+njvr6+eu+991KU29latmxpeO0fEhJi6l7ffPONhg4dahj39PTU4sWL1bx5c1P3NUPhwoUTvd765Zdfkp13yJAhhpM2/8prdFeNxMTHx6tbt25auXKl4TF58+bV+vXrVaBAgRTt4SwrVqxQ9+7d7U70//bbb9WnTx8XVpVyp06d0rlz52zGWrZs6eJqAAAA4Eo0qgIAAAAAgHQpX758mjBhgmE8Li5OH3/8sSpVqqSZM2caTgqMi4tTSEiIunbtqsaNG+vs2bOPxe29CZteDBgwwO6bflu2bPn7cXJ0EufVq1f1/fffq169eqpZs6aWLFmiuLi4JJ1rtVq1fv16NW/eXOXLl9fnn3+e4PuRXEFBQWrWrJmioqJsxp999tkk39Yzb968+t///mcYv3Hjhp5++mm701v/LTY2ViNGjFCfPn0MJyS5ubnpu+++S9Yb8xERERo8eLDy5cun119/XRs3bnTodvXXrl3Ts88+q23bttmMe3h4qFu3binOb6aRI0cqS5YsNmNWq1W9evXSqFGjkvxzKf355n/btm0fu5Wt2U3svr6+mjdvnuFEyqioKL388svq1KmTjh075tBe8fHx2rRpk958803ly5dPgwYN0vnz5x3KmV4cPHhQPXv2VN68efX2228rNDTU4ZyPHj3SmDFjVL16dcOJw9Kfz2VHpxSmdS+//LLd6cCSdP78ebVo0UJt2rQx5fE/dOiQ+vXrpyJFimjevHkO54P5WrRoYfd23YcPH1b16tU1ceJEhyfa3759W3PnzlWLFi1UpkwZTZ8+3fAawJ4GDRoYToxcvXq1unXrplOnTjlUa2qyWCyaMmWK4bVFfHy8evXqpf/973+G0y1tWbp0qRo0aKCbN28aHjNixIg0N63yL/nz51edOnVsxlLSeGkksVu4e3h4aP78+WrVqpVpe5pt9OjRhlNx4+Pj1aFDB82dOzdJuWJjY/Xmm2/q888/Nzymfv36Kb6lvdVqVb9+/bRgwQLDY3LlyqX169erSJEiKdrDWdauXauOHTvafR5OnjxZr7zyigurcoxRs7C3t7deeOEFF1cDAAAAVzK+Dw8AAAAAAEAaN2DAAG3cuFHz5883PCYsLEy9e/eWp6enKlasqDx58ihHjhy6c+eOLl++rMOHDxvehrhatWoaMmSIpk6dajOenqatzpkzR/Xq1VNYWJjN+NWrV9W7d299+OGHGjBggBo3bqwqVarYvY2z9Gdz5r59+7Rnzx6tWrVKmzZtcqgh8i8HDx7U4MGDNXjwYFWvXl2tW7dWtWrVVKVKFWXLls3uuffv31dQUJB++OEH/fbbb4bHWSwWu42ntrz11lv67bfftH79epvx27dv6/nnn1fz5s01ePBg1a9f32ajy4MHD7Rs2TKNGTNGhw8ftrvnRx99pBo1aiSrzn/W88033+ibb75Rrly51K5dO9WtW1dVqlRR8eLFE/0Z3rNnjxYsWKApU6bYnc46YMAA5cmTJ0U1mi179uz67rvv9OKLL9qMx8fH66OPPtKiRYv0wQcfqE2bNsqQIUOC42JiYvT7779r3LhxNht0+/XrZ/jakFK1atXSN998o5dfftnwmIULF2rx4sV67rnn1LlzZz399NOJNlvHxcXp6NGj2rdvnzZv3qzly5fr8uXLptae3ty8eVOTJk3SpEmTlD9/fjVr1kwNGzZUtWrVVLhw4USfGw8ePNDu3bu1aNEiLVy4UFeuXEl0zyFDhqh69epmfQlp1vjx4+Xu7q5PP/3U7nErV67UypUrVbFiRXXq1EmNGjVSxYoV5enpafe86Oho7dixQyEhIVq8eHGKJ1nDtT7//HMdPnzYcCrlvXv3NGjQII0aNUovvfSSnn32WdWoUUPe3t528965c0f79+/X3r17tWbNGgUFBT32oYKUypYtm5o2bWo4/XfevHmaN2+eihUrptKlSyswMFA+Pj6Grx1vvvmmihUr5nBdZqpevbo++ugjw2uh+Ph4jRgxQgsXLtR7772nF154webdDOLj47VhwwaNHz8+0WnJDRs21Ntvv21K/c7So0cPbd68OcF6cHCwIiMjDT8Mk1QHDx5U//79DT+gJP15i/v169cbXm+aoUWLFmrRokWKz8+aNaumTp1q2FgYFRWl7t27a/Hixfrwww9VtWrVBMfExsZq1apV+uijj3Tw4EHDvbJkyaLp06enuNYpU6ZoxowZdo8pW7as06clJ/d14Pr162rXrp3d17SnnnpKx48f1+uvv25GiTbVrFnT1A+kLV682OZ6q1atFBAQYNo+AAAASHtoVAUAAAAAAOnazJkzdeXKlURvxxkTE6OdO3cmOW/evHm1fPlyu28MJtbEmZYEBARozZo1atiwoY4fP2543IULF/Thhx/qww8/lK+vr8qWLausWbMqMDBQvr6+unPnjm7fvq3IyEidP3/eJVMYd+zYoR07dvz9/wsUKKB8+fIpICBAAQEBypgxox48eKDIyEidOHFC4eHhSZoMO3ToUJtvmtvj7u6u+fPnq3bt2goPDzc8bvXq1Vq9erWyZMmiKlWqKF++fMqQIYPu3bunkydPas+ePUma8Pb8889r+PDhyarRyOXLlzVlyhRNmTJFkpQpUyaVKFFCgYGBCgwMlL+/v2JiYnTv3j2dO3dOR48etXsL878UL15co0ePNqVGs3Tq1EmbNm3SN998Y3jMgQMH9OKLL8rHx0dVqlRR4cKF5evrqwcPHujs2bPavXu3YXNu27Zt1aVLF9MbVSXppZdeUmRkpN3bAcfHx2v58uVavny5JKlo0aLKly/f39/L2NjYv5+nN2/e1LFjx/Tw4UPTa31SnDt3TlOnTv37+5kpUyYVKVLk79eZjBkzKjY2Vnfv3tWdO3d08uRJhYeHJ6spv02bNho1apSzvoQ0Z9y4ccqRI4eGDh2a6DTGffv2ad++fZKkDBkyqEiRIipYsODfj730ZxPjzZs3dfLkSZ05c8bh6d9wPW9vby1fvlzNmzc3nM4t/dlEPm7cOI0bN07e3t4qV66csmXLpsDAQGXKlEn37t37+/Xt0qVLOnXqlN2GP0e8//77iTZenjhxQidOnEg0V/v27dNco6okffzxxzpw4IDdafCHDx9Wjx491L9/f1WqVElFixaVn5+foqKidP78ee3Zs8fuBNW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",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10), dpi=150)\n",
    "plt.barh(lineas.index,lineas[\"ingreso\"], color='#008B8B')\n",
    "plt.xlabel('Ingreso (100 millones de pesos)', fontdict = {'fontsize':35 })\n",
    "plt.ylabel('Lineas',fontdict = {'fontsize':35 })\n",
    "plt.title('Ingresos del STC Metro (2022)', fontsize = 35)\n",
    "plt.tick_params(labelsize=30, pad=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd3867d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e765faf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6516686f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f7058d8d",
   "metadata": {},
   "source": [
    "# Eliminación de duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "21de22df",
   "metadata": {},
   "outputs": [],
   "source": [
    "cal = pd.read_csv('https://raw.githubusercontent.com/jamc88/TSMCI-Analisis-de-datos-con-Python/main/Datos/calif_dup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "74364287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>EXAMEN 1</th>\n",
       "      <th>EXAMEN 2</th>\n",
       "      <th>EXAMEN 3</th>\n",
       "      <th>TAREAS</th>\n",
       "      <th>CALIFICACIÓN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACEVEDO URIBE ULISES</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALVAREZ MARTINEZ MICHEL</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALVAREZ SILVA LIZZETE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALVAREZ DEL CASTILLO OLVERA DANIELA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARIAS SOSA INGRID ARLETTE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BAILON ARELLANO MARIA FERNANDA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BAILON ARELLANO MARIA FERNANDA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BRAMBILA ORTIZ ODEMARIS MAYUBI</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CANTO FLORES NURIT DANAE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CARBAJAL PACHECO MARIA FERNANDA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHAVEZ ANTONIO DIANA SUSANA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CORTES GONZALEZ MEIBYT MALENI</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CRUZ ESCOBEDO YUBIRY</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DIAZ PEREZ TANIA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GREGORIO BARRAGAN ERIKA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RUBALCAVA GONZALEZ MONICA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FRIAS CASTELLANOS ADRIAN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GONZALEZ MIGUEL OSVALDO YAHIR</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GREGORIO BARRAGAN ERIKA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HERNANDEZ DE LA CRUZ LIZETH</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HERNANDEZ MATIAS ANALY</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HERNANDEZ RAMOS CAROLINA YAZMIN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>S(G)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LOPEZ ARMENDARIZ JANICE KATHERINE</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LOPEZ BAZAN LAURA ESTEFANIA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6.8</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LOPEZ LOPEZ LUZ ADRIANA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LOPEZ REYES TANIA JACQUELINE</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LOPEZ RUIZ IVAN ENRIQUE</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MARTINEZ GUTIERREZ DENISSE ALEJANDRA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MARTINEZ MIRANDA EVELYN CAROLINA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MECALCO HERNANDEZ DIANA IVONNE</td>\n",
       "      <td>9.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MORALES MORALES FRIDA SABINA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>OLVERA RAMIREZ DIANA EMILY</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PAEZ REYES JOSE ANTONIO</td>\n",
       "      <td>9.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PEREZ LUJAN MELINA ITZEL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PRADO MENDOZA EDITH MONTSERRAT</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RODRIGUEZ MALDONADO PAOLA</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ROMERO ROSALES IVON ITZEL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ROQUE RODRIGUEZ MARIA FERNANDA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>RUBALCAVA GONZALEZ MONICA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ROMERO ROSALES IVON ITZEL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>RUIZ VELAZCO ZAIRA JOCELYN</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SALINAS GUILLEN YOTZAJANI</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TAFOYA LOPEZ ADRIANA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TORAL SANCHEZ ALEJANDRA ANIELLA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>VALDEZ BAEZ ALEXIS GABRIEL</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>VELAZQUEZ MARTINEZ CECILIA ITZAMA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ZEPEDA ARROYO SABRINA MICHELLE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Nombre  EXAMEN 1  EXAMEN 2  EXAMEN 3   \n",
       "0                   ACEVEDO URIBE ULISES      10.0      10.0      10.0  \\\n",
       "1                ALVAREZ MARTINEZ MICHEL       8.5       7.5       9.0   \n",
       "2                  ALVAREZ SILVA LIZZETE       9.5       6.4       6.8   \n",
       "3    ALVAREZ DEL CASTILLO OLVERA DANIELA       8.0       9.3       8.8   \n",
       "4              ARIAS SOSA INGRID ARLETTE      10.0       8.0       9.8   \n",
       "5         BAILON ARELLANO MARIA FERNANDA       9.0       7.0       6.6   \n",
       "6         BAILON ARELLANO MARIA FERNANDA       9.0       7.0       6.6   \n",
       "7         BRAMBILA ORTIZ ODEMARIS MAYUBI       9.0       7.0       8.5   \n",
       "8               CANTO FLORES NURIT DANAE       9.5       9.0      10.0   \n",
       "9        CARBAJAL PACHECO MARIA FERNANDA       8.0       8.5       9.0   \n",
       "10           CHAVEZ ANTONIO DIANA SUSANA       9.5       7.5       7.6   \n",
       "11         CORTES GONZALEZ MEIBYT MALENI       9.0       9.0      10.0   \n",
       "12                  CRUZ ESCOBEDO YUBIRY       9.5       8.8      10.0   \n",
       "13                      DIAZ PEREZ TANIA       8.0       6.0       7.5   \n",
       "14               GREGORIO BARRAGAN ERIKA       7.0       6.3       7.5   \n",
       "15             RUBALCAVA GONZALEZ MONICA       7.0       6.5       8.3   \n",
       "16              FRIAS CASTELLANOS ADRIAN      10.0       9.3       6.0   \n",
       "17         GONZALEZ MIGUEL OSVALDO YAHIR       9.0       9.2       9.5   \n",
       "18               GREGORIO BARRAGAN ERIKA       7.0       6.3       7.5   \n",
       "19           HERNANDEZ DE LA CRUZ LIZETH       9.0      10.0       6.8   \n",
       "20                HERNANDEZ MATIAS ANALY      10.0       8.7      10.0   \n",
       "21       HERNANDEZ RAMOS CAROLINA YAZMIN       6.0       5.0       4.8   \n",
       "22     LOPEZ ARMENDARIZ JANICE KATHERINE       9.8       6.5       6.1   \n",
       "23           LOPEZ BAZAN LAURA ESTEFANIA      10.0       9.5       6.8   \n",
       "24               LOPEZ LOPEZ LUZ ADRIANA       7.0       4.0       3.3   \n",
       "25          LOPEZ REYES TANIA JACQUELINE       6.0       8.5       7.8   \n",
       "26               LOPEZ RUIZ IVAN ENRIQUE       9.0       8.5       9.5   \n",
       "27  MARTINEZ GUTIERREZ DENISSE ALEJANDRA       9.5       7.8       9.8   \n",
       "28      MARTINEZ MIRANDA EVELYN CAROLINA       8.0       6.0       6.7   \n",
       "29        MECALCO HERNANDEZ DIANA IVONNE       9.8       7.7      10.0   \n",
       "30          MORALES MORALES FRIDA SABINA      10.0       7.5       7.0   \n",
       "31            OLVERA RAMIREZ DIANA EMILY       9.8       9.0       8.2   \n",
       "32               PAEZ REYES JOSE ANTONIO       9.8       7.3       9.5   \n",
       "33              PEREZ LUJAN MELINA ITZEL      10.0       8.5      10.0   \n",
       "34        PRADO MENDOZA EDITH MONTSERRAT       9.0      10.0       9.3   \n",
       "35             RODRIGUEZ MALDONADO PAOLA       9.8       8.8      10.0   \n",
       "36             ROMERO ROSALES IVON ITZEL      10.0      10.0       8.6   \n",
       "37        ROQUE RODRIGUEZ MARIA FERNANDA       9.5       7.9       7.9   \n",
       "38             RUBALCAVA GONZALEZ MONICA       7.0       6.5       8.3   \n",
       "39             ROMERO ROSALES IVON ITZEL      10.0      10.0       8.6   \n",
       "40            RUIZ VELAZCO ZAIRA JOCELYN       9.5       8.0       7.8   \n",
       "41             SALINAS GUILLEN YOTZAJANI      10.0       9.0      10.0   \n",
       "42                  TAFOYA LOPEZ ADRIANA       8.0       6.0       8.8   \n",
       "43       TORAL SANCHEZ ALEJANDRA ANIELLA      10.0      10.0       9.7   \n",
       "44            VALDEZ BAEZ ALEXIS GABRIEL       9.0       9.0       6.0   \n",
       "45     VELAZQUEZ MARTINEZ CECILIA ITZAMA       9.5      10.0       8.0   \n",
       "46        ZEPEDA ARROYO SABRINA MICHELLE       9.5       9.2      10.0   \n",
       "\n",
       "    TAREAS CALIFICACIÓN  \n",
       "0     10.0           MB  \n",
       "1      8.7            B  \n",
       "2      8.1            B  \n",
       "3      9.8           MB  \n",
       "4     10.0           MB  \n",
       "5      8.9            B  \n",
       "6      8.9            B  \n",
       "7      9.6           MB  \n",
       "8      9.4           MB  \n",
       "9      8.7           MB  \n",
       "10     9.1           MB  \n",
       "11     9.8           MB  \n",
       "12    10.0           MB  \n",
       "13     8.0            S  \n",
       "14     9.0            S  \n",
       "15     9.2            B  \n",
       "16     8.1            B  \n",
       "17     7.8           MB  \n",
       "18     9.0            S  \n",
       "19     9.5           MB  \n",
       "20     9.8           MB  \n",
       "21     8.8         S(G)  \n",
       "22     8.7            B  \n",
       "23     9.1           MB  \n",
       "24     8.2          NaN  \n",
       "25     9.0            B  \n",
       "26    10.0           MB  \n",
       "27     9.8           MB  \n",
       "28     8.7            B  \n",
       "29     9.1           MB  \n",
       "30     9.6           MB  \n",
       "31     9.1           MB  \n",
       "32    10.0           MB  \n",
       "33     9.9           MB  \n",
       "34     9.6           MB  \n",
       "35    10.0           MB  \n",
       "36     9.5           MB  \n",
       "37     8.2            B  \n",
       "38     9.2            B  \n",
       "39     9.5           MB  \n",
       "40     6.6            B  \n",
       "41    10.0           MB  \n",
       "42     9.5            B  \n",
       "43     8.9           MB  \n",
       "44     9.0            B  \n",
       "45     9.0           MB  \n",
       "46     9.1           MB  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f5d7fbd",
   "metadata": {},
   "source": [
    "El método **duplicated** de DataFrame devuelve una serie booleana indicando si cada fila es un duplicado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e0e33b58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6      True\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18     True\n",
       "19    False\n",
       "20    False\n",
       "21    False\n",
       "22    False\n",
       "23    False\n",
       "24    False\n",
       "25    False\n",
       "26    False\n",
       "27    False\n",
       "28    False\n",
       "29    False\n",
       "30    False\n",
       "31    False\n",
       "32    False\n",
       "33    False\n",
       "34    False\n",
       "35    False\n",
       "36    False\n",
       "37    False\n",
       "38     True\n",
       "39     True\n",
       "40    False\n",
       "41    False\n",
       "42    False\n",
       "43    False\n",
       "44    False\n",
       "45    False\n",
       "46    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75fe3e30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "70afa0e4",
   "metadata": {},
   "source": [
    "**drop_duplicates** devuelve un dataframe con filas en las que el array duplicate es False al ser filtrado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8a927eac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>EXAMEN 1</th>\n",
       "      <th>EXAMEN 2</th>\n",
       "      <th>EXAMEN 3</th>\n",
       "      <th>TAREAS</th>\n",
       "      <th>CALIFICACIÓN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACEVEDO URIBE ULISES</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALVAREZ MARTINEZ MICHEL</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALVAREZ SILVA LIZZETE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALVAREZ DEL CASTILLO OLVERA DANIELA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARIAS SOSA INGRID ARLETTE</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BAILON ARELLANO MARIA FERNANDA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BRAMBILA ORTIZ ODEMARIS MAYUBI</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CANTO FLORES NURIT DANAE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CARBAJAL PACHECO MARIA FERNANDA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHAVEZ ANTONIO DIANA SUSANA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CORTES GONZALEZ MEIBYT MALENI</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CRUZ ESCOBEDO YUBIRY</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DIAZ PEREZ TANIA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GREGORIO BARRAGAN ERIKA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RUBALCAVA GONZALEZ MONICA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FRIAS CASTELLANOS ADRIAN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GONZALEZ MIGUEL OSVALDO YAHIR</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HERNANDEZ DE LA CRUZ LIZETH</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HERNANDEZ MATIAS ANALY</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HERNANDEZ RAMOS CAROLINA YAZMIN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>S(G)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LOPEZ ARMENDARIZ JANICE KATHERINE</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LOPEZ BAZAN LAURA ESTEFANIA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6.8</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LOPEZ LOPEZ LUZ ADRIANA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LOPEZ REYES TANIA JACQUELINE</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LOPEZ RUIZ IVAN ENRIQUE</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MARTINEZ GUTIERREZ DENISSE ALEJANDRA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MARTINEZ MIRANDA EVELYN CAROLINA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MECALCO HERNANDEZ DIANA IVONNE</td>\n",
       "      <td>9.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MORALES MORALES FRIDA SABINA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>OLVERA RAMIREZ DIANA EMILY</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PAEZ REYES JOSE ANTONIO</td>\n",
       "      <td>9.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PEREZ LUJAN MELINA ITZEL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PRADO MENDOZA EDITH MONTSERRAT</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RODRIGUEZ MALDONADO PAOLA</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ROMERO ROSALES IVON ITZEL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ROQUE RODRIGUEZ MARIA FERNANDA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>RUIZ VELAZCO ZAIRA JOCELYN</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SALINAS GUILLEN YOTZAJANI</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TAFOYA LOPEZ ADRIANA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TORAL SANCHEZ ALEJANDRA ANIELLA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>VALDEZ BAEZ ALEXIS GABRIEL</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>VELAZQUEZ MARTINEZ CECILIA ITZAMA</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ZEPEDA ARROYO SABRINA MICHELLE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Nombre  EXAMEN 1  EXAMEN 2  EXAMEN 3   \n",
       "0                   ACEVEDO URIBE ULISES      10.0      10.0      10.0  \\\n",
       "1                ALVAREZ MARTINEZ MICHEL       8.5       7.5       9.0   \n",
       "2                  ALVAREZ SILVA LIZZETE       9.5       6.4       6.8   \n",
       "3    ALVAREZ DEL CASTILLO OLVERA DANIELA       8.0       9.3       8.8   \n",
       "4              ARIAS SOSA INGRID ARLETTE      10.0       8.0       9.8   \n",
       "5         BAILON ARELLANO MARIA FERNANDA       9.0       7.0       6.6   \n",
       "7         BRAMBILA ORTIZ ODEMARIS MAYUBI       9.0       7.0       8.5   \n",
       "8               CANTO FLORES NURIT DANAE       9.5       9.0      10.0   \n",
       "9        CARBAJAL PACHECO MARIA FERNANDA       8.0       8.5       9.0   \n",
       "10           CHAVEZ ANTONIO DIANA SUSANA       9.5       7.5       7.6   \n",
       "11         CORTES GONZALEZ MEIBYT MALENI       9.0       9.0      10.0   \n",
       "12                  CRUZ ESCOBEDO YUBIRY       9.5       8.8      10.0   \n",
       "13                      DIAZ PEREZ TANIA       8.0       6.0       7.5   \n",
       "14               GREGORIO BARRAGAN ERIKA       7.0       6.3       7.5   \n",
       "15             RUBALCAVA GONZALEZ MONICA       7.0       6.5       8.3   \n",
       "16              FRIAS CASTELLANOS ADRIAN      10.0       9.3       6.0   \n",
       "17         GONZALEZ MIGUEL OSVALDO YAHIR       9.0       9.2       9.5   \n",
       "19           HERNANDEZ DE LA CRUZ LIZETH       9.0      10.0       6.8   \n",
       "20                HERNANDEZ MATIAS ANALY      10.0       8.7      10.0   \n",
       "21       HERNANDEZ RAMOS CAROLINA YAZMIN       6.0       5.0       4.8   \n",
       "22     LOPEZ ARMENDARIZ JANICE KATHERINE       9.8       6.5       6.1   \n",
       "23           LOPEZ BAZAN LAURA ESTEFANIA      10.0       9.5       6.8   \n",
       "24               LOPEZ LOPEZ LUZ ADRIANA       7.0       4.0       3.3   \n",
       "25          LOPEZ REYES TANIA JACQUELINE       6.0       8.5       7.8   \n",
       "26               LOPEZ RUIZ IVAN ENRIQUE       9.0       8.5       9.5   \n",
       "27  MARTINEZ GUTIERREZ DENISSE ALEJANDRA       9.5       7.8       9.8   \n",
       "28      MARTINEZ MIRANDA EVELYN CAROLINA       8.0       6.0       6.7   \n",
       "29        MECALCO HERNANDEZ DIANA IVONNE       9.8       7.7      10.0   \n",
       "30          MORALES MORALES FRIDA SABINA      10.0       7.5       7.0   \n",
       "31            OLVERA RAMIREZ DIANA EMILY       9.8       9.0       8.2   \n",
       "32               PAEZ REYES JOSE ANTONIO       9.8       7.3       9.5   \n",
       "33              PEREZ LUJAN MELINA ITZEL      10.0       8.5      10.0   \n",
       "34        PRADO MENDOZA EDITH MONTSERRAT       9.0      10.0       9.3   \n",
       "35             RODRIGUEZ MALDONADO PAOLA       9.8       8.8      10.0   \n",
       "36             ROMERO ROSALES IVON ITZEL      10.0      10.0       8.6   \n",
       "37        ROQUE RODRIGUEZ MARIA FERNANDA       9.5       7.9       7.9   \n",
       "40            RUIZ VELAZCO ZAIRA JOCELYN       9.5       8.0       7.8   \n",
       "41             SALINAS GUILLEN YOTZAJANI      10.0       9.0      10.0   \n",
       "42                  TAFOYA LOPEZ ADRIANA       8.0       6.0       8.8   \n",
       "43       TORAL SANCHEZ ALEJANDRA ANIELLA      10.0      10.0       9.7   \n",
       "44            VALDEZ BAEZ ALEXIS GABRIEL       9.0       9.0       6.0   \n",
       "45     VELAZQUEZ MARTINEZ CECILIA ITZAMA       9.5      10.0       8.0   \n",
       "46        ZEPEDA ARROYO SABRINA MICHELLE       9.5       9.2      10.0   \n",
       "\n",
       "    TAREAS CALIFICACIÓN  \n",
       "0     10.0           MB  \n",
       "1      8.7            B  \n",
       "2      8.1            B  \n",
       "3      9.8           MB  \n",
       "4     10.0           MB  \n",
       "5      8.9            B  \n",
       "7      9.6           MB  \n",
       "8      9.4           MB  \n",
       "9      8.7           MB  \n",
       "10     9.1           MB  \n",
       "11     9.8           MB  \n",
       "12    10.0           MB  \n",
       "13     8.0            S  \n",
       "14     9.0            S  \n",
       "15     9.2            B  \n",
       "16     8.1            B  \n",
       "17     7.8           MB  \n",
       "19     9.5           MB  \n",
       "20     9.8           MB  \n",
       "21     8.8         S(G)  \n",
       "22     8.7            B  \n",
       "23     9.1           MB  \n",
       "24     8.2          NaN  \n",
       "25     9.0            B  \n",
       "26    10.0           MB  \n",
       "27     9.8           MB  \n",
       "28     8.7            B  \n",
       "29     9.1           MB  \n",
       "30     9.6           MB  \n",
       "31     9.1           MB  \n",
       "32    10.0           MB  \n",
       "33     9.9           MB  \n",
       "34     9.6           MB  \n",
       "35    10.0           MB  \n",
       "36     9.5           MB  \n",
       "37     8.2            B  \n",
       "40     6.6            B  \n",
       "41    10.0           MB  \n",
       "42     9.5            B  \n",
       "43     8.9           MB  \n",
       "44     9.0            B  \n",
       "45     9.0           MB  \n",
       "46     9.1           MB  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e466167",
   "metadata": {},
   "source": [
    "Supongamos que queremos filtrar los duplicados basándonos solamente en la columna 'CALIFICACIÓN':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4ff590f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Nombre</th>\n",
       "      <th>EXAMEN 1</th>\n",
       "      <th>EXAMEN 2</th>\n",
       "      <th>EXAMEN 3</th>\n",
       "      <th>TAREAS</th>\n",
       "      <th>CALIFICACIÓN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACEVEDO URIBE ULISES</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALVAREZ MARTINEZ MICHEL</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DIAZ PEREZ TANIA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HERNANDEZ RAMOS CAROLINA YAZMIN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>S(G)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LOPEZ LOPEZ LUZ ADRIANA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Nombre  EXAMEN 1  EXAMEN 2  EXAMEN 3  TAREAS   \n",
       "0              ACEVEDO URIBE ULISES      10.0      10.0      10.0    10.0  \\\n",
       "1           ALVAREZ MARTINEZ MICHEL       8.5       7.5       9.0     8.7   \n",
       "13                 DIAZ PEREZ TANIA       8.0       6.0       7.5     8.0   \n",
       "21  HERNANDEZ RAMOS CAROLINA YAZMIN       6.0       5.0       4.8     8.8   \n",
       "24          LOPEZ LOPEZ LUZ ADRIANA       7.0       4.0       3.3     8.2   \n",
       "\n",
       "   CALIFICACIÓN  \n",
       "0            MB  \n",
       "1             B  \n",
       "13            S  \n",
       "21         S(G)  \n",
       "24          NaN  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.drop_duplicates(subset=['CALIFICACIÓN'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6abe27",
   "metadata": {},
   "source": [
    "*duplicated* y *drop_duplicates* conservan por defecto la primera combinación de valor observada."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf31eb9",
   "metadata": {},
   "source": [
    "Pasar keep='last' devolverá la última:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "22f33255",
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>EXAMEN 1</th>\n",
       "      <th>EXAMEN 2</th>\n",
       "      <th>EXAMEN 3</th>\n",
       "      <th>TAREAS</th>\n",
       "      <th>CALIFICACIÓN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GREGORIO BARRAGAN ERIKA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HERNANDEZ RAMOS CAROLINA YAZMIN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>S(G)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LOPEZ LOPEZ LUZ ADRIANA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>VALDEZ BAEZ ALEXIS GABRIEL</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ZEPEDA ARROYO SABRINA MICHELLE</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>MB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Nombre  EXAMEN 1  EXAMEN 2  EXAMEN 3  TAREAS   \n",
       "18          GREGORIO BARRAGAN ERIKA       7.0       6.3       7.5     9.0  \\\n",
       "21  HERNANDEZ RAMOS CAROLINA YAZMIN       6.0       5.0       4.8     8.8   \n",
       "24          LOPEZ LOPEZ LUZ ADRIANA       7.0       4.0       3.3     8.2   \n",
       "44       VALDEZ BAEZ ALEXIS GABRIEL       9.0       9.0       6.0     9.0   \n",
       "46   ZEPEDA ARROYO SABRINA MICHELLE       9.5       9.2      10.0     9.1   \n",
       "\n",
       "   CALIFICACIÓN  \n",
       "18            S  \n",
       "21         S(G)  \n",
       "24          NaN  \n",
       "44            B  \n",
       "46           MB  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.drop_duplicates(subset=['CALIFICACIÓN'], keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a582d04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "71a7e52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "agua = pd.read_csv('consumo_agua_historico_2019.csv',index_col=['fecha_referencia'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92617a5",
   "metadata": {},
   "source": [
    "Datos: https://datos.cdmx.gob.mx/dataset/consumo-agua"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d2ff1682",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anio</th>\n",
       "      <th>bimestre</th>\n",
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       "      <th>colonia</th>\n",
       "      <th>alcaldia</th>\n",
       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha_referencia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>159.72</td>\n",
       "      <td>42.566364</td>\n",
       "      <td>468.23</td>\n",
       "      <td>53.240000</td>\n",
       "      <td>631.00</td>\n",
       "      <td>42.066667</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>3.05</td>\n",
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       "      <td>GUSTAVO A. MADERO</td>\n",
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       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.936667</td>\n",
       "      <td>107.81</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>115.13</td>\n",
       "      <td>28.782500</td>\n",
       "      <td>7.320000</td>\n",
       "      <td>7.32</td>\n",
       "      <td>MEDIO</td>\n",
       "      <td>7 DE NOVIEMBRE</td>\n",
       "      <td>GUSTAVO A. MADERO</td>\n",
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       "      <th>2019-06-30</th>\n",
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       "      <td>122.93</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>197.96</td>\n",
       "      <td>32.993333</td>\n",
       "      <td>75.030000</td>\n",
       "      <td>75.03</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>7 DE NOVIEMBRE</td>\n",
       "      <td>GUSTAVO A. MADERO</td>\n",
       "      <td>19.455720</td>\n",
       "      <td>-99.113582</td>\n",
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       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>253.53</td>\n",
       "      <td>84.510000</td>\n",
       "      <td>84.510000</td>\n",
       "      <td>253.53</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>GERTRUDIS SANCHEZ 3A SECCION</td>\n",
       "      <td>GUSTAVO A. MADERO</td>\n",
       "      <td>19.459647</td>\n",
       "      <td>-99.104469</td>\n",
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       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>56.72</td>\n",
       "      <td>67.436250</td>\n",
       "      <td>539.49</td>\n",
       "      <td>56.720000</td>\n",
       "      <td>839.35</td>\n",
       "      <td>76.304545</td>\n",
       "      <td>121.570000</td>\n",
       "      <td>243.14</td>\n",
       "      <td>BAJO</td>\n",
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       "      <td>AZCAPOTZALCO</td>\n",
       "      <td>19.474161</td>\n",
       "      <td>-99.146750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.053112</td>\n",
       "      <td>3930.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4286.28</td>\n",
       "      <td>19.307568</td>\n",
       "      <td>13.687308</td>\n",
       "      <td>355.87</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>GUERRERO I</td>\n",
       "      <td>CUAUHTEMOC</td>\n",
       "      <td>19.448564</td>\n",
       "      <td>-99.139940</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>71.30</td>\n",
       "      <td>21.126615</td>\n",
       "      <td>9549.24</td>\n",
       "      <td>35.650001</td>\n",
       "      <td>9796.12</td>\n",
       "      <td>20.976702</td>\n",
       "      <td>13.506923</td>\n",
       "      <td>175.59</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>GUERRERO I</td>\n",
       "      <td>CUAUHTEMOC</td>\n",
       "      <td>19.449339</td>\n",
       "      <td>-99.145719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>759.16</td>\n",
       "      <td>27.527778</td>\n",
       "      <td>4707.25</td>\n",
       "      <td>94.894999</td>\n",
       "      <td>5692.81</td>\n",
       "      <td>29.344381</td>\n",
       "      <td>15.093334</td>\n",
       "      <td>226.40</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>GUERRERO I</td>\n",
       "      <td>CUAUHTEMOC</td>\n",
       "      <td>19.448392</td>\n",
       "      <td>-99.145930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>402.65</td>\n",
       "      <td>30.605000</td>\n",
       "      <td>550.89</td>\n",
       "      <td>100.662498</td>\n",
       "      <td>963.15</td>\n",
       "      <td>41.876087</td>\n",
       "      <td>9.610000</td>\n",
       "      <td>9.61</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>GUERRERO I</td>\n",
       "      <td>CUAUHTEMOC</td>\n",
       "      <td>19.447587</td>\n",
       "      <td>-99.142509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>41.20</td>\n",
       "      <td>22.507710</td>\n",
       "      <td>8552.94</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>9000.07</td>\n",
       "      <td>21.951366</td>\n",
       "      <td>15.034444</td>\n",
       "      <td>405.93</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>GUERRERO II</td>\n",
       "      <td>CUAUHTEMOC</td>\n",
       "      <td>19.447402</td>\n",
       "      <td>-99.139725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71102 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  anio  bimestre  consumo_total_mixto  consumo_prom_dom   \n",
       "fecha_referencia                                                          \n",
       "2019-06-30        2019         3               159.72         42.566364  \\\n",
       "2019-06-30        2019         3                 0.00         35.936667   \n",
       "2019-06-30        2019         3                 0.00         24.586000   \n",
       "2019-06-30        2019         3                 0.00          0.000000   \n",
       "2019-06-30        2019         3                56.72         67.436250   \n",
       "...                ...       ...                  ...               ...   \n",
       "2019-02-28        2019         1                  NaN         20.053112   \n",
       "2019-02-28        2019         1                71.30         21.126615   \n",
       "2019-02-28        2019         1               759.16         27.527778   \n",
       "2019-02-28        2019         1               402.65         30.605000   \n",
       "2019-02-28        2019         1                41.20         22.507710   \n",
       "\n",
       "                  consumo_total_dom  consumo_prom_mixto  consumo_total   \n",
       "fecha_referencia                                                         \n",
       "2019-06-30                   468.23           53.240000         631.00  \\\n",
       "2019-06-30                   107.81            0.000000         115.13   \n",
       "2019-06-30                   122.93            0.000000         197.96   \n",
       "2019-06-30                     0.00            0.000000         253.53   \n",
       "2019-06-30                   539.49           56.720000         839.35   \n",
       "...                             ...                 ...            ...   \n",
       "2019-02-28                  3930.41                 NaN        4286.28   \n",
       "2019-02-28                  9549.24           35.650001        9796.12   \n",
       "2019-02-28                  4707.25           94.894999        5692.81   \n",
       "2019-02-28                   550.89          100.662498         963.15   \n",
       "2019-02-28                  8552.94           13.733333        9000.07   \n",
       "\n",
       "                  consumo_prom  consumo_prom_no_dom  consumo_total_no_dom   \n",
       "fecha_referencia                                                            \n",
       "2019-06-30           42.066667             3.050000                  3.05  \\\n",
       "2019-06-30           28.782500             7.320000                  7.32   \n",
       "2019-06-30           32.993333            75.030000                 75.03   \n",
       "2019-06-30           84.510000            84.510000                253.53   \n",
       "2019-06-30           76.304545           121.570000                243.14   \n",
       "...                        ...                  ...                   ...   \n",
       "2019-02-28           19.307568            13.687308                355.87   \n",
       "2019-02-28           20.976702            13.506923                175.59   \n",
       "2019-02-28           29.344381            15.093334                226.40   \n",
       "2019-02-28           41.876087             9.610000                  9.61   \n",
       "2019-02-28           21.951366            15.034444                405.93   \n",
       "\n",
       "                 indice_des                       colonia           alcaldia   \n",
       "fecha_referencia                                                               \n",
       "2019-06-30             ALTO                7 DE NOVIEMBRE  GUSTAVO A. MADERO  \\\n",
       "2019-06-30            MEDIO                7 DE NOVIEMBRE  GUSTAVO A. MADERO   \n",
       "2019-06-30          POPULAR                7 DE NOVIEMBRE  GUSTAVO A. MADERO   \n",
       "2019-06-30             BAJO  GERTRUDIS SANCHEZ 3A SECCION  GUSTAVO A. MADERO   \n",
       "2019-06-30             BAJO                   PRO HOGAR I       AZCAPOTZALCO   \n",
       "...                     ...                           ...                ...   \n",
       "2019-02-28             BAJO                    GUERRERO I         CUAUHTEMOC   \n",
       "2019-02-28          POPULAR                    GUERRERO I         CUAUHTEMOC   \n",
       "2019-02-28             BAJO                    GUERRERO I         CUAUHTEMOC   \n",
       "2019-02-28             BAJO                    GUERRERO I         CUAUHTEMOC   \n",
       "2019-02-28             BAJO                   GUERRERO II         CUAUHTEMOC   \n",
       "\n",
       "                    latitud   longitud  \n",
       "fecha_referencia                        \n",
       "2019-06-30        19.455260 -99.112662  \n",
       "2019-06-30        19.455260 -99.112662  \n",
       "2019-06-30        19.455720 -99.113582  \n",
       "2019-06-30        19.459647 -99.104469  \n",
       "2019-06-30        19.474161 -99.146750  \n",
       "...                     ...        ...  \n",
       "2019-02-28        19.448564 -99.139940  \n",
       "2019-02-28        19.449339 -99.145719  \n",
       "2019-02-28        19.448392 -99.145930  \n",
       "2019-02-28        19.447587 -99.142509  \n",
       "2019-02-28        19.447402 -99.139725  \n",
       "\n",
       "[71102 rows x 15 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agua"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ede386c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "anio                       0\n",
       "bimestre                   0\n",
       "consumo_total_mixto     8327\n",
       "consumo_prom_dom        4820\n",
       "consumo_total_dom       4820\n",
       "consumo_prom_mixto      8327\n",
       "consumo_total              0\n",
       "consumo_prom               0\n",
       "consumo_prom_no_dom        0\n",
       "consumo_total_no_dom       0\n",
       "indice_des                 0\n",
       "colonia                  216\n",
       "alcaldia                 216\n",
       "latitud                    0\n",
       "longitud                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agua.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8e531db6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anio</th>\n",
       "      <th>bimestre</th>\n",
       "      <th>consumo_total_mixto</th>\n",
       "      <th>consumo_prom_dom</th>\n",
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       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha_referencia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>426.39</td>\n",
       "      <td>213.195000</td>\n",
       "      <td>213.195000</td>\n",
       "      <td>426.39</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>CONSTITUCION DE 1917 II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.350551</td>\n",
       "      <td>-99.058122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>SANTA MARTHA ACATITLA</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.372634</td>\n",
       "      <td>-99.026868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>839.36</td>\n",
       "      <td>279.786667</td>\n",
       "      <td>279.786667</td>\n",
       "      <td>839.36</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>SANTA CRUZ MEYEHUALCO (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.351650</td>\n",
       "      <td>-99.047017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.03</td>\n",
       "      <td>14.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.32</td>\n",
       "      <td>4.580000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>4.29</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>SANTA CRUZ MEYEHUALCO (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.350693</td>\n",
       "      <td>-99.044110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.35</td>\n",
       "      <td>21.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.60</td>\n",
       "      <td>52.866667</td>\n",
       "      <td>68.625000</td>\n",
       "      <td>137.25</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>SANTA CRUZ MEYEHUALCO (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.350773</td>\n",
       "      <td>-99.044983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>2019-02-28</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3734.10</td>\n",
       "      <td>466.762509</td>\n",
       "      <td>466.762509</td>\n",
       "      <td>3734.10</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>ERMITA ZARAGOZA (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.364859</td>\n",
       "      <td>-98.997922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.170000</td>\n",
       "      <td>9.170000</td>\n",
       "      <td>9.17</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>ERMITA ZARAGOZA (U HAB) I</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.362170</td>\n",
       "      <td>-98.996594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>ERMITA ZARAGOZA (U HAB) I</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.364762</td>\n",
       "      <td>-99.000788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.66</td>\n",
       "      <td>110.329998</td>\n",
       "      <td>110.329998</td>\n",
       "      <td>220.66</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>ERMITA ZARAGOZA (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.367369</td>\n",
       "      <td>-98.997828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108.60</td>\n",
       "      <td>54.300001</td>\n",
       "      <td>54.300001</td>\n",
       "      <td>108.60</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>ERMITA ZARAGOZA (U HAB) I</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.362148</td>\n",
       "      <td>-98.995599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10518 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  anio  bimestre  consumo_total_mixto  consumo_prom_dom   \n",
       "fecha_referencia                                                          \n",
       "2019-06-30        2019         3                  0.0              0.00  \\\n",
       "2019-06-30        2019         3                  0.0              0.00   \n",
       "2019-06-30        2019         3                  0.0              0.00   \n",
       "2019-06-30        2019         3                  0.0             14.03   \n",
       "2019-06-30        2019         3                  0.0             21.35   \n",
       "...                ...       ...                  ...               ...   \n",
       "2019-02-28        2019         1                  NaN               NaN   \n",
       "2019-02-28        2019         1                  NaN               NaN   \n",
       "2019-02-28        2019         1                  NaN               NaN   \n",
       "2019-02-28        2019         1                  NaN               NaN   \n",
       "2019-02-28        2019         1                  NaN               NaN   \n",
       "\n",
       "                  consumo_total_dom  consumo_prom_mixto  consumo_total   \n",
       "fecha_referencia                                                         \n",
       "2019-06-30                     0.00                 0.0         426.39  \\\n",
       "2019-06-30                     0.00                 0.0           0.00   \n",
       "2019-06-30                     0.00                 0.0         839.36   \n",
       "2019-06-30                    14.03                 0.0          18.32   \n",
       "2019-06-30                    21.35                 0.0         158.60   \n",
       "...                             ...                 ...            ...   \n",
       "2019-02-28                      NaN                 NaN        3734.10   \n",
       "2019-02-28                      NaN                 NaN           9.17   \n",
       "2019-02-28                      NaN                 NaN           0.00   \n",
       "2019-02-28                      NaN                 NaN         220.66   \n",
       "2019-02-28                      NaN                 NaN         108.60   \n",
       "\n",
       "                  consumo_prom  consumo_prom_no_dom  consumo_total_no_dom   \n",
       "fecha_referencia                                                            \n",
       "2019-06-30          213.195000           213.195000                426.39  \\\n",
       "2019-06-30            0.000000             0.000000                  0.00   \n",
       "2019-06-30          279.786667           279.786667                839.36   \n",
       "2019-06-30            4.580000             1.430000                  4.29   \n",
       "2019-06-30           52.866667            68.625000                137.25   \n",
       "...                        ...                  ...                   ...   \n",
       "2019-02-28          466.762509           466.762509               3734.10   \n",
       "2019-02-28            9.170000             9.170000                  9.17   \n",
       "2019-02-28            0.000000             0.000000                  0.00   \n",
       "2019-02-28          110.329998           110.329998                220.66   \n",
       "2019-02-28           54.300001            54.300001                108.60   \n",
       "\n",
       "                 indice_des                           colonia    alcaldia   \n",
       "fecha_referencia                                                            \n",
       "2019-06-30          POPULAR           CONSTITUCION DE 1917 II  IZTAPALAPA  \\\n",
       "2019-06-30             BAJO             SANTA MARTHA ACATITLA  IZTAPALAPA   \n",
       "2019-06-30          POPULAR  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "2019-06-30             BAJO  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "2019-06-30             BAJO  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "...                     ...                               ...         ...   \n",
       "2019-02-28          POPULAR        ERMITA ZARAGOZA (U HAB) II  IZTAPALAPA   \n",
       "2019-02-28             BAJO         ERMITA ZARAGOZA (U HAB) I  IZTAPALAPA   \n",
       "2019-02-28          POPULAR         ERMITA ZARAGOZA (U HAB) I  IZTAPALAPA   \n",
       "2019-02-28          POPULAR        ERMITA ZARAGOZA (U HAB) II  IZTAPALAPA   \n",
       "2019-02-28          POPULAR         ERMITA ZARAGOZA (U HAB) I  IZTAPALAPA   \n",
       "\n",
       "                    latitud   longitud  \n",
       "fecha_referencia                        \n",
       "2019-06-30        19.350551 -99.058122  \n",
       "2019-06-30        19.372634 -99.026868  \n",
       "2019-06-30        19.351650 -99.047017  \n",
       "2019-06-30        19.350693 -99.044110  \n",
       "2019-06-30        19.350773 -99.044983  \n",
       "...                     ...        ...  \n",
       "2019-02-28        19.364859 -98.997922  \n",
       "2019-02-28        19.362170 -98.996594  \n",
       "2019-02-28        19.364762 -99.000788  \n",
       "2019-02-28        19.367369 -98.997828  \n",
       "2019-02-28        19.362148 -98.995599  \n",
       "\n",
       "[10518 rows x 15 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtro = (agua['alcaldia']=='IZTAPALAPA')\n",
    "agua[filtro]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4dac770a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_iz = agua[filtro]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "62fe3205",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anio</th>\n",
       "      <th>bimestre</th>\n",
       "      <th>consumo_total_mixto</th>\n",
       "      <th>consumo_prom_dom</th>\n",
       "      <th>consumo_total_dom</th>\n",
       "      <th>consumo_prom_mixto</th>\n",
       "      <th>consumo_total</th>\n",
       "      <th>consumo_prom</th>\n",
       "      <th>consumo_prom_no_dom</th>\n",
       "      <th>consumo_total_no_dom</th>\n",
       "      <th>indice_des</th>\n",
       "      <th>colonia</th>\n",
       "      <th>alcaldia</th>\n",
       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fecha_referencia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>426.39</td>\n",
       "      <td>213.195000</td>\n",
       "      <td>213.195000</td>\n",
       "      <td>426.39</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>CONSTITUCION DE 1917 II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.350551</td>\n",
       "      <td>-99.058122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>SANTA MARTHA ACATITLA</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.372634</td>\n",
       "      <td>-99.026868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>839.36</td>\n",
       "      <td>279.786667</td>\n",
       "      <td>279.786667</td>\n",
       "      <td>839.36</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>SANTA CRUZ MEYEHUALCO (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.351650</td>\n",
       "      <td>-99.047017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.03</td>\n",
       "      <td>14.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.32</td>\n",
       "      <td>4.580000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>4.29</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>SANTA CRUZ MEYEHUALCO (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.350693</td>\n",
       "      <td>-99.044110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.35</td>\n",
       "      <td>21.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.60</td>\n",
       "      <td>52.866667</td>\n",
       "      <td>68.625000</td>\n",
       "      <td>137.25</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>SANTA CRUZ MEYEHUALCO (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.350773</td>\n",
       "      <td>-99.044983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.87</td>\n",
       "      <td>162.870000</td>\n",
       "      <td>162.870000</td>\n",
       "      <td>162.87</td>\n",
       "      <td>BAJO</td>\n",
       "      <td>SANTA CRUZ MEYEHUALCO (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.349552</td>\n",
       "      <td>-99.047087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  anio  bimestre  consumo_total_mixto  consumo_prom_dom   \n",
       "fecha_referencia                                                          \n",
       "2019-06-30        2019         3                  0.0              0.00  \\\n",
       "2019-06-30        2019         3                  0.0              0.00   \n",
       "2019-06-30        2019         3                  0.0              0.00   \n",
       "2019-06-30        2019         3                  0.0             14.03   \n",
       "2019-06-30        2019         3                  0.0             21.35   \n",
       "2019-06-30        2019         3                  0.0              0.00   \n",
       "\n",
       "                  consumo_total_dom  consumo_prom_mixto  consumo_total   \n",
       "fecha_referencia                                                         \n",
       "2019-06-30                     0.00                 0.0         426.39  \\\n",
       "2019-06-30                     0.00                 0.0           0.00   \n",
       "2019-06-30                     0.00                 0.0         839.36   \n",
       "2019-06-30                    14.03                 0.0          18.32   \n",
       "2019-06-30                    21.35                 0.0         158.60   \n",
       "2019-06-30                     0.00                 0.0         162.87   \n",
       "\n",
       "                  consumo_prom  consumo_prom_no_dom  consumo_total_no_dom   \n",
       "fecha_referencia                                                            \n",
       "2019-06-30          213.195000           213.195000                426.39  \\\n",
       "2019-06-30            0.000000             0.000000                  0.00   \n",
       "2019-06-30          279.786667           279.786667                839.36   \n",
       "2019-06-30            4.580000             1.430000                  4.29   \n",
       "2019-06-30           52.866667            68.625000                137.25   \n",
       "2019-06-30          162.870000           162.870000                162.87   \n",
       "\n",
       "                 indice_des                           colonia    alcaldia   \n",
       "fecha_referencia                                                            \n",
       "2019-06-30          POPULAR           CONSTITUCION DE 1917 II  IZTAPALAPA  \\\n",
       "2019-06-30             BAJO             SANTA MARTHA ACATITLA  IZTAPALAPA   \n",
       "2019-06-30          POPULAR  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "2019-06-30             BAJO  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "2019-06-30             BAJO  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "2019-06-30             BAJO  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "\n",
       "                    latitud   longitud  \n",
       "fecha_referencia                        \n",
       "2019-06-30        19.350551 -99.058122  \n",
       "2019-06-30        19.372634 -99.026868  \n",
       "2019-06-30        19.351650 -99.047017  \n",
       "2019-06-30        19.350693 -99.044110  \n",
       "2019-06-30        19.350773 -99.044983  \n",
       "2019-06-30        19.349552 -99.047087  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_iz.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c1e33e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "anio                       0\n",
       "bimestre                   0\n",
       "consumo_total_mixto     1796\n",
       "consumo_prom_dom        1578\n",
       "consumo_total_dom       1578\n",
       "consumo_prom_mixto      1796\n",
       "consumo_total              0\n",
       "consumo_prom               0\n",
       "consumo_prom_no_dom        0\n",
       "consumo_total_no_dom       0\n",
       "indice_des                 0\n",
       "colonia                    0\n",
       "alcaldia                   0\n",
       "latitud                    0\n",
       "longitud                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_iz.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61af8717",
   "metadata": {},
   "source": [
    "Podemos reemplazar los valores NA por 0 con la función **replace**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f30be2f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2019-06-30</th>\n",
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       "      <th>2019-06-30</th>\n",
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       "      <td>279.786667</td>\n",
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       "      <td>SANTA CRUZ MEYEHUALCO (U HAB) II</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.351650</td>\n",
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       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
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       "      <th>2019-06-30</th>\n",
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       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
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       "      <td>-98.996594</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
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       "      <td>POPULAR</td>\n",
       "      <td>ERMITA ZARAGOZA (U HAB) I</td>\n",
       "      <td>IZTAPALAPA</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>2019</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>IZTAPALAPA</td>\n",
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       "      <th>2019-02-28</th>\n",
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       "      <td>54.300001</td>\n",
       "      <td>54.300001</td>\n",
       "      <td>108.60</td>\n",
       "      <td>POPULAR</td>\n",
       "      <td>ERMITA ZARAGOZA (U HAB) I</td>\n",
       "      <td>IZTAPALAPA</td>\n",
       "      <td>19.362148</td>\n",
       "      <td>-98.995599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10518 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  anio  bimestre  consumo_total_mixto  consumo_prom_dom   \n",
       "fecha_referencia                                                          \n",
       "2019-06-30        2019         3                  0.0              0.00  \\\n",
       "2019-06-30        2019         3                  0.0              0.00   \n",
       "2019-06-30        2019         3                  0.0              0.00   \n",
       "2019-06-30        2019         3                  0.0             14.03   \n",
       "2019-06-30        2019         3                  0.0             21.35   \n",
       "...                ...       ...                  ...               ...   \n",
       "2019-02-28        2019         1                  0.0              0.00   \n",
       "2019-02-28        2019         1                  0.0              0.00   \n",
       "2019-02-28        2019         1                  0.0              0.00   \n",
       "2019-02-28        2019         1                  0.0              0.00   \n",
       "2019-02-28        2019         1                  0.0              0.00   \n",
       "\n",
       "                  consumo_total_dom  consumo_prom_mixto  consumo_total   \n",
       "fecha_referencia                                                         \n",
       "2019-06-30                     0.00                 0.0         426.39  \\\n",
       "2019-06-30                     0.00                 0.0           0.00   \n",
       "2019-06-30                     0.00                 0.0         839.36   \n",
       "2019-06-30                    14.03                 0.0          18.32   \n",
       "2019-06-30                    21.35                 0.0         158.60   \n",
       "...                             ...                 ...            ...   \n",
       "2019-02-28                     0.00                 0.0        3734.10   \n",
       "2019-02-28                     0.00                 0.0           9.17   \n",
       "2019-02-28                     0.00                 0.0           0.00   \n",
       "2019-02-28                     0.00                 0.0         220.66   \n",
       "2019-02-28                     0.00                 0.0         108.60   \n",
       "\n",
       "                  consumo_prom  consumo_prom_no_dom  consumo_total_no_dom   \n",
       "fecha_referencia                                                            \n",
       "2019-06-30          213.195000           213.195000                426.39  \\\n",
       "2019-06-30            0.000000             0.000000                  0.00   \n",
       "2019-06-30          279.786667           279.786667                839.36   \n",
       "2019-06-30            4.580000             1.430000                  4.29   \n",
       "2019-06-30           52.866667            68.625000                137.25   \n",
       "...                        ...                  ...                   ...   \n",
       "2019-02-28          466.762509           466.762509               3734.10   \n",
       "2019-02-28            9.170000             9.170000                  9.17   \n",
       "2019-02-28            0.000000             0.000000                  0.00   \n",
       "2019-02-28          110.329998           110.329998                220.66   \n",
       "2019-02-28           54.300001            54.300001                108.60   \n",
       "\n",
       "                 indice_des                           colonia    alcaldia   \n",
       "fecha_referencia                                                            \n",
       "2019-06-30          POPULAR           CONSTITUCION DE 1917 II  IZTAPALAPA  \\\n",
       "2019-06-30             BAJO             SANTA MARTHA ACATITLA  IZTAPALAPA   \n",
       "2019-06-30          POPULAR  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "2019-06-30             BAJO  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "2019-06-30             BAJO  SANTA CRUZ MEYEHUALCO (U HAB) II  IZTAPALAPA   \n",
       "...                     ...                               ...         ...   \n",
       "2019-02-28          POPULAR        ERMITA ZARAGOZA (U HAB) II  IZTAPALAPA   \n",
       "2019-02-28             BAJO         ERMITA ZARAGOZA (U HAB) I  IZTAPALAPA   \n",
       "2019-02-28          POPULAR         ERMITA ZARAGOZA (U HAB) I  IZTAPALAPA   \n",
       "2019-02-28          POPULAR        ERMITA ZARAGOZA (U HAB) II  IZTAPALAPA   \n",
       "2019-02-28          POPULAR         ERMITA ZARAGOZA (U HAB) I  IZTAPALAPA   \n",
       "\n",
       "                    latitud   longitud  \n",
       "fecha_referencia                        \n",
       "2019-06-30        19.350551 -99.058122  \n",
       "2019-06-30        19.372634 -99.026868  \n",
       "2019-06-30        19.351650 -99.047017  \n",
       "2019-06-30        19.350693 -99.044110  \n",
       "2019-06-30        19.350773 -99.044983  \n",
       "...                     ...        ...  \n",
       "2019-02-28        19.364859 -98.997922  \n",
       "2019-02-28        19.362170 -98.996594  \n",
       "2019-02-28        19.364762 -99.000788  \n",
       "2019-02-28        19.367369 -98.997828  \n",
       "2019-02-28        19.362148 -98.995599  \n",
       "\n",
       "[10518 rows x 15 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_iz.replace(np.nan, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0621c948",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_iz=a_iz.replace(np.nan, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "333135f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "anio                    0\n",
       "bimestre                0\n",
       "consumo_total_mixto     0\n",
       "consumo_prom_dom        0\n",
       "consumo_total_dom       0\n",
       "consumo_prom_mixto      0\n",
       "consumo_total           0\n",
       "consumo_prom            0\n",
       "consumo_prom_no_dom     0\n",
       "consumo_total_no_dom    0\n",
       "indice_des              0\n",
       "colonia                 0\n",
       "alcaldia                0\n",
       "latitud                 0\n",
       "longitud                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_iz.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c81a5a2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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