Proyecto 1 - Estadística descriptiva.ipynb

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    "# Análisis de datos - Dr. Jorge Velázquez Castro\n",
    "\n",
    "## Proyecto 1: Estadística Descriptiva\n",
    "\n",
    "## Francisco Valentín Valerio López\n",
    "\n",
    "### Introducción\n",
    "En los últimos años, se ha propagado una enfermedad de las plantas de cítricos a nivel mundial. La enfermedad se conoce como *dragón amarillo* y es generada por una bacteria que la transmite un psilido procedente de la región asiática llamado Diaphorina.\n",
    "\n",
    "Esta enfermedad reduce la producción de cítricos, además de que reduce su tamaño. México no está exento de este problema. Para estimar la magnitud, ubicación y posible propagación geográfica del problema, se colocan trampas de Diaphorinas en los plantíos. En el archivo llamado \"`capturas_2015-2020.xls`\" está el monitoreo de dichas trampas desde 2015 hasta 2020.\n",
    "\n",
    "<div>\n",
    "<img src = \"src\\hlb.jpg\", width = \"640\" height = \"480\" align = \"center\"/>\n",
    "<img src = \"src\\diaph.jpg\", width = \"640\" height = \"480\" align = \"center\"/>\n",
    "</div>\n",
    "\n",
    "\n",
    "Un indicador de la dispersión es el índice de dispersión ($Id$ ) dado por:\n",
    "\n",
    "$$ Id = \\frac{\\sigma^2}{\\bar{x}} \\tag{1}$$\n",
    "\n",
    "donde $\\sigma^2$ es la varianza de la muestra y $\\bar{x}$ es la media muestral. Es importante notar que los datos de donde se obtiene $\\sigma$ y $\\bar{x}$ son los correspondientes a cada ubicación geográfica (trampa) y no a a cada toma de la muestra. Es decir, es necesario primero obtener el promedio temporal de Diaphorinas por ubicación (trampa o municipio) y posteriormente se podrá calcular el índica con estos datos por ubicación. \n",
    "Los valores $Id = 1$ indican una disposición espacial aleatoria, si $Id < 1$, entonces se muestra una disposición espacial regular o uniforme, y finalmente los valores $Id >> 1$ indican una disposición agregada.\n",
    "\n",
    "El coeficiente de Green ($C_x$) es otra métrica para evaluar la agregación o uniformidad de una distribución y está dado por:\n",
    "\n",
    "$$ C_x = \\frac{(\\sigma^2 / \\bar{x})-1}{\\sum x - 1} \\tag{2} $$\n",
    "\n",
    "Si $C_x < 0$ entonces hay un patrón uniforme, si $C_x > 0$ entonces hay un patrón de agregación y si $C_x = 0$ entonces hay una distribución aleatoria.\n",
    "\n",
    "### Problema\n",
    "\n",
    "1. Hacer una gráfica del promedio de Diaphorinas por trampa para cada mes de los años 2015, 2017 y 2020. Es decir, una gráfica por cada año donde en el eje horizontal esté el mes y en el vertical el promedio de Diaphorinas por trampa.\n",
    "\n",
    "2. Hacer una gráfica del promedio de Diaphorinas por trampa para cada mes del año 2020 pero sólo de un municipio.\n",
    "\n",
    "3. Hacer una tabla donde la primera columna sea el mes, la segunda el índice $Id$ (por trampa) y la tercera el índice $C_x$ para el año 2015.\n",
    "\n",
    "4. Hacer una gráfica del índice $Id$ (por trampa) como función del mes que abarque desde 2015 hasta 2020.\n",
    "\n",
    "5. Hacer una gráfica del centro de masa de las Diaphorinas como función del mes que abarque desde 2017 hasta 2020. Nota: $CM = \\text{pos} \\cdot \\text{Diaph} / N_{\\text{trampas}}$, donde $CM$ es el centro de masa, pos es la posición de la trampa, Diaph es el promedio de Diaphorinas en la trampa y $N_{\\text{trampas}}$ es el número de trampas que se consideraron en el cálculo.\n",
    "\n",
    "### Solución:\n",
    "\n",
    "#### 1. Vamos a importar las bibliotecas necesarias"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Nota*: Para poder leer el archivo de Excel, es necesario instalar la dependencia `openpyxl` debido a cuestiones de compatibilidad con versiones de MS Excel 2010, el siguiente comando lo instala:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install openpyxl"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a importar los datos, Pandas tiene una función para leer archivos de Excel directamente. \n",
    "\n",
    "Es importante comentar que el archivo viene dividido por hojas de cálculo, cada una representa un año desde 2015 hasta 2020, la función `pd.read_excel()` nos permite leer el archivo .xlsx, y el parámetro `sheet_name` nos permite escoger la hoja. Por simplicidad, extraeremos cada uno de los años y los manejaremos como dataframes individuales. \n",
    "\n",
    "### Nota:\n",
    "*En el archivo .xlsx una de hay 2 hojas de cálculo correspondientes al año 2016. Como no hay diferencias aparentes, se considerará sólo la primera hoja para el 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos_2015 = pd.read_excel('capturas_2015-2020.xlsx', sheet_name=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizamos la dataframe para los datos del 2015:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Latitud</th>\n",
       "      <th>Longitud</th>\n",
       "      <th>Ubicacion</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Comunidad</th>\n",
       "      <th>Productor</th>\n",
       "      <th>Cultivo</th>\n",
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       "      <th>No_Trampas_Revisadas</th>\n",
       "      <th>No_DiaphorinasXtrampa</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Semana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-22</td>\n",
       "      <td>24.11564</td>\n",
       "      <td>-99.03734</td>\n",
       "      <td>177_THUE05-28-030-0415T01</td>\n",
       "      <td>PADILLA</td>\n",
       "      <td>Ej. Marte R. Gomez</td>\n",
       "      <td>MARTINEZ_ZAMBRANO_ROBERTA</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>2015-01-29</td>\n",
       "      <td>24.11564</td>\n",
       "      <td>-99.03734</td>\n",
       "      <td>177_THUE05-28-030-0415T01</td>\n",
       "      <td>PADILLA</td>\n",
       "      <td>Ej. Marte R. Gomez</td>\n",
       "      <td>MARTINEZ_ZAMBRANO_ROBERTA</td>\n",
       "      <td>LIMÓN</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>24.11562</td>\n",
       "      <td>-99.03560</td>\n",
       "      <td>177_THUE05-28-030-0415T02</td>\n",
       "      <td>PADILLA</td>\n",
       "      <td>Ej. Marte R. Gomez</td>\n",
       "      <td>MARTINEZ_ZAMBRANO_ROBERTA</td>\n",
       "      <td>LIMÓN</td>\n",
       "      <td>DESARROLLO (crecimiento)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-22</td>\n",
       "      <td>24.11562</td>\n",
       "      <td>-99.03560</td>\n",
       "      <td>177_THUE05-28-030-0415T02</td>\n",
       "      <td>PADILLA</td>\n",
       "      <td>Ej. Marte R. Gomez</td>\n",
       "      <td>MARTINEZ_ZAMBRANO_ROBERTA</td>\n",
       "      <td>LIMÓN</td>\n",
       "      <td>DESARROLLO (crecimiento)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>24.11726</td>\n",
       "      <td>-99.03528</td>\n",
       "      <td>177_THUE05-28-030-0415T03</td>\n",
       "      <td>PADILLA</td>\n",
       "      <td>Ej. Marte R. Gomez</td>\n",
       "      <td>MARTINEZ_ZAMBRANO_ROBERTA</td>\n",
       "      <td>LIMÓN</td>\n",
       "      <td>DESARROLLO (crecimiento)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "       Fecha   Latitud  Longitud                  Ubicacion Municipio  \\\n",
       "0 2015-01-22  24.11564 -99.03734  177_THUE05-28-030-0415T01   PADILLA   \n",
       "1 2015-01-29  24.11564 -99.03734  177_THUE05-28-030-0415T01   PADILLA   \n",
       "2 2015-01-29  24.11562 -99.03560  177_THUE05-28-030-0415T02   PADILLA   \n",
       "3 2015-01-22  24.11562 -99.03560  177_THUE05-28-030-0415T02   PADILLA   \n",
       "4 2015-01-29  24.11726 -99.03528  177_THUE05-28-030-0415T03   PADILLA   \n",
       "\n",
       "            Comunidad                  Productor Cultivo  \\\n",
       "0  Ej. Marte R. Gomez  MARTINEZ_ZAMBRANO_ROBERTA   LIMÓN   \n",
       "1  Ej. Marte R. Gomez  MARTINEZ_ZAMBRANO_ROBERTA   LIMÓN   \n",
       "2  Ej. Marte R. Gomez  MARTINEZ_ZAMBRANO_ROBERTA   LIMÓN   \n",
       "3  Ej. Marte R. Gomez  MARTINEZ_ZAMBRANO_ROBERTA   LIMÓN   \n",
       "4  Ej. Marte R. Gomez  MARTINEZ_ZAMBRANO_ROBERTA   LIMÓN   \n",
       "\n",
       "                  Fenologia  Superficie_ha  No_Trampas_Instaladas  \\\n",
       "0  DESARROLLO (crecimiento)           25.0                    0.0   \n",
       "1  DESARROLLO (crecimiento)           25.0                    1.0   \n",
       "2  DESARROLLO (crecimiento)            0.0                    1.0   \n",
       "3  DESARROLLO (crecimiento)            0.0                    0.0   \n",
       "4  DESARROLLO (crecimiento)            0.0                    1.0   \n",
       "\n",
       "   No_Trampas_Revisadas  No_DiaphorinasXtrampa  Mes  Semana  \n",
       "0                   1.0                    0.0  1.0     4.0  \n",
       "1                   1.0                    0.0  1.0     5.0  \n",
       "2                   1.0                    0.0  1.0     5.0  \n",
       "3                   1.0                    0.0  1.0     4.0  \n",
       "4                   1.0                    0.0  1.0     5.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_2015.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora, podemos importar las demás hojas de cálculo para los años restantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos_2016 = pd.read_excel('capturas_2015-2020.xlsx', sheet_name = 1)\n",
    "datos_2017 = pd.read_excel('capturas_2015-2020.xlsx', sheet_name = 3)\n",
    "datos_2018 = pd.read_excel('capturas_2015-2020.xlsx', sheet_name = 4)\n",
    "datos_2019 = pd.read_excel('capturas_2015-2020.xlsx', sheet_name = 5)\n",
    "datos_2020 = pd.read_excel('capturas_2015-2020.xlsx', sheet_name = 6)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploremos rápidamente el contenido de las tablas de los años siguientes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Latitud</th>\n",
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       "      <th>Mes</th>\n",
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       "      <th>0</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>23.31379</td>\n",
       "      <td>-98.95510</td>\n",
       "      <td>177_THUE05-28-019-0030T15</td>\n",
       "      <td>LLERA</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2016-01-04</td>\n",
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       "      <td>-98.86220</td>\n",
       "      <td>177_THUE05-28-019-0089T03</td>\n",
       "      <td>LLERA</td>\n",
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       "      <td>VARGAS_OSCAR</td>\n",
       "      <td>NARANJA</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>23.31596</td>\n",
       "      <td>-98.95559</td>\n",
       "      <td>177_THUE05-28-019-0030T05</td>\n",
       "      <td>LLERA</td>\n",
       "      <td>EJIDO_PEDRO_JOSE_MENDEZ</td>\n",
       "      <td>MANCILLA_ZEFERINO</td>\n",
       "      <td>NARANJA</td>\n",
       "      <td>DESARROLLO (crecimiento)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>23.27890</td>\n",
       "      <td>-98.87410</td>\n",
       "      <td>177_THUE05-28-019-0098T03</td>\n",
       "      <td>LLERA</td>\n",
       "      <td>EJIDO_COMPUERTAS</td>\n",
       "      <td>VAZQUEZ_CAMACHO_PAULINO</td>\n",
       "      <td>NARANJA</td>\n",
       "      <td>DESARROLLO (crecimiento)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-04</td>\n",
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       "      <td>-98.87282</td>\n",
       "      <td>177_THUE05-28-019-0098T06</td>\n",
       "      <td>LLERA</td>\n",
       "      <td>EJIDO_COMPUERTAS</td>\n",
       "      <td>VAZQUEZ_CAMACHO_PAULINO</td>\n",
       "      <td>NARANJA</td>\n",
       "      <td>DESARROLLO (crecimiento)</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "       Fecha   Latitud  Longitud                  Ubicacion Municipio  \\\n",
       "0 2016-01-04  23.31379 -98.95510  177_THUE05-28-019-0030T15     LLERA   \n",
       "1 2016-01-04  23.27431 -98.86220  177_THUE05-28-019-0089T03     LLERA   \n",
       "2 2016-01-04  23.31596 -98.95559  177_THUE05-28-019-0030T05     LLERA   \n",
       "3 2016-01-04  23.27890 -98.87410  177_THUE05-28-019-0098T03     LLERA   \n",
       "4 2016-01-04  23.27791 -98.87282  177_THUE05-28-019-0098T06     LLERA   \n",
       "\n",
       "                 Comunidad                Productor  Cultivo  \\\n",
       "0  EJIDO_PEDRO_JOSE_MENDEZ       MANCILLA_ZEFERINO   NARANJA   \n",
       "1         EJIDO_COMPUERTAS            VARGAS_OSCAR   NARANJA   \n",
       "2  EJIDO_PEDRO_JOSE_MENDEZ       MANCILLA_ZEFERINO   NARANJA   \n",
       "3         EJIDO_COMPUERTAS  VAZQUEZ_CAMACHO_PAULINO  NARANJA   \n",
       "4         EJIDO_COMPUERTAS  VAZQUEZ_CAMACHO_PAULINO  NARANJA   \n",
       "\n",
       "                  Fenologia  Superficie_ha  No_Trampas_Instaladas  \\\n",
       "0  DESARROLLO (crecimiento)            0.0                      0   \n",
       "1  DESARROLLO (crecimiento)            0.0                      0   \n",
       "2  DESARROLLO (crecimiento)            0.0                      0   \n",
       "3  DESARROLLO (crecimiento)            0.0                      0   \n",
       "4  DESARROLLO (crecimiento)            0.0                      0   \n",
       "\n",
       "   No_Trampas_Revisadas  No_DiaphorinasXtrampa  Mes  Semana  \n",
       "0                     0                      0    1       1  \n",
       "1                     0                      0    1       1  \n",
       "2                     0                      0    1       1  \n",
       "3                     0                      0    1       1  \n",
       "4                     0                      0    1       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "datos_2016.head()"
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  {
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    {
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       "       Fecha   Latitud  Longitud                  Ubicacion Municipio  \\\n",
       "0 2017-05-24  23.88426 -99.19092  177_THUE05-28-041-0143T02  VICTORIA   \n",
       "1 2017-05-10  23.88426 -99.19092  177_THUE05-28-041-0143T02  VICTORIA   \n",
       "2 2017-06-07  23.88426 -99.19092  177_THUE05-28-041-0143T02  VICTORIA   \n",
       "3 2017-04-12  23.88426 -99.19092  177_THUE05-28-041-0143T02  VICTORIA   \n",
       "4 2017-01-18  23.88426 -99.19092  177_THUE05-28-041-0143T02  VICTORIA   \n",
       "\n",
       "         Comunidad               Productor  Cultivo       Fenologia  \\\n",
       "0  HUERTA SANTA FE  SILVIA MONTELONGO CANO  NARANJA  FRUCTIFICACIÓN   \n",
       "1  HUERTA SANTA FE  SILVIA MONTELONGO CANO  NARANJA  FRUCTIFICACIÓN   \n",
       "2  HUERTA SANTA FE  SILVIA MONTELONGO CANO  NARANJA  FRUCTIFICACIÓN   \n",
       "3  HUERTA SANTA FE  SILVIA MONTELONGO CANO  NARANJA  FRUCTIFICACIÓN   \n",
       "4  HUERTA SANTA FE  SILVIA MONTELONGO CANO  NARANJA       DORMANCIA   \n",
       "\n",
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       "0            0.0                      0                     1   \n",
       "1            0.0                      0                     1   \n",
       "2            0.0                      0                     1   \n",
       "3            0.0                      0                     1   \n",
       "4            0.0                      0                     1   \n",
       "\n",
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       "0                      0    5      21  \n",
       "1                      0    5      19  \n",
       "2                      3    6      23  \n",
       "3                      0    4      15  \n",
       "4                      0    1       3  "
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "datos_2017.head()"
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   "execution_count": 8,
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    {
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       "       fecha   ano  semana                   name_thue    latitud   longitud  \\\n",
       "0 2018-01-03  2018       1  177_THUE05-28-013-0639T001  23.920757 -99.108700   \n",
       "1 2018-01-03  2018       1  177_THUE05-28-013-0639T002  23.920738 -99.108536   \n",
       "2 2018-01-03  2018       1  177_THUE05-28-013-0639T003  23.920712 -99.108370   \n",
       "3 2018-01-03  2018       1  177_THUE05-28-013-0639T004  23.920694 -99.108220   \n",
       "4 2018-01-03  2018       1  177_THUE05-28-013-0639T005  23.920660 -99.108055   \n",
       "\n",
       "        fenologia Revisada  captura                campo  superficie  \n",
       "0  FRUCTIFICACION       Si      0.0  HUERTA SANTA MARTHA        26.0  \n",
       "1  FRUCTIFICACION       Si      0.0  HUERTA SANTA MARTHA         0.0  \n",
       "2  FRUCTIFICACION       Si      0.0  HUERTA SANTA MARTHA         0.0  \n",
       "3  FRUCTIFICACION       Si      0.0  HUERTA SANTA MARTHA         0.0  \n",
       "4  FRUCTIFICACION       Si      0.0  HUERTA SANTA MARTHA         0.0  "
      ]
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       "        Fecha              Ubicacion   Latitud  Longitud  \\\n",
       "0  16/01/2019  THUE05/28-019/2953T06  23.29010 -98.86182   \n",
       "1  13/02/2019  THUE05/28-019/2953T09  23.28970 -98.86197   \n",
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       "3  13/02/2019  THUE05/28-019/2953T15  23.28952 -98.86261   \n",
       "4  13/02/2019  THUE05/28-019/2953T10  23.28956 -98.86204   \n",
       "\n",
       "               Productor Municipio         Localidad  Cultivo  Superficie  \\\n",
       "0  ONESIMO OLVERA ZUÑIGA     LLERA  EJIDO COMPUERTAS  NARANJA         0.0   \n",
       "1  ONESIMO OLVERA ZUÑIGA     LLERA  EJIDO COMPUERTAS  NARANJA         0.0   \n",
       "2  ONESIMO OLVERA ZUÑIGA     LLERA  EJIDO COMPUERTAS  NARANJA         0.0   \n",
       "3  ONESIMO OLVERA ZUÑIGA     LLERA  EJIDO COMPUERTAS  NARANJA         0.0   \n",
       "4  ONESIMO OLVERA ZUÑIGA     LLERA  EJIDO COMPUERTAS  NARANJA         0.0   \n",
       "\n",
       "   NoTrampasInst  ...  Adultos_Brote_2  Ninfas_Brote_2 Fenologia_Brote_3  \\\n",
       "0              0  ...              0.0             0.0               NaN   \n",
       "1              0  ...              0.0             0.0               NaN   \n",
       "2              0  ...              0.0             0.0               NaN   \n",
       "3              0  ...              0.0             0.0               NaN   \n",
       "4              0  ...              0.0             0.0               NaN   \n",
       "\n",
       "   Adultos_Brote_3  Ninfas_Brote_3 Fenologia_Brote_4  Adultos_Brote_4  \\\n",
       "0              0.0             0.0               NaN              0.0   \n",
       "1              0.0             0.0               NaN              0.0   \n",
       "2              0.0             0.0               NaN              0.0   \n",
       "3              0.0             0.0               NaN              0.0   \n",
       "4              0.0             0.0               NaN              0.0   \n",
       "\n",
       "   Ninfas_Brote_4 Mes  Semana  \n",
       "0             0.0   1       3  \n",
       "1             0.0   2       7  \n",
       "2             0.0   2       7  \n",
       "3             0.0   2       7  \n",
       "4             0.0   2       7  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_2019.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Ubicacion</th>\n",
       "      <th>Latitud</th>\n",
       "      <th>Longitud</th>\n",
       "      <th>Productor</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Cultivo</th>\n",
       "      <th>Variedad</th>\n",
       "      <th>noProductores</th>\n",
       "      <th>...</th>\n",
       "      <th>Ninfas_Brote_2</th>\n",
       "      <th>Fenologia_Brote_3</th>\n",
       "      <th>Adultos_Brote_3</th>\n",
       "      <th>Ninfas_Brote_3</th>\n",
       "      <th>Fenologia_Brote_4</th>\n",
       "      <th>Adultos_Brote_4</th>\n",
       "      <th>Ninfas_Brote_4</th>\n",
       "      <th>Mes</th>\n",
       "      <th>Semana</th>\n",
       "      <th>Tecnico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02/01/2020</td>\n",
       "      <td>THUE05/28-019/2974T06</td>\n",
       "      <td>23.25613</td>\n",
       "      <td>-98.82374</td>\n",
       "      <td>PALMIRA CASTRO ALVAREZ</td>\n",
       "      <td>LLERA</td>\n",
       "      <td>EJIDO JOSE SILVA SANCHEZ.</td>\n",
       "      <td>NARANJA</td>\n",
       "      <td>VALENCIA</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>V6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GERARDO GUEVARA SANCHEZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16/01/2020</td>\n",
       "      <td>THUE05/28-019/2974T06</td>\n",
       "      <td>23.25613</td>\n",
       "      <td>-98.82374</td>\n",
       "      <td>PALMIRA CASTRO ALVAREZ</td>\n",
       "      <td>LLERA</td>\n",
       "      <td>EJIDO JOSE SILVA SANCHEZ.</td>\n",
       "      <td>NARANJA</td>\n",
       "      <td>VALENCIA</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>V5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>V4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>GERARDO GUEVARA SANCHEZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02/01/2020</td>\n",
       "      <td>THUE05/28-019/2974T11</td>\n",
       "      <td>23.25575</td>\n",
       "      <td>-98.82317</td>\n",
       "      <td>PALMIRA CASTRO ALVAREZ</td>\n",
       "      <td>LLERA</td>\n",
       "      <td>EJIDO JOSE SILVA SANCHEZ.</td>\n",
       "      <td>NARANJA</td>\n",
       "      <td>VALENCIA</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>V6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>V5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GERARDO GUEVARA SANCHEZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16/01/2020</td>\n",
       "      <td>THUE05/28-019/2974T02</td>\n",
       "      <td>23.25653</td>\n",
       "      <td>-98.82423</td>\n",
       "      <td>PALMIRA CASTRO ALVAREZ</td>\n",
       "      <td>LLERA</td>\n",
       "      <td>EJIDO JOSE SILVA SANCHEZ.</td>\n",
       "      <td>NARANJA</td>\n",
       "      <td>VALENCIA</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>V5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>V4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>GERARDO GUEVARA SANCHEZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16/01/2020</td>\n",
       "      <td>THUE05/28-019/2974T17</td>\n",
       "      <td>23.25618</td>\n",
       "      <td>-98.82309</td>\n",
       "      <td>PALMIRA CASTRO ALVAREZ</td>\n",
       "      <td>LLERA</td>\n",
       "      <td>EJIDO JOSE SILVA SANCHEZ.</td>\n",
       "      <td>NARANJA</td>\n",
       "      <td>VALENCIA</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>V5</td>\n",
       "      <td>0</td>\n",
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       "      <td>V5</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>GERARDO GUEVARA SANCHEZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fecha              Ubicacion   Latitud  Longitud  \\\n",
       "0  02/01/2020  THUE05/28-019/2974T06  23.25613 -98.82374   \n",
       "1  16/01/2020  THUE05/28-019/2974T06  23.25613 -98.82374   \n",
       "2  02/01/2020  THUE05/28-019/2974T11  23.25575 -98.82317   \n",
       "3  16/01/2020  THUE05/28-019/2974T02  23.25653 -98.82423   \n",
       "4  16/01/2020  THUE05/28-019/2974T17  23.25618 -98.82309   \n",
       "\n",
       "                Productor Municipio                  Localidad  Cultivo  \\\n",
       "0  PALMIRA CASTRO ALVAREZ     LLERA  EJIDO JOSE SILVA SANCHEZ.  NARANJA   \n",
       "1  PALMIRA CASTRO ALVAREZ     LLERA  EJIDO JOSE SILVA SANCHEZ.  NARANJA   \n",
       "2  PALMIRA CASTRO ALVAREZ     LLERA  EJIDO JOSE SILVA SANCHEZ.  NARANJA   \n",
       "3  PALMIRA CASTRO ALVAREZ     LLERA  EJIDO JOSE SILVA SANCHEZ.  NARANJA   \n",
       "4  PALMIRA CASTRO ALVAREZ     LLERA  EJIDO JOSE SILVA SANCHEZ.  NARANJA   \n",
       "\n",
       "   Variedad  noProductores  ...  Ninfas_Brote_2  Fenologia_Brote_3  \\\n",
       "0  VALENCIA              1  ...               0                 V6   \n",
       "1  VALENCIA              1  ...               0                 V5   \n",
       "2  VALENCIA              1  ...               0                 V6   \n",
       "3  VALENCIA              1  ...               0                 V5   \n",
       "4  VALENCIA              1  ...               0                 V5   \n",
       "\n",
       "   Adultos_Brote_3  Ninfas_Brote_3  Fenologia_Brote_4  Adultos_Brote_4  \\\n",
       "0                0               0                 V5                0   \n",
       "1                0               0                 V4                0   \n",
       "2                1               0                 V5                1   \n",
       "3                0               0                 V4                0   \n",
       "4                0               0                 V5                1   \n",
       "\n",
       "  Ninfas_Brote_4  Mes  Semana                  Tecnico  \n",
       "0              0    1       1  GERARDO GUEVARA SANCHEZ  \n",
       "1              0    1       3  GERARDO GUEVARA SANCHEZ  \n",
       "2              0    1       1  GERARDO GUEVARA SANCHEZ  \n",
       "3              0    1       3  GERARDO GUEVARA SANCHEZ  \n",
       "4              0    1       3  GERARDO GUEVARA SANCHEZ  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_2020.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notamos que para la dataframe `datos_2018` la información está estructurada de manera distinta a todas las demás, así que habrá que tener cuidado al momento de manejar su información."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También podemos obtener información de cada una de las tablas, pero para ejemplificar, lo haremos sólo para el año 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 421360 entries, 0 to 421359\n",
      "Data columns (total 15 columns):\n",
      " #   Column                 Non-Null Count   Dtype         \n",
      "---  ------                 --------------   -----         \n",
      " 0   Fecha                  384663 non-null  datetime64[ns]\n",
      " 1   Latitud                384663 non-null  float64       \n",
      " 2   Longitud               384663 non-null  float64       \n",
      " 3   Ubicacion              384663 non-null  object        \n",
      " 4   Municipio              384663 non-null  object        \n",
      " 5   Comunidad              384663 non-null  object        \n",
      " 6   Productor              384663 non-null  object        \n",
      " 7   Cultivo                384663 non-null  object        \n",
      " 8   Fenologia              384663 non-null  object        \n",
      " 9   Superficie_ha          384663 non-null  float64       \n",
      " 10  No_Trampas_Instaladas  384663 non-null  float64       \n",
      " 11  No_Trampas_Revisadas   384663 non-null  float64       \n",
      " 12  No_DiaphorinasXtrampa  384663 non-null  float64       \n",
      " 13  Mes                    384663 non-null  float64       \n",
      " 14  Semana                 384663 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(8), object(6)\n",
      "memory usage: 48.2+ MB\n"
     ]
    }
   ],
   "source": [
    "datos_2015.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordemos que primero queremos obtener una gráfica que muestre el promedio de diaphorinas detectadas en las trampas para cada mes de los años 2015, 2017 y 2020. Así que la variable de interés está en la columna `No_DiaphorinasXtrampa`. \n",
    "\n",
    "Podemos utilizar `groupby` para agrupar por `Mes` y después aplicar una función que calcule el promedio de diaphorinas, usaremos `agg` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No_DiaphorinasXtrampa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mes</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.116510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.160865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.105462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.037114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.033045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.039220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.114808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.156186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.175519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.074683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.028856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      No_DiaphorinasXtrampa\n",
       "Mes                        \n",
       "1.0                0.092308\n",
       "2.0                0.116510\n",
       "3.0                0.160865\n",
       "4.0                0.105462\n",
       "5.0                0.037114\n",
       "6.0                0.033045\n",
       "7.0                0.039220\n",
       "8.0                0.114808\n",
       "9.0                0.156186\n",
       "10.0               0.175519\n",
       "11.0               0.074683\n",
       "12.0               0.028856"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diaph_avg_2015 = datos_2015.groupby('Mes').agg({'No_DiaphorinasXtrampa': np.mean})\n",
    "\n",
    "diaph_avg_2015"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repetimos esto para los años que se soicitaron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"Column(s) ['No_DiaphorinasXtrampa'] do not exist\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [13], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m diaph_avg_2017 \u001b[39m=\u001b[39m datos_2017\u001b[39m.\u001b[39mgroupby(\u001b[39m'\u001b[39m\u001b[39mMes\u001b[39m\u001b[39m'\u001b[39m)\u001b[39m.\u001b[39magg({\u001b[39m'\u001b[39m\u001b[39mNo_DiaphorinasXtrampa\u001b[39m\u001b[39m'\u001b[39m: np\u001b[39m.\u001b[39mmean})\n\u001b[1;32m----> 3\u001b[0m diaph_avg_2020 \u001b[39m=\u001b[39m datos_2020\u001b[39m.\u001b[39mgroupby(\u001b[39m'\u001b[39m\u001b[39mMes\u001b[39m\u001b[39m'\u001b[39m)\u001b[39m.\u001b[39magg({\u001b[39m'\u001b[39m\u001b[39mNo_DiaphorinasXtrampa\u001b[39m\u001b[39m'\u001b[39m: np\u001b[39m.\u001b[39mmean})\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\pandas\\core\\groupby\\generic.py:890\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    887\u001b[0m func \u001b[39m=\u001b[39m maybe_mangle_lambdas(func)\n\u001b[0;32m    889\u001b[0m op \u001b[39m=\u001b[39m GroupByApply(\u001b[39mself\u001b[39m, func, args, kwargs)\n\u001b[1;32m--> 890\u001b[0m result \u001b[39m=\u001b[39m op\u001b[39m.\u001b[39;49magg()\n\u001b[0;32m    891\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m is_dict_like(func) \u001b[39mand\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    892\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\pandas\\core\\apply.py:169\u001b[0m, in \u001b[0;36mApply.agg\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    166\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_str()\n\u001b[0;32m    168\u001b[0m \u001b[39mif\u001b[39;00m is_dict_like(arg):\n\u001b[1;32m--> 169\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49magg_dict_like()\n\u001b[0;32m    170\u001b[0m \u001b[39melif\u001b[39;00m is_list_like(arg):\n\u001b[0;32m    171\u001b[0m     \u001b[39m# we require a list, but not a 'str'\u001b[39;00m\n\u001b[0;32m    172\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39magg_list_like()\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\pandas\\core\\apply.py:478\u001b[0m, in \u001b[0;36mApply.agg_dict_like\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    475\u001b[0m     selected_obj \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39m_selected_obj\n\u001b[0;32m    476\u001b[0m     selection \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39m_selection\n\u001b[1;32m--> 478\u001b[0m arg \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnormalize_dictlike_arg(\u001b[39m\"\u001b[39;49m\u001b[39magg\u001b[39;49m\u001b[39m\"\u001b[39;49m, selected_obj, arg)\n\u001b[0;32m    480\u001b[0m \u001b[39mif\u001b[39;00m selected_obj\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m    481\u001b[0m     \u001b[39m# key only used for output\u001b[39;00m\n\u001b[0;32m    482\u001b[0m     colg \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39m_gotitem(selection, ndim\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\pandas\\core\\apply.py:596\u001b[0m, in \u001b[0;36mApply.normalize_dictlike_arg\u001b[1;34m(self, how, obj, func)\u001b[0m\n\u001b[0;32m    594\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(cols) \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m    595\u001b[0m         cols_sorted \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(safe_sort(\u001b[39mlist\u001b[39m(cols)))\n\u001b[1;32m--> 596\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mColumn(s) \u001b[39m\u001b[39m{\u001b[39;00mcols_sorted\u001b[39m}\u001b[39;00m\u001b[39m do not exist\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    598\u001b[0m aggregator_types \u001b[39m=\u001b[39m (\u001b[39mlist\u001b[39m, \u001b[39mtuple\u001b[39m, \u001b[39mdict\u001b[39m)\n\u001b[0;32m    600\u001b[0m \u001b[39m# if we have a dict of any non-scalars\u001b[39;00m\n\u001b[0;32m    601\u001b[0m \u001b[39m# eg. {'A' : ['mean']}, normalize all to\u001b[39;00m\n\u001b[0;32m    602\u001b[0m \u001b[39m# be list-likes\u001b[39;00m\n\u001b[0;32m    603\u001b[0m \u001b[39m# Cannot use func.values() because arg may be a Series\u001b[39;00m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"Column(s) ['No_DiaphorinasXtrampa'] do not exist\""
     ]
    }
   ],
   "source": [
    "diaph_avg_2017 = datos_2017.groupby('Mes').agg({'No_DiaphorinasXtrampa': np.mean})\n",
    "\n",
    "diaph_avg_2020 = datos_2020.groupby('Mes').agg({'No_DiaphorinasXtrampa': np.mean})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parece haber un problema en el nombre de la columna de los datos para 2020, veamos qué nombres tienen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fecha', 'Ubicacion', 'Latitud', 'Longitud', 'Productor', 'Municipio',\n",
       "       'Localidad', 'Cultivo', 'Variedad', 'noProductores', 'Superficie',\n",
       "       'NoTrampasInst', 'Trampas_revisadas', 'DiaphorinaXTrampa',\n",
       "       'Observacion', 'Incidencia_Psilido', 'Fenologia_Brote_1',\n",
       "       'Adultos_Brote_1', 'Ninfas_Brote_1', 'Fenologia_Brote_2',\n",
       "       'Adultos_Brote_2', 'Ninfas_Brote_2', 'Fenologia_Brote_3',\n",
       "       'Adultos_Brote_3', 'Ninfas_Brote_3', 'Fenologia_Brote_4',\n",
       "       'Adultos_Brote_4', 'Ninfas_Brote_4', 'Mes', 'Semana', 'Tecnico'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_2020.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es una diferencia muy sutil, pero debemos cambiar el nombre de la columna que queremos filtrar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "diaph_avg_2020 = datos_2020.groupby('Mes').agg({'DiaphorinaXTrampa': np.mean})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También notamos que los meses están asignados como números ordinales, así que opcionalmente podemos cambiarles el nombre para que tengan un poco más de sentido al graficar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DiaphorinaXTrampa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Enero</th>\n",
       "      <td>0.378565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Febrero</th>\n",
       "      <td>0.750304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marzo</th>\n",
       "      <td>0.502849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abril</th>\n",
       "      <td>0.446075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mayo</th>\n",
       "      <td>0.249759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Junio</th>\n",
       "      <td>0.212220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Julio</th>\n",
       "      <td>0.305785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agosto</th>\n",
       "      <td>0.289525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Septiembre</th>\n",
       "      <td>0.191505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Octubre</th>\n",
       "      <td>0.189819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noviembre</th>\n",
       "      <td>0.390792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diciembre</th>\n",
       "      <td>0.779727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DiaphorinaXTrampa\n",
       "Enero                0.378565\n",
       "Febrero              0.750304\n",
       "Marzo                0.502849\n",
       "Abril                0.446075\n",
       "Mayo                 0.249759\n",
       "Junio                0.212220\n",
       "Julio                0.305785\n",
       "Agosto               0.289525\n",
       "Septiembre           0.191505\n",
       "Octubre              0.189819\n",
       "Noviembre            0.390792\n",
       "Diciembre            0.779727"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diaph_avg_2015.set_axis(['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre',\n",
    "                        'Noviembre', 'Diciembre'], axis = 0)\n",
    "\n",
    "diaph_avg_2017.set_axis(['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre',\n",
    "                        'Noviembre', 'Diciembre'], axis = 0)\n",
    "\n",
    "diaph_avg_2020.set_axis(['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre',\n",
    "                        'Noviembre', 'Diciembre'], axis = 0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora graficamos cada una de las tablas obtenidas, lo haremos utilizando gráficos de barra en vez de una línea continua, ya que tendrá una mayor representación visual la distribución de las detecciones de Diaphorinas cada mes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Diaphorinas por trampa (2015)'}>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diaph_avg_2015.plot.bar(figsize = (8,6), title = 'Diaphorinas por trampa (2015)', color = 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Diaphorinas por trampa (2017)'}>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diaph_avg_2017.plot.bar(figsize = (8,6), title = 'Diaphorinas por trampa (2017)', color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Diaphorinas por trampa (2020)'}>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diaph_avg_2020.plot.bar(figsize = (8,6), title = 'Diaphorinas por trampa (2020)', color = 'purple')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como comentario: Es interesante observar que se registraron mayor número de Diaphorinas en las trampas durante los meses de enero a abril, en el caso de los años 2015 y 2020, después hay una marcada disminución durante los meses siguientes y a partir de octubre-noviembre vuelven a tener mayor número de detección. Eso podría sugerir una distribución temporal, pero son muy pocos datos como para asegurarlo.\n",
    "\n",
    "#### 2. Hacer una gráfica del promedio de Diaphorinas por trampa para cada mes del año 2020 pero sólo de un municipio.\n",
    "\n",
    "Para completar este ejercicio, podemos volver a utilizar `groupby` para agrupar los datos del año 2020 por mes y también por municipio. Para hacer que la función no nos arroje errores nos conviene revisar primero los nombres de las columnas para saber a quiénes llamar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fecha', 'Ubicacion', 'Latitud', 'Longitud', 'Productor', 'Municipio',\n",
       "       'Localidad', 'Cultivo', 'Variedad', 'noProductores', 'Superficie',\n",
       "       'NoTrampasInst', 'Trampas_revisadas', 'DiaphorinaXTrampa',\n",
       "       'Observacion', 'Incidencia_Psilido', 'Fenologia_Brote_1',\n",
       "       'Adultos_Brote_1', 'Ninfas_Brote_1', 'Fenologia_Brote_2',\n",
       "       'Adultos_Brote_2', 'Ninfas_Brote_2', 'Fenologia_Brote_3',\n",
       "       'Adultos_Brote_3', 'Ninfas_Brote_3', 'Fenologia_Brote_4',\n",
       "       'Adultos_Brote_4', 'Ninfas_Brote_4', 'Mes', 'Semana', 'Tecnico'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_2020.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También podemos conocer todos los posibles valores que aparecen en una columna, utilizando el método `pd.unique`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LLERA', 'HIDALGO', 'GUEMEZ', 'PADILLA', 'VICTORIA'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_2020.Municipio.unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y de la misma forma que en el punto anterior, una vez agrupados imponemos una función de agregación que calcule el promedio en la columna Diaphorinas. \n",
    "\n",
    "Es importante notar que importa el orden del nombre de las columnas, al ser índices jerárquicos Pandas realizará la organización en el orden que le indiquemos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>DiaphorinaXTrampa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Municipio</th>\n",
       "      <th>Mes</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">GUEMEZ</th>\n",
       "      <th>1</th>\n",
       "      <td>0.408345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.785759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.630379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.513235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.295492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.230448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.007463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.160682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.209453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.273210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.425111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.822393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">HIDALGO</th>\n",
       "      <th>1</th>\n",
       "      <td>0.218427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.791475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.603687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.210767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.228637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.249655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.360882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.255043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.173432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.132941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.272418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.418440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">LLERA</th>\n",
       "      <th>1</th>\n",
       "      <td>0.575518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.302721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.707447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.386850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.320827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.247656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.434690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.349813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.332610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.731200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.585185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">PADILLA</th>\n",
       "      <th>1</th>\n",
       "      <td>0.077373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.326296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.305161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.112924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.141002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.821962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.135866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.050822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.015918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.203060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.323619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">VICTORIA</th>\n",
       "      <th>1</th>\n",
       "      <td>0.908224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.906367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.242308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.201923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.079812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.745718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.301676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.136905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.370679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.696697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               DiaphorinaXTrampa\n",
       "Municipio Mes                   \n",
       "GUEMEZ    1             0.408345\n",
       "          2             0.785759\n",
       "          3             0.630379\n",
       "          4             0.513235\n",
       "          5             0.295492\n",
       "          6             0.230448\n",
       "          7             0.007463\n",
       "          8             0.160682\n",
       "          9             0.209453\n",
       "          10            0.273210\n",
       "          11            0.425111\n",
       "          12            0.822393\n",
       "HIDALGO   1             0.218427\n",
       "          2             0.791475\n",
       "          3             0.603687\n",
       "          4             0.210767\n",
       "          5             0.228637\n",
       "          6             0.249655\n",
       "          7             0.360882\n",
       "          8             0.255043\n",
       "          9             0.173432\n",
       "          10            0.132941\n",
       "          11            0.272418\n",
       "          12            0.418440\n",
       "LLERA     1             0.575518\n",
       "          2             1.302721\n",
       "          3             0.707447\n",
       "          4             0.386850\n",
       "          5             0.320827\n",
       "          6             0.247656\n",
       "          7                  NaN\n",
       "          8             0.434690\n",
       "          9             0.349813\n",
       "          10            0.332610\n",
       "          11            0.731200\n",
       "          12            1.585185\n",
       "PADILLA   1             0.077373\n",
       "          2             0.326296\n",
       "          3             0.175463\n",
       "          4             0.305161\n",
       "          5             0.112924\n",
       "          6             0.141002\n",
       "          7             0.821962\n",
       "          8             0.135866\n",
       "          9             0.050822\n",
       "          10            0.015918\n",
       "          11            0.203060\n",
       "          12            0.323619\n",
       "VICTORIA  1             0.908224\n",
       "          2             0.906367\n",
       "          3             0.550000\n",
       "          4             0.966667\n",
       "          5             0.242308\n",
       "          6             0.201923\n",
       "          7             0.079812\n",
       "          8             0.745718\n",
       "          9             0.301676\n",
       "          10            0.136905\n",
       "          11            0.370679\n",
       "          12            0.696697"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diaph_avg_2020_municipios = datos_2020.groupby(['Municipio', 'Mes']).agg({'DiaphorinaXTrampa': np.mean})\n",
    "\n",
    "diaph_avg_2020_municipios"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seleccionamos el municipio de `VICTORIA` para graficar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Promedio de Diaphorinas (2020) en Victoria, Tamaulipas'}, xlabel='Mes'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diaph_avg_2020_municipios.loc['VICTORIA'].plot.bar(figsize = (8,6), title = 'Promedio de Diaphorinas (2020) en Victoria, Tamaulipas', color = 'green')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Hacer una tabla donde la primera columna sea el mes, la segunda el índice $Id$ (por trampa) y la tercera el índice $C_x$ para el año 2015.\n",
    "\n",
    "Recordemos que tanto el índice $Id$ como el coeficiente $C_x$ están definidos en las expresiones (1) y (2). Primero debemos preprarar los datos, nos enfocaremos en la dataframe del año 2015, `datos_2015`. \n",
    "\n",
    "Lo primero que debemos hacer es verificar si la dataframe tiene valores `NaN`, ya que como vamos a hacer operaciones entre las filas, queremos evitar errores y advertencias. Podemos verificarlo y además contar cuántos valores `Nan` hay en una columna con el siguiente comando:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36697"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_2015['No_DiaphorinasXtrampa'].isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tenemos casi 37 mil valores nulos en la columna del número de Diaphorinas por trampa. Queremos removerlos, así que utilizamos el método `dropna`, indicando que sólo queremos que remueva los que estén en la columna `No_DiaphorinasXtrampa` con `subset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos_2015_filtrados = datos_2015.dropna(subset = ['No_DiaphorinasXtrampa'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También, considerando las definiciones (1) y (2), si $\\bar{x} = 0$, tendríamos división entre 0.\n",
    "\n",
    "Filtramos entonces también esa columna, para que tengamos únicamente los valores distintos a 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "datos_2015_filtrados = datos_2015_filtrados[datos_2015_filtrados['No_DiaphorinasXtrampa'] != 0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, verificamos cuántos valores nulos tenemos para comprobar que funcionaron los filtros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_2015_filtrados['No_DiaphorinasXtrampa'].isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordemos que el objetivo es el construir una nueva tabla, donde los atributos sean el mes, después el índice $Id$ y la última columna el índice $C_x$, para cada una de las trampas.\n",
    "\n",
    "La columna `Ubicacion` nos da información sobre cada una de la trampas individuales, ya que están etiquetadas con una clave específica. Eso nos sugiere que podemos agrupar en índices multinivel, empezando por esa columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "trampas = datos_2015_filtrados.groupby(['Ubicacion', 'Mes']).agg({'No_DiaphorinasXtrampa': sum})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "                                No_DiaphorinasXtrampa\n",
       "Ubicacion                 Mes                        \n",
       "177_THUE05-28-011-0001T01 9.0                     1.0\n",
       "177_THUE05-28-011-0001T02 6.0                     1.0\n",
       "                          8.0                     1.0\n",
       "                          10.0                    3.0\n",
       "                          11.0                    2.0\n",
       "...                                               ...\n",
       "177_THUE05-28-08-0002T16  11.0                    2.0\n",
       "177_THUE05-28-08-0002T17  6.0                     3.0\n",
       "                          8.0                     2.0\n",
       "                          10.0                    3.0\n",
       "                          11.0                    2.0\n",
       "\n",
       "[11761 rows x 1 columns]"
      ]
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trampas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "trampas['Indice_Id'] = trampas['No_DiaphorinasXtrampa'].apply(lambda x: np.var(x)**2 / np.mean(x))\n",
    "\n",
    "trampas['Cx'] = trampas['No_DiaphorinasXtrampa'].apply(lambda x: ((np.var(x)**2 /np.mean(x)) -1)/(np.sum(x)-1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "                                No_DiaphorinasXtrampa  Indice_Id   Cx\n",
       "Ubicacion                 Mes                                        \n",
       "177_THUE05-28-011-0001T01 9.0                     1.0        0.0 -inf\n",
       "177_THUE05-28-011-0001T02 6.0                     1.0        0.0 -inf\n",
       "                          8.0                     1.0        0.0 -inf\n",
       "                          10.0                    3.0        0.0 -0.5\n",
       "                          11.0                    2.0        0.0 -1.0\n",
       "...                                               ...        ...  ...\n",
       "177_THUE05-28-08-0002T16  11.0                    2.0        0.0 -1.0\n",
       "177_THUE05-28-08-0002T17  6.0                     3.0        0.0 -0.5\n",
       "                          8.0                     2.0        0.0 -1.0\n",
       "                          10.0                    3.0        0.0 -0.5\n",
       "                          11.0                    2.0        0.0 -1.0\n",
       "\n",
       "[11761 rows x 3 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trampas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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