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@@ -0,0 +1,3203 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "e66a7ab6",
+      "metadata": {
+        "id": "e66a7ab6"
+      },
+      "source": [
+        "# Analisis de datos"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* HabrÃa que diferenciar entre dataset y dataframe de pandas. \n",
+        "* Hay varias columnas con NaN. Se podrÃa tratar los mismos antes de empezar un anÃ¡lisis.\n",
+        "* EstarÃa bueno incluir comentarios sobre los resultados de cada funciÃ³n aplicada. Por ejemplo, 17 registros dentro de las comidas rÃ¡pidas pertenecen a PIZZA HUT. \n",
+        "* En la funciÃ³n de agrupaciÃ³n agg, por quÃ© usa llaves en vez de corchetes? * Poner una tabla de posibles operaciones de agregaciÃ³n, o referencia a documentaciÃ³n.\n",
+        "* Al aplicar la suma/promedio/mÃ¡ximo sobre el contenido graso del grupos de alimentos, QuÃ© estamos buscando? QuÃ© conclusiones podemos sacar?\n",
+        "* En los grÃ¡ficos de torta, hablÃ¡s de calorÃas pero parece que el dato es de grasas. TendrÃas que explicar que estÃ¡s proyectando las calorÃas de los grupos que tienen grasa > 80. Falta explicar quÃ© se observan en los grÃ¡ficos.\n",
+        "* El grÃ¡fico â€œValor de las proteÃnas y las calorÃas de cada pescadoâ€, no se visualizan bien los textos."
+      ],
+      "metadata": {
+        "id": "Yx617OK4j3S1"
+      },
+      "id": "Yx617OK4j3S1"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a549d204",
+      "metadata": {
+        "id": "a549d204"
+      },
+      "source": [
+        "### En este notebook se vera la forma de procesar archivos csv con la libreria pandas y asi obtener cualquier tipo de informacion sobre un dataset. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d004e9fa",
+      "metadata": {
+        "id": "d004e9fa"
+      },
+      "source": [
+        "Antes que nada, hay que tener en claro que es un dataset.\n",
+        "Un dataset es una colecciÃ³n de datos habitualmente tabulada. Cada columna de la tabla representa una variable en particular, y cada fila representa a un miembro determinado del conjunto de datos que estamos tratando. En un conjunto de datos o dataset tenemos todos los valores que puede tener cada una de las variables, como por ejemplo la altura y el peso de un objeto, que corresponden a cada miembro del conjunto de datos. Cada uno de estos valores se conoce con el nombre de dato. El conjunto de datos puede incluir datos para uno o mÃ¡s miembros en funciÃ³n de su nÃºmero de filas.\n",
+        "\n",
+        "En esta explicacion, vamos a tratar con un dataset que proviene de la Base de datos nacional de nutrientes del USDA (Departamento de Agricultura de Estados Unidos. Link del dataset: https://data.world/exercises/principal-components-exercise-1/workspace/file?filename=nndb_flat.csv \n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "190730c1",
+      "metadata": {
+        "id": "190730c1"
+      },
+      "source": [
+        "Con las siguientes lineas de codigo, abrimos el archivo con el que vamos a trabajar. En este caso, el archivo se llama \"tabla_nutricion.csv\". La terminacion *csv* significa \"Comma Separated Values\" (valores separados con comas, en espaÃ±ol). Esto quiere decir, como su nombre lo indica, que toda la informacion de este archivo se encuentra delimitada por comas. Si intentamos abrir este archivo, no lo podriamos entender ya que parece todo muy desorganizado y compactado. Pero en cambio, con la libreria Pandas, una de las mas famosas para Python que se utiliza para el analisis de datos, podemos facilmente abrir el archivo y verlo de una forma mucho mas ordenada y simple"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8ec491bf",
+      "metadata": {
+        "id": "8ec491bf"
+      },
+      "source": [
+        "Abro el archivo en df. Por convencion se utiliza el nombre df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b46f081a",
+      "metadata": {
+        "id": "b46f081a",
+        "outputId": "e50e57ab-9705-4bb0-face-cfdd3633cc20"
+      },
+      "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>ID</th>\n",
+              "      <th>FoodGroup</th>\n",
+              "      <th>ShortDescrip</th>\n",
+              "      <th>Descrip</th>\n",
+              "      <th>CommonName</th>\n",
+              "      <th>MfgName</th>\n",
+              "      <th>ScientificName</th>\n",
+              "      <th>Energy_kcal</th>\n",
+              "      <th>Protein_g</th>\n",
+              "      <th>Fat_g</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Folate_USRDA</th>\n",
+              "      <th>Niacin_USRDA</th>\n",
+              "      <th>Riboflavin_USRDA</th>\n",
+              "      <th>Thiamin_USRDA</th>\n",
+              "      <th>Calcium_USRDA</th>\n",
+              "      <th>Copper_USRDA</th>\n",
+              "      <th>Magnesium_USRDA</th>\n",
+              "      <th>Phosphorus_USRDA</th>\n",
+              "      <th>Selenium_USRDA</th>\n",
+              "      <th>Zinc_USRDA</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1001</td>\n",
+              "      <td>Dairy and Egg Products</td>\n",
+              "      <td>BUTTER,WITH SALT</td>\n",
+              "      <td>Butter, salted</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>717.0</td>\n",
+              "      <td>0.85</td>\n",
+              "      <td>81.11</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0075</td>\n",
+              "      <td>0.002625</td>\n",
+              "      <td>0.026154</td>\n",
+              "      <td>0.004167</td>\n",
+              "      <td>0.020000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.004762</td>\n",
+              "      <td>0.034286</td>\n",
+              "      <td>0.018182</td>\n",
+              "      <td>0.008182</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1002</td>\n",
+              "      <td>Dairy and Egg Products</td>\n",
+              "      <td>BUTTER,WHIPPED,WITH SALT</td>\n",
+              "      <td>Butter, whipped, with salt</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>717.0</td>\n",
+              "      <td>0.85</td>\n",
+              "      <td>81.11</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0075</td>\n",
+              "      <td>0.002625</td>\n",
+              "      <td>0.026154</td>\n",
+              "      <td>0.004167</td>\n",
+              "      <td>0.020000</td>\n",
+              "      <td>0.000018</td>\n",
+              "      <td>0.004762</td>\n",
+              "      <td>0.032857</td>\n",
+              "      <td>0.018182</td>\n",
+              "      <td>0.004545</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1003</td>\n",
+              "      <td>Dairy and Egg Products</td>\n",
+              "      <td>BUTTER OIL,ANHYDROUS</td>\n",
+              "      <td>Butter oil, anhydrous</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>876.0</td>\n",
+              "      <td>0.28</td>\n",
+              "      <td>99.48</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.000188</td>\n",
+              "      <td>0.003846</td>\n",
+              "      <td>0.000833</td>\n",
+              "      <td>0.003333</td>\n",
+              "      <td>0.000001</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.004286</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1004</td>\n",
+              "      <td>Dairy and Egg Products</td>\n",
+              "      <td>CHEESE,BLUE</td>\n",
+              "      <td>Cheese, blue</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>353.0</td>\n",
+              "      <td>21.40</td>\n",
+              "      <td>28.74</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0900</td>\n",
+              "      <td>0.063500</td>\n",
+              "      <td>0.293846</td>\n",
+              "      <td>0.024167</td>\n",
+              "      <td>0.440000</td>\n",
+              "      <td>0.000044</td>\n",
+              "      <td>0.054762</td>\n",
+              "      <td>0.552857</td>\n",
+              "      <td>0.263636</td>\n",
+              "      <td>0.241818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1005</td>\n",
+              "      <td>Dairy and Egg Products</td>\n",
+              "      <td>CHEESE,BRICK</td>\n",
+              "      <td>Cheese, brick</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>371.0</td>\n",
+              "      <td>23.24</td>\n",
+              "      <td>29.68</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0500</td>\n",
+              "      <td>0.007375</td>\n",
+              "      <td>0.270000</td>\n",
+              "      <td>0.011667</td>\n",
+              "      <td>0.561667</td>\n",
+              "      <td>0.000027</td>\n",
+              "      <td>0.057143</td>\n",
+              "      <td>0.644286</td>\n",
+              "      <td>0.263636</td>\n",
+              "      <td>0.236364</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8613</th>\n",
+              "      <td>83110</td>\n",
+              "      <td>Finfish and Shellfish Products</td>\n",
+              "      <td>MACKEREL,SALTED</td>\n",
+              "      <td>Fish, mackerel, salted</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>305.0</td>\n",
+              "      <td>18.50</td>\n",
+              "      <td>25.10</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0375</td>\n",
+              "      <td>0.206250</td>\n",
+              "      <td>0.146154</td>\n",
+              "      <td>0.016667</td>\n",
+              "      <td>0.055000</td>\n",
+              "      <td>0.000111</td>\n",
+              "      <td>0.142857</td>\n",
+              "      <td>0.362857</td>\n",
+              "      <td>1.334545</td>\n",
+              "      <td>0.100000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8614</th>\n",
+              "      <td>90240</td>\n",
+              "      <td>Finfish and Shellfish Products</td>\n",
+              "      <td>SCALLOP,(BAY&amp;SEA),CKD,STMD</td>\n",
+              "      <td>Mollusks, scallop, (bay and sea), cooked, steamed</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>111.0</td>\n",
+              "      <td>20.54</td>\n",
+              "      <td>0.84</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0500</td>\n",
+              "      <td>0.067250</td>\n",
+              "      <td>0.018462</td>\n",
+              "      <td>0.010000</td>\n",
+              "      <td>0.008333</td>\n",
+              "      <td>0.000037</td>\n",
+              "      <td>0.088095</td>\n",
+              "      <td>0.608571</td>\n",
+              "      <td>0.394545</td>\n",
+              "      <td>0.140909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8615</th>\n",
+              "      <td>90480</td>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>SYRUP,CANE</td>\n",
+              "      <td>Syrup, Cane</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>269.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.006250</td>\n",
+              "      <td>0.046154</td>\n",
+              "      <td>0.108333</td>\n",
+              "      <td>0.010833</td>\n",
+              "      <td>0.000022</td>\n",
+              "      <td>0.023810</td>\n",
+              "      <td>0.011429</td>\n",
+              "      <td>0.012727</td>\n",
+              "      <td>0.017273</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8616</th>\n",
+              "      <td>90560</td>\n",
+              "      <td>Finfish and Shellfish Products</td>\n",
+              "      <td>SNAIL,RAW</td>\n",
+              "      <td>Mollusks, snail, raw</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>16.10</td>\n",
+              "      <td>1.40</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0150</td>\n",
+              "      <td>0.087500</td>\n",
+              "      <td>0.092308</td>\n",
+              "      <td>0.008333</td>\n",
+              "      <td>0.008333</td>\n",
+              "      <td>0.000444</td>\n",
+              "      <td>0.595238</td>\n",
+              "      <td>0.388571</td>\n",
+              "      <td>0.498182</td>\n",
+              "      <td>0.090909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8617</th>\n",
+              "      <td>93600</td>\n",
+              "      <td>Finfish and Shellfish Products</td>\n",
+              "      <td>TURTLE,GREEN,RAW</td>\n",
+              "      <td>Turtle, green, raw</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>19.80</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0375</td>\n",
+              "      <td>0.068750</td>\n",
+              "      <td>0.115385</td>\n",
+              "      <td>0.100000</td>\n",
+              "      <td>0.098333</td>\n",
+              "      <td>0.000278</td>\n",
+              "      <td>0.047619</td>\n",
+              "      <td>0.257143</td>\n",
+              "      <td>0.305455</td>\n",
+              "      <td>0.090909</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8618 rows Ã— 45 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         ID                       FoodGroup                ShortDescrip  \\\n",
+              "0      1001          Dairy and Egg Products            BUTTER,WITH SALT   \n",
+              "1      1002          Dairy and Egg Products    BUTTER,WHIPPED,WITH SALT   \n",
+              "2      1003          Dairy and Egg Products        BUTTER OIL,ANHYDROUS   \n",
+              "3      1004          Dairy and Egg Products                 CHEESE,BLUE   \n",
+              "4      1005          Dairy and Egg Products                CHEESE,BRICK   \n",
+              "...     ...                             ...                         ...   \n",
+              "8613  83110  Finfish and Shellfish Products             MACKEREL,SALTED   \n",
+              "8614  90240  Finfish and Shellfish Products  SCALLOP,(BAY&SEA),CKD,STMD   \n",
+              "8615  90480                          Sweets                  SYRUP,CANE   \n",
+              "8616  90560  Finfish and Shellfish Products                   SNAIL,RAW   \n",
+              "8617  93600  Finfish and Shellfish Products            TURTLE,GREEN,RAW   \n",
+              "\n",
+              "                                                Descrip CommonName MfgName  \\\n",
+              "0                                        Butter, salted        NaN     NaN   \n",
+              "1                            Butter, whipped, with salt        NaN     NaN   \n",
+              "2                                 Butter oil, anhydrous        NaN     NaN   \n",
+              "3                                          Cheese, blue        NaN     NaN   \n",
+              "4                                         Cheese, brick        NaN     NaN   \n",
+              "...                                                 ...        ...     ...   \n",
+              "8613                             Fish, mackerel, salted        NaN     NaN   \n",
+              "8614  Mollusks, scallop, (bay and sea), cooked, steamed        NaN     NaN   \n",
+              "8615                                        Syrup, Cane        NaN     NaN   \n",
+              "8616                               Mollusks, snail, raw        NaN     NaN   \n",
+              "8617                                 Turtle, green, raw        NaN     NaN   \n",
+              "\n",
+              "     ScientificName  Energy_kcal  Protein_g  Fat_g  ...  Folate_USRDA  \\\n",
+              "0               NaN        717.0       0.85  81.11  ...        0.0075   \n",
+              "1               NaN        717.0       0.85  81.11  ...        0.0075   \n",
+              "2               NaN        876.0       0.28  99.48  ...        0.0000   \n",
+              "3               NaN        353.0      21.40  28.74  ...        0.0900   \n",
+              "4               NaN        371.0      23.24  29.68  ...        0.0500   \n",
+              "...             ...          ...        ...    ...  ...           ...   \n",
+              "8613            NaN        305.0      18.50  25.10  ...        0.0375   \n",
+              "8614            NaN        111.0      20.54   0.84  ...        0.0500   \n",
+              "8615            NaN        269.0       0.00   0.00  ...        0.0000   \n",
+              "8616            NaN         90.0      16.10   1.40  ...        0.0150   \n",
+              "8617            NaN         89.0      19.80   0.50  ...        0.0375   \n",
+              "\n",
+              "      Niacin_USRDA  Riboflavin_USRDA  Thiamin_USRDA  Calcium_USRDA  \\\n",
+              "0         0.002625          0.026154       0.004167       0.020000   \n",
+              "1         0.002625          0.026154       0.004167       0.020000   \n",
+              "2         0.000188          0.003846       0.000833       0.003333   \n",
+              "3         0.063500          0.293846       0.024167       0.440000   \n",
+              "4         0.007375          0.270000       0.011667       0.561667   \n",
+              "...            ...               ...            ...            ...   \n",
+              "8613      0.206250          0.146154       0.016667       0.055000   \n",
+              "8614      0.067250          0.018462       0.010000       0.008333   \n",
+              "8615      0.006250          0.046154       0.108333       0.010833   \n",
+              "8616      0.087500          0.092308       0.008333       0.008333   \n",
+              "8617      0.068750          0.115385       0.100000       0.098333   \n",
+              "\n",
+              "      Copper_USRDA  Magnesium_USRDA  Phosphorus_USRDA  Selenium_USRDA  \\\n",
+              "0         0.000000         0.004762          0.034286        0.018182   \n",
+              "1         0.000018         0.004762          0.032857        0.018182   \n",
+              "2         0.000001         0.000000          0.004286        0.000000   \n",
+              "3         0.000044         0.054762          0.552857        0.263636   \n",
+              "4         0.000027         0.057143          0.644286        0.263636   \n",
+              "...            ...              ...               ...             ...   \n",
+              "8613      0.000111         0.142857          0.362857        1.334545   \n",
+              "8614      0.000037         0.088095          0.608571        0.394545   \n",
+              "8615      0.000022         0.023810          0.011429        0.012727   \n",
+              "8616      0.000444         0.595238          0.388571        0.498182   \n",
+              "8617      0.000278         0.047619          0.257143        0.305455   \n",
+              "\n",
+              "      Zinc_USRDA  \n",
+              "0       0.008182  \n",
+              "1       0.004545  \n",
+              "2       0.000909  \n",
+              "3       0.241818  \n",
+              "4       0.236364  \n",
+              "...          ...  \n",
+              "8613    0.100000  \n",
+              "8614    0.140909  \n",
+              "8615    0.017273  \n",
+              "8616    0.090909  \n",
+              "8617    0.090909  \n",
+              "\n",
+              "[8618 rows x 45 columns]"
+            ]
+          },
+          "execution_count": 1,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df=pd.read_csv('files/nndb_flat.csv', encoding='utf-8') \n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d92fef72",
+      "metadata": {
+        "id": "d92fef72"
+      },
+      "source": [
+        "\n",
+        "Los datasets suelen tener cantidades enormes de informacion, por lo que Pandas solo nos mostrara un pantallazo de lo que es toda la tabla.\n",
+        "\n",
+        "Para saber cual es la cantidad de filas y columnas con las que voy a trabajar, se puede usar la siguiente funcion. El primer valor que devuelve es la cantidad de filas, y el segundo la cantidad de columnas."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "337dfd9e",
+      "metadata": {
+        "id": "337dfd9e",
+        "outputId": "c7cbecac-ad38-4133-a123-06e98e903c14"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(8618, 45)"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "06bccd44",
+      "metadata": {
+        "id": "06bccd44"
+      },
+      "source": [
+        "Si se quiere saber que informacion contiene cada columna, se puede llamar a la siguiente funcion:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "dd1a163c",
+      "metadata": {
+        "scrolled": true,
+        "id": "dd1a163c",
+        "outputId": "b06c5c7b-f134-4148-8984-62c8e42fa564"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Index(['ID', 'FoodGroup', 'ShortDescrip', 'Descrip', 'CommonName', 'MfgName',\n",
+              "       'ScientificName', 'Energy_kcal', 'Protein_g', 'Fat_g', 'Carb_g',\n",
+              "       'Sugar_g', 'Fiber_g', 'VitA_mcg', 'VitB6_mg', 'VitB12_mcg', 'VitC_mg',\n",
+              "       'VitE_mg', 'Folate_mcg', 'Niacin_mg', 'Riboflavin_mg', 'Thiamin_mg',\n",
+              "       'Calcium_mg', 'Copper_mcg', 'Iron_mg', 'Magnesium_mg', 'Manganese_mg',\n",
+              "       'Phosphorus_mg', 'Selenium_mcg', 'Zinc_mg', 'VitA_USRDA', 'VitB6_USRDA',\n",
+              "       'VitB12_USRDA', 'VitC_USRDA', 'VitE_USRDA', 'Folate_USRDA',\n",
+              "       'Niacin_USRDA', 'Riboflavin_USRDA', 'Thiamin_USRDA', 'Calcium_USRDA',\n",
+              "       'Copper_USRDA', 'Magnesium_USRDA', 'Phosphorus_USRDA', 'Selenium_USRDA',\n",
+              "       'Zinc_USRDA'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e8366b10",
+      "metadata": {
+        "id": "e8366b10"
+      },
+      "source": [
+        "## Realizamos nuestro primer grafico usando MatPlotLib"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "53d6cd80",
+      "metadata": {
+        "id": "53d6cd80"
+      },
+      "source": [
+        "Ahora realicemos un grafico para poder saber la proporcion de grupos de alimentos que hay en este dataset\n",
+        "Para esto, primero debemos importar la libreria que nos va a facilitar la realizacion de estos graficos.\n",
+        "Esta libreria se llama MatPlotLib, y es una de las mas conocidas dentro de Python para realizar distintos tipos de graficos.\n",
+        "\n",
+        "Se importa de la siguiente forma:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "551b3ef4",
+      "metadata": {
+        "id": "551b3ef4"
+      },
+      "outputs": [],
+      "source": [
+        "from matplotlib import pyplot as plt #Esta linea basicamente significa: \"De matplotlib(libreria),\n",
+        "                                    #traeme solamente pyplot(la parte que usaremos de la libreria), y nombrala como plt\n",
+        "                                    # (por lo que cada vez que quiera usar esta parte de la libreria, lo voy a invocar como plt)\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a0859e5f",
+      "metadata": {
+        "id": "a0859e5f"
+      },
+      "source": [
+        "Primero, agrupamos los elementos con los que vamos a trabajar para realizar el grafico. En este caso, yo quiero ver la proporcion de grupos de alimentos en el dataset. Por lo que voy a contar cuantos alimentos pertenecen a cada grupo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "94540e23",
+      "metadata": {
+        "id": "94540e23",
+        "outputId": "0d4d930f-318d-4a9c-896f-72bceaf52ada"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Beef Products                          946\n",
+              "Vegetables and Vegetable Products      828\n",
+              "Baked Products                         797\n",
+              "Soups, Sauces, and Gravies             452\n",
+              "Lamb, Veal, and Game Products          438\n",
+              "Poultry Products                       390\n",
+              "Legumes and Legume Products            389\n",
+              "Fast Foods                             371\n",
+              "Breakfast Cereals                      363\n",
+              "Baby Foods                             362\n",
+              "Sweets                                 347\n",
+              "Fruits and Fruit Juices                346\n",
+              "Pork Products                          343\n",
+              "Beverages                              315\n",
+              "Finfish and Shellfish Products         267\n",
+              "Dairy and Egg Products                 264\n",
+              "Sausages and Luncheon Meats            244\n",
+              "Fats and Oils                          219\n",
+              "Cereal Grains and Pasta                183\n",
+              "Snacks                                 171\n",
+              "American Indian/Alaska Native Foods    165\n",
+              "Nut and Seed Products                  133\n",
+              "Meals, Entrees, and Side Dishes        113\n",
+              "Restaurant Foods                       108\n",
+              "Spices and Herbs                        64\n",
+              "Name: FoodGroup, dtype: int64"
+            ]
+          },
+          "execution_count": 51,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "proporcion=df['FoodGroup'].value_counts() #cuento cuantos alimentos pertenecen a cada grupo de alimentos\n",
+        "proporcion"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fe02a389",
+      "metadata": {
+        "id": "fe02a389"
+      },
+      "source": [
+        "Ahora vamos a separar los valores que necesitamos para hacer el grafico. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "55b80832",
+      "metadata": {
+        "id": "55b80832",
+        "outputId": "d78ad9cf-b617-48c1-97db-5e891b6e1ba3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'PROPORCION DE GRUPOS DE COMIDAS')"
+            ]
+          },
+          "execution_count": 53,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "proporcion=df['FoodGroup'].value_counts() #cuento cuantos alimentos pertenecen a cada grupo de alimentos\n",
+        "\n",
+        "data=proporcion.values #guardo la cantidad de veces que aparece cada elemento\n",
+        "keys=proporcion.keys() #guardo el nombre de los grupo de alimentos\n",
+        "explode=[1 for i in range(0,25)] #agrego en una lista 25 veces el valor 1 (que sera la separacion entre las partes de la torta)\n",
+        "plt.pie(data,explode=explode,shadow=True, autopct='%1.1f%%', labeldistance=5) #asigno los valores que defini previamente\n",
+        "plt.legend(keys, bbox_to_anchor=(1,0)) #determino como se veran las claves\n",
+        "plt.title('PROPORCION DE GRUPOS DE COMIDAS') #Asigno el titulo del grafico\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3c8b92b6",
+      "metadata": {
+        "id": "3c8b92b6"
+      },
+      "source": [
+        "## Filtrando elementos y generando un nuevo archivo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ff6092f8",
+      "metadata": {
+        "id": "ff6092f8"
+      },
+      "source": [
+        "Si yo quiero quedarme solo con la informacion de algun grupo de alimentos en particular, puedo hacerlo facilmente de la siguiente forma. En este caso vamos a quedarnos solamente con la comida rapida."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1f7b7ba6",
+      "metadata": {
+        "scrolled": true,
+        "id": "1f7b7ba6",
+        "outputId": "f91af24d-a882-4ebb-b954-4d2445c0c3c8"
+      },
+      "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>ID</th>\n",
+              "      <th>FoodGroup</th>\n",
+              "      <th>ShortDescrip</th>\n",
+              "      <th>Descrip</th>\n",
+              "      <th>CommonName</th>\n",
+              "      <th>MfgName</th>\n",
+              "      <th>ScientificName</th>\n",
+              "      <th>Energy_kcal</th>\n",
+              "      <th>Protein_g</th>\n",
+              "      <th>Fat_g</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Folate_USRDA</th>\n",
+              "      <th>Niacin_USRDA</th>\n",
+              "      <th>Riboflavin_USRDA</th>\n",
+              "      <th>Thiamin_USRDA</th>\n",
+              "      <th>Calcium_USRDA</th>\n",
+              "      <th>Copper_USRDA</th>\n",
+              "      <th>Magnesium_USRDA</th>\n",
+              "      <th>Phosphorus_USRDA</th>\n",
+              "      <th>Selenium_USRDA</th>\n",
+              "      <th>Zinc_USRDA</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>6601</th>\n",
+              "      <td>21002</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>FAST FOODS  BISCUIT  W/ EGG</td>\n",
+              "      <td>Fast foods, biscuit, with egg</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>274.0</td>\n",
+              "      <td>8.53</td>\n",
+              "      <td>16.23</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.1475</td>\n",
+              "      <td>0.098937</td>\n",
+              "      <td>0.275385</td>\n",
+              "      <td>0.185833</td>\n",
+              "      <td>0.050000</td>\n",
+              "      <td>0.000050</td>\n",
+              "      <td>0.033333</td>\n",
+              "      <td>0.407143</td>\n",
+              "      <td>0.365455</td>\n",
+              "      <td>0.066364</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6602</th>\n",
+              "      <td>21003</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>FAST FOODS,BISCUIT,W/EGG&amp;BACON</td>\n",
+              "      <td>Fast foods, biscuit, with egg and bacon</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>305.0</td>\n",
+              "      <td>11.33</td>\n",
+              "      <td>20.73</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.1350</td>\n",
+              "      <td>0.100000</td>\n",
+              "      <td>0.115385</td>\n",
+              "      <td>0.075000</td>\n",
+              "      <td>0.105000</td>\n",
+              "      <td>0.000083</td>\n",
+              "      <td>0.038095</td>\n",
+              "      <td>0.227143</td>\n",
+              "      <td>0.374545</td>\n",
+              "      <td>0.099091</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6603</th>\n",
+              "      <td>21004</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>FAST FOODS,BISCUIT,W/EGG&amp;HAM</td>\n",
+              "      <td>Fast foods, biscuit, with egg and ham</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>233.0</td>\n",
+              "      <td>10.64</td>\n",
+              "      <td>14.08</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.1150</td>\n",
+              "      <td>0.065000</td>\n",
+              "      <td>0.238462</td>\n",
+              "      <td>0.291667</td>\n",
+              "      <td>0.095833</td>\n",
+              "      <td>0.000080</td>\n",
+              "      <td>0.038095</td>\n",
+              "      <td>0.235714</td>\n",
+              "      <td>0.349091</td>\n",
+              "      <td>0.105455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6604</th>\n",
+              "      <td>21005</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>BREAKFAST ITEMS,BISCUIT W/EGG&amp;SAUSAGE</td>\n",
+              "      <td>Fast Foods, biscuit, with egg and sausage</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>312.0</td>\n",
+              "      <td>11.13</td>\n",
+              "      <td>20.77</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.1650</td>\n",
+              "      <td>0.154188</td>\n",
+              "      <td>0.202308</td>\n",
+              "      <td>0.235000</td>\n",
+              "      <td>0.042500</td>\n",
+              "      <td>0.000271</td>\n",
+              "      <td>0.030952</td>\n",
+              "      <td>0.445714</td>\n",
+              "      <td>0.410909</td>\n",
+              "      <td>0.081818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6605</th>\n",
+              "      <td>21006</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>FAST FOODS,BISCUIT W/ EGG &amp; STEAK</td>\n",
+              "      <td>Fast foods, biscuit with egg and steak</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>277.0</td>\n",
+              "      <td>12.12</td>\n",
+              "      <td>19.21</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.1275</td>\n",
+              "      <td>0.129375</td>\n",
+              "      <td>0.269231</td>\n",
+              "      <td>0.200000</td>\n",
+              "      <td>0.077500</td>\n",
+              "      <td>0.000080</td>\n",
+              "      <td>0.040476</td>\n",
+              "      <td>0.217143</td>\n",
+              "      <td>0.385455</td>\n",
+              "      <td>0.171818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6967</th>\n",
+              "      <td>21512</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" CHS PIZZA,STUFFED CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Cheese Pizza, Stuffed Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>274.0</td>\n",
+              "      <td>12.23</td>\n",
+              "      <td>11.63</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.235187</td>\n",
+              "      <td>0.256154</td>\n",
+              "      <td>0.269167</td>\n",
+              "      <td>0.198333</td>\n",
+              "      <td>0.000090</td>\n",
+              "      <td>0.052381</td>\n",
+              "      <td>0.355714</td>\n",
+              "      <td>0.290909</td>\n",
+              "      <td>0.135455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6968</th>\n",
+              "      <td>21513</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>KASHI PIZZA,GREEK TZATZIKI,SINGLE SERVE</td>\n",
+              "      <td>KASHI Pizza, Greek Tzatziki, single serve</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Kellogg, Co.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>223.0</td>\n",
+              "      <td>11.10</td>\n",
+              "      <td>6.60</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.168750</td>\n",
+              "      <td>0.153846</td>\n",
+              "      <td>0.166667</td>\n",
+              "      <td>0.096667</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.038095</td>\n",
+              "      <td>0.121429</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.045455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6969</th>\n",
+              "      <td>21514</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>KASHI PIZZA,TIKKA MASALA,SINGLE SERVE</td>\n",
+              "      <td>KASHI Pizza, Tikka Masala, single serve</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Kellogg, Co.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>10.20</td>\n",
+              "      <td>5.90</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.156250</td>\n",
+              "      <td>0.076923</td>\n",
+              "      <td>0.166667</td>\n",
+              "      <td>0.080833</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.035714</td>\n",
+              "      <td>0.110000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.045455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6970</th>\n",
+              "      <td>21515</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>MORNINGSTAR FARMS PIZZA,BAJA BLACK BEAN,SINGLE...</td>\n",
+              "      <td>MORNINGSTAR FARMS Pizza, Baja Black Bean, sing...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Kellogg, Co.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>252.0</td>\n",
+              "      <td>10.60</td>\n",
+              "      <td>6.90</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.237500</td>\n",
+              "      <td>0.153846</td>\n",
+              "      <td>0.250000</td>\n",
+              "      <td>0.100833</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.023810</td>\n",
+              "      <td>0.120000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.036364</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6989</th>\n",
+              "      <td>22903</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA,PEPPERONI TOPPING,REG CRUST,FRZ,CKD</td>\n",
+              "      <td>Pizza, pepperoni topping, regular crust, froze...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>11.21</td>\n",
+              "      <td>15.20</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.154375</td>\n",
+              "      <td>0.176923</td>\n",
+              "      <td>0.186667</td>\n",
+              "      <td>0.125833</td>\n",
+              "      <td>0.000117</td>\n",
+              "      <td>0.057143</td>\n",
+              "      <td>0.295714</td>\n",
+              "      <td>0.356364</td>\n",
+              "      <td>0.134545</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>371 rows Ã— 45 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         ID   FoodGroup                                       ShortDescrip  \\\n",
+              "6601  21002  Fast Foods                        FAST FOODS  BISCUIT  W/ EGG   \n",
+              "6602  21003  Fast Foods                     FAST FOODS,BISCUIT,W/EGG&BACON   \n",
+              "6603  21004  Fast Foods                       FAST FOODS,BISCUIT,W/EGG&HAM   \n",
+              "6604  21005  Fast Foods              BREAKFAST ITEMS,BISCUIT W/EGG&SAUSAGE   \n",
+              "6605  21006  Fast Foods                  FAST FOODS,BISCUIT W/ EGG & STEAK   \n",
+              "...     ...         ...                                                ...   \n",
+              "6967  21512  Fast Foods              PIZZA HUT 14\" CHS PIZZA,STUFFED CRUST   \n",
+              "6968  21513  Fast Foods            KASHI PIZZA,GREEK TZATZIKI,SINGLE SERVE   \n",
+              "6969  21514  Fast Foods              KASHI PIZZA,TIKKA MASALA,SINGLE SERVE   \n",
+              "6970  21515  Fast Foods  MORNINGSTAR FARMS PIZZA,BAJA BLACK BEAN,SINGLE...   \n",
+              "6989  22903  Fast Foods          PIZZA,PEPPERONI TOPPING,REG CRUST,FRZ,CKD   \n",
+              "\n",
+              "                                                Descrip CommonName  \\\n",
+              "6601                      Fast foods, biscuit, with egg        NaN   \n",
+              "6602            Fast foods, biscuit, with egg and bacon        NaN   \n",
+              "6603              Fast foods, biscuit, with egg and ham        NaN   \n",
+              "6604          Fast Foods, biscuit, with egg and sausage        NaN   \n",
+              "6605             Fast foods, biscuit with egg and steak        NaN   \n",
+              "...                                                 ...        ...   \n",
+              "6967          PIZZA HUT 14\" Cheese Pizza, Stuffed Crust        NaN   \n",
+              "6968          KASHI Pizza, Greek Tzatziki, single serve        NaN   \n",
+              "6969            KASHI Pizza, Tikka Masala, single serve        NaN   \n",
+              "6970  MORNINGSTAR FARMS Pizza, Baja Black Bean, sing...        NaN   \n",
+              "6989  Pizza, pepperoni topping, regular crust, froze...        NaN   \n",
+              "\n",
+              "              MfgName ScientificName  Energy_kcal  Protein_g  Fat_g  ...  \\\n",
+              "6601              NaN            NaN        274.0       8.53  16.23  ...   \n",
+              "6602              NaN            NaN        305.0      11.33  20.73  ...   \n",
+              "6603              NaN            NaN        233.0      10.64  14.08  ...   \n",
+              "6604              NaN            NaN        312.0      11.13  20.77  ...   \n",
+              "6605              NaN            NaN        277.0      12.12  19.21  ...   \n",
+              "...               ...            ...          ...        ...    ...  ...   \n",
+              "6967  Pizza Hut, Inc.            NaN        274.0      12.23  11.63  ...   \n",
+              "6968     Kellogg, Co.            NaN        223.0      11.10   6.60  ...   \n",
+              "6969     Kellogg, Co.            NaN        212.0      10.20   5.90  ...   \n",
+              "6970     Kellogg, Co.            NaN        252.0      10.60   6.90  ...   \n",
+              "6989              NaN            NaN        296.0      11.21  15.20  ...   \n",
+              "\n",
+              "      Folate_USRDA  Niacin_USRDA  Riboflavin_USRDA  Thiamin_USRDA  \\\n",
+              "6601        0.1475      0.098937          0.275385       0.185833   \n",
+              "6602        0.1350      0.100000          0.115385       0.075000   \n",
+              "6603        0.1150      0.065000          0.238462       0.291667   \n",
+              "6604        0.1650      0.154188          0.202308       0.235000   \n",
+              "6605        0.1275      0.129375          0.269231       0.200000   \n",
+              "...            ...           ...               ...            ...   \n",
+              "6967        0.0000      0.235187          0.256154       0.269167   \n",
+              "6968        0.0000      0.168750          0.153846       0.166667   \n",
+              "6969        0.0000      0.156250          0.076923       0.166667   \n",
+              "6970        0.0000      0.237500          0.153846       0.250000   \n",
+              "6989        0.0000      0.154375          0.176923       0.186667   \n",
+              "\n",
+              "      Calcium_USRDA  Copper_USRDA  Magnesium_USRDA  Phosphorus_USRDA  \\\n",
+              "6601       0.050000      0.000050         0.033333          0.407143   \n",
+              "6602       0.105000      0.000083         0.038095          0.227143   \n",
+              "6603       0.095833      0.000080         0.038095          0.235714   \n",
+              "6604       0.042500      0.000271         0.030952          0.445714   \n",
+              "6605       0.077500      0.000080         0.040476          0.217143   \n",
+              "...             ...           ...              ...               ...   \n",
+              "6967       0.198333      0.000090         0.052381          0.355714   \n",
+              "6968       0.096667      0.000000         0.038095          0.121429   \n",
+              "6969       0.080833      0.000000         0.035714          0.110000   \n",
+              "6970       0.100833      0.000000         0.023810          0.120000   \n",
+              "6989       0.125833      0.000117         0.057143          0.295714   \n",
+              "\n",
+              "      Selenium_USRDA  Zinc_USRDA  \n",
+              "6601        0.365455    0.066364  \n",
+              "6602        0.374545    0.099091  \n",
+              "6603        0.349091    0.105455  \n",
+              "6604        0.410909    0.081818  \n",
+              "6605        0.385455    0.171818  \n",
+              "...              ...         ...  \n",
+              "6967        0.290909    0.135455  \n",
+              "6968        0.000000    0.045455  \n",
+              "6969        0.000000    0.045455  \n",
+              "6970        0.000000    0.036364  \n",
+              "6989        0.356364    0.134545  \n",
+              "\n",
+              "[371 rows x 45 columns]"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "comida_rapida=df[df['FoodGroup']==\"Fast Foods\"] #me quedo solamente con los grupos de alimentos que sean Fast Foods\n",
+        "comida_rapida\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9bffc12e",
+      "metadata": {
+        "id": "9bffc12e"
+      },
+      "source": [
+        "Ahora, si yo ademas quiero saber los valores nutricionales de la comida rapida, pero especificamente la comida rapida de Pizza Hut, puedo hacer lo siguiente:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "20dd98b5",
+      "metadata": {
+        "id": "20dd98b5",
+        "outputId": "d4ce0a4e-6522-4d7c-fc76-6466daec9ba7"
+      },
+      "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>ID</th>\n",
+              "      <th>FoodGroup</th>\n",
+              "      <th>ShortDescrip</th>\n",
+              "      <th>Descrip</th>\n",
+              "      <th>CommonName</th>\n",
+              "      <th>MfgName</th>\n",
+              "      <th>ScientificName</th>\n",
+              "      <th>Energy_kcal</th>\n",
+              "      <th>Protein_g</th>\n",
+              "      <th>Fat_g</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Folate_USRDA</th>\n",
+              "      <th>Niacin_USRDA</th>\n",
+              "      <th>Riboflavin_USRDA</th>\n",
+              "      <th>Thiamin_USRDA</th>\n",
+              "      <th>Calcium_USRDA</th>\n",
+              "      <th>Copper_USRDA</th>\n",
+              "      <th>Magnesium_USRDA</th>\n",
+              "      <th>Phosphorus_USRDA</th>\n",
+              "      <th>Selenium_USRDA</th>\n",
+              "      <th>Zinc_USRDA</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>6773</th>\n",
+              "      <td>21271</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 12\" CHS PIZZA,HAND-TOSSED CRUST</td>\n",
+              "      <td>PIZZA HUT 12\" Cheese Pizza, Hand-Tossed Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>271.0</td>\n",
+              "      <td>11.93</td>\n",
+              "      <td>10.89</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.205937</td>\n",
+              "      <td>0.196923</td>\n",
+              "      <td>0.220000</td>\n",
+              "      <td>0.174167</td>\n",
+              "      <td>0.000102</td>\n",
+              "      <td>0.052381</td>\n",
+              "      <td>0.355714</td>\n",
+              "      <td>0.372727</td>\n",
+              "      <td>0.150000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6774</th>\n",
+              "      <td>21272</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 12\" CHS PIZZA,PAN CRUST</td>\n",
+              "      <td>PIZZA HUT 12\" Cheese Pizza, Pan Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>11.73</td>\n",
+              "      <td>12.56</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.244375</td>\n",
+              "      <td>0.193077</td>\n",
+              "      <td>0.202500</td>\n",
+              "      <td>0.173333</td>\n",
+              "      <td>0.000112</td>\n",
+              "      <td>0.050000</td>\n",
+              "      <td>0.344286</td>\n",
+              "      <td>0.352727</td>\n",
+              "      <td>0.148182</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6775</th>\n",
+              "      <td>21273</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 12\" CHS PIZZA,THIN 'N CRISPY CRUST</td>\n",
+              "      <td>PIZZA HUT 12\" Cheese Pizza, THIN 'N CRISPY Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>303.0</td>\n",
+              "      <td>15.29</td>\n",
+              "      <td>14.10</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.180938</td>\n",
+              "      <td>0.212308</td>\n",
+              "      <td>0.181667</td>\n",
+              "      <td>0.231667</td>\n",
+              "      <td>0.000107</td>\n",
+              "      <td>0.057143</td>\n",
+              "      <td>0.452857</td>\n",
+              "      <td>0.420000</td>\n",
+              "      <td>0.170000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6776</th>\n",
+              "      <td>21274</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 12\" PEPPERONI PIZZA,HAND-TOSSED CRUST</td>\n",
+              "      <td>PIZZA HUT 12\" Pepperoni Pizza, Hand-Tossed Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>12.86</td>\n",
+              "      <td>11.38</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.251750</td>\n",
+              "      <td>0.208462</td>\n",
+              "      <td>0.266667</td>\n",
+              "      <td>0.129167</td>\n",
+              "      <td>0.000116</td>\n",
+              "      <td>0.054762</td>\n",
+              "      <td>0.311429</td>\n",
+              "      <td>0.387273</td>\n",
+              "      <td>0.152727</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6777</th>\n",
+              "      <td>21275</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 12\" PEPPERONI PIZZA,PAN CRUST</td>\n",
+              "      <td>PIZZA HUT 12\" Pepperoni Pizza, Pan Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>298.0</td>\n",
+              "      <td>11.97</td>\n",
+              "      <td>14.21</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.240000</td>\n",
+              "      <td>0.195385</td>\n",
+              "      <td>0.239167</td>\n",
+              "      <td>0.121667</td>\n",
+              "      <td>0.000117</td>\n",
+              "      <td>0.052381</td>\n",
+              "      <td>0.292857</td>\n",
+              "      <td>0.374545</td>\n",
+              "      <td>0.140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6778</th>\n",
+              "      <td>21276</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 12\" SUPER SUPREME PIZZA,HAND-TOSSED ...</td>\n",
+              "      <td>PIZZA HUT 12\" Super Supreme Pizza, Hand-Tossed...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>243.0</td>\n",
+              "      <td>10.90</td>\n",
+              "      <td>10.72</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.223875</td>\n",
+              "      <td>0.186154</td>\n",
+              "      <td>0.221667</td>\n",
+              "      <td>0.107500</td>\n",
+              "      <td>0.000141</td>\n",
+              "      <td>0.054762</td>\n",
+              "      <td>0.285714</td>\n",
+              "      <td>0.354545</td>\n",
+              "      <td>0.131818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6795</th>\n",
+              "      <td>21293</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" CHS PIZZA,HAND-TOSSED CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Cheese Pizza, Hand-Tossed Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>275.0</td>\n",
+              "      <td>11.98</td>\n",
+              "      <td>10.42</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.3725</td>\n",
+              "      <td>0.268750</td>\n",
+              "      <td>0.169231</td>\n",
+              "      <td>0.383333</td>\n",
+              "      <td>0.174167</td>\n",
+              "      <td>0.000114</td>\n",
+              "      <td>0.057143</td>\n",
+              "      <td>0.325714</td>\n",
+              "      <td>0.305455</td>\n",
+              "      <td>0.130909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6796</th>\n",
+              "      <td>21294</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" CHS PIZZA,PAN CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Cheese Pizza, Pan Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>276.0</td>\n",
+              "      <td>10.85</td>\n",
+              "      <td>11.25</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.3800</td>\n",
+              "      <td>0.217500</td>\n",
+              "      <td>0.169231</td>\n",
+              "      <td>0.325000</td>\n",
+              "      <td>0.160000</td>\n",
+              "      <td>0.000109</td>\n",
+              "      <td>0.050000</td>\n",
+              "      <td>0.308571</td>\n",
+              "      <td>0.290909</td>\n",
+              "      <td>0.119091</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6797</th>\n",
+              "      <td>21295</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" CHS PIZZA,THIN 'N CRISPY CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Cheese Pizza, THIN 'N CRISPY Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>306.0</td>\n",
+              "      <td>13.37</td>\n",
+              "      <td>12.80</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.3375</td>\n",
+              "      <td>0.261250</td>\n",
+              "      <td>0.184615</td>\n",
+              "      <td>0.291667</td>\n",
+              "      <td>0.236667</td>\n",
+              "      <td>0.000114</td>\n",
+              "      <td>0.057143</td>\n",
+              "      <td>0.425714</td>\n",
+              "      <td>0.369091</td>\n",
+              "      <td>0.149091</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6798</th>\n",
+              "      <td>21296</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" PEPPERONI PIZZA,HAND-TOSSED CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Pepperoni Pizza, Hand-Tossed Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>291.0</td>\n",
+              "      <td>12.23</td>\n",
+              "      <td>12.63</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.3975</td>\n",
+              "      <td>0.301250</td>\n",
+              "      <td>0.184615</td>\n",
+              "      <td>0.458333</td>\n",
+              "      <td>0.138333</td>\n",
+              "      <td>0.000118</td>\n",
+              "      <td>0.057143</td>\n",
+              "      <td>0.294286</td>\n",
+              "      <td>0.345455</td>\n",
+              "      <td>0.135455</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6799</th>\n",
+              "      <td>21297</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" PEPPERONI PIZZA,PAN CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Pepperoni Pizza, Pan Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>291.0</td>\n",
+              "      <td>11.47</td>\n",
+              "      <td>13.07</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.3575</td>\n",
+              "      <td>0.234375</td>\n",
+              "      <td>0.161538</td>\n",
+              "      <td>0.350000</td>\n",
+              "      <td>0.122500</td>\n",
+              "      <td>0.000116</td>\n",
+              "      <td>0.052381</td>\n",
+              "      <td>0.275714</td>\n",
+              "      <td>0.281818</td>\n",
+              "      <td>0.123636</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6800</th>\n",
+              "      <td>21298</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" SUPER SUPREME PIZZA,HAND-TOSSED ...</td>\n",
+              "      <td>PIZZA HUT 14\" Super Supreme Pizza, Hand-Tossed...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>248.0</td>\n",
+              "      <td>11.34</td>\n",
+              "      <td>10.95</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.2075</td>\n",
+              "      <td>0.148750</td>\n",
+              "      <td>0.223077</td>\n",
+              "      <td>0.305833</td>\n",
+              "      <td>0.086667</td>\n",
+              "      <td>0.000166</td>\n",
+              "      <td>0.054762</td>\n",
+              "      <td>0.261429</td>\n",
+              "      <td>0.263636</td>\n",
+              "      <td>0.104545</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6947</th>\n",
+              "      <td>21491</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" PEPPERONI PIZZA,THIN 'N CRISPY C...</td>\n",
+              "      <td>PIZZA HUT 14\" Pepperoni Pizza, THIN 'N CRISPY ...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>333.0</td>\n",
+              "      <td>14.13</td>\n",
+              "      <td>16.17</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.284375</td>\n",
+              "      <td>0.176923</td>\n",
+              "      <td>0.283333</td>\n",
+              "      <td>0.163333</td>\n",
+              "      <td>0.000129</td>\n",
+              "      <td>0.059524</td>\n",
+              "      <td>0.348571</td>\n",
+              "      <td>0.418182</td>\n",
+              "      <td>0.148182</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6952</th>\n",
+              "      <td>21496</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" SAUSAGE PIZZA,THIN 'N CRISPY CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Sausage Pizza, THIN 'N CRISPY Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>323.0</td>\n",
+              "      <td>13.95</td>\n",
+              "      <td>16.90</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.281250</td>\n",
+              "      <td>0.169231</td>\n",
+              "      <td>0.300000</td>\n",
+              "      <td>0.160833</td>\n",
+              "      <td>0.000112</td>\n",
+              "      <td>0.057143</td>\n",
+              "      <td>0.340000</td>\n",
+              "      <td>0.318182</td>\n",
+              "      <td>0.140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6953</th>\n",
+              "      <td>21497</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" SAUSAGE PIZZA,HAND-TOSSED CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Sausage Pizza, Hand-Tossed Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>287.0</td>\n",
+              "      <td>11.92</td>\n",
+              "      <td>13.50</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.265625</td>\n",
+              "      <td>0.169231</td>\n",
+              "      <td>0.300000</td>\n",
+              "      <td>0.125000</td>\n",
+              "      <td>0.000116</td>\n",
+              "      <td>0.054762</td>\n",
+              "      <td>0.272857</td>\n",
+              "      <td>0.336364</td>\n",
+              "      <td>0.127273</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6954</th>\n",
+              "      <td>21498</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" SAUSAGE PIZZA,PAN CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Sausage Pizza, Pan Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>287.0</td>\n",
+              "      <td>11.08</td>\n",
+              "      <td>13.85</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.239375</td>\n",
+              "      <td>0.153846</td>\n",
+              "      <td>0.333333</td>\n",
+              "      <td>0.110000</td>\n",
+              "      <td>0.000112</td>\n",
+              "      <td>0.050000</td>\n",
+              "      <td>0.255714</td>\n",
+              "      <td>0.287273</td>\n",
+              "      <td>0.113636</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6967</th>\n",
+              "      <td>21512</td>\n",
+              "      <td>Fast Foods</td>\n",
+              "      <td>PIZZA HUT 14\" CHS PIZZA,STUFFED CRUST</td>\n",
+              "      <td>PIZZA HUT 14\" Cheese Pizza, Stuffed Crust</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Pizza Hut, Inc.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>274.0</td>\n",
+              "      <td>12.23</td>\n",
+              "      <td>11.63</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>0.235187</td>\n",
+              "      <td>0.256154</td>\n",
+              "      <td>0.269167</td>\n",
+              "      <td>0.198333</td>\n",
+              "      <td>0.000090</td>\n",
+              "      <td>0.052381</td>\n",
+              "      <td>0.355714</td>\n",
+              "      <td>0.290909</td>\n",
+              "      <td>0.135455</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17 rows Ã— 45 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         ID   FoodGroup                                       ShortDescrip  \\\n",
+              "6773  21271  Fast Foods          PIZZA HUT 12\" CHS PIZZA,HAND-TOSSED CRUST   \n",
+              "6774  21272  Fast Foods                  PIZZA HUT 12\" CHS PIZZA,PAN CRUST   \n",
+              "6775  21273  Fast Foods       PIZZA HUT 12\" CHS PIZZA,THIN 'N CRISPY CRUST   \n",
+              "6776  21274  Fast Foods    PIZZA HUT 12\" PEPPERONI PIZZA,HAND-TOSSED CRUST   \n",
+              "6777  21275  Fast Foods            PIZZA HUT 12\" PEPPERONI PIZZA,PAN CRUST   \n",
+              "6778  21276  Fast Foods  PIZZA HUT 12\" SUPER SUPREME PIZZA,HAND-TOSSED ...   \n",
+              "6795  21293  Fast Foods          PIZZA HUT 14\" CHS PIZZA,HAND-TOSSED CRUST   \n",
+              "6796  21294  Fast Foods                  PIZZA HUT 14\" CHS PIZZA,PAN CRUST   \n",
+              "6797  21295  Fast Foods       PIZZA HUT 14\" CHS PIZZA,THIN 'N CRISPY CRUST   \n",
+              "6798  21296  Fast Foods    PIZZA HUT 14\" PEPPERONI PIZZA,HAND-TOSSED CRUST   \n",
+              "6799  21297  Fast Foods            PIZZA HUT 14\" PEPPERONI PIZZA,PAN CRUST   \n",
+              "6800  21298  Fast Foods  PIZZA HUT 14\" SUPER SUPREME PIZZA,HAND-TOSSED ...   \n",
+              "6947  21491  Fast Foods  PIZZA HUT 14\" PEPPERONI PIZZA,THIN 'N CRISPY C...   \n",
+              "6952  21496  Fast Foods   PIZZA HUT 14\" SAUSAGE PIZZA,THIN 'N CRISPY CRUST   \n",
+              "6953  21497  Fast Foods      PIZZA HUT 14\" SAUSAGE PIZZA,HAND-TOSSED CRUST   \n",
+              "6954  21498  Fast Foods              PIZZA HUT 14\" SAUSAGE PIZZA,PAN CRUST   \n",
+              "6967  21512  Fast Foods              PIZZA HUT 14\" CHS PIZZA,STUFFED CRUST   \n",
+              "\n",
+              "                                                Descrip CommonName  \\\n",
+              "6773      PIZZA HUT 12\" Cheese Pizza, Hand-Tossed Crust        NaN   \n",
+              "6774              PIZZA HUT 12\" Cheese Pizza, Pan Crust        NaN   \n",
+              "6775   PIZZA HUT 12\" Cheese Pizza, THIN 'N CRISPY Crust        NaN   \n",
+              "6776   PIZZA HUT 12\" Pepperoni Pizza, Hand-Tossed Crust        NaN   \n",
+              "6777           PIZZA HUT 12\" Pepperoni Pizza, Pan Crust        NaN   \n",
+              "6778  PIZZA HUT 12\" Super Supreme Pizza, Hand-Tossed...        NaN   \n",
+              "6795      PIZZA HUT 14\" Cheese Pizza, Hand-Tossed Crust        NaN   \n",
+              "6796              PIZZA HUT 14\" Cheese Pizza, Pan Crust        NaN   \n",
+              "6797   PIZZA HUT 14\" Cheese Pizza, THIN 'N CRISPY Crust        NaN   \n",
+              "6798   PIZZA HUT 14\" Pepperoni Pizza, Hand-Tossed Crust        NaN   \n",
+              "6799           PIZZA HUT 14\" Pepperoni Pizza, Pan Crust        NaN   \n",
+              "6800  PIZZA HUT 14\" Super Supreme Pizza, Hand-Tossed...        NaN   \n",
+              "6947  PIZZA HUT 14\" Pepperoni Pizza, THIN 'N CRISPY ...        NaN   \n",
+              "6952  PIZZA HUT 14\" Sausage Pizza, THIN 'N CRISPY Crust        NaN   \n",
+              "6953     PIZZA HUT 14\" Sausage Pizza, Hand-Tossed Crust        NaN   \n",
+              "6954             PIZZA HUT 14\" Sausage Pizza, Pan Crust        NaN   \n",
+              "6967          PIZZA HUT 14\" Cheese Pizza, Stuffed Crust        NaN   \n",
+              "\n",
+              "              MfgName ScientificName  Energy_kcal  Protein_g  Fat_g  ...  \\\n",
+              "6773  Pizza Hut, Inc.            NaN        271.0      11.93  10.89  ...   \n",
+              "6774  Pizza Hut, Inc.            NaN        280.0      11.73  12.56  ...   \n",
+              "6775  Pizza Hut, Inc.            NaN        303.0      15.29  14.10  ...   \n",
+              "6776  Pizza Hut, Inc.            NaN        280.0      12.86  11.38  ...   \n",
+              "6777  Pizza Hut, Inc.            NaN        298.0      11.97  14.21  ...   \n",
+              "6778  Pizza Hut, Inc.            NaN        243.0      10.90  10.72  ...   \n",
+              "6795  Pizza Hut, Inc.            NaN        275.0      11.98  10.42  ...   \n",
+              "6796  Pizza Hut, Inc.            NaN        276.0      10.85  11.25  ...   \n",
+              "6797  Pizza Hut, Inc.            NaN        306.0      13.37  12.80  ...   \n",
+              "6798  Pizza Hut, Inc.            NaN        291.0      12.23  12.63  ...   \n",
+              "6799  Pizza Hut, Inc.            NaN        291.0      11.47  13.07  ...   \n",
+              "6800  Pizza Hut, Inc.            NaN        248.0      11.34  10.95  ...   \n",
+              "6947  Pizza Hut, Inc.            NaN        333.0      14.13  16.17  ...   \n",
+              "6952  Pizza Hut, Inc.            NaN        323.0      13.95  16.90  ...   \n",
+              "6953  Pizza Hut, Inc.            NaN        287.0      11.92  13.50  ...   \n",
+              "6954  Pizza Hut, Inc.            NaN        287.0      11.08  13.85  ...   \n",
+              "6967  Pizza Hut, Inc.            NaN        274.0      12.23  11.63  ...   \n",
+              "\n",
+              "      Folate_USRDA  Niacin_USRDA  Riboflavin_USRDA  Thiamin_USRDA  \\\n",
+              "6773        0.0000      0.205937          0.196923       0.220000   \n",
+              "6774        0.0000      0.244375          0.193077       0.202500   \n",
+              "6775        0.0000      0.180938          0.212308       0.181667   \n",
+              "6776        0.0000      0.251750          0.208462       0.266667   \n",
+              "6777        0.0000      0.240000          0.195385       0.239167   \n",
+              "6778        0.0000      0.223875          0.186154       0.221667   \n",
+              "6795        0.3725      0.268750          0.169231       0.383333   \n",
+              "6796        0.3800      0.217500          0.169231       0.325000   \n",
+              "6797        0.3375      0.261250          0.184615       0.291667   \n",
+              "6798        0.3975      0.301250          0.184615       0.458333   \n",
+              "6799        0.3575      0.234375          0.161538       0.350000   \n",
+              "6800        0.2075      0.148750          0.223077       0.305833   \n",
+              "6947        0.0000      0.284375          0.176923       0.283333   \n",
+              "6952        0.0000      0.281250          0.169231       0.300000   \n",
+              "6953        0.0000      0.265625          0.169231       0.300000   \n",
+              "6954        0.0000      0.239375          0.153846       0.333333   \n",
+              "6967        0.0000      0.235187          0.256154       0.269167   \n",
+              "\n",
+              "      Calcium_USRDA  Copper_USRDA  Magnesium_USRDA  Phosphorus_USRDA  \\\n",
+              "6773       0.174167      0.000102         0.052381          0.355714   \n",
+              "6774       0.173333      0.000112         0.050000          0.344286   \n",
+              "6775       0.231667      0.000107         0.057143          0.452857   \n",
+              "6776       0.129167      0.000116         0.054762          0.311429   \n",
+              "6777       0.121667      0.000117         0.052381          0.292857   \n",
+              "6778       0.107500      0.000141         0.054762          0.285714   \n",
+              "6795       0.174167      0.000114         0.057143          0.325714   \n",
+              "6796       0.160000      0.000109         0.050000          0.308571   \n",
+              "6797       0.236667      0.000114         0.057143          0.425714   \n",
+              "6798       0.138333      0.000118         0.057143          0.294286   \n",
+              "6799       0.122500      0.000116         0.052381          0.275714   \n",
+              "6800       0.086667      0.000166         0.054762          0.261429   \n",
+              "6947       0.163333      0.000129         0.059524          0.348571   \n",
+              "6952       0.160833      0.000112         0.057143          0.340000   \n",
+              "6953       0.125000      0.000116         0.054762          0.272857   \n",
+              "6954       0.110000      0.000112         0.050000          0.255714   \n",
+              "6967       0.198333      0.000090         0.052381          0.355714   \n",
+              "\n",
+              "      Selenium_USRDA  Zinc_USRDA  \n",
+              "6773        0.372727    0.150000  \n",
+              "6774        0.352727    0.148182  \n",
+              "6775        0.420000    0.170000  \n",
+              "6776        0.387273    0.152727  \n",
+              "6777        0.374545    0.140000  \n",
+              "6778        0.354545    0.131818  \n",
+              "6795        0.305455    0.130909  \n",
+              "6796        0.290909    0.119091  \n",
+              "6797        0.369091    0.149091  \n",
+              "6798        0.345455    0.135455  \n",
+              "6799        0.281818    0.123636  \n",
+              "6800        0.263636    0.104545  \n",
+              "6947        0.418182    0.148182  \n",
+              "6952        0.318182    0.140000  \n",
+              "6953        0.336364    0.127273  \n",
+              "6954        0.287273    0.113636  \n",
+              "6967        0.290909    0.135455  \n",
+              "\n",
+              "[17 rows x 45 columns]"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "comida_rapida_hut=comida_rapida[comida_rapida[\"MfgName\"]==\"Pizza Hut, Inc.\"] # De la tabla anterior solo me quedo con los que\n",
+        "                                                                            # el nombre del manufactorador sea Pizza Hut\n",
+        "comida_rapida_hut"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a40da285",
+      "metadata": {
+        "id": "a40da285"
+      },
+      "source": [
+        "Con un par de lineas de codigo, puedo tener toda la informacion nutricional de todos los alimentos de comida rapida de Pizza Hut, e incluso puedo guardarlo en un archivo para no perder esta informacion."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9bb6ad7c",
+      "metadata": {
+        "id": "9bb6ad7c"
+      },
+      "outputs": [],
+      "source": [
+        "comida_rapida_hut.to_csv(\"ValorNutricionalHut.csv\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "53829ff4",
+      "metadata": {
+        "id": "53829ff4"
+      },
+      "source": [
+        "### Agrupando\n",
+        "Si yo quiero saber el promedio de proteinas de cada grupo de alimentos puedo hacerlo de la siguiente forma:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1f1e84ae",
+      "metadata": {
+        "id": "1f1e84ae"
+      },
+      "outputs": [],
+      "source": [
+        "grouped=df.groupby(\"FoodGroup\").agg({'Protein_g':'mean'}) #agg es la funcion para aplicar a cada columna la operacion que deseemos"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "373d4fb7",
+      "metadata": {
+        "id": "373d4fb7"
+      },
+      "source": [
+        "Lo que estoy haciendo aca, es que con el groupby elijo el criterio por el cual voy a agrupar a los elementos, y luego con la funcion agg indico que quiero que de la grasa me informe el promedio. Los elementos se pueden agrupar por el elemento que quiera y en vez de indicar el promedio de proteina, podria haber decidido indicar la suma total de grasa de todos los productos de un mismo grupo. Ej:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a1dc092f",
+      "metadata": {
+        "id": "a1dc092f"
+      },
+      "outputs": [],
+      "source": [
+        "grouped_sum=df.groupby(\"FoodGroup\").agg({'Fat_g':'sum'})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1739f67e",
+      "metadata": {
+        "id": "1739f67e"
+      },
+      "source": [
+        "Tambien puedo aplicar distintas funciones a multiples columnas.\n",
+        "\n",
+        "Si yo quiero saber el promedio de proteinas, y la suma de todas las grasas por Grupo de Alimentos, puedo hacer lo siguiente:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "16222dfc",
+      "metadata": {
+        "id": "16222dfc",
+        "outputId": "7ebd7ffc-c084-4c75-d3c6-d4e3ee12df5b"
+      },
+      "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>Fat_g</th>\n",
+              "      <th>Protein_g</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>FoodGroup</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>American Indian/Alaska Native Foods</th>\n",
+              "      <td>1833.11</td>\n",
+              "      <td>82.60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Baby Foods</th>\n",
+              "      <td>2020.12</td>\n",
+              "      <td>25.40</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Baked Products</th>\n",
+              "      <td>11690.32</td>\n",
+              "      <td>40.44</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Beef Products</th>\n",
+              "      <td>11693.84</td>\n",
+              "      <td>36.12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Beverages</th>\n",
+              "      <td>310.24</td>\n",
+              "      <td>78.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Breakfast Cereals</th>\n",
+              "      <td>1453.52</td>\n",
+              "      <td>31.43</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Cereal Grains and Pasta</th>\n",
+              "      <td>394.91</td>\n",
+              "      <td>75.16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Dairy and Egg Products</th>\n",
+              "      <td>3690.94</td>\n",
+              "      <td>84.63</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fast Foods</th>\n",
+              "      <td>4698.03</td>\n",
+              "      <td>28.98</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fats and Oils</th>\n",
+              "      <td>14911.86</td>\n",
+              "      <td>5.95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Finfish and Shellfish Products</th>\n",
+              "      <td>1260.21</td>\n",
+              "      <td>62.82</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fruits and Fruit Juices</th>\n",
+              "      <td>186.78</td>\n",
+              "      <td>4.90</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Lamb, Veal, and Game Products</th>\n",
+              "      <td>5687.52</td>\n",
+              "      <td>36.71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Legumes and Legume Products</th>\n",
+              "      <td>2922.43</td>\n",
+              "      <td>88.32</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Meals, Entrees, and Side Dishes</th>\n",
+              "      <td>544.25</td>\n",
+              "      <td>18.66</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nut and Seed Products</th>\n",
+              "      <td>5126.13</td>\n",
+              "      <td>50.14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pork Products</th>\n",
+              "      <td>4448.63</td>\n",
+              "      <td>39.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poultry Products</th>\n",
+              "      <td>4154.56</td>\n",
+              "      <td>33.67</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Restaurant Foods</th>\n",
+              "      <td>1207.02</td>\n",
+              "      <td>31.52</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Sausages and Luncheon Meats</th>\n",
+              "      <td>4421.81</td>\n",
+              "      <td>31.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Snacks</th>\n",
+              "      <td>2941.13</td>\n",
+              "      <td>61.30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Soups, Sauces, and Gravies</th>\n",
+              "      <td>1126.43</td>\n",
+              "      <td>17.30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Spices and Herbs</th>\n",
+              "      <td>526.88</td>\n",
+              "      <td>26.63</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Sweets</th>\n",
+              "      <td>3689.61</td>\n",
+              "      <td>85.60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Vegetables and Vegetable Products</th>\n",
+              "      <td>815.77</td>\n",
+              "      <td>57.47</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                        Fat_g  Protein_g\n",
+              "FoodGroup                                               \n",
+              "American Indian/Alaska Native Foods   1833.11      82.60\n",
+              "Baby Foods                            2020.12      25.40\n",
+              "Baked Products                       11690.32      40.44\n",
+              "Beef Products                        11693.84      36.12\n",
+              "Beverages                              310.24      78.13\n",
+              "Breakfast Cereals                     1453.52      31.43\n",
+              "Cereal Grains and Pasta                394.91      75.16\n",
+              "Dairy and Egg Products                3690.94      84.63\n",
+              "Fast Foods                            4698.03      28.98\n",
+              "Fats and Oils                        14911.86       5.95\n",
+              "Finfish and Shellfish Products        1260.21      62.82\n",
+              "Fruits and Fruit Juices                186.78       4.90\n",
+              "Lamb, Veal, and Game Products         5687.52      36.71\n",
+              "Legumes and Legume Products           2922.43      88.32\n",
+              "Meals, Entrees, and Side Dishes        544.25      18.66\n",
+              "Nut and Seed Products                 5126.13      50.14\n",
+              "Pork Products                         4448.63      39.01\n",
+              "Poultry Products                      4154.56      33.67\n",
+              "Restaurant Foods                      1207.02      31.52\n",
+              "Sausages and Luncheon Meats           4421.81      31.10\n",
+              "Snacks                                2941.13      61.30\n",
+              "Soups, Sauces, and Gravies            1126.43      17.30\n",
+              "Spices and Herbs                       526.88      26.63\n",
+              "Sweets                                3689.61      85.60\n",
+              "Vegetables and Vegetable Products      815.77      57.47"
+            ]
+          },
+          "execution_count": 57,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.groupby(\"FoodGroup\").agg({'Fat_g':'sum','Protein_g':'max'}) #saco el total de grasas por grupo de alimento y \n",
+        "                                                                            #la cantidad de proteinas maxima por grupo de alimentos"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6caae58d",
+      "metadata": {
+        "id": "6caae58d"
+      },
+      "source": [
+        "Ahora vamos a realizar otro tipo de grafico a partir de la agrupacion que realizamos por grupo de alimento, comparando el promedio de proteina entre cada grupo. Para esto, lo podemos elegir facilmente con la siguiente linea de codigo \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0fb6c9fc",
+      "metadata": {
+        "id": "0fb6c9fc",
+        "outputId": "656d021f-5fcb-4815-bbb6-ba60397449a7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<AxesSubplot:xlabel='FoodGroup'>"
+            ]
+          },
+          "execution_count": 58,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "grouped['Protein_g'].plot(kind='bar')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "56030a02",
+      "metadata": {
+        "id": "56030a02"
+      },
+      "source": [
+        "Aca voy a crear un sub-dataframe que solo contenga todos los alimentos que tengan mas de 80 calorias. En la linea de abajo, agrupo el nuevo dataframe por Grupo de Alimento, y cuento cuantos alimentos pertenecientes a cada grupo tienen mas de 80 calorias."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6484e601",
+      "metadata": {
+        "scrolled": true,
+        "id": "6484e601",
+        "outputId": "620dbfac-4f71-468c-efea-f54696dc111b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "FoodGroup\n",
+              "American Indian/Alaska Native Foods    127\n",
+              "Baby Foods                             145\n",
+              "Baked Products                         794\n",
+              "Beef Products                          945\n",
+              "Beverages                              126\n",
+              "Breakfast Cereals                      333\n",
+              "Cereal Grains and Pasta                180\n",
+              "Dairy and Egg Products                 211\n",
+              "Fast Foods                             365\n",
+              "Fats and Oils                          212\n",
+              "Finfish and Shellfish Products         242\n",
+              "Fruits and Fruit Juices                111\n",
+              "Lamb, Veal, and Game Products          436\n",
+              "Legumes and Legume Products            325\n",
+              "Meals, Entrees, and Side Dishes        104\n",
+              "Nut and Seed Products                  131\n",
+              "Pork Products                          343\n",
+              "Poultry Products                       389\n",
+              "Restaurant Foods                       105\n",
+              "Sausages and Luncheon Meats            237\n",
+              "Snacks                                 171\n",
+              "Soups, Sauces, and Gravies             106\n",
+              "Spices and Herbs                        51\n",
+              "Sweets                                 332\n",
+              "Vegetables and Vegetable Products      218\n",
+              "Name: Energy_kcal, dtype: int64"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grasa_mayor80=df[df['Energy_kcal'] > 80] #creo el nuevo df quedandome solo con los alimentos que tengan mas de 80gr de calorias\n",
+        "grasa_mayor80=grasa_mayor80.groupby(['FoodGroup'])[\"Energy_kcal\"].count() #agrupo por grupo de alimento y cuento los que tienen\n",
+        "                                                                    #mas de 80 calorias por grupo de comida\n",
+        "grasa_mayor80"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "661dccf2",
+      "metadata": {
+        "id": "661dccf2"
+      },
+      "source": [
+        "Si quiero imprimir un grafico que muestre la diferencias de promedio de grasa entre los diferentes grupos de comida, usando la agrupacion realizada anteriormente, puedo crear un nuevo grafico."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ff7d9d09",
+      "metadata": {
+        "collapsed": true,
+        "id": "ff7d9d09"
+      },
+      "source": [
+        "Primero ordeno los valores en orden descendente y me quedo con los 5 primeros, y luego con la ultima linea de codigo graficamos, pero esta vez le indicamos que el tipo de grafico sera de torta."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7d073733",
+      "metadata": {
+        "scrolled": true,
+        "id": "7d073733",
+        "outputId": "b556e16a-46e4-4052-949a-47571bcbb039"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<AxesSubplot:ylabel='Energy_kcal'>"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "dibujar=grasa_mayor80.sort_values(ascending=False).head(5)\n",
+        "dibujar\n",
+        "dibujar.plot(kind='pie')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "28e95458",
+      "metadata": {
+        "id": "28e95458"
+      },
+      "source": [
+        "### Ahora quiero realizar un grafico mas detallado con la informacion filtrada anteriormente"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7108c454",
+      "metadata": {
+        "id": "7108c454"
+      },
+      "outputs": [],
+      "source": [
+        "cal = top10.values # me da los valores de las calorias \n",
+        "claves = top10.keys() # me da las claves FoodGroup\n",
+        "explode = (0.1,0.05,0.05,0.05,0.05) # Separacion"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "907fccfd",
+      "metadata": {
+        "id": "907fccfd",
+        "outputId": "43ee9de2-3dcb-43b7-b4e8-cfeca88d1ba6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Los 5 grupos de comida con mayor cantidad de calorias')"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.pie(cal, explode=explode, shadow=True, autopct='%1.1f%%', labeldistance=5)\n",
+        "plt.legend(claves, bbox_to_anchor=(1,0))\n",
+        "plt.title('Los 5 grupos de comida con mayor cantidad de calorias')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9df01263",
+      "metadata": {
+        "id": "9df01263"
+      },
+      "source": [
+        "### Otro ejemplo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6297515a",
+      "metadata": {
+        "id": "6297515a"
+      },
+      "outputs": [],
+      "source": [
+        "# Consigo los alimentos que sean pescados\n",
+        "\n",
+        "fish = df[df[\"Descrip\"].str.contains('Fish')]\n",
+        "\n",
+        "\n",
+        "fish = fish[fish[\"FoodGroup\"] != 'Fats and Oils'] # No me interesan los aceites"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f3556633",
+      "metadata": {
+        "id": "f3556633"
+      },
+      "outputs": [],
+      "source": [
+        "# Me interesan solo las columnas que hablen de calorias, proteinas, grasas, carbohidratos, azucares y fibras\n",
+        "\n",
+        "fish = fish[['FoodGroup', 'ShortDescrip', 'Descrip', 'Energy_kcal', 'Protein_g', 'Fat_g', 'Carb_g','Sugar_g', 'Fiber_g']]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5b5eb635",
+      "metadata": {
+        "id": "5b5eb635",
+        "outputId": "ca7429f7-1701-41f7-d7bf-8262a480145d"
+      },
+      "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>ShortDescrip</th>\n",
+              "      <th>Energy_kcal</th>\n",
+              "      <th>Protein_g</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4572</th>\n",
+              "      <td>TUNA,FRSH,BLUEFIN,CKD,DRY HEAT</td>\n",
+              "      <td>184.0</td>\n",
+              "      <td>29.91</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8160</th>\n",
+              "      <td>FISH,SALMON,KING,CHINOOK,KIPPERED,CND (ALASKA ...</td>\n",
+              "      <td>266.0</td>\n",
+              "      <td>30.70</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4560</th>\n",
+              "      <td>STURGEON,MXD SP,SMOKED</td>\n",
+              "      <td>173.0</td>\n",
+              "      <td>31.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8220</th>\n",
+              "      <td>FISH,SALMON,RED,(SOCKEYE),CND,SMOKED (ALASKA N...</td>\n",
+              "      <td>206.0</td>\n",
+              "      <td>35.19</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8162</th>\n",
+              "      <td>FISH,SALMON,KING,CHINOOK,SMOKED,BRINED (ALASKA...</td>\n",
+              "      <td>430.0</td>\n",
+              "      <td>39.90</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8141</th>\n",
+              "      <td>FISH,HERRING,PACIFIC,FLSH,AIR-DRIED,PACK OIL (...</td>\n",
+              "      <td>489.0</td>\n",
+              "      <td>44.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8139</th>\n",
+              "      <td>FISH,HERRING EGGS,PACIFIC,DRY (ALASKA NATIVE)</td>\n",
+              "      <td>312.0</td>\n",
+              "      <td>60.40</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8225</th>\n",
+              "      <td>FISH,SALMON,CHUM,DRIED (ALASKA NATIVE)</td>\n",
+              "      <td>378.0</td>\n",
+              "      <td>62.09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8219</th>\n",
+              "      <td>FISH,WHITEFISH,DRIED (ALASKA NATIVE)</td>\n",
+              "      <td>371.0</td>\n",
+              "      <td>62.44</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4472</th>\n",
+              "      <td>COD,ATLANTIC,DRIED&amp;SALTED</td>\n",
+              "      <td>290.0</td>\n",
+              "      <td>62.82</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                           ShortDescrip  Energy_kcal  \\\n",
+              "4572                     TUNA,FRSH,BLUEFIN,CKD,DRY HEAT        184.0   \n",
+              "8160  FISH,SALMON,KING,CHINOOK,KIPPERED,CND (ALASKA ...        266.0   \n",
+              "4560                             STURGEON,MXD SP,SMOKED        173.0   \n",
+              "8220  FISH,SALMON,RED,(SOCKEYE),CND,SMOKED (ALASKA N...        206.0   \n",
+              "8162  FISH,SALMON,KING,CHINOOK,SMOKED,BRINED (ALASKA...        430.0   \n",
+              "8141  FISH,HERRING,PACIFIC,FLSH,AIR-DRIED,PACK OIL (...        489.0   \n",
+              "8139      FISH,HERRING EGGS,PACIFIC,DRY (ALASKA NATIVE)        312.0   \n",
+              "8225             FISH,SALMON,CHUM,DRIED (ALASKA NATIVE)        378.0   \n",
+              "8219               FISH,WHITEFISH,DRIED (ALASKA NATIVE)        371.0   \n",
+              "4472                          COD,ATLANTIC,DRIED&SALTED        290.0   \n",
+              "\n",
+              "      Protein_g  \n",
+              "4572      29.91  \n",
+              "8160      30.70  \n",
+              "4560      31.20  \n",
+              "8220      35.19  \n",
+              "8162      39.90  \n",
+              "8141      44.50  \n",
+              "8139      60.40  \n",
+              "8225      62.09  \n",
+              "8219      62.44  \n",
+              "4472      62.82  "
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Como los pescados son ricos en proteinas, quiero conseguir aquellos que mÃ¡s tengan, y ademas me interesa saber sus calorias\n",
+        "# Consigo los ultimos 10 (porque la lista estÃ¡ de menor a mayor)\n",
+        "\n",
+        "fish = fish.sort_values(by=['Protein_g'], ascending=True).tail(10)\n",
+        "fish = fish[['ShortDescrip', 'Energy_kcal', 'Protein_g']]\n",
+        "fish"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "aef7ef78",
+      "metadata": {
+        "id": "aef7ef78",
+        "outputId": "3f858ee3-dd29-4ea4-bc19-d8927f32b0d2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Quiero hacer una comparacion entre la cantidad de proteinas y la de calorias en los pescados que seleccione, y +\n",
+        "# + puedo mostrarla en un grafico\n",
+        "\n",
+        "claves = fish['ShortDescrip'].values.tolist()\n",
+        "calorias = fish['Energy_kcal'].values.tolist()\n",
+        "proteinas = fish['Protein_g'].values.tolist()\n",
+        "\n",
+        "# Necesitaremos numpy\n",
+        "import numpy as np\n",
+        "\n",
+        "x = np.arange(len(claves))\n",
+        "width = 0.35  # Ancho de las barritas\n",
+        "\n",
+        "fig, ax = plt.subplots()\n",
+        "rects1 = ax.bar(x - width/2, calorias, width, label='Calorias')\n",
+        "rects2 = ax.bar(x + width/2, proteinas, width, label='Proteinas')\n",
+        "\n",
+        "ax.set_ylabel('Valor')\n",
+        "ax.set_title('Valor de las proteinas y las calorias de cada pescado')\n",
+        "ax.set_xticks(x, claves)\n",
+        "ax.legend()\n",
+        "\n",
+        "ax.bar_label(rects1, padding=3)\n",
+        "ax.bar_label(rects2, padding=3)\n",
+        "\n",
+        "fig.tight_layout()\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72fbbe6d",
+      "metadata": {
+        "id": "72fbbe6d"
+      },
+      "source": [
+        "### Me gustaria conocer aquellos alimentos que tengan igual proporcion de proteinas y de grasas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ea7d8ec4",
+      "metadata": {
+        "id": "ea7d8ec4",
+        "outputId": "b429dcc5-50e7-4469-b35c-f44efb3f5081"
+      },
+      "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>FoodGroup</th>\n",
+              "      <th>ShortDescrip</th>\n",
+              "      <th>Descrip</th>\n",
+              "      <th>Protein_g</th>\n",
+              "      <th>Fat_g</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>173</th>\n",
+              "      <td>Dairy and Egg Products</td>\n",
+              "      <td>CHEESE,PARMESAN,DRY GRATED,RED FAT</td>\n",
+              "      <td>Cheese, parmesan, dry grated, reduced fat</td>\n",
+              "      <td>20.00</td>\n",
+              "      <td>20.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>279</th>\n",
+              "      <td>Spices and Herbs</td>\n",
+              "      <td>VANILLA EXTRACT</td>\n",
+              "      <td>Vanilla extract</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>0.06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>330</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,DINNER,VEG&amp;LAMB,STR</td>\n",
+              "      <td>Babyfood, dinner, vegetables and lamb, strained</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>2.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>340</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,DINNER,VEG&amp;NOODLES&amp;TURKEY,STR</td>\n",
+              "      <td>Babyfood, dinner, vegetables and noodles and t...</td>\n",
+              "      <td>1.20</td>\n",
+              "      <td>1.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>362</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPLSAUC,STR</td>\n",
+              "      <td>Babyfood, fruit, applesauce, strained</td>\n",
+              "      <td>0.20</td>\n",
+              "      <td>0.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>383</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPLSAUC&amp;APRICOTS,STR</td>\n",
+              "      <td>Babyfood, fruit, applesauce and apricots, stra...</td>\n",
+              "      <td>0.20</td>\n",
+              "      <td>0.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>384</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPLSAUC&amp;APRICOTS,JR</td>\n",
+              "      <td>Babyfood, fruit, applesauce and apricots, junior</td>\n",
+              "      <td>0.20</td>\n",
+              "      <td>0.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>388</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPLSAUC&amp;PNAPPL,STR</td>\n",
+              "      <td>Babyfood, fruit, applesauce and pineapple, str...</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>389</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPLSAUC&amp;PNAPPL,JR</td>\n",
+              "      <td>Babyfood, fruit, applesauce and pineapple, junior</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>390</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPL &amp; RASPBERRY,STR</td>\n",
+              "      <td>Babyfood, fruit, apple and raspberry, strained</td>\n",
+              "      <td>0.20</td>\n",
+              "      <td>0.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>391</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPL &amp; RASPBERRY,JR</td>\n",
+              "      <td>Babyfood, fruit, apple and raspberry, junior</td>\n",
+              "      <td>0.20</td>\n",
+              "      <td>0.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>401</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPL&amp;BLUEBERRY,STR</td>\n",
+              "      <td>Babyfood, fruit, apple and blueberry, strained</td>\n",
+              "      <td>0.20</td>\n",
+              "      <td>0.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>402</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,APPL&amp;BLUEBERRY,JR</td>\n",
+              "      <td>Babyfood, fruit, apple and blueberry, junior</td>\n",
+              "      <td>0.20</td>\n",
+              "      <td>0.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>416</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,JUC,MXD FRUIT</td>\n",
+              "      <td>Babyfood, juice, mixed fruit</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>449</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,DSSRT,CHERRY VANILLA PUDD,JR</td>\n",
+              "      <td>Babyfood, dessert, cherry vanilla pudding, junior</td>\n",
+              "      <td>0.20</td>\n",
+              "      <td>0.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>606</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,MULTIGRAIN WHL GRAIN CRL,DRY</td>\n",
+              "      <td>Babyfood, Multigrain whole grain cereal, dry</td>\n",
+              "      <td>6.25</td>\n",
+              "      <td>6.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1231</th>\n",
+              "      <td>Soups, Sauces, and Gravies</td>\n",
+              "      <td>CAMPBELL'S RED&amp;WHITE,CHICK BROTH,COND</td>\n",
+              "      <td>CAMPBELL'S Red and White, Chicken Broth, conde...</td>\n",
+              "      <td>0.81</td>\n",
+              "      <td>0.81</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1304</th>\n",
+              "      <td>Soups, Sauces, and Gravies</td>\n",
+              "      <td>CAMPBELL'S,98% FAT FREE CRM OF BROCCOLI SOUP,COND</td>\n",
+              "      <td>CAMPBELL'S, 98% Fat Free Cream of Broccoli Sou...</td>\n",
+              "      <td>1.61</td>\n",
+              "      <td>1.61</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1321</th>\n",
+              "      <td>Soups, Sauces, and Gravies</td>\n",
+              "      <td>PREGO PASTA,ITAL SAUSGE &amp; GARLIC ITAL SAU,RTS</td>\n",
+              "      <td>PREGO Pasta, Italian Sausage and Garlic Italia...</td>\n",
+              "      <td>2.40</td>\n",
+              "      <td>2.40</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1363</th>\n",
+              "      <td>Soups, Sauces, and Gravies</td>\n",
+              "      <td>CAMPBELL'S MICROWAVABLE BF GRAVY</td>\n",
+              "      <td>CAMPBELL'S Microwavable Beef Gravy</td>\n",
+              "      <td>1.69</td>\n",
+              "      <td>1.69</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1365</th>\n",
+              "      <td>Soups, Sauces, and Gravies</td>\n",
+              "      <td>CAMPBELL'S TURKEY GRAVY</td>\n",
+              "      <td>CAMPBELL'S Turkey Gravy</td>\n",
+              "      <td>1.69</td>\n",
+              "      <td>1.69</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1371</th>\n",
+              "      <td>Soups, Sauces, and Gravies</td>\n",
+              "      <td>CAMPBELL'S MICROWAVABLE TURKEY GRAVY</td>\n",
+              "      <td>CAMPBELL'S Microwavable Turkey Gravy</td>\n",
+              "      <td>1.67</td>\n",
+              "      <td>1.67</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1588</th>\n",
+              "      <td>Soups, Sauces, and Gravies</td>\n",
+              "      <td>CAMPBELL'S BF GRAVY</td>\n",
+              "      <td>CAMPBELL'S Beef Gravy</td>\n",
+              "      <td>1.69</td>\n",
+              "      <td>1.69</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2042</th>\n",
+              "      <td>Snacks</td>\n",
+              "      <td>RICE &amp; WHEAT CRL BAR</td>\n",
+              "      <td>Rice and Wheat cereal bar</td>\n",
+              "      <td>9.09</td>\n",
+              "      <td>9.09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2074</th>\n",
+              "      <td>Breakfast Cereals</td>\n",
+              "      <td>CEREALS RTE,GENERAL MILLS,FIBER ONE,CARAMEL DE...</td>\n",
+              "      <td>Cereals ready-to-eat, GENERAL MILLS, FIBER ONE...</td>\n",
+              "      <td>5.80</td>\n",
+              "      <td>5.80</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2126</th>\n",
+              "      <td>Breakfast Cereals</td>\n",
+              "      <td>CEREALS RTE,GENERAL MILLS,COCOA PUFFS BROWNIE ...</td>\n",
+              "      <td>Cereals ready-to-eat, GENERAL MILLS, COCOA PUF...</td>\n",
+              "      <td>6.00</td>\n",
+              "      <td>6.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2309</th>\n",
+              "      <td>Fruits and Fruit Juices</td>\n",
+              "      <td>BLUEBERRIES,DRIED,SWTND</td>\n",
+              "      <td>Blueberries, dried, sweetened</td>\n",
+              "      <td>2.50</td>\n",
+              "      <td>2.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2316</th>\n",
+              "      <td>Fruits and Fruit Juices</td>\n",
+              "      <td>MAMMY-APPLE,(MAMEY),RAW</td>\n",
+              "      <td>Mammy-apple, (mamey), raw</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>0.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2454</th>\n",
+              "      <td>Fruits and Fruit Juices</td>\n",
+              "      <td>APPLESAUCE,CND,SWTND,W/SALT</td>\n",
+              "      <td>Applesauce, canned, sweetened, with salt</td>\n",
+              "      <td>0.18</td>\n",
+              "      <td>0.18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4157</th>\n",
+              "      <td>Beverages</td>\n",
+              "      <td>ALCOHOLIC BEV,DAIQUIRI,PREPARED-FROM-RECIPE</td>\n",
+              "      <td>Alcoholic beverage, daiquiri, prepared-from-re...</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>0.06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4169</th>\n",
+              "      <td>Beverages</td>\n",
+              "      <td>WHISKEY SOUR MIX,BTLD</td>\n",
+              "      <td>Whiskey sour mix, bottled</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4170</th>\n",
+              "      <td>Beverages</td>\n",
+              "      <td>ALCOHOLIC BEV,WHISKEY SOUR,PREP FROM ITEM 14028</td>\n",
+              "      <td>Alcoholic beverage, whiskey sour, prepared fro...</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>0.06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4179</th>\n",
+              "      <td>Beverages</td>\n",
+              "      <td>BEVERAGES,NESTLE,BOOST PLUS,NUTRITIONAL DRK,RTD</td>\n",
+              "      <td>Beverages, NESTLE, Boost plus, nutritional dri...</td>\n",
+              "      <td>5.38</td>\n",
+              "      <td>5.38</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4197</th>\n",
+              "      <td>Beverages</td>\n",
+              "      <td>BEVERAGES,CHOC PDR,NO SUGAR ADDED</td>\n",
+              "      <td>Beverages, chocolate powder, no sugar added</td>\n",
+              "      <td>9.09</td>\n",
+              "      <td>9.09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4361</th>\n",
+              "      <td>Beverages</td>\n",
+              "      <td>COFFEE SUB,CRL GRAIN BEV,PDR,PREP W/ WHL MILK</td>\n",
+              "      <td>Coffee substitute, cereal grain beverage, powd...</td>\n",
+              "      <td>3.30</td>\n",
+              "      <td>3.30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4386</th>\n",
+              "      <td>Beverages</td>\n",
+              "      <td>WHISKEY SOUR MIX,BTLD,W/ K&amp;NA</td>\n",
+              "      <td>Whiskey sour mix, bottled, with added potassiu...</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6160</th>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>PUDDINGS,CHOC,DRY MIX,INST,PREP W/ WHL MILK</td>\n",
+              "      <td>Puddings, chocolate, dry mix, instant, prepare...</td>\n",
+              "      <td>3.10</td>\n",
+              "      <td>3.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6170</th>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>PUDDINGS,TAPIOCA,DRY MIX</td>\n",
+              "      <td>Puddings, tapioca, dry mix</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6248</th>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>PECTIN,UNSWTND,DRY MIX</td>\n",
+              "      <td>Pectin, unsweetened, dry mix</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>0.30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6249</th>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>PIE FILLINGS,APPL,CND</td>\n",
+              "      <td>Pie fillings, apple, canned</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6288</th>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>TOPPINGS,PINEAPPLE</td>\n",
+              "      <td>Toppings, pineapple</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6344</th>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>PUDDINGS,TAPIOCA,DRY MIX,W/ NO ADDED SALT</td>\n",
+              "      <td>Puddings, tapioca, dry mix, with no added salt</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7047</th>\n",
+              "      <td>Meals, Entrees, and Side Dishes</td>\n",
+              "      <td>KASHI BLACK BEAN MANGO,FRZ,UNPREP</td>\n",
+              "      <td>KASHI Black Bean Mango, frozen, unprepared</td>\n",
+              "      <td>3.00</td>\n",
+              "      <td>3.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7055</th>\n",
+              "      <td>Meals, Entrees, and Side Dishes</td>\n",
+              "      <td>KASHI PESTO PASTA PRIMAVERA,FRZ,UNPREP</td>\n",
+              "      <td>KASHI Pesto Pasta Primavera, frozen, unprepared</td>\n",
+              "      <td>3.80</td>\n",
+              "      <td>3.80</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7676</th>\n",
+              "      <td>Snacks</td>\n",
+              "      <td>SNACKS,GRANOLA BARS,QUAKER OATMEAL TO GO,ALL F...</td>\n",
+              "      <td>Snacks, granola bars, QUAKER OATMEAL TO GO, al...</td>\n",
+              "      <td>6.67</td>\n",
+              "      <td>6.67</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7678</th>\n",
+              "      <td>Snacks</td>\n",
+              "      <td>SNACKS,GRANOLA BAR,KASHI TLC BAR,CRUNCHY,MXD F...</td>\n",
+              "      <td>Snacks, granola bar, KASHI TLC Bar, crunchy, m...</td>\n",
+              "      <td>15.00</td>\n",
+              "      <td>15.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8029</th>\n",
+              "      <td>Baked Products</td>\n",
+              "      <td>TORTILLAS,RTB OR -FRY,WHL WHEAT</td>\n",
+              "      <td>Tortillas, ready-to-bake or -fry, whole wheat</td>\n",
+              "      <td>9.76</td>\n",
+              "      <td>9.76</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8036</th>\n",
+              "      <td>Baked Products</td>\n",
+              "      <td>COOKIES,GRAHAM CRACKERS,PLN OR HONEY,LOWFAT</td>\n",
+              "      <td>Cookies, graham crackers, plain or honey, lowfat</td>\n",
+              "      <td>5.71</td>\n",
+              "      <td>5.71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8466</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,FRUIT,TUTTI FRUTTI,JR</td>\n",
+              "      <td>Babyfood, fruit, tutti frutti, junior</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>0.40</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8475</th>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>JAMS &amp; PRESERVES,DIETETIC (WITH NA SACCHARIN),...</td>\n",
+              "      <td>Jams and preserves, dietetic (with sodium sacc...</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>0.30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8487</th>\n",
+              "      <td>Legumes and Legume Products</td>\n",
+              "      <td>VERMICELLI,MADE FROM SOY</td>\n",
+              "      <td>Vermicelli, made from soy</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8570</th>\n",
+              "      <td>Sweets</td>\n",
+              "      <td>FROZEN NOVELTIES,JUC TYPE,JUC W/ CRM</td>\n",
+              "      <td>Frozen novelties, juice type, juice with cream</td>\n",
+              "      <td>1.41</td>\n",
+              "      <td>1.41</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8579</th>\n",
+              "      <td>Baby Foods</td>\n",
+              "      <td>BABYFOOD,MXD FRUIT YOGURT,STR</td>\n",
+              "      <td>Babyfood, mixed fruit yogurt, strained</td>\n",
+              "      <td>0.80</td>\n",
+              "      <td>0.80</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            FoodGroup  \\\n",
+              "173            Dairy and Egg Products   \n",
+              "279                  Spices and Herbs   \n",
+              "330                        Baby Foods   \n",
+              "340                        Baby Foods   \n",
+              "362                        Baby Foods   \n",
+              "383                        Baby Foods   \n",
+              "384                        Baby Foods   \n",
+              "388                        Baby Foods   \n",
+              "389                        Baby Foods   \n",
+              "390                        Baby Foods   \n",
+              "391                        Baby Foods   \n",
+              "401                        Baby Foods   \n",
+              "402                        Baby Foods   \n",
+              "416                        Baby Foods   \n",
+              "449                        Baby Foods   \n",
+              "606                        Baby Foods   \n",
+              "1231       Soups, Sauces, and Gravies   \n",
+              "1304       Soups, Sauces, and Gravies   \n",
+              "1321       Soups, Sauces, and Gravies   \n",
+              "1363       Soups, Sauces, and Gravies   \n",
+              "1365       Soups, Sauces, and Gravies   \n",
+              "1371       Soups, Sauces, and Gravies   \n",
+              "1588       Soups, Sauces, and Gravies   \n",
+              "2042                           Snacks   \n",
+              "2074                Breakfast Cereals   \n",
+              "2126                Breakfast Cereals   \n",
+              "2309          Fruits and Fruit Juices   \n",
+              "2316          Fruits and Fruit Juices   \n",
+              "2454          Fruits and Fruit Juices   \n",
+              "4157                        Beverages   \n",
+              "4169                        Beverages   \n",
+              "4170                        Beverages   \n",
+              "4179                        Beverages   \n",
+              "4197                        Beverages   \n",
+              "4361                        Beverages   \n",
+              "4386                        Beverages   \n",
+              "6160                           Sweets   \n",
+              "6170                           Sweets   \n",
+              "6248                           Sweets   \n",
+              "6249                           Sweets   \n",
+              "6288                           Sweets   \n",
+              "6344                           Sweets   \n",
+              "7047  Meals, Entrees, and Side Dishes   \n",
+              "7055  Meals, Entrees, and Side Dishes   \n",
+              "7676                           Snacks   \n",
+              "7678                           Snacks   \n",
+              "8029                   Baked Products   \n",
+              "8036                   Baked Products   \n",
+              "8466                       Baby Foods   \n",
+              "8475                           Sweets   \n",
+              "8487      Legumes and Legume Products   \n",
+              "8570                           Sweets   \n",
+              "8579                       Baby Foods   \n",
+              "\n",
+              "                                           ShortDescrip  \\\n",
+              "173                  CHEESE,PARMESAN,DRY GRATED,RED FAT   \n",
+              "279                                     VANILLA EXTRACT   \n",
+              "330                        BABYFOOD,DINNER,VEG&LAMB,STR   \n",
+              "340              BABYFOOD,DINNER,VEG&NOODLES&TURKEY,STR   \n",
+              "362                         BABYFOOD,FRUIT,APPLSAUC,STR   \n",
+              "383                BABYFOOD,FRUIT,APPLSAUC&APRICOTS,STR   \n",
+              "384                 BABYFOOD,FRUIT,APPLSAUC&APRICOTS,JR   \n",
+              "388                  BABYFOOD,FRUIT,APPLSAUC&PNAPPL,STR   \n",
+              "389                   BABYFOOD,FRUIT,APPLSAUC&PNAPPL,JR   \n",
+              "390                 BABYFOOD,FRUIT,APPL & RASPBERRY,STR   \n",
+              "391                  BABYFOOD,FRUIT,APPL & RASPBERRY,JR   \n",
+              "401                   BABYFOOD,FRUIT,APPL&BLUEBERRY,STR   \n",
+              "402                    BABYFOOD,FRUIT,APPL&BLUEBERRY,JR   \n",
+              "416                              BABYFOOD,JUC,MXD FRUIT   \n",
+              "449               BABYFOOD,DSSRT,CHERRY VANILLA PUDD,JR   \n",
+              "606               BABYFOOD,MULTIGRAIN WHL GRAIN CRL,DRY   \n",
+              "1231              CAMPBELL'S RED&WHITE,CHICK BROTH,COND   \n",
+              "1304  CAMPBELL'S,98% FAT FREE CRM OF BROCCOLI SOUP,COND   \n",
+              "1321      PREGO PASTA,ITAL SAUSGE & GARLIC ITAL SAU,RTS   \n",
+              "1363                   CAMPBELL'S MICROWAVABLE BF GRAVY   \n",
+              "1365                            CAMPBELL'S TURKEY GRAVY   \n",
+              "1371               CAMPBELL'S MICROWAVABLE TURKEY GRAVY   \n",
+              "1588                                CAMPBELL'S BF GRAVY   \n",
+              "2042                               RICE & WHEAT CRL BAR   \n",
+              "2074  CEREALS RTE,GENERAL MILLS,FIBER ONE,CARAMEL DE...   \n",
+              "2126  CEREALS RTE,GENERAL MILLS,COCOA PUFFS BROWNIE ...   \n",
+              "2309                            BLUEBERRIES,DRIED,SWTND   \n",
+              "2316                            MAMMY-APPLE,(MAMEY),RAW   \n",
+              "2454                        APPLESAUCE,CND,SWTND,W/SALT   \n",
+              "4157        ALCOHOLIC BEV,DAIQUIRI,PREPARED-FROM-RECIPE   \n",
+              "4169                              WHISKEY SOUR MIX,BTLD   \n",
+              "4170    ALCOHOLIC BEV,WHISKEY SOUR,PREP FROM ITEM 14028   \n",
+              "4179    BEVERAGES,NESTLE,BOOST PLUS,NUTRITIONAL DRK,RTD   \n",
+              "4197                  BEVERAGES,CHOC PDR,NO SUGAR ADDED   \n",
+              "4361      COFFEE SUB,CRL GRAIN BEV,PDR,PREP W/ WHL MILK   \n",
+              "4386                      WHISKEY SOUR MIX,BTLD,W/ K&NA   \n",
+              "6160        PUDDINGS,CHOC,DRY MIX,INST,PREP W/ WHL MILK   \n",
+              "6170                           PUDDINGS,TAPIOCA,DRY MIX   \n",
+              "6248                             PECTIN,UNSWTND,DRY MIX   \n",
+              "6249                              PIE FILLINGS,APPL,CND   \n",
+              "6288                                 TOPPINGS,PINEAPPLE   \n",
+              "6344          PUDDINGS,TAPIOCA,DRY MIX,W/ NO ADDED SALT   \n",
+              "7047                  KASHI BLACK BEAN MANGO,FRZ,UNPREP   \n",
+              "7055             KASHI PESTO PASTA PRIMAVERA,FRZ,UNPREP   \n",
+              "7676  SNACKS,GRANOLA BARS,QUAKER OATMEAL TO GO,ALL F...   \n",
+              "7678  SNACKS,GRANOLA BAR,KASHI TLC BAR,CRUNCHY,MXD F...   \n",
+              "8029                    TORTILLAS,RTB OR -FRY,WHL WHEAT   \n",
+              "8036        COOKIES,GRAHAM CRACKERS,PLN OR HONEY,LOWFAT   \n",
+              "8466                     BABYFOOD,FRUIT,TUTTI FRUTTI,JR   \n",
+              "8475  JAMS & PRESERVES,DIETETIC (WITH NA SACCHARIN),...   \n",
+              "8487                           VERMICELLI,MADE FROM SOY   \n",
+              "8570               FROZEN NOVELTIES,JUC TYPE,JUC W/ CRM   \n",
+              "8579                      BABYFOOD,MXD FRUIT YOGURT,STR   \n",
+              "\n",
+              "                                                Descrip  Protein_g  Fat_g  \n",
+              "173           Cheese, parmesan, dry grated, reduced fat      20.00  20.00  \n",
+              "279                                     Vanilla extract       0.06   0.06  \n",
+              "330     Babyfood, dinner, vegetables and lamb, strained       2.00   2.00  \n",
+              "340   Babyfood, dinner, vegetables and noodles and t...       1.20   1.20  \n",
+              "362               Babyfood, fruit, applesauce, strained       0.20   0.20  \n",
+              "383   Babyfood, fruit, applesauce and apricots, stra...       0.20   0.20  \n",
+              "384    Babyfood, fruit, applesauce and apricots, junior       0.20   0.20  \n",
+              "388   Babyfood, fruit, applesauce and pineapple, str...       0.10   0.10  \n",
+              "389   Babyfood, fruit, applesauce and pineapple, junior       0.10   0.10  \n",
+              "390      Babyfood, fruit, apple and raspberry, strained       0.20   0.20  \n",
+              "391        Babyfood, fruit, apple and raspberry, junior       0.20   0.20  \n",
+              "401      Babyfood, fruit, apple and blueberry, strained       0.20   0.20  \n",
+              "402        Babyfood, fruit, apple and blueberry, junior       0.20   0.20  \n",
+              "416                        Babyfood, juice, mixed fruit       0.10   0.10  \n",
+              "449   Babyfood, dessert, cherry vanilla pudding, junior       0.20   0.20  \n",
+              "606        Babyfood, Multigrain whole grain cereal, dry       6.25   6.25  \n",
+              "1231  CAMPBELL'S Red and White, Chicken Broth, conde...       0.81   0.81  \n",
+              "1304  CAMPBELL'S, 98% Fat Free Cream of Broccoli Sou...       1.61   1.61  \n",
+              "1321  PREGO Pasta, Italian Sausage and Garlic Italia...       2.40   2.40  \n",
+              "1363                 CAMPBELL'S Microwavable Beef Gravy       1.69   1.69  \n",
+              "1365                            CAMPBELL'S Turkey Gravy       1.69   1.69  \n",
+              "1371               CAMPBELL'S Microwavable Turkey Gravy       1.67   1.67  \n",
+              "1588                              CAMPBELL'S Beef Gravy       1.69   1.69  \n",
+              "2042                          Rice and Wheat cereal bar       9.09   9.09  \n",
+              "2074  Cereals ready-to-eat, GENERAL MILLS, FIBER ONE...       5.80   5.80  \n",
+              "2126  Cereals ready-to-eat, GENERAL MILLS, COCOA PUF...       6.00   6.00  \n",
+              "2309                      Blueberries, dried, sweetened       2.50   2.50  \n",
+              "2316                          Mammy-apple, (mamey), raw       0.50   0.50  \n",
+              "2454           Applesauce, canned, sweetened, with salt       0.18   0.18  \n",
+              "4157  Alcoholic beverage, daiquiri, prepared-from-re...       0.06   0.06  \n",
+              "4169                          Whiskey sour mix, bottled       0.10   0.10  \n",
+              "4170  Alcoholic beverage, whiskey sour, prepared fro...       0.06   0.06  \n",
+              "4179  Beverages, NESTLE, Boost plus, nutritional dri...       5.38   5.38  \n",
+              "4197        Beverages, chocolate powder, no sugar added       9.09   9.09  \n",
+              "4361  Coffee substitute, cereal grain beverage, powd...       3.30   3.30  \n",
+              "4386  Whiskey sour mix, bottled, with added potassiu...       0.10   0.10  \n",
+              "6160  Puddings, chocolate, dry mix, instant, prepare...       3.10   3.10  \n",
+              "6170                         Puddings, tapioca, dry mix       0.10   0.10  \n",
+              "6248                       Pectin, unsweetened, dry mix       0.30   0.30  \n",
+              "6249                        Pie fillings, apple, canned       0.10   0.10  \n",
+              "6288                                Toppings, pineapple       0.10   0.10  \n",
+              "6344     Puddings, tapioca, dry mix, with no added salt       0.10   0.10  \n",
+              "7047         KASHI Black Bean Mango, frozen, unprepared       3.00   3.00  \n",
+              "7055    KASHI Pesto Pasta Primavera, frozen, unprepared       3.80   3.80  \n",
+              "7676  Snacks, granola bars, QUAKER OATMEAL TO GO, al...       6.67   6.67  \n",
+              "7678  Snacks, granola bar, KASHI TLC Bar, crunchy, m...      15.00  15.00  \n",
+              "8029      Tortillas, ready-to-bake or -fry, whole wheat       9.76   9.76  \n",
+              "8036   Cookies, graham crackers, plain or honey, lowfat       5.71   5.71  \n",
+              "8466              Babyfood, fruit, tutti frutti, junior       0.40   0.40  \n",
+              "8475  Jams and preserves, dietetic (with sodium sacc...       0.30   0.30  \n",
+              "8487                          Vermicelli, made from soy       0.10   0.10  \n",
+              "8570     Frozen novelties, juice type, juice with cream       1.41   1.41  \n",
+              "8579             Babyfood, mixed fruit yogurt, strained       0.80   0.80  "
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "igual_pf = df[df['Protein_g'] == df['Fat_g']] # Filtro\n",
+        "igual_pf = igual_pf[['FoodGroup', 'ShortDescrip', 'Descrip', 'Protein_g', 'Fat_g']] # Me quedo con las columnas necesarias\n",
+        "igual_pf = igual_pf[igual_pf['Protein_g'] != 0.00] # No quiero aquellas columnas que no tengan nada\n",
+        "igual_pf"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "aedf4c64",
+      "metadata": {
+        "id": "aedf4c64"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file