cereal.ipynb

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   "outputs": [],
   "source": [
    "#Librerias\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                        name     sugars\n",
       "0                  100% Bran  18.181818\n",
       "1          100% Natural Bran   8.000000\n",
       "2                   All-Bran  15.151515\n",
       "3  All-Bran with Extra Fiber   0.000000\n",
       "4             Almond Delight  10.666667"
      ],
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     },
     "metadata": {},
     "execution_count": 2
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   ],
   "source": [
    "#Datos: Azucar (gramos) por tasa de 77 cereales \n",
    "data = pd.read_excel('Datasets/cereal.xlsx')[['name', 'sugars']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "data": {
      "text/plain": [
       "          sugars\n",
       "count  77.000000\n",
       "mean    9.201024\n",
       "std     5.985441\n",
       "min    -1.492537\n",
       "25%     3.000000\n",
       "50%    10.666667\n",
       "75%    13.333333\n",
       "max    20.895522"
      ],
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     "metadata": {},
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   ],
   "source": [
    "#Estadisticas basicas\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Histograma\n",
    "data.hist(color='lightblue', ec='gray', bins=15)\n",
    "plt.title('Azucar gr')\n",
    "plt.xlabel('Gramos')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True     42\n",
       "False    35\n",
       "Name: sugars, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "#Datos con azucar igual o mayor a 10 gr por cada 30 gr\n",
    "od = data['sugars'] >= 10 \n",
    "od.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                      name     sugars\n",
       "50                       Nutri-grain Wheat   2.000000\n",
       "40                                     Kix   2.000000\n",
       "45       Muesli Raisins; Peaches; & Pecans  11.000000\n",
       "14                             Cocoa Puffs  13.000000\n",
       "33                              Grape-Nuts  12.000000\n",
       "70                       Total Raisin Bran  14.000000\n",
       "43                                   Maypo   3.000000\n",
       "5                  Apple Cinnamon Cheerios  13.333333\n",
       "34                      Great Grains Pecan  12.121212\n",
       "30                            Golden Crisp  17.045455\n",
       "31                          Golden Grahams  12.000000\n",
       "57                          Quaker Oatmeal  -1.492537\n",
       "58                             Raisin Bran  16.000000\n",
       "12                   Cinnamon Toast Crunch  12.000000\n",
       "18                           Count Chocula  13.000000\n",
       "38             Just Right Crunchy  Nuggets   6.000000\n",
       "65               Shredded Wheat spoon size   0.000000\n",
       "32                       Grape Nuts Flakes   5.681818\n",
       "26                     Frosted Mini-Wheats   8.750000\n",
       "35                        Honey Graham Ohs  11.000000\n",
       "0                                100% Bran  18.181818\n",
       "27  Fruit & Fibre Dates; Walnuts; and Oats  14.925373\n",
       "22                  Crispy Wheat & Raisins  13.333333\n",
       "74                              Wheat Chex   4.477612\n",
       "66                                  Smacks  20.000000\n",
       "24                             Froot Loops  13.000000\n",
       "1                        100% Natural Bran   8.000000\n",
       "20                  Cream of Wheat (Quick)   0.000000\n",
       "42                            Lucky Charms  12.000000\n",
       "37                              Honey-comb   8.270677"
      ],
      "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>name</th>\n      <th>sugars</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>50</th>\n      <td>Nutri-grain Wheat</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Kix</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Muesli Raisins; Peaches; &amp; Pecans</td>\n      <td>11.000000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Cocoa Puffs</td>\n      <td>13.000000</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Grape-Nuts</td>\n      <td>12.000000</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>Total Raisin Bran</td>\n      <td>14.000000</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>Maypo</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Apple Cinnamon Cheerios</td>\n      <td>13.333333</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Great Grains Pecan</td>\n      <td>12.121212</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Golden Crisp</td>\n      <td>17.045455</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Golden Grahams</td>\n      <td>12.000000</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>Quaker Oatmeal</td>\n      <td>-1.492537</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>Raisin Bran</td>\n      <td>16.000000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Cinnamon Toast Crunch</td>\n      <td>12.000000</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Count Chocula</td>\n      <td>13.000000</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Just Right Crunchy  Nuggets</td>\n      <td>6.000000</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>Shredded Wheat spoon size</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Grape Nuts Flakes</td>\n      <td>5.681818</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Frosted Mini-Wheats</td>\n      <td>8.750000</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Honey Graham Ohs</td>\n      <td>11.000000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>100% Bran</td>\n      <td>18.181818</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Fruit &amp; Fibre Dates; Walnuts; and Oats</td>\n      <td>14.925373</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Crispy Wheat &amp; Raisins</td>\n      <td>13.333333</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Wheat Chex</td>\n      <td>4.477612</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Smacks</td>\n      <td>20.000000</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Froot Loops</td>\n      <td>13.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100% Natural Bran</td>\n      <td>8.000000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Cream of Wheat (Quick)</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>Lucky Charms</td>\n      <td>12.000000</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Honey-comb</td>\n      <td>8.270677</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "#Muestra n=30\n",
    "sample = data.sample(30)\n",
    "sample\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          sugars\n",
       "count  30.000000\n",
       "mean    9.820936\n",
       "std     5.712583\n",
       "min    -1.492537\n",
       "25%     5.761364\n",
       "50%    12.000000\n",
       "75%    13.250000\n",
       "max    20.000000"
      ],
      "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>sugars</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>9.820936</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>5.712583</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-1.492537</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>5.761364</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>12.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>13.250000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>20.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "#Estadisticas de la muestra\n",
    "sample.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "sugars    8.078963\ndtype: float64 sugars    10.323086\ndtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Intervalo de confianza para la media (mu) para el azucar\n",
    "n = 77\n",
    "xbar = data.mean()\n",
    "sigma = data.std()\n",
    "z = 1.645 # nivel de confianza 90%\n",
    "\n",
    "mu_min = xbar - z* (sigma / np.sqrt(n))\n",
    "mu_max = xbar + z* (sigma / np.sqrt(n))\n",
    "\n",
    "print(mu_min, mu_max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}