{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6112efd-e44e-4322-8161-8deca5a2f223",
   "metadata": {},
   "source": [
    "# Calidad del dato 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2702e8f-d8e6-4ae9-ad2f-f3d03bb65bbe",
   "metadata": {},
   "source": [
    "## Caso Fabricación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd0392d7-0c25-4f0f-be62-374e41b747ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.options.display.float_format = '{:.2f}'.format #Desactivar notación científica en pandas:\n",
    "np.set_printoptions(suppress=True) #Desactivar notación científica en numpy:\n",
    "pd.set_option('display.max_columns', None) #comando para mostrar todas las columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d9e515a-5fc5-416c-930a-560d58b22fb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"./datos/calidad_3.csv\", sep=\",\", decimal=\".\", encoding=\"WINDOWS-1252\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb2f49d1-7d6d-4b5b-96d5-dd66baea8f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>G4</th>\n",
       "      <th>G5</th>\n",
       "      <th>G6</th>\n",
       "      <th>G7</th>\n",
       "      <th>G8</th>\n",
       "      <th>G9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.37</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    G1   G2    G3   G4   G5    G6    G7    G8    G9\n",
       "0 0.17 0.19 -0.16 0.10 0.37 -0.24 -0.80  0.16 -0.21\n",
       "1 0.10 0.07  0.14 0.18 0.14 -0.06 -0.07  0.07  0.07\n",
       "2 0.61 0.71  0.88 0.77 0.59  0.19  0.43  0.34  0.19\n",
       "3 0.77 0.82  0.62 0.37 0.72  0.44 -0.03 -0.26  0.50\n",
       "4 0.50 0.64  0.84 0.68 0.50  0.15  0.40  0.30  0.26"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35eadd0d-0c10-4556-970b-2ed271445b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>G4</th>\n",
       "      <th>G5</th>\n",
       "      <th>G6</th>\n",
       "      <th>G7</th>\n",
       "      <th>G8</th>\n",
       "      <th>G9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>184.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>184.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.49</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.20</td>\n",
       "      <td>8.06</td>\n",
       "      <td>7.22</td>\n",
       "      <td>7.59</td>\n",
       "      <td>7.26</td>\n",
       "      <td>8.31</td>\n",
       "      <td>7.83</td>\n",
       "      <td>8.93</td>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          G1     G2     G3     G4     G5     G6     G7     G8     G9\n",
       "count 184.00 184.00 184.00 184.00 184.00 184.00 184.00 184.00 184.00\n",
       "mean    0.25   0.29   0.35   0.32   0.30   0.14   0.16   0.30   0.13\n",
       "std     0.72   0.78   0.73   0.74   0.74   0.92   0.82   0.90   0.82\n",
       "min    -1.17  -1.19  -0.97  -1.19  -1.09  -1.25  -1.47  -1.28  -1.42\n",
       "25%    -0.05  -0.06   0.05   0.03  -0.07  -0.38  -0.26  -0.14  -0.37\n",
       "50%     0.18   0.24   0.28   0.29   0.27   0.07  -0.02   0.27   0.01\n",
       "75%     0.49   0.55   0.59   0.55   0.54   0.48   0.50   0.73   0.50\n",
       "max     7.20   8.06   7.22   7.59   7.26   8.31   7.83   8.93   7.82"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "476dd733-c702-40f4-bc64-696a1248dad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 184 entries, 0 to 183\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   G1      184 non-null    float64\n",
      " 1   G2      184 non-null    float64\n",
      " 2   G3      184 non-null    float64\n",
      " 3   G4      184 non-null    float64\n",
      " 4   G5      184 non-null    float64\n",
      " 5   G6      184 non-null    float64\n",
      " 6   G7      184 non-null    float64\n",
      " 7   G8      184 non-null    float64\n",
      " 8   G9      184 non-null    float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 13.1 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c780e25-800e-40bd-abb3-376580bd24a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='G1', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "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": [
    "sns.histplot(df1.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bd65691a-413d-4935-9301-9865b4693726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7f0/PHrqmITjGP7BuCYvbgZMZTXX9CnBmVf3noIWtIUmmgemquinJYcA24KUeo5+V5PXADPCYqnrJ0PWsRUkuA/6tqi7uZhU+qqruHbisifOMf3guYbGCqpqrqpu6x/cB24Gjhq1qbUlyNPDrwMVD17JWJXkMcCJwCUBV/aTF0AeDfy04Cvj2gud3Y6gtKclG4DjgxoFLWWveA/wp8MDAdaxlTwTmgb/thsQuTvLooYsagsE/vLGWsBAkORS4Gji/qn4wdD1rRZKXADuratvQtaxxDweeAVxUVccBPwKa/E7N4B+eS1iMIcnBjEL/iqr62ND1rDEnAL+R5E5GQ4UnJbl82JLWpLuBu6vqwf8tfpTRB0FzDP7huYTFCpKE0bjs9qp619D1rDVV9edVdXRVbWT07+dzVXX2wGWtOVX1X8C3kzyla3o+0OQEgaavubsWuITFWE4AzgG+luTmru1NVfXp4UrSOvVa4IruJGsH8LsD1zMIp3NKUmMc6pGkxhj8ktQYg1+SGmPwS1JjDH5JaozBL40pyZFJPpxkR5JtSb6U5PQkv9CtHvrDJH8zdJ3SSgx+aQzdj8j+EfhiVT2xqn6F0Y+ljgb+F/hL4E+Gq1Aan8Evjeck4CdV9f4HG6rqrqp6b1X9qKpuYPQBIK15Br80nl8Gbhq6CGl/MPilVUjyviRfTfKVoWuR9pXBL43nNhas5FhVr2G0yNfUYBVJq2TwS+P5HHBIkj9c0PaooYqRHgoXaZPG1F37993AsxhdyelHwPur6spuLfzHAI8A7gVe6DWBtVYZ/JLUGId6JKkxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqzP8B+6KCjfPj704AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dVPvO4h9eU0tWVNWoqm7r7u9iXAxHTzdVf5IcA/w28L5pZ+lbkscDzwfeD1BV91XVj6caql8HAockORA4lEfw548s/uEdDXx33uO7WcFFOF+S1cBJwC1TjtKndwF/BTw45RxDeBKwA/hAd2jrfUkOm3aoPlTV94BLgLuAEfDfVXXDdFPtO4t/eBMtWbHSJHks8FHgDVW1c9p5+pDkd4DtVXXrtLMM5EDgmcBlVXUS8BNgRf7NKsnhjP9nfhzwq8BhSf5ouqn2ncU/vOaWrEhyEOPSv6Kqrpl2nh6dCvxukjsZH8Jbm+SfphupV3cDd1fVQ/+Du5rxL4KV6EXAf1TVjqr6OXAN8LwpZ9pnFv/wmlqyIkkYHwPeVlWXTjtPn6rqLVV1TFWtZvzv+tmqesS+K1xKVf0n8N0kT+mGTgP+fYqR+nQXcEqSQ7uf6dN4BP8h22vuDqzBJStOBc4Gvp5kczd2YVV9anqRtIxeD1zRvYn5NvAnU87Ti6q6JcnVwG2Mz1T7Ko/gpRtcskGSGuOhHklqjMUvSY2x+CWpMRa/JDXG4pekxlj80oSSHJXkw0m+neTWJF9KclaSg5JsTPL1boXKt0w7q7QYi1+aQPehnWuBL1TVk6rqWYw/pHUM8ArgMVX1m8CzgHO7dYmk/ZLFL01mLXBfVb3noYGq+k5V/T3jtZYO61ZtPAS4D1iR6xFpZbD4pcmcwPhTmwu5mvECZSPGH+2/pKp+NFQw6eGy+KV9kOTdSb6W5CuMr7HwAONVG48Dzk/ypKkGlBZh8UuT2cq8lSer6nWMF+qaAf6A8VWofl5V24F/A+amklKagMUvTeazwMFJ/nze2KHd7V2Ml2BOdyGSU4A7hg4oTcpF2qQJJZkF3gk8h/GVp34CvAf4JPABxtdQDvCBqvrbaeWUlmLxS1JjPNQjSY2x+CWpMRa/JDXG4pekxlj8ktQYi1+SGmPxS1Jj/g+Htl4NoMym0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for a in range(len(df1.columns)):\n",
    "    sns.histplot(df1.iloc[:,a])\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "#ns.histplot(df1.G2)\n",
    "#lt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bfe1f8a-49d9-400b-a657-b022b51c76cb",
   "metadata": {},
   "source": [
    "Se pueden ver valores dispersos en todas las zonas de medida (G1:G9).\n",
    "Un buen método para ver si son outliers es a través de Boxplot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c1942460-041c-4258-9613-098c22106baa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKKUlEQVR4nO3dXYied1rH8d+Vl2VTtchOS1mmi2MZUfBoNSiy4MHaSmoXX84UtMUTQTQdKyIq8SzHahvFpVRtgisKq4jYNNKyggoiTbortbbKQ5myia5mZ1l3Tbu2k/w9mGk7DZPOS2ZyPZP5fI4y/2e45+Ii+c7dezozNcYIALfege4BAPYrAQZoIsAATQQYoIkAAzQ5tJV3vuuuu8bc3NwujQJwe7pw4cKXxxh3X3++pQDPzc3l/PnzOzcVwD5QVa+vd+4RBEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE229DvhbmenTp3KZDK54euXLl1KkszOzm7qevPz8zl+/PiOzAbcngR41WQyyRf+5ZVcveMj675+8I3/SZJ86f82XtnBN76yo7MBtycBXuPqHR/Jm9/1I+u+duTVs0lyw9fXe1+AD+IZMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAk6kP8KlTp3Lq1KnuMaaePcHec6h7gI1MJpPuEfYEe4K9Z+rvgAFuVwIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNbkmAl5aW8uijj2ZpaWnLZ2zP2l3eql1PJpM89NBDmUwmO3ZN6LabXbolAT59+nReeumlnDlzZstnbM/aXd6qXZ88eTJXrlzJyZMnd+ya0G03u7TrAV5aWsq5c+cyxsi5c+fevSPbzBnbs3aXzz777C3Z9WQyyeLiYpJkcXHRXTC3hd3u0qEdvdo6Tp8+nWvXriVJrl69mjNnzmSMsamzxx57LJcuXcqbb76ZhYWFXZ1zMpnkwFtjR6514Btfy2Ty9V2fea3JZJIjR44kef/O33777XffZ6Nd34zr73pPnjyZp59++qauCd3W69fN/ltZa8M74Kr6uao6X1XnL1++vOUP8Pzzz2d5eTlJsry8nOeee27TZ2zP2l2OMTLGyieW3dz1O3e/N3ob9qLd7tKGd8BjjCeTPJkkR48e3fIt4v3335+zZ89meXk5hw4dygMPPJAxxqbOkmR2djZJ8vjjj2/1Q2/JwsJCLrz2XztyrWsfvjPz992z6zOvtfZue+3OqyrJSog32vXNmJube1905+bmbvqa0G29fu2kXX8G/Mgjj+TAgZUPc/DgwTz88MObPmN71u7y8OHDOXz4cJLd3fWJEyc+8G3Yi3a7S7se4JmZmRw7dixVlWPHjmVmZmbTZ2zP2l0++OCDt2TX8/Pz7971zs3NZX5+/qavCd12u0u7/kW4ZOWzyOLi4vs+e2z2jO25fpe3YtcnTpzIwsKCu19uK7vZpVsS4JmZmTzxxBPbOmN7rt/lrdj1/Px8nnnmmR29JnTbzS75VmSAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNDnUPsJH5+fnuEfYEe4K9Z+oDfPz48e4R9gR7gr3HIwiAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNDnUPcA0OfjGV3Lk1bM3eG0pSW74+vXXSe7ZydGA25AAr5qfn//A1y9dWk6SzM5uJqz3bHg9AAFedfz48e4RgH3GM2CAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAkxpjbP6dqy4neX33xpkadyX5cvcQU8x+NmZHG9tPO/q2Mcbd1x9uKcD7RVWdH2Mc7Z5jWtnPxuxoY3bkEQRAGwEGaCLA63uye4ApZz8bs6ON7fsdeQYM0MQdMEATAQZoIsBrVNWxqvq3qppU1a91zzNtqupjVfW3VfVKVb1cVQvdM02jqjpYVZ+vqr/unmUaVdW3VtVnq+rV1b9LP9A9UxfPgFdV1cEk/57kgSQXk7yQ5KfGGP/aOtgUqaqPJvnoGOPFqvqWJBeS/LgdvV9V/XKSo0nuHGN8qnueaVNVp5P8/Rjjqar6UJI7xhhfbR6rhTvg93xfkskY47UxxltJ/jTJjzXPNFXGGP85xnhx9c9fT/JKktneqaZLVd2b5KEkT3XPMo2q6s4kP5jkD5JkjPHWfo1vIsBrzSb54pq3L0Zcbqiq5pJ8PMk/NY8ybX4nya8mudY8x7S6L8nlJH+0+pjmqar6pu6hugjwe2qdM89n1lFV35zkz5P80hjja93zTIuq+lSS/x5jXOieZYodSvI9SX5/jPHxJFeS7Nuvtwjwey4m+diat+9N8h9Ns0ytqjqclfh+ZozxF93zTJlPJPnRqlrMyiOsT1bVH/eONHUuJrk4xnjnv5w+m5Ug70sC/J4XknxHVX376hcGfjLJXzXPNFWqqrLy7O6VMcZvdc8zbcYYvz7GuHeMMZeVvz+fG2P8dPNYU2WM8aUkX6yq71w9+qEk+/aLuIe6B5gWY4zlqvrFJH+T5GCSPxxjvNw81rT5RJKfSfJSVX1h9ew3xhhn+0ZiDzqe5DOrNzqvJfnZ5nna+N/QAJp4BAHQRIABmggwQBMBBmgiwABNBJg9p6ruqao/qarXqupCVf1jVf1EVc2s/rS2/62q3+2eEzYiwOwpq98M8pdJ/m6Mcd8Y43uz8k0P9yb5RpLfTPIrfRPC5gkwe80nk7w1xvj0OwdjjNfHGKfGGFfGGP+QlRDD1BNg9prvTvJi9xCwEwSYPa2qfq+q/rmqXuieBbZKgNlrXs6an541xviFrPxAl7vbJoJtEmD2ms8l+XBV/fyaszu6hoGb4YfxsOes/m66307y/Vn57QpXknx6jPFnqz+L984kH0ry1SQ/7HfWMa0EGKCJRxAATQQYoIkAAzQRYIAmAgzQRIABmggwQJP/B79oYTk6vcJQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for a in range(len(df1.columns)):\n",
    "    sns.boxplot(df1.iloc[:,a])\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b49cacc7-c7a8-4c05-8a50-fd5a26687a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Existen diferentes métodos para detectar outliers que veremos más adelante.  Para este ejemplo usaremos la fórmula:\n",
    "# Rango Outliers superior = Q3 + 1.5*IRQ\n",
    "# Rango Outliers inferior = Q1 - 1.5*IRQ\n",
    "\n",
    "def deteccion_outliers(x):\n",
    "    sorted(x)\n",
    "    Q1,Q3 = np.percentile(x , [25,75])\n",
    "    IQR = Q3 - Q1\n",
    "    rango_inf = Q1 - (1.5 * IQR)\n",
    "    rango_sup = Q3 + (1.5 * IQR)\n",
    "    \n",
    "    x = x[x>rango_inf]\n",
    "    x = x[x<rango_sup]\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8eb67f42-ed14-43c7-80c8-6f088450c06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "g1 = deteccion_outliers(df1.G1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9f1b9bcc-8971-463d-885a-ecd0ff56b9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.17\n",
       "1      0.10\n",
       "2      0.61\n",
       "3      0.77\n",
       "4      0.50\n",
       "       ... \n",
       "179    0.54\n",
       "180    0.04\n",
       "181    0.51\n",
       "182   -0.03\n",
       "183    0.47\n",
       "Name: G1, Length: 175, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "be26f60c-1a52-4f6f-b47e-fa835425a67f",
   "metadata": {},
   "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"
    },
    {
     "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": [
    "#Pintamos el resultado de los datos, con y sin outliers\n",
    "sns.boxplot(df1.G1)\n",
    "plt.show()\n",
    "sns.boxplot(g1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b9997b-371f-4714-b6c5-c2b6807ec49e",
   "metadata": {},
   "source": [
    "Ejercicio:\n",
    "- Localiza y elimina los outliers en el resto de las columnas.\n",
    "- Genera un nuevo dataset sin outliers\n",
    "- Comprueba mediante histograma que las variables y no tienen valores atípicos."
   ]
  }
 ],
 "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}