T2-4-Data Cleaning-Concatenación de datos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "merah_wine=pd.read_csv('../GitHub/python-ml-course/datasets/wine/winequality-red.csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99512</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.076</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99574</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.075</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99547</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.2</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.067</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99549</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
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       "<p>1599 rows × 12 columns</p>\n",
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      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4             0.700         0.00             1.9      0.076   \n",
       "1               7.8             0.880         0.00             2.6      0.098   \n",
       "2               7.8             0.760         0.04             2.3      0.092   \n",
       "3              11.2             0.280         0.56             1.9      0.075   \n",
       "4               7.4             0.700         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "1594            6.2             0.600         0.08             2.0      0.090   \n",
       "1595            5.9             0.550         0.10             2.2      0.062   \n",
       "1596            6.3             0.510         0.13             2.3      0.076   \n",
       "1597            5.9             0.645         0.12             2.0      0.075   \n",
       "1598            6.0             0.310         0.47             3.6      0.067   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
       "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
       "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
       "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
       "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
       "\n",
       "      alcohol  quality  \n",
       "0         9.4        5  \n",
       "1         9.8        5  \n",
       "2         9.8        5  \n",
       "3         9.8        6  \n",
       "4         9.4        5  \n",
       "...       ...      ...  \n",
       "1594     10.5        5  \n",
       "1595     11.2        6  \n",
       "1596     11.0        6  \n",
       "1597     10.2        5  \n",
       "1598     11.0        6  \n",
       "\n",
       "[1599 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merah_wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fixed acidity', 'volatile acidity', 'citric acid',\n",
       "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
       "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
       "       'quality'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merah_wine.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merah_wine.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "putih_wine=pd.read_csv('../GitHub/python-ml-course/datasets/wine/winequality-white.csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
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       "      <th>sulphates</th>\n",
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       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.00100</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.99400</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.99510</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.99560</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.99560</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4893</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4894</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4895</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4896</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4898 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.0              0.27         0.36            20.7      0.045   \n",
       "1               6.3              0.30         0.34             1.6      0.049   \n",
       "2               8.1              0.28         0.40             6.9      0.050   \n",
       "3               7.2              0.23         0.32             8.5      0.058   \n",
       "4               7.2              0.23         0.32             8.5      0.058   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "4893            6.2              0.21         0.29             1.6      0.039   \n",
       "4894            6.6              0.32         0.36             8.0      0.047   \n",
       "4895            6.5              0.24         0.19             1.2      0.041   \n",
       "4896            5.5              0.29         0.30             1.1      0.022   \n",
       "4897            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    45.0                 170.0  1.00100  3.00       0.45   \n",
       "1                    14.0                 132.0  0.99400  3.30       0.49   \n",
       "2                    30.0                  97.0  0.99510  3.26       0.44   \n",
       "3                    47.0                 186.0  0.99560  3.19       0.40   \n",
       "4                    47.0                 186.0  0.99560  3.19       0.40   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality  \n",
       "0         8.8        6  \n",
       "1         9.5        6  \n",
       "2        10.1        6  \n",
       "3         9.9        6  \n",
       "4         9.9        6  \n",
       "...       ...      ...  \n",
       "4893     11.2        6  \n",
       "4894      9.6        5  \n",
       "4895      9.4        6  \n",
       "4896     12.8        7  \n",
       "4897     11.8        6  \n",
       "\n",
       "[4898 rows x 12 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "putih_wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4898, 12)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "putih_wine.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En python hay dos tipos de ejes:\n",
    "\n",
    "* Eje horizontal: Denotado como axis= 0\n",
    "* Eje vertical: Denotado como axis= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_data=pd.concat((merah_wine,putih_wine),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
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       "      <th>fixed acidity</th>\n",
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       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4              0.70         0.00             1.9      0.076   \n",
       "1               7.8              0.88         0.00             2.6      0.098   \n",
       "2               7.8              0.76         0.04             2.3      0.092   \n",
       "3              11.2              0.28         0.56             1.9      0.075   \n",
       "4               7.4              0.70         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "4893            6.2              0.21         0.29             1.6      0.039   \n",
       "4894            6.6              0.32         0.36             8.0      0.047   \n",
       "4895            6.5              0.24         0.19             1.2      0.041   \n",
       "4896            5.5              0.29         0.30             1.1      0.022   \n",
       "4897            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
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       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality  \n",
       "0         9.4        5  \n",
       "1         9.8        5  \n",
       "2         9.8        5  \n",
       "3         9.8        6  \n",
       "4         9.4        5  \n",
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       "4895      9.4        6  \n",
       "4896     12.8        7  \n",
       "4897     11.8        6  \n",
       "\n",
       "[6497 rows x 12 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "wine_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=wine_data[:10]\n",
    "data2=wine_data[300:310]\n",
    "data3=wine_data.tail(10)\n",
    "\n",
    "wine_data_2=pd.concat((data2,data1,data3),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>303</th>\n",
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       "      <th>304</th>\n",
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       "      <td>0.112</td>\n",
       "      <td>12.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.99730</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.52</td>\n",
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       "      <th>305</th>\n",
       "      <td>10.3</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.063</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.99980</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.59</td>\n",
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       "      <th>306</th>\n",
       "      <td>7.6</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.082</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>0.99660</td>\n",
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       "      <th>307</th>\n",
       "      <td>10.3</td>\n",
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       "      <td>0.42</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.99940</td>\n",
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       "      <td>0.62</td>\n",
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       "      <th>308</th>\n",
       "      <td>10.3</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.214</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.99940</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.63</td>\n",
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       "      <th>309</th>\n",
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       "      <td>1.70</td>\n",
       "      <td>0.062</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.41</td>\n",
       "      <td>0.53</td>\n",
       "      <td>9.5</td>\n",
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       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
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       "      <td>0.56</td>\n",
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       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.9</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.069</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.99640</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.3</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.065</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.99460</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.073</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.10</td>\n",
       "      <td>0.071</td>\n",
       "      <td>17.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4888</th>\n",
       "      <td>6.8</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.052</td>\n",
       "      <td>38.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>0.99330</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.54</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4889</th>\n",
       "      <td>4.9</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.27</td>\n",
       "      <td>11.75</td>\n",
       "      <td>0.030</td>\n",
       "      <td>34.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.99540</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.50</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4890</th>\n",
       "      <td>6.1</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.036</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.98938</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.44</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4891</th>\n",
       "      <td>5.7</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.038</td>\n",
       "      <td>38.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.99074</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.46</td>\n",
       "      <td>10.6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4892</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.032</td>\n",
       "      <td>29.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.99298</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.54</td>\n",
       "      <td>9.7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4893</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4894</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4895</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4896</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "300             7.5             0.530         0.06            2.60      0.086   \n",
       "301            11.1             0.180         0.48            1.50      0.068   \n",
       "302             8.3             0.705         0.12            2.60      0.092   \n",
       "303             7.4             0.670         0.12            1.60      0.186   \n",
       "304             8.4             0.650         0.60            2.10      0.112   \n",
       "305            10.3             0.530         0.48            2.50      0.063   \n",
       "306             7.6             0.620         0.32            2.20      0.082   \n",
       "307            10.3             0.410         0.42            2.40      0.213   \n",
       "308            10.3             0.430         0.44            2.40      0.214   \n",
       "309             7.4             0.290         0.38            1.70      0.062   \n",
       "0               7.4             0.700         0.00            1.90      0.076   \n",
       "1               7.8             0.880         0.00            2.60      0.098   \n",
       "2               7.8             0.760         0.04            2.30      0.092   \n",
       "3              11.2             0.280         0.56            1.90      0.075   \n",
       "4               7.4             0.700         0.00            1.90      0.076   \n",
       "5               7.4             0.660         0.00            1.80      0.075   \n",
       "6               7.9             0.600         0.06            1.60      0.069   \n",
       "7               7.3             0.650         0.00            1.20      0.065   \n",
       "8               7.8             0.580         0.02            2.00      0.073   \n",
       "9               7.5             0.500         0.36            6.10      0.071   \n",
       "4888            6.8             0.220         0.36            1.20      0.052   \n",
       "4889            4.9             0.235         0.27           11.75      0.030   \n",
       "4890            6.1             0.340         0.29            2.20      0.036   \n",
       "4891            5.7             0.210         0.32            0.90      0.038   \n",
       "4892            6.5             0.230         0.38            1.30      0.032   \n",
       "4893            6.2             0.210         0.29            1.60      0.039   \n",
       "4894            6.6             0.320         0.36            8.00      0.047   \n",
       "4895            6.5             0.240         0.19            1.20      0.041   \n",
       "4896            5.5             0.290         0.30            1.10      0.022   \n",
       "4897            6.0             0.210         0.38            0.80      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "300                  20.0                  44.0  0.99650  3.38       0.59   \n",
       "301                   7.0                  15.0  0.99730  3.22       0.64   \n",
       "302                  12.0                  28.0  0.99940  3.51       0.72   \n",
       "303                   5.0                  21.0  0.99600  3.39       0.54   \n",
       "304                  12.0                  90.0  0.99730  3.20       0.52   \n",
       "305                   6.0                  25.0  0.99980  3.12       0.59   \n",
       "306                   7.0                  54.0  0.99660  3.36       0.52   \n",
       "307                   6.0                  14.0  0.99940  3.19       0.62   \n",
       "308                   5.0                  12.0  0.99940  3.19       0.63   \n",
       "309                   9.0                  30.0  0.99680  3.41       0.53   \n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "5                    13.0                  40.0  0.99780  3.51       0.56   \n",
       "6                    15.0                  59.0  0.99640  3.30       0.46   \n",
       "7                    15.0                  21.0  0.99460  3.39       0.47   \n",
       "8                     9.0                  18.0  0.99680  3.36       0.57   \n",
       "9                    17.0                 102.0  0.99780  3.35       0.80   \n",
       "4888                 38.0                 127.0  0.99330  3.04       0.54   \n",
       "4889                 34.0                 118.0  0.99540  3.07       0.50   \n",
       "4890                 25.0                 100.0  0.98938  3.06       0.44   \n",
       "4891                 38.0                 121.0  0.99074  3.24       0.46   \n",
       "4892                 29.0                 112.0  0.99298  3.29       0.54   \n",
       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality  \n",
       "300      10.7        6  \n",
       "301      10.1        6  \n",
       "302      10.0        5  \n",
       "303       9.5        5  \n",
       "304       9.2        5  \n",
       "305       9.3        6  \n",
       "306       9.4        5  \n",
       "307       9.5        6  \n",
       "308       9.5        6  \n",
       "309       9.5        6  \n",
       "0         9.4        5  \n",
       "1         9.8        5  \n",
       "2         9.8        5  \n",
       "3         9.8        6  \n",
       "4         9.4        5  \n",
       "5         9.4        5  \n",
       "6         9.4        5  \n",
       "7        10.0        7  \n",
       "8         9.5        7  \n",
       "9        10.5        5  \n",
       "4888      9.2        5  \n",
       "4889      9.4        6  \n",
       "4890     11.8        6  \n",
       "4891     10.6        6  \n",
       "4892      9.7        5  \n",
       "4893     11.2        6  \n",
       "4894      9.6        5  \n",
       "4895      9.4        6  \n",
       "4896     12.8        7  \n",
       "4897     11.8        6  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No sé porque la carga no la hace según el orden del directorio, por lo tanto hay que aplicar la función **sorted**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths=sorted(glob.glob('/home/franklin/Documentos/GitHub/python-ml-course/datasets/distributed-data/*.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este tipo de iteraciones recordar siempre que el primero se llama igual al último."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(paths[0])\n",
    "verify=len(df1)\n",
    "for i in range(1,len(paths)):\n",
    "    df=pd.read_csv(paths[i])\n",
    "    df1=pd.concat((df1,df),axis=0)\n",
    "    verify+=len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>nitrate</th>\n",
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       "      <td>2004-12-29</td>\n",
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       "      <th>730</th>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>772087 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  sulfate  nitrate   ID\n",
       "0    2003-01-01      NaN      NaN    1\n",
       "1    2003-01-02      NaN      NaN    1\n",
       "2    2003-01-03      NaN      NaN    1\n",
       "3    2003-01-04      NaN      NaN    1\n",
       "4    2003-01-05      NaN      NaN    1\n",
       "..          ...      ...      ...  ...\n",
       "726  2004-12-27      NaN      NaN  332\n",
       "727  2004-12-28      NaN      NaN  332\n",
       "728  2004-12-29      NaN      NaN  332\n",
       "729  2004-12-30      NaN      NaN  332\n",
       "730  2004-12-31      NaN      NaN  332\n",
       "\n",
       "[772087 rows x 4 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(772087, 4)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verify==df1.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Join Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_medals=pd.read_csv('../GitHub/python-ml-course/datasets/athletes/Medals.csv',encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aleksey Nemov</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "4     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
       "\n",
       "   Silver Medals  Bronze Medals  Total Medals  \n",
       "0              0              0             8  \n",
       "1              0              2             8  \n",
       "2              2              0             6  \n",
       "3              2              3             6  \n",
       "4              1              3             6  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_medals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6956"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=data_medals.Athlete.unique().tolist()\n",
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8618, 8)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_medals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_country=pd.read_csv('../GitHub/python-ml-course/datasets/athletes/Athelete_Country_Map.csv',encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aleksey Nemov</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alicia Coutts</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Missy Franklin</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6965</th>\n",
       "      <td>Olena Sadovnycha</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6966</th>\n",
       "      <td>Kateryna Serdiuk</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>Wietse van Alten</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>Sandra Wagner-Sachse</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>Rod White</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6970 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Athlete        Country\n",
       "0           Michael Phelps  United States\n",
       "1         Natalie Coughlin  United States\n",
       "2            Aleksey Nemov         Russia\n",
       "3            Alicia Coutts      Australia\n",
       "4           Missy Franklin  United States\n",
       "...                    ...            ...\n",
       "6965      Olena Sadovnycha        Ukraine\n",
       "6966      Kateryna Serdiuk        Ukraine\n",
       "6967      Wietse van Alten    Netherlands\n",
       "6968  Sandra Wagner-Sachse        Germany\n",
       "6969             Rod White  United States\n",
       "\n",
       "[6970 rows x 2 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A veces aparecen registros que están duplicados y dónde el único camino que queda es eliminar los duplicados y dejar uno solo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>Serbia and Montenegro</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Athlete                Country\n",
       "1029  Aleksandar Ciric                 Serbia\n",
       "1086  Aleksandar Ciric  Serbia and Montenegro"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_country[data_country[\"Athlete\"]==\"Aleksandar Ciric\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sports=pd.read_csv('../GitHub/python-ml-course/datasets/athletes/Athelete_Sports_Map.csv',encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Sport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aleksey Nemov</td>\n",
       "      <td>Gymnastics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alicia Coutts</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Missy Franklin</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6970</th>\n",
       "      <td>Olena Sadovnycha</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6971</th>\n",
       "      <td>Kateryna Serdiuk</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6972</th>\n",
       "      <td>Wietse van Alten</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6973</th>\n",
       "      <td>Sandra Wagner-Sachse</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>Rod White</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6975 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Athlete       Sport\n",
       "0           Michael Phelps    Swimming\n",
       "1         Natalie Coughlin    Swimming\n",
       "2            Aleksey Nemov  Gymnastics\n",
       "3            Alicia Coutts    Swimming\n",
       "4           Missy Franklin    Swimming\n",
       "...                    ...         ...\n",
       "6970      Olena Sadovnycha     Archery\n",
       "6971      Kateryna Serdiuk     Archery\n",
       "6972      Wietse van Alten     Archery\n",
       "6973  Sandra Wagner-Sachse     Archery\n",
       "6974             Rod White     Archery\n",
       "\n",
       "[6975 rows x 2 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El conjunto de reglas lógicas en python debe encerrarse en parentesis para ser distinguido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Sport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>Richard Thompson</td>\n",
       "      <td>Athletics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>Chen Jing</td>\n",
       "      <td>Volleyball</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>Chen Jing</td>\n",
       "      <td>Table Tennis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5691</th>\n",
       "      <td>Richard Thompson</td>\n",
       "      <td>Baseball</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Athlete         Sport\n",
       "528   Richard Thompson     Athletics\n",
       "1308         Chen Jing    Volleyball\n",
       "1419         Chen Jing  Table Tennis\n",
       "5691  Richard Thompson      Baseball"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sports[(data_sports[\"Athlete\"]==\"Chen Jing\")|\n",
    "           (data_sports[\"Athlete\"]==\"Richard Thompson\")\n",
    "           ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Borrando los duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6956"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_out_dp=data_country.drop_duplicates(subset=\"Athlete\")\n",
    "len(data_out_dp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_main_country=pd.merge(data_medals,data_country,left_on=\"Athlete\",right_on=\"Athlete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>United States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8652</th>\n",
       "      <td>Olena Sadovnycha</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8653</th>\n",
       "      <td>Kateryna Serdiuk</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8654</th>\n",
       "      <td>Wietse van Alten</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8655</th>\n",
       "      <td>Sandra Wagner-Sachse</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8656</th>\n",
       "      <td>Rod White</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8657 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0           Michael Phelps  23.0  2008            08/24/2008            8   \n",
       "1           Michael Phelps  19.0  2004            08/29/2004            6   \n",
       "2           Michael Phelps  27.0  2012            08/12/2012            4   \n",
       "3         Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "4         Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
       "...                    ...   ...   ...                   ...          ...   \n",
       "8652      Olena Sadovnycha  32.0  2000            10/01/2000            0   \n",
       "8653      Kateryna Serdiuk  17.0  2000            10/01/2000            0   \n",
       "8654      Wietse van Alten  21.0  2000            10/01/2000            0   \n",
       "8655  Sandra Wagner-Sachse  31.0  2000            10/01/2000            0   \n",
       "8656             Rod White  23.0  2000            10/01/2000            0   \n",
       "\n",
       "      Silver Medals  Bronze Medals  Total Medals        Country  \n",
       "0                 0              0             8  United States  \n",
       "1                 0              2             8  United States  \n",
       "2                 2              0             6  United States  \n",
       "3                 2              3             6  United States  \n",
       "4                 2              1             5  United States  \n",
       "...             ...            ...           ...            ...  \n",
       "8652              1              0             1        Ukraine  \n",
       "8653              1              0             1        Ukraine  \n",
       "8654              0              1             1    Netherlands  \n",
       "8655              0              1             1        Germany  \n",
       "8656              0              1             1  United States  \n",
       "\n",
       "[8657 rows x 9 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8657, 9)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main_country.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8618, 8)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_medals.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Puede notarse que algunos registros se duplicaron al hacer el inner join porque su registro estaba 2 veces en country. Esto debió a que el fichero de países lo tenía duplicado, relación de uno a muchos. Debe ser una relación uno a uno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia and Montenegro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia and Montenegro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia and Montenegro</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "1503  Aleksandar Ciric  30.0  2008            08/24/2008            0   \n",
       "1504  Aleksandar Ciric  30.0  2008            08/24/2008            0   \n",
       "1505  Aleksandar Ciric  26.0  2004            08/29/2004            0   \n",
       "1506  Aleksandar Ciric  26.0  2004            08/29/2004            0   \n",
       "1507  Aleksandar Ciric  22.0  2000            10/01/2000            0   \n",
       "1508  Aleksandar Ciric  22.0  2000            10/01/2000            0   \n",
       "\n",
       "      Silver Medals  Bronze Medals  Total Medals                Country  \n",
       "1503              0              1             1                 Serbia  \n",
       "1504              0              1             1  Serbia and Montenegro  \n",
       "1505              1              0             1                 Serbia  \n",
       "1506              1              0             1  Serbia and Montenegro  \n",
       "1507              0              1             1                 Serbia  \n",
       "1508              0              1             1  Serbia and Montenegro  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main_country[data_main_country.Athlete==\"Aleksandar Ciric\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6956, (6956, 2), (8618, 8))"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a),data_out_dp.shape,data_medals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_main_country=pd.merge(data_medals,data_out_dp,left_on=\"Athlete\",right_on=\"Athlete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>United States</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>Olena Sadovnycha</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8614</th>\n",
       "      <td>Kateryna Serdiuk</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8615</th>\n",
       "      <td>Wietse van Alten</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>Sandra Wagner-Sachse</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>Rod White</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8618 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0           Michael Phelps  23.0  2008            08/24/2008            8   \n",
       "1           Michael Phelps  19.0  2004            08/29/2004            6   \n",
       "2           Michael Phelps  27.0  2012            08/12/2012            4   \n",
       "3         Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "4         Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
       "...                    ...   ...   ...                   ...          ...   \n",
       "8613      Olena Sadovnycha  32.0  2000            10/01/2000            0   \n",
       "8614      Kateryna Serdiuk  17.0  2000            10/01/2000            0   \n",
       "8615      Wietse van Alten  21.0  2000            10/01/2000            0   \n",
       "8616  Sandra Wagner-Sachse  31.0  2000            10/01/2000            0   \n",
       "8617             Rod White  23.0  2000            10/01/2000            0   \n",
       "\n",
       "      Silver Medals  Bronze Medals  Total Medals        Country  \n",
       "0                 0              0             8  United States  \n",
       "1                 0              2             8  United States  \n",
       "2                 2              0             6  United States  \n",
       "3                 2              3             6  United States  \n",
       "4                 2              1             5  United States  \n",
       "...             ...            ...           ...            ...  \n",
       "8613              1              0             1        Ukraine  \n",
       "8614              1              0             1        Ukraine  \n",
       "8615              0              1             1    Netherlands  \n",
       "8616              0              1             1        Germany  \n",
       "8617              0              1             1  United States  \n",
       "\n",
       "[8618 rows x 9 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>Aleksandar Ciric</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "1491  Aleksandar Ciric  30.0  2008            08/24/2008            0   \n",
       "1492  Aleksandar Ciric  26.0  2004            08/29/2004            0   \n",
       "1493  Aleksandar Ciric  22.0  2000            10/01/2000            0   \n",
       "\n",
       "      Silver Medals  Bronze Medals  Total Medals Country  \n",
       "1491              0              1             1  Serbia  \n",
       "1492              1              0             1  Serbia  \n",
       "1493              0              1             1  Serbia  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main_country[data_main_country.Athlete==\"Aleksandar Ciric\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sport_dp=data_sports.drop_duplicates(subset=\"Athlete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6956, 2), 6956)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sport_dp.shape,len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final=pd.merge(left=data_main_country,right=data_sport_dp,left_on=\"Athlete\",right_on=\"Athlete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "      <th>Sport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>Olena Sadovnycha</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8614</th>\n",
       "      <td>Kateryna Serdiuk</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8615</th>\n",
       "      <td>Wietse van Alten</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>Sandra Wagner-Sachse</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>Rod White</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>Archery</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8618 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0           Michael Phelps  23.0  2008            08/24/2008            8   \n",
       "1           Michael Phelps  19.0  2004            08/29/2004            6   \n",
       "2           Michael Phelps  27.0  2012            08/12/2012            4   \n",
       "3         Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "4         Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
       "...                    ...   ...   ...                   ...          ...   \n",
       "8613      Olena Sadovnycha  32.0  2000            10/01/2000            0   \n",
       "8614      Kateryna Serdiuk  17.0  2000            10/01/2000            0   \n",
       "8615      Wietse van Alten  21.0  2000            10/01/2000            0   \n",
       "8616  Sandra Wagner-Sachse  31.0  2000            10/01/2000            0   \n",
       "8617             Rod White  23.0  2000            10/01/2000            0   \n",
       "\n",
       "      Silver Medals  Bronze Medals  Total Medals        Country     Sport  \n",
       "0                 0              0             8  United States  Swimming  \n",
       "1                 0              2             8  United States  Swimming  \n",
       "2                 2              0             6  United States  Swimming  \n",
       "3                 2              3             6  United States  Swimming  \n",
       "4                 2              1             5  United States  Swimming  \n",
       "...             ...            ...           ...            ...       ...  \n",
       "8613              1              0             1        Ukraine   Archery  \n",
       "8614              1              0             1        Ukraine   Archery  \n",
       "8615              0              1             1    Netherlands   Archery  \n",
       "8616              0              1             1        Germany   Archery  \n",
       "8617              0              1             1  United States   Archery  \n",
       "\n",
       "[8618 rows x 10 columns]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tipos de joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inner Join <= A(left Join),B (right Join)<= Outer Join**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_athletes=np.random.choice(data_medals[\"Athlete\"],size=6,replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_country_dlt=data_out_dp[~(data_out_dp.Athlete).isin(out_athlete)&\n",
    "                             (data_out_dp.Athlete!=\"Michael Phelps\")]\n",
    "\n",
    "data_sports_dlt=data_sport_dp[~(data_sport_dp.Athlete).isin(out_athlete)&\n",
    "                              (data_sport_dp.Athlete!=\"Michael Phelps\")]\n",
    "\n",
    "data_medals_dlt=data_medals[~(data_medals.Athlete).isin(out_athlete)&\n",
    "                            (data_medals.Athlete!=\"Michael Phelps\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordar que:\n",
    "\n",
    "- Data_medals tiene toda la info,\n",
    "- Data_country_dlt le hacen falta 7 registros\n",
    "- Data_sports_dlt le hacen falta 7 registros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner Join\n",
    "* Devuelve un DataFrame con las filas que tienen valor tanto en el primer como en el segundo data frame que estamos uniendo\n",
    "* El número de filas será igual a filas **comunes** que tengan ambos data sets\n",
    "* Intersección de 2 conjuntos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"../GitHub/python-ml-course/notebooks/resources/inner-join.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_inner=pd.merge(left=data_medals,right=data_country_dlt,\n",
    "                     how=\"inner\",left_on=\"Athlete\",right_on=\"Athlete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aleksey Nemov</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alicia Coutts</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "1  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
       "2  Natalie Coughlin  29.0  2012            08/12/2012            0   \n",
       "3     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
       "4     Alicia Coutts  24.0  2012            08/12/2012            1   \n",
       "\n",
       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
       "0              2              3             6  United States  \n",
       "1              2              1             5  United States  \n",
       "2              0              1             1  United States  \n",
       "3              1              3             6         Russia  \n",
       "4              3              1             5      Australia  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_inner.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"../GitHub/python-ml-course/notebooks/resources/left-join.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_left=pd.merge(left=data_medals,right=data_country_dlt,\n",
    "                     how=\"left\",left_on=\"Athlete\",right_on=\"Athlete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aleksey Nemov</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Russia</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>Olena Sadovnycha</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8614</th>\n",
       "      <td>Kateryna Serdiuk</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8615</th>\n",
       "      <td>Wietse van Alten</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>Sandra Wagner-Sachse</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>Rod White</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8618 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0           Michael Phelps  23.0  2008            08/24/2008            8   \n",
       "1           Michael Phelps  19.0  2004            08/29/2004            6   \n",
       "2           Michael Phelps  27.0  2012            08/12/2012            4   \n",
       "3         Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "4            Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
       "...                    ...   ...   ...                   ...          ...   \n",
       "8613      Olena Sadovnycha  32.0  2000            10/01/2000            0   \n",
       "8614      Kateryna Serdiuk  17.0  2000            10/01/2000            0   \n",
       "8615      Wietse van Alten  21.0  2000            10/01/2000            0   \n",
       "8616  Sandra Wagner-Sachse  31.0  2000            10/01/2000            0   \n",
       "8617             Rod White  23.0  2000            10/01/2000            0   \n",
       "\n",
       "      Silver Medals  Bronze Medals  Total Medals        Country  \n",
       "0                 0              0             8            NaN  \n",
       "1                 0              2             8            NaN  \n",
       "2                 2              0             6            NaN  \n",
       "3                 2              3             6  United States  \n",
       "4                 1              3             6         Russia  \n",
       "...             ...            ...           ...            ...  \n",
       "8613              1              0             1        Ukraine  \n",
       "8614              1              0             1        Ukraine  \n",
       "8615              0              1             1    Netherlands  \n",
       "8616              0              1             1        Germany  \n",
       "8617              0              1             1  United States  \n",
       "\n",
       "[8618 rows x 9 columns]"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8618"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged_left)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Right Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"../GitHub/python-ml-course/notebooks/resources/right-join.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_right=pd.merge(left=data_medals_dlt,right=data_country,\n",
    "                     how=\"right\",left_on=\"Athlete\",right_on=\"Athlete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se colocan de primeros todos los que hacen parte del data set del left (ese dataset domina) y después los de dataset que no cabe se apendica al final."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8642</th>\n",
       "      <td>Wietse van Alten</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8643</th>\n",
       "      <td>Sandra Wagner-Sachse</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8644</th>\n",
       "      <td>Rod White</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8645</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8646</th>\n",
       "      <td>Maurice Greene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8647</th>\n",
       "      <td>Norbert Madaras</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hungary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8648</th>\n",
       "      <td>Dimitri Peters</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8649</th>\n",
       "      <td>Viola Odebrecht</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8650</th>\n",
       "      <td>Aleksandr Vinokurov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kazakhstan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8651</th>\n",
       "      <td>Kim Cheong-Tae</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Korea</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Athlete   Age    Year Closing Ceremony Date  Gold Medals  \\\n",
       "8642      Wietse van Alten  21.0  2000.0            10/01/2000          0.0   \n",
       "8643  Sandra Wagner-Sachse  31.0  2000.0            10/01/2000          0.0   \n",
       "8644             Rod White  23.0  2000.0            10/01/2000          0.0   \n",
       "8645        Michael Phelps   NaN     NaN                   NaN          NaN   \n",
       "8646        Maurice Greene   NaN     NaN                   NaN          NaN   \n",
       "8647       Norbert Madaras   NaN     NaN                   NaN          NaN   \n",
       "8648        Dimitri Peters   NaN     NaN                   NaN          NaN   \n",
       "8649       Viola Odebrecht   NaN     NaN                   NaN          NaN   \n",
       "8650   Aleksandr Vinokurov   NaN     NaN                   NaN          NaN   \n",
       "8651        Kim Cheong-Tae   NaN     NaN                   NaN          NaN   \n",
       "\n",
       "      Silver Medals  Bronze Medals  Total Medals        Country  \n",
       "8642            0.0            1.0           1.0    Netherlands  \n",
       "8643            0.0            1.0           1.0        Germany  \n",
       "8644            0.0            1.0           1.0  United States  \n",
       "8645            NaN            NaN           NaN  United States  \n",
       "8646            NaN            NaN           NaN  United States  \n",
       "8647            NaN            NaN           NaN        Hungary  \n",
       "8648            NaN            NaN           NaN        Germany  \n",
       "8649            NaN            NaN           NaN        Germany  \n",
       "8650            NaN            NaN           NaN     Kazakhstan  \n",
       "8651            NaN            NaN           NaN    South Korea  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_right.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outer Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"../GitHub/python-ml-course/notebooks/resources/outer-join.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_country_fa=data_country_dlt.append(\n",
    "    {\"Athlete\":\"Franklin Ayala\",\n",
    "    \"Country\":\"Colombia\"\n",
    "    },ignore_index=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_outer=pd.merge(left=data_medals,right=data_country_fa,\n",
    "                     how=\"outer\",left_on=\"Athlete\",right_on=\"Athlete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>United States</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8614</th>\n",
       "      <td>Kateryna Serdiuk</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8615</th>\n",
       "      <td>Wietse van Alten</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>Sandra Wagner-Sachse</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8617</th>\n",
       "      <td>Rod White</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8618</th>\n",
       "      <td>Franklin Ayala</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Colombia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8619 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Athlete   Age    Year Closing Ceremony Date  Gold Medals  \\\n",
       "0           Michael Phelps  23.0  2008.0            08/24/2008          8.0   \n",
       "1           Michael Phelps  19.0  2004.0            08/29/2004          6.0   \n",
       "2           Michael Phelps  27.0  2012.0            08/12/2012          4.0   \n",
       "3         Natalie Coughlin  25.0  2008.0            08/24/2008          1.0   \n",
       "4         Natalie Coughlin  21.0  2004.0            08/29/2004          2.0   \n",
       "...                    ...   ...     ...                   ...          ...   \n",
       "8614      Kateryna Serdiuk  17.0  2000.0            10/01/2000          0.0   \n",
       "8615      Wietse van Alten  21.0  2000.0            10/01/2000          0.0   \n",
       "8616  Sandra Wagner-Sachse  31.0  2000.0            10/01/2000          0.0   \n",
       "8617             Rod White  23.0  2000.0            10/01/2000          0.0   \n",
       "8618        Franklin Ayala   NaN     NaN                   NaN          NaN   \n",
       "\n",
       "      Silver Medals  Bronze Medals  Total Medals        Country  \n",
       "0               0.0            0.0           8.0            NaN  \n",
       "1               0.0            2.0           8.0            NaN  \n",
       "2               2.0            0.0           6.0            NaN  \n",
       "3               2.0            3.0           6.0  United States  \n",
       "4               2.0            1.0           5.0  United States  \n",
       "...             ...            ...           ...            ...  \n",
       "8614            1.0            0.0           1.0        Ukraine  \n",
       "8615            0.0            1.0           1.0    Netherlands  \n",
       "8616            0.0            1.0           1.0        Germany  \n",
       "8617            0.0            1.0           1.0  United States  \n",
       "8618            NaN            NaN           NaN       Colombia  \n",
       "\n",
       "[8619 rows x 9 columns]"
      ]
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