Final_Orden.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "#############                                                                                        #############\n",
    "\n",
    "##### Óscar Angarita(1201748), Andrés Lindarte(1201397), James Romero(1201786), Daniel Sandoval(1201770) ########\n",
    "\n",
    "############                                                                                         #############\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell \n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "# Permite mostrar el resultado de cada linea de la misma celda\n",
    "import numpy as np #librería para computación científica\n",
    "import pandas as pd #librería para analisis de datos\n",
    "import seaborn as sns #librería para visualización de datos estadísticos\n",
    "import matplotlib.pyplot as plt #librería para crear graficas 2D\n",
    "import sklearn.linear_model as lm #libreria incluye modelo de regresión lineal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(271116, 15)"
      ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>271116.000000</td>\n",
       "      <td>261642.000000</td>\n",
       "      <td>210945.000000</td>\n",
       "      <td>208241.000000</td>\n",
       "      <td>271116.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>68248.954396</td>\n",
       "      <td>25.556898</td>\n",
       "      <td>175.338970</td>\n",
       "      <td>70.702393</td>\n",
       "      <td>1978.378480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39022.286345</td>\n",
       "      <td>6.393561</td>\n",
       "      <td>10.518462</td>\n",
       "      <td>14.348020</td>\n",
       "      <td>29.877632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1896.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34643.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>68205.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>102097.250000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>135571.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID            Age         Height         Weight  \\\n",
       "count  271116.000000  261642.000000  210945.000000  208241.000000   \n",
       "mean    68248.954396      25.556898     175.338970      70.702393   \n",
       "std     39022.286345       6.393561      10.518462      14.348020   \n",
       "min         1.000000      10.000000     127.000000      25.000000   \n",
       "25%     34643.000000      21.000000     168.000000      60.000000   \n",
       "50%     68205.000000      24.000000     175.000000      70.000000   \n",
       "75%    102097.250000      28.000000     183.000000      79.000000   \n",
       "max    135571.000000      97.000000     226.000000     214.000000   \n",
       "\n",
       "                Year  \n",
       "count  271116.000000  \n",
       "mean     1978.378480  \n",
       "std        29.877632  \n",
       "min      1896.000000  \n",
       "25%      1960.000000  \n",
       "50%      1988.000000  \n",
       "75%      2002.000000  \n",
       "max      2016.000000  "
      ]
     },
     "execution_count": 2,
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       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight            Team  \\\n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0           China   \n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0           China   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN         Denmark   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN  Denmark/Sweden   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
       "\n",
       "   NOC        Games  Year  Season       City          Sport  \\\n",
       "0  CHN  1992 Summer  1992  Summer  Barcelona     Basketball   \n",
       "1  CHN  2012 Summer  2012  Summer     London           Judo   \n",
       "2  DEN  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  DEN  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  NED  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event Medal  \n",
       "0       Basketball Men's Basketball   NaN  \n",
       "1      Judo Men's Extra-Lightweight   NaN  \n",
       "2           Football Men's Football   NaN  \n",
       "3       Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4  Speed Skating Women's 500 metres   NaN  "
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 271116 entries, 0 to 271115\n",
      "Data columns (total 15 columns):\n",
      "ID        271116 non-null int64\n",
      "Name      271116 non-null object\n",
      "Sex       271116 non-null object\n",
      "Age       261642 non-null float64\n",
      "Height    210945 non-null float64\n",
      "Weight    208241 non-null float64\n",
      "Team      271116 non-null object\n",
      "NOC       271116 non-null object\n",
      "Games     271116 non-null object\n",
      "Year      271116 non-null int64\n",
      "Season    271116 non-null object\n",
      "City      271116 non-null object\n",
      "Sport     271116 non-null object\n",
      "Event     271116 non-null object\n",
      "Medal     39783 non-null object\n",
      "dtypes: float64(3), int64(2), object(10)\n",
      "memory usage: 31.0+ MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 206165 entries, 0 to 206164\n",
      "Data columns (total 15 columns):\n",
      "ID             206165 non-null int64\n",
      "Name           206165 non-null object\n",
      "Sex            206165 non-null object\n",
      "Age (Years)    206165 non-null float64\n",
      "Height (cm)    206165 non-null float64\n",
      "Weight (kg)    206165 non-null float64\n",
      "Team           206165 non-null object\n",
      "NOC            206165 non-null object\n",
      "Games          206165 non-null object\n",
      "Year           206165 non-null int64\n",
      "Season         206165 non-null object\n",
      "City           206165 non-null object\n",
      "Sport          206165 non-null object\n",
      "Event          206165 non-null object\n",
      "Medal          206165 non-null object\n",
      "dtypes: float64(3), int64(2), object(10)\n",
      "memory usage: 23.6+ MB\n"
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       "      <th>0</th>\n",
       "      <td>38860</td>\n",
       "      <td>Robert \"Bob\" Garrett</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1896 Summer</td>\n",
       "      <td>1896</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Men's Shot Put</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16616</td>\n",
       "      <td>Thomas Edmund \"Tom\" Burke</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1896 Summer</td>\n",
       "      <td>1896</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Men's 400 metres</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38860</td>\n",
       "      <td>Robert \"Bob\" Garrett</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1896 Summer</td>\n",
       "      <td>1896</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Men's Discus Throw</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22700</td>\n",
       "      <td>James Brendan Bennet Connolly</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1896 Summer</td>\n",
       "      <td>1896</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Men's Long Jump</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38860</td>\n",
       "      <td>Robert \"Bob\" Garrett</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1896 Summer</td>\n",
       "      <td>1896</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Men's Long Jump</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      ID                           Name Sex  Age (Years)  Height (cm)  \\\n",
       "0  38860           Robert \"Bob\" Garrett   M         20.0        188.0   \n",
       "1  16616      Thomas Edmund \"Tom\" Burke   M         21.0        183.0   \n",
       "2  38860           Robert \"Bob\" Garrett   M         20.0        188.0   \n",
       "3  22700  James Brendan Bennet Connolly   M         27.0        175.0   \n",
       "4  38860           Robert \"Bob\" Garrett   M         20.0        188.0   \n",
       "\n",
       "   Weight (kg)           Team  NOC        Games  Year  Season    City  \\\n",
       "0         81.0  United States  USA  1896 Summer  1896  Summer  Athina   \n",
       "1         66.0  United States  USA  1896 Summer  1896  Summer  Athina   \n",
       "2         81.0  United States  USA  1896 Summer  1896  Summer  Athina   \n",
       "3         72.0  United States  USA  1896 Summer  1896  Summer  Athina   \n",
       "4         81.0  United States  USA  1896 Summer  1896  Summer  Athina   \n",
       "\n",
       "       Sport                         Event   Medal  \n",
       "0  Athletics      Athletics Men's Shot Put    Gold  \n",
       "1  Athletics    Athletics Men's 400 metres    Gold  \n",
       "2  Athletics  Athletics Men's Discus Throw    Gold  \n",
       "3  Athletics     Athletics Men's Long Jump  Bronze  \n",
       "4  Athletics     Athletics Men's Long Jump  Silver  "
      ]
     },
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       "      <th>ID</th>\n",
       "      <th>Age (Years)</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Year</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>206165.000000</td>\n",
       "      <td>206165.000000</td>\n",
       "      <td>206165.000000</td>\n",
       "      <td>206165.000000</td>\n",
       "      <td>206165.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>68616.017675</td>\n",
       "      <td>25.055509</td>\n",
       "      <td>175.371950</td>\n",
       "      <td>70.688337</td>\n",
       "      <td>1989.674678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38996.514355</td>\n",
       "      <td>5.483096</td>\n",
       "      <td>10.546088</td>\n",
       "      <td>14.340338</td>\n",
       "      <td>20.130865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1896.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35194.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1976.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>68629.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>102313.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>135571.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID    Age (Years)    Height (cm)    Weight (kg)  \\\n",
       "count  206165.000000  206165.000000  206165.000000  206165.000000   \n",
       "mean    68616.017675      25.055509     175.371950      70.688337   \n",
       "std     38996.514355       5.483096      10.546088      14.340338   \n",
       "min         1.000000      11.000000     127.000000      25.000000   \n",
       "25%     35194.000000      21.000000     168.000000      60.000000   \n",
       "50%     68629.000000      24.000000     175.000000      70.000000   \n",
       "75%    102313.000000      28.000000     183.000000      79.000000   \n",
       "max    135571.000000      71.000000     226.000000     214.000000   \n",
       "\n",
       "                Year  \n",
       "count  206165.000000  \n",
       "mean     1989.674678  \n",
       "std        20.130865  \n",
       "min      1896.000000  \n",
       "25%      1976.000000  \n",
       "50%      1992.000000  \n",
       "75%      2006.000000  \n",
       "max      2016.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('athlete_events.csv') #se guarda el dataset como un DF de Pandas\n",
    "df.shape #para ver el tamaño(filasxcolumnas) del DF\n",
    "df.describe() #Genera estadísticas descriptivas que resumen la tendencia central, la dispersión y la forma de la distribución de un conjunto de datos, excluyendo los valores NaN\n",
    "df.head() #Muestra las 5 primeras filas del DF\n",
    "df.info() #Muestra un resumen conciso del DF\n",
    "df = df[np.isfinite(df['Age'])] #Prueba los elementos para determinar la finitud (no infinito o no es un número). El resultado se devuelve como un arreglo booleano\n",
    "df = df[np.isfinite(df['Height'])] #Esto para limpiar algunos datos NaN en estas tres columnas\n",
    "df = df[np.isfinite(df['Weight'])]\n",
    "df=df.sort_values('Year') #Se ordena el DF por año. Originalmente ordenado por 'ID'\n",
    "#Reestablece el índice del DF y usa el predeterminado en su lugar\n",
    "#El drop es para que no intente insertar un índice en las columnas del marco de datos. Esto restablece el índice al índice entero predeterminado \n",
    "df = df.reset_index(drop=True)\n",
    "#Renombrar algunas columnas del DF para hacer clara las unidades que se manejan\n",
    "df=df.rename(columns = {'Age':'Age (Years)','Height':'Height (cm)','Weight':'Weight (kg)'})\n",
    "#Se crea un diccionario para poder cambiar los valores NaN de la columna 'Medal' por 'No Medal' y dejar claro que se refiere a que no se ganó medallas en ese espacio\n",
    "values = {'Medal': 'No Medal'}\n",
    "df=df.fillna(value=values) #fillna() es para rellenar los valores NaN de la forma que se especif\n",
    "df.info() #Se imprime un resumen nuevo para ver los cambios\n",
    "df.head() #Se imprime un head nuevo para ver los cambios\n",
    "df.describe() #Se imprime un describe nuevo para ver los cambios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    24013\n",
       "True        27\n",
       "Name: NOC, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x720 with 0 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6433d358>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of medals per NOC')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Medals')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(64, 500, '')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(64, 500, 'COLOMBIA')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Se guarda en una variable los datos del DF que cumplan con la condición especificada\n",
    "totalYears=df['Year']>=1948 #Que el año sea mayor/igual a 1948 porque fue la 1ra aparición de Colombia en los olímpicos\n",
    "totalMed=df['Medal']!='No Medal' #Especifica que se hayan ganado medallas\n",
    "totalSummer=df['Season']=='Summer' #Especifica que sean juegos de verano ya que es donde Colombia participa comúnmente\n",
    "#Se crea un arreglo booleano a partir del DF original con los datos que cumplan las 3 condiciones anteriores\n",
    "totalMedals=df[totalMed & totalSummer & totalYears]\n",
    "#Se crea un arreglo booleano a partir del anterior que tenga todos los datos de Colombia con las 3 condiciones anteriores\n",
    "totalCOL=totalMedals['NOC']=='COL'\n",
    "totalCOL.value_counts() #Para saber cuantos elementos verdaderos tiene el arreglo, que dice la cantidad de medallas de Colombia\n",
    "#El contenedor de nivel superior para todos los elementos de graficado\n",
    "plt.figure(figsize=(25,10)) #figsize define el tamaño(anchoxalto) de la gráfica\n",
    "#Crea diagrama de barras a partir de las veces que aparezca un elemento de 'NOC' en el arreglo\n",
    "totalMedals.NOC.value_counts().plot.bar()\n",
    "plt.title('Number of medals per NOC')#Título del gráfico\n",
    "plt.ylabel('Total Medals')#Label del eje Y\n",
    "#Para dibujar la flecha y/o escribir texto, dibujar\n",
    "plt.annotate('', #String con texto a escribir\n",
    "            xy=(64, 50), #Posición donde apunta la flecha\n",
    "            xycoords='data', #Tipo de coordenadas de la flecha y el texto\n",
    "            xytext=(64, 500), #Posición donde se dibuja el texto\n",
    "            arrowprops=\n",
    "                {'facecolor':'black','shrink':0.05}) #Parametros de la flecha (color y compresión)\n",
    "#Para escribir texto (posición, texto, atributos, encerrado en)\n",
    "plt.text( 64,500, 'COLOMBIA',{'color': 'black', 'fontsize': 24, 'ha': 'center', 'va': 'center',\n",
    "          'bbox': dict(boxstyle=\"round\", fc=\"white\", ec=\"black\", pad=0.2)})\n",
    "#Dibujar una gráfica\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(945, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age (Years)</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4742</th>\n",
       "      <td>4681</td>\n",
       "      <td>Jaime Aparicio Rodewalt</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>COL</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Men's 400 metres Hurdles</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4743</th>\n",
       "      <td>4681</td>\n",
       "      <td>Jaime Aparicio Rodewalt</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>COL</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Men's 400 metres</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8292</th>\n",
       "      <td>31316</td>\n",
       "      <td>Emilio Echeverry de la Roche</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>COL</td>\n",
       "      <td>1956 Summer</td>\n",
       "      <td>1956</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Fencing</td>\n",
       "      <td>Fencing Men's Sabre, Individual</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8293</th>\n",
       "      <td>31316</td>\n",
       "      <td>Emilio Echeverry de la Roche</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>COL</td>\n",
       "      <td>1956 Summer</td>\n",
       "      <td>1956</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Fencing</td>\n",
       "      <td>Fencing Men's epee, Team</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8305</th>\n",
       "      <td>31316</td>\n",
       "      <td>Emilio Echeverry de la Roche</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>COL</td>\n",
       "      <td>1956 Summer</td>\n",
       "      <td>1956</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Fencing</td>\n",
       "      <td>Fencing Men's epee, Individual</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID                          Name Sex  Age (Years)  Height (cm)  \\\n",
       "4742   4681      Jaime Aparicio Rodewalt    M         18.0        170.0   \n",
       "4743   4681      Jaime Aparicio Rodewalt    M         18.0        170.0   \n",
       "8292  31316  Emilio Echeverry de la Roche   M         27.0        175.0   \n",
       "8293  31316  Emilio Echeverry de la Roche   M         27.0        175.0   \n",
       "8305  31316  Emilio Echeverry de la Roche   M         27.0        175.0   \n",
       "\n",
       "      Weight (kg)      Team  NOC        Games  Year  Season       City  \\\n",
       "4742         63.0  Colombia  COL  1948 Summer  1948  Summer     London   \n",
       "4743         63.0  Colombia  COL  1948 Summer  1948  Summer     London   \n",
       "8292         68.0  Colombia  COL  1956 Summer  1956  Summer  Melbourne   \n",
       "8293         68.0  Colombia  COL  1956 Summer  1956  Summer  Melbourne   \n",
       "8305         68.0  Colombia  COL  1956 Summer  1956  Summer  Melbourne   \n",
       "\n",
       "          Sport                               Event     Medal  \n",
       "4742  Athletics  Athletics Men's 400 metres Hurdles  No Medal  \n",
       "4743  Athletics          Athletics Men's 400 metres  No Medal  \n",
       "8292    Fencing     Fencing Men's Sabre, Individual  No Medal  \n",
       "8293    Fencing            Fencing Men's epee, Team  No Medal  \n",
       "8305    Fencing      Fencing Men's epee, Individual  No Medal  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age (Years)</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>945.000000</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>945.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>63965.737566</td>\n",
       "      <td>24.985185</td>\n",
       "      <td>169.752381</td>\n",
       "      <td>64.868254</td>\n",
       "      <td>1994.205291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39962.490656</td>\n",
       "      <td>6.017963</td>\n",
       "      <td>9.047547</td>\n",
       "      <td>10.337798</td>\n",
       "      <td>18.654422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>574.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1948.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25877.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>1976.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>74563.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>99964.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>135441.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  Age (Years)  Height (cm)  Weight (kg)         Year\n",
       "count     945.000000   945.000000   945.000000   945.000000   945.000000\n",
       "mean    63965.737566    24.985185   169.752381    64.868254  1994.205291\n",
       "std     39962.490656     6.017963     9.047547    10.337798    18.654422\n",
       "min       574.000000    12.000000   138.000000    38.000000  1948.000000\n",
       "25%     25877.000000    21.000000   164.000000    58.000000  1976.000000\n",
       "50%     74563.000000    24.000000   170.000000    64.000000  2000.000000\n",
       "75%     99964.000000    28.000000   175.000000    71.000000  2012.000000\n",
       "max    135441.000000    52.000000   205.000000   110.000000  2016.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupedCol=df[df['NOC']=='COL']#Crea DF a partir del original con datos solo de Colombia\n",
    "groupedCol.shape #Para revisar los cambios recientes\n",
    "groupedCol.head() #Para revisar los cambios recientes\n",
    "groupedCol.describe() #Para revisar los cambios recientes\n",
    "#Crea DF a partir del anterior pero con datos de medallistas de sexo femenino\n",
    "groupedFem=groupedCol[groupedCol['Sex']=='F'][groupedCol['Medal']!='No Medal']\n",
    "#Guarda en una variable la cantidad de datos únicos que hay de cada atleta femenina medallista\n",
    "totFem=len(groupedFem['ID'].unique())\n",
    "#Crea DF a partir del anterior pero con datos de medallistas de sexo masculino\n",
    "groupedMal=groupedCol[groupedCol['Sex']=='M'][groupedCol['Medal']!='No Medal']\n",
    "#Guarda en una variable la cantidad de datos únicos que hay de cada atleta masculino medallista\n",
    "totMal=len(groupedMal['ID'].unique())\n",
    "#Crea DF a partir del anterior pero con datos de medallistas tanto femeninos como masculinos\n",
    "groupedTm=groupedCol[groupedCol['Medal']!='No Medal']\n",
    "#Guarda en una variable la cantidad de datos únicos que hay de cada atleta masculino y femenino medallista\n",
    "tot=len(groupedTm['ID'].unique())\n",
    "#El contenedor de nivel superior para todos los elementos de graficado\n",
    "plt.figure(figsize=(20,10)) #figsize define el tamaño(anchoxalto) de la gráfica\n",
    "#Agrega una subgráfica a la figura actual\n",
    "plt.subplot(131) #(N. filasxN.columnasxposición)\n",
    "# nunique() se utiliza para obtener un recuento de valores únicos\n",
    "#Se agrupan las mujeres por medalla y 'ID' único para hacer un diagrama de pie\n",
    "#autopct da formato a los porcentajes(float con 1 punto decimal)\n",
    "#shadow para dibujar una sombra al diagrama de pie\n",
    "#pctdistance es la distancia del valor porcentaje al centro del diagrama\n",
    "#colors son para dibujar cada parte del pie distinta\n",
    "groupedFem.groupby(\"Medal\")[\"ID\"].nunique().plot.pie(autopct = \"%1.0f%%\",\n",
    "                                              shadow = True ,pctdistance=0.85, \n",
    "                                               colors = [\"#998456\",\"gold\",\"silver\"]) \n",
    "plt.ylabel(\"\")#Label eje Y\n",
    "#se dibuja un circulo blanco en el centro del diagrama de pie(y más pequeño) para dar efecto de dona\n",
    "circ = plt.Circle((0,0),.7,color = \"white\")\n",
    "#gca obtiene la instancia actual de Axes en la figura actual que coincide con los argumentos de palabra clave dados, o cree uno\n",
    "#add_artist agrega un artista(Clase base para objetos que se representan en una FigureCanvas) a los ejes y devuelve el artista\n",
    "plt.gca().add_artist(circ)\n",
    "#Título del diagrama de pie para las mujeres convirtiendo en cadena el total calculado anteriormente\n",
    "plt.title(str(totFem)+\" Mujeres Medallistas\")\n",
    "\n",
    "plt.subplot(132)\n",
    "groupedMal.groupby(\"Medal\")[\"ID\"].nunique().plot.pie(autopct = \"%1.0f%%\",\n",
    "                                              shadow = True ,pctdistance=0.85, \n",
    "                                               colors = [\"#998456\",\"gold\",\"silver\"]) \n",
    "plt.ylabel(\"\")\n",
    "circ = plt.Circle((0,0),.7,color = \"white\")\n",
    "plt.gca().add_artist(circ)\n",
    "plt.title(str(totMal)+\" Hombres Medallistas\")\n",
    "\n",
    "\n",
    "plt.subplot(133)\n",
    "groupedTm.groupby(\"Medal\")[\"ID\"].nunique().plot.pie(autopct = \"%1.0f%%\",\n",
    "                                              shadow = True ,pctdistance=0.85, \n",
    "                                               colors = [\"#998456\",\"gold\",\"silver\"])\n",
    "\n",
    "plt.ylabel(\"\")\n",
    "circ = plt.Circle((0,0),.7,color = \"white\")\n",
    "plt.gca().add_artist(circ)\n",
    "plt.title(str(tot)+\" Medallistas Totales\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17]), <a list of 18 Text xticklabel objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Colombian athletes by gender over years for Summer Olympics')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26f6cd6f128>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Crea DF a partir de DF con datos de Colombia agrupado por Year, Sex y ID único de cada atleta con index por defecto \n",
    "groupedSex = groupedCol.groupby([\"Year\",\"Sex\"])[\"ID\"].nunique().reset_index()\n",
    "\n",
    "fig = plt.figure(figsize=(14,6))\n",
    "#crea pointplot de Seaborn\n",
    "#grafo de Year vs ID único, dividido en lineas que representan a un Sex (Male en azul, Female en púrpura)\n",
    "ax = sns.pointplot(x = groupedSex[\"Year\"] , y = groupedSex[\"ID\"],\n",
    "                   markers=\"h\" , hue = groupedSex[\"Sex\"],palette = [\"b\",\"purple\"])\n",
    "#Crea grilla sobre el grafo y rota las variables del ejeX 60°\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation = 60)\n",
    "#Pasa el color de fondo del grafo en gris\n",
    "ax.set_facecolor(\"lightgrey\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Colombian athletes by gender over years for Summer Olympics\")\n",
    "#Crea una leyenda en la mejor posición que considere Seaborn de tamaño 15\n",
    "plt.legend(loc = \"best\" ,prop={\"size\":15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Athletics'),\n",
       " Text(0, 0, 'Cycling'),\n",
       " Text(0, 0, 'Swimming'),\n",
       " Text(0, 0, 'Football'),\n",
       " Text(0, 0, 'Shooting'),\n",
       " Text(0, 0, 'Weightlifting'),\n",
       " Text(0, 0, 'Fencing'),\n",
       " Text(0, 0, 'Boxing'),\n",
       " Text(0, 0, 'Gymnastics'),\n",
       " Text(0, 0, 'Diving'),\n",
       " Text(0, 0, 'Wrestling'),\n",
       " Text(0, 0, 'Equestrianism'),\n",
       " Text(0, 0, 'Archery'),\n",
       " Text(0, 0, 'Tennis'),\n",
       " Text(0, 0, 'Rugby Sevens'),\n",
       " Text(0, 0, 'Judo'),\n",
       " Text(0, 0, 'Taekwondo'),\n",
       " Text(0, 0, 'Sailing'),\n",
       " Text(0, 0, 'Triathlon'),\n",
       " Text(0, 0, 'Table Tennis'),\n",
       " Text(0, 0, 'Synchronized Swimming'),\n",
       " Text(0, 0, 'Alpine Skiing'),\n",
       " Text(0, 0, 'Golf'),\n",
       " Text(0, 0, 'Rowing')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Most sports participations of Colombia')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sportsCol=groupedCol['Sport'] #guarda en una variable todos los valores de la columna 'Sport' del DF de Colombia  \n",
    "#El contenedor de nivel superior para todos los elementos de graficado\n",
    "plt.figure(figsize=(20, 10)) #figsize asigna el anchoxalto del grafo\n",
    "#Countplot de Seaborn muestra los conteos de observaciones en cada contenedor categórico usando barras\n",
    "#(datos,orden) el orden en este caso es por valores repetidos de mayor a menor\n",
    "graph=sns.countplot(sportsCol,order = sportsCol.value_counts().index)\n",
    "#Para dar formato y especificar los datos de los valores del eje X\n",
    "graph.set_xticklabels(graph.get_xticklabels(),rotation=90)\n",
    "plt.title('Most sports participations of Colombia')#titulo del grafo\n",
    "#patches hace referencia a los elementos que se pueden dibujar sobre un grafo de Seaborn\n",
    "#En este caso se anota la cantidad de atletas por deporte teniendo en cuenta el alto y ancho de las barras en el grafo\n",
    "#para su visualización\n",
    "for p in graph.patches:\n",
    "        graph.annotate('{:}'.format(p.get_height()), (p.get_x()+0.2, p.get_height()+2.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6af54748>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6af54748>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x26f6fe0d940>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '945 Medallas de Oro')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6fe04748>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6fe04748>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x26f6f8f3eb8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '945 Medallas de Plata')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6f904d30>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6f904d30>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x26f6f942cc0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '14 Medallas de Bronce')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#En un DF se guarda el DF de los datos de Colombia pero teniendo en cuenta solo los ganadores de oro\n",
    "groupedGold=groupedCol[groupedCol['Medal']=='Gold']\n",
    "#En una variable se guarda la cantidad total de medallas de oro a partir del DF anterior\n",
    "totGold=len(groupedCol['ID'])\n",
    "#En un DF se guarda el DF de los datos de Colombia pero teniendo en cuenta solo los ganadores de plata\n",
    "groupedSilv=groupedCol[groupedCol['Medal']=='Silver']\n",
    "#En una variable se guarda la cantidad total de medallas de plata a partir del DF anterior\n",
    "totSilv=len(groupedCol['ID'])\n",
    "#En un DF se guarda el DF de los datos de Colombia pero teniendo en cuenta solo los ganadores de bronce\n",
    "groupedBro=groupedCol[groupedCol['Medal']=='Bronze']\n",
    "#En una variable se guarda la cantidad total de medallas de bronce a partir del DF anterior\n",
    "totBro=len(groupedBro['ID'])\n",
    "#El contenedor de nivel superior para todos los elementos de graficado\n",
    "plt.figure(figsize=(20,10))#figsize asigna el anchoxalto del grafo\n",
    "#Agrega una subgráfica a la figura actual\n",
    "plt.subplot(131) #(N. filasxN.columnasxposición)\n",
    "#Se crea un diagrama de pie a partir del conteo de los valores de la columna 'Sport' del DF de oro\n",
    "#autopct le da formato con 1 decimal\n",
    "#shadow le dibuja una sombra\n",
    "#pctdistance es la distancia de los valores al centro del pie\n",
    "#colors son los colores que representan cada deporte\n",
    "groupedGold.Sport.value_counts().plot.pie(autopct = \"%1.0f%%\",\n",
    "                                              shadow = True ,pctdistance=0.85,\n",
    "                                                      colors=['red','pink','gray'])\n",
    "plt.ylabel(\"\") #label del eje Y\n",
    "#Crea circulo blanco en el centro y más pequeño que el pie para dar el efecto de dona\n",
    "circ = plt.Circle((0,0),.7,color = \"white\")\n",
    "#gca obtiene la instancia actual de Axes en la figura actual que coincide con los argumentos de palabra clave dados, o cree uno\n",
    "#add_artist agrega un artista(Clase base para objetos que se representan en una FigureCanvas) a los ejes y devuelve el artista\n",
    "plt.gca().add_artist(circ)\n",
    "#título del diagrama + cantidad de medallas de oro\n",
    "plt.title(str(totGold)+\" Medallas de Oro\")\n",
    "\n",
    "plt.subplot(132)\n",
    "groupedSilv.Sport.value_counts().plot.pie(autopct = \"%1.0f%%\",\n",
    "                                              shadow = True ,pctdistance=0.85,\n",
    "                                                     colors=['pink','blue','red','purple','green',\n",
    "                                                              'gray']) \n",
    "plt.ylabel(\"\")\n",
    "circ = plt.Circle((0,0),.7,color = \"white\")\n",
    "plt.gca().add_artist(circ)\n",
    "plt.title(str(totSilv)+\" Medallas de Plata\")\n",
    "\n",
    "\n",
    "plt.subplot(133)\n",
    "groupedBro.Sport.value_counts().plot.pie(autopct = \"%1.0f%%\",\n",
    "                                              shadow = True ,pctdistance=0.85,\n",
    "                                                    colors=['green','red','pink','yellow','purple',\n",
    "                                                              'royalblue','gray'])\n",
    "\n",
    "plt.ylabel(\"\")\n",
    "circ = plt.Circle((0,0),.7,color = \"white\")\n",
    "plt.gca().add_artist(circ)\n",
    "plt.title(str(totBro)+\" Medallas de Bronce\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 15)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age (Years)</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>32330</td>\n",
       "      <td>Launceston Elliot</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>GBR</td>\n",
       "      <td>1896 Summer</td>\n",
       "      <td>1896</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Men's Unlimited, Two Hands</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>32330</td>\n",
       "      <td>Launceston Elliot</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>GBR</td>\n",
       "      <td>1896 Summer</td>\n",
       "      <td>1896</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Men's Unlimited, One Hand</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>64683</td>\n",
       "      <td>Frank X. Kugler</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>GER</td>\n",
       "      <td>1904 Summer</td>\n",
       "      <td>1904</td>\n",
       "      <td>Summer</td>\n",
       "      <td>St. Louis</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Men's All-Around Dumbbell Contest</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>64683</td>\n",
       "      <td>Frank X. Kugler</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>GER</td>\n",
       "      <td>1904 Summer</td>\n",
       "      <td>1904</td>\n",
       "      <td>Summer</td>\n",
       "      <td>St. Louis</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Men's Unlimited, Two Hands</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>17647</td>\n",
       "      <td>Tullio Camillotti</td>\n",
       "      <td>M</td>\n",
       "      <td>26.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>ITA</td>\n",
       "      <td>1906 Summer</td>\n",
       "      <td>1906</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Men's Unlimited, One Hand</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID               Name Sex  Age (Years)  Height (cm)  Weight (kg)  \\\n",
       "29   32330  Launceston Elliot   M         21.0        188.0        102.0   \n",
       "37   32330  Launceston Elliot   M         21.0        188.0        102.0   \n",
       "179  64683    Frank X. Kugler   M         25.0        170.0         75.0   \n",
       "183  64683    Frank X. Kugler   M         25.0        170.0         75.0   \n",
       "234  17647  Tullio Camillotti   M         26.0        175.0         98.0   \n",
       "\n",
       "              Team  NOC        Games  Year  Season       City          Sport  \\\n",
       "29   Great Britain  GBR  1896 Summer  1896  Summer     Athina  Weightlifting   \n",
       "37   Great Britain  GBR  1896 Summer  1896  Summer     Athina  Weightlifting   \n",
       "179        Germany  GER  1904 Summer  1904  Summer  St. Louis  Weightlifting   \n",
       "183        Germany  GER  1904 Summer  1904  Summer  St. Louis  Weightlifting   \n",
       "234          Italy  ITA  1906 Summer  1906  Summer     Athina  Weightlifting   \n",
       "\n",
       "                                               Event   Medal  \n",
       "29          Weightlifting Men's Unlimited, Two Hands  Silver  \n",
       "37           Weightlifting Men's Unlimited, One Hand    Gold  \n",
       "179  Weightlifting Men's All-Around Dumbbell Contest  Bronze  \n",
       "183         Weightlifting Men's Unlimited, Two Hands  Bronze  \n",
       "234          Weightlifting Men's Unlimited, One Hand  Silver  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x720 with 0 Axes>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26f6e88f828>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of medals in weightlifting per NOC')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Medals')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(26, 20, '')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(26, 20, 'COLOMBIA')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Crea un arreglo booleano a partir del DF original donde aloja la condición de que los datos correspondan a 'Weightlifting'\n",
    "totalWeightLift=df['Sport']=='Weightlifting'\n",
    "#Crea un DF a partir del original cuyos datos cumplan con ser de medallistas, en los juegos de verano, y que el deporte sea Weightlifting\n",
    "dfWeightLift=df[totalMed & totalSummer & totalWeightLift]\n",
    "Colombia=dfWeightLift[dfWeightLift['NOC']=='COL']\n",
    "Colombia.shape\n",
    "dfWeightLift.head()#Imprime las 5 primeras filas para ver el resultado\n",
    "#Crea una figura con tamaño determinado\n",
    "plt.figure(figsize=(25,10))\n",
    "#Crea un diagrama de barras a partir del número de medallas por NOC de la columna NOC del DF anterior\n",
    "dfWeightLift.NOC.value_counts().plot.bar()\n",
    "plt.title('Number of medals in weightlifting per NOC')\n",
    "plt.ylabel('Total Medals')\n",
    "\n",
    "#para dibujar la flecha de color negro apuntando a (27,6)\n",
    "plt.annotate('',\n",
    "            xy=(26, 6),\n",
    "            xycoords='data',\n",
    "            xytext=(26, 20),\n",
    "            arrowprops=\n",
    "                {'facecolor':'black','shrink':0.05})\n",
    "#para dibujar el texto de color negro, centrado, y en la posición (27,20), rodeado por una caja redondeada\n",
    "plt.text( 26,20, 'COLOMBIA',{'color': 'black', 'fontsize': 24, 'ha': 'center', 'va': 'center',\n",
    "          'bbox': dict(boxstyle=\"round\", fc=\"white\", ec=\"black\", pad=0.2)})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1984</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1988</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1996</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  ID\n",
       "0  1984  10\n",
       "1  1988  10\n",
       "2  1992   3\n",
       "3  1996  10\n",
       "4  2000  10"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>Year</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1972</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  ID\n",
       "0  1956   1\n",
       "1  1960   2\n",
       "2  1972   2\n",
       "3  1976   2\n",
       "4  1984   5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1956</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1960</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1968</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  ID\n",
       "0  1952   2\n",
       "1  1956   2\n",
       "2  1960   6\n",
       "3  1964   7\n",
       "4  1968   7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "china=df['NOC']=='CHN' #Crea variable donde se guardan los datos del DF original con datos de China\n",
    "colom=df['NOC']=='COL' #Crea variable donde se guardan los datos del DF original con datos de Colombia\n",
    "urs=df['NOC']=='URS' #Crea variable donde se guardan los datos del DF original con datos de la URS\n",
    "wei=df['Sport']=='Weightlifting' #Crea variable donde se guardan los datos del DF original con datos de Weightlifting\n",
    "#Crea DF a partir del original con datos del Weightlifting en China\n",
    "dfChi=df[china & wei ]\n",
    "#Crea DF a partir del original con datos del Weightlifting en Colombia\n",
    "dfColo=df[colom & wei]\n",
    "#Crea DF a partir del original con datos del Weightlifting en la URS\n",
    "dfUrs=df[urs & wei]\n",
    "#Agrupa el DF de China por año y por ID(atleta) único\n",
    "#Restableece el índice del DF y use el predeterminado en su lugar.\n",
    "dfChi = dfChi.groupby(\"Year\")[\"ID\"].nunique().reset_index()\n",
    "#Agrupa el DF de Colombia por año y por ID(atleta) único\n",
    "#Restableece el índice del DF y use el predeterminado en su lugar.\n",
    "dfColo = dfColo.groupby(\"Year\")[\"ID\"].nunique().reset_index()\n",
    "#Agrupa el DF de la URS por año y por ID(atleta) único\n",
    "#Restableece el índice del DF y use el predeterminado en su lugar.\n",
    "dfUrs = dfUrs.groupby(\"Year\")[\"ID\"].nunique().reset_index()\n",
    "#Muestra el head de cada uno para visualizar los cambios\n",
    "dfChi.head()\n",
    "dfColo.head()\n",
    "dfUrs.head()\n",
    "#Crea un diagrama de lineas punteadas de Year vs ID con cada DF\n",
    "_=plt.plot(dfChi['Year'],dfChi['ID'],'--r',color='red',label='China')\n",
    "_=plt.plot(dfColo['Year'],dfColo['ID'],'--b',color='blue',label='Colombia')\n",
    "_=plt.plot(dfUrs['Year'],dfUrs['ID'],'--g',color='green',label='URS')\n",
    "#Crea una leyenda para diferenciar visualmente a qué DF corresponde cada linea\n",
    "_=plt.legend(loc='upper left')\n",
    "_=plt.xlabel('Year')#label eje x\n",
    "_=plt.ylabel('Number of Athletes')#label eje y\n",
    "_=plt.title('Chinese and Colombian weightlifting athletes over the years for Summer Olympics')\n",
    "plt.grid()#Para dibujar una grilla de lineas sobre el grafo\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Year</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1896</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1900</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1904</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1906</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1908</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   ID\n",
       "0  1896    9\n",
       "1  1900   20\n",
       "2  1904   34\n",
       "3  1906   45\n",
       "4  1908  106"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>Year</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1896</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1900</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1904</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1906</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1908</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   ID\n",
       "0  1896   11\n",
       "1  1900   34\n",
       "2  1904   52\n",
       "3  1906   77\n",
       "4  1908  187"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pendiente:  0.16979518389053866\n",
      "Intercepción:  60.540333143973726\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([145.43792509])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.9815923529980868"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Crea DF a partir del original con los datos de los medallistas\n",
    "dfMedallas=df[df['Medal']!='No Medal']\n",
    "#Agrupa el DF de los medallistas por Year, deja el index por defecto\n",
    "#agg= Agregación usando una o más operaciones sobre el eje especificado. \n",
    "#En este caso se agregan al grupo los ID únicos\n",
    "dfWinners=dfMedallas.groupby(['Year'],as_index=False).agg({'ID': pd.Series.nunique})\n",
    "#Agrupa un DF de todos los atletas por Year, deja el index por defecto y agrega al grupo los ID únicos\n",
    "dfComplete=df.groupby(['Year'],as_index=False).agg({'ID': pd.Series.nunique})\n",
    "#imprime las 1ras 5 filas de ambos DF para ver su contenido\n",
    "dfWinners.head()\n",
    "dfComplete.head()\n",
    "#Crea un gráfico Scatter= en el que los valores de 2 variables se trazan a lo largo de 2 ejes, el patrón de los puntos resultantes revela cualquier correlación presente \n",
    "#(Datax,Datay)\n",
    "#Grafo lo crea de ID de todos los atletas vs ID de todos los medallistas\n",
    "_=plt.scatter(dfComplete['ID'],dfWinners['ID'])\n",
    "_=plt.xlabel('ID')#label eje x\n",
    "_=plt.ylabel('IDWin')#label eje y\n",
    "plt.show()#muestra el grafo\n",
    "#newaxis=utilizado para aumentar la dimensión de la matriz existente en una dimensión más, cuando se utiliza una vez\n",
    "#Al DF de ID de todos los atletas se le vuelve 2D con todos sus valores y se guarda en x \n",
    "x=dfComplete.ID[:,np.newaxis]\n",
    "#Al DF de ID de medallistas se le toman todos sus valores en forma de serie y se guardan en y\n",
    "y=dfWinners['ID'].values\n",
    "#La regresión lineal es una forma de predecir una respuesta Y sobre la base de una sola variable predictiva X\n",
    "#Se supone que existe una relación lineal aproximadamente entre X e Y\n",
    "lr=lm.LinearRegression() # Crear instancia de linea de regresión\n",
    "lr.fit(x,y)#fit se usa para entrenar el modelo a partir de los datos x,y \n",
    "b=lr.coef_[0]#hace referencia a la pendiente de la recta resultante\n",
    "a=lr.intercept_#hace referencia al punto de intercepción de la recta resultante\n",
    "print('Pendiente: ',b)\n",
    "print('Intercepción: ',a)\n",
    "\n",
    "lr.predict([[500]]) # Dada una delegación de 500 atletas el modelo predice que podrían haber 145 medallistas aprox.\n",
    "lr.score(x,y) # El score 0.98 dice que el modelo fue bueno, pero, siempre entre más datos se tomen, mejor la regresión\n",
    "_2=plt.scatter(x,y)\n",
    "#Se dibuja en rojo la recta resultante de la predicción sobre el grafo Scatter de la distribución que se tenía previamente\n",
    "_2=plt.xlabel('IDTotal')\n",
    "_2=plt.ylabel('IDWin')\n",
    "_2=plt.plot(x,lr.predict(x),color='red',linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}