EDA, athlete_events analysis using python.ipynb

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   "source": [
    "import pandas as pd\n",
    "athlete = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Data/athlete_events.csv\")"
   ]
  },
  {
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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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <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",
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       "   ID                      Name  ...                             Event  Medal\n",
       "0   1                 A Dijiang  ...       Basketball Men's Basketball    NaN\n",
       "1   2                  A Lamusi  ...      Judo Men's Extra-Lightweight    NaN\n",
       "2   3       Gunnar Nielsen Aaby  ...           Football Men's Football    NaN\n",
       "3   4      Edgar Lindenau Aabye  ...       Tug-Of-War Men's Tug-Of-War   Gold\n",
       "4   5  Christine Jacoba Aaftink  ...  Speed Skating Women's 500 metres    NaN\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
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     "execution_count": 8,
     "metadata": {
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   ],
   "source": [
    "athlete.head()           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XBto1iw5XphZ"
   },
   "source": [
    "## Data Description\n",
    "\n",
    "**Each row corresponds to an individual athlete competing in an individual Olympic event (athlete-events).**\n",
    "\n",
    "Kaggle page for data: https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c3ljYDsLWAN4"
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   "source": [
    "## Section 1: What does peak performance look like?\n",
    "\n",
    "Olympians are world-class athletes, but peak performance body types and ages can differ drastically depending on the sport/event.  Let's analyze this by specifically looking at those athletes that **won medals** as a proxy for peak performance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JIFJ7tKiW14D"
   },
   "source": [
    "### Part 1: Data Prep\n",
    "\n",
    "Using the original dataframe loaded from the CSV, create a new dataframe `medalists` with the following characteristics\n",
    "\n",
    "\n",
    "*   Data is filtered to only the rows that have `\"Gold\", \"Silver\", or \"Bronze\"` in `Medals`.  *Hint: you can use `.isin()` here, just as we did in the lecture*\n",
    "*   Any rows that have NA or NaN are dropped using `dropna()`. \n",
    "\n"
   ]
  },
  {
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   "source": [
    "# create a variable after droppin all NaN\n",
    "medalists = athlete.dropna()"
   ]
  },
  {
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       "(30181, 15)"
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     },
     "execution_count": 10,
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   ],
   "source": [
    "#validate number of rows\n",
    "medalists.shape"
   ]
  },
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    "id": "z3W7N8zotZ8u"
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   "source": [
    "### Part 2: Biometrics by Event\n",
    "\n",
    "Using `medalists`, answer the below questions by aggregating the column of interest by `Event` and sorting the result values.\n",
    "\n",
    "\n",
    "1.   Which **Event** has the **smallest** average **height** for **female medalists**? What is it?\n",
    "2.   Which **Event** has the **highest** average **age** for **female medalists**? What is it?\n",
    "3.   Which **Event** has the **highest** average **weight** for **male medalists**? What is it?\n",
    "4.   Which **Event** has the **highest** average **height** for **male medalists**? What is it?\n",
    "\n"
   ]
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    "# Write your code and answers below\n",
    "#create variables for groupby columns and getting mean for Height, Age, Weight\n",
    "height_by_sex = medalists.groupby(by= ['Event', 'Sex'], as_index=False)['Height'].mean()\n",
    "age_by_sex = medalists.groupby(by= ['Event', 'Sex'], as_index=False)['Age'].mean()\n",
    "weight_by_sex = medalists.groupby(by= ['Event', 'Sex'], as_index=False)['Weight'].mean()"
   ]
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       "      <td>Alpine Skiing Men's Combined</td>\n",
       "      <td>M</td>\n",
       "      <td>178.923077</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>Alpine Skiing Men's Downhill</td>\n",
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       "      <td>181.121951</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>Alpine Skiing Men's Giant Slalom</td>\n",
       "      <td>M</td>\n",
       "      <td>178.439024</td>\n",
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       "      <td>Alpine Skiing Men's Slalom</td>\n",
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       "<p>578 rows × 3 columns</p>\n",
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       "                                              Event Sex      Height\n",
       "0                      Alpine Skiing Men's Combined   M  178.923077\n",
       "1                      Alpine Skiing Men's Downhill   M  181.121951\n",
       "2                  Alpine Skiing Men's Giant Slalom   M  178.439024\n",
       "3                        Alpine Skiing Men's Slalom   M  178.452381\n",
       "4                       Alpine Skiing Men's Super G   M  179.166667\n",
       "..                                              ...  ..         ...\n",
       "573          Wrestling Women's Flyweight, Freestyle   F  155.133333\n",
       "574        Wrestling Women's Heavyweight, Freestyle   F  172.066667\n",
       "575  Wrestling Women's Light-Heavyweight, Freestyle   F  168.500000\n",
       "576        Wrestling Women's Lightweight, Freestyle   F  162.666667\n",
       "577       Wrestling Women's Middleweight, Freestyle   F  164.933333\n",
       "\n",
       "[578 rows x 3 columns]"
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   "source": [
    "medalists.groupby(by= ['Event', 'Sex'], as_index=False)['Height'].mean()"
   ]
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    "#Set up to filter sex\n",
    "height_by_sex = height_by_sex.rename(columns={'Height': 'avg_height'})\n",
    "age_by_sex = age_by_sex.rename(columns={'Age': 'avg_age'})\n",
    "weight_by_sex = weight_by_sex.rename(columns={'Weight': 'avg_weight'})\n",
    "\n",
    "is_male = height_by_sex[(height_by_sex.Sex == 'M')]"
   ]
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       "      <td>Alpine Skiing Men's Combined</td>\n",
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       "      <td>Alpine Skiing Men's Giant Slalom</td>\n",
       "      <td>M</td>\n",
       "      <td>25.341463</td>\n",
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       "      <td>Alpine Skiing Men's Slalom</td>\n",
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       "      <td>Wrestling Women's Middleweight, Freestyle</td>\n",
       "      <td>F</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "<p>578 rows × 3 columns</p>\n",
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      ],
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       "                                              Event Sex    avg_age\n",
       "0                      Alpine Skiing Men's Combined   M  25.538462\n",
       "1                      Alpine Skiing Men's Downhill   M  26.146341\n",
       "2                  Alpine Skiing Men's Giant Slalom   M  25.341463\n",
       "3                        Alpine Skiing Men's Slalom   M  24.666667\n",
       "4                       Alpine Skiing Men's Super G   M  27.333333\n",
       "..                                              ...  ..        ...\n",
       "573          Wrestling Women's Flyweight, Freestyle   F  25.200000\n",
       "574        Wrestling Women's Heavyweight, Freestyle   F  27.266667\n",
       "575  Wrestling Women's Light-Heavyweight, Freestyle   F  24.750000\n",
       "576        Wrestling Women's Lightweight, Freestyle   F  26.200000\n",
       "577       Wrestling Women's Middleweight, Freestyle   F  24.666667\n",
       "\n",
       "[578 rows x 3 columns]"
      ]
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   "source": [
    "\n",
    "age_by_sex"
   ]
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       "      <th>1</th>\n",
       "      <td>Alpine Skiing Men's Downhill</td>\n",
       "      <td>M</td>\n",
       "      <td>83.268293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alpine Skiing Men's Giant Slalom</td>\n",
       "      <td>M</td>\n",
       "      <td>78.829268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alpine Skiing Men's Slalom</td>\n",
       "      <td>M</td>\n",
       "      <td>79.595238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alpine Skiing Men's Super G</td>\n",
       "      <td>M</td>\n",
       "      <td>85.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>Wrestling Women's Flyweight, Freestyle</td>\n",
       "      <td>F</td>\n",
       "      <td>48.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>Wrestling Women's Heavyweight, Freestyle</td>\n",
       "      <td>F</td>\n",
       "      <td>73.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>Wrestling Women's Light-Heavyweight, Freestyle</td>\n",
       "      <td>F</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>Wrestling Women's Lightweight, Freestyle</td>\n",
       "      <td>F</td>\n",
       "      <td>56.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>Wrestling Women's Middleweight, Freestyle</td>\n",
       "      <td>F</td>\n",
       "      <td>62.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>578 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Event Sex  avg_weight\n",
       "0                      Alpine Skiing Men's Combined   M   83.769231\n",
       "1                      Alpine Skiing Men's Downhill   M   83.268293\n",
       "2                  Alpine Skiing Men's Giant Slalom   M   78.829268\n",
       "3                        Alpine Skiing Men's Slalom   M   79.595238\n",
       "4                       Alpine Skiing Men's Super G   M   85.583333\n",
       "..                                              ...  ..         ...\n",
       "573          Wrestling Women's Flyweight, Freestyle   F   48.200000\n",
       "574        Wrestling Women's Heavyweight, Freestyle   F   73.133333\n",
       "575  Wrestling Women's Light-Heavyweight, Freestyle   F   70.000000\n",
       "576        Wrestling Women's Lightweight, Freestyle   F   56.600000\n",
       "577       Wrestling Women's Middleweight, Freestyle   F   62.800000\n",
       "\n",
       "[578 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_by_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0Swm292E9PWG"
   },
   "outputs": [],
   "source": [
    "height_by_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_dPmwrq2DN9X"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "35wJZI-eRWUg",
    "outputId": "12202fb5-fe1d-4de8-fab7-c8b7a9d57d19"
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Event</th>\n",
       "      <th>Sex</th>\n",
       "      <th>avg_height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Weightlifting Women's Flyweight</td>\n",
       "      <td>F</td>\n",
       "      <td>149.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Gymnastics Women's Balance Beam</td>\n",
       "      <td>F</td>\n",
       "      <td>153.297872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Weightlifting Women's Lightweight</td>\n",
       "      <td>F</td>\n",
       "      <td>154.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Judo Women's Extra-Lightweight</td>\n",
       "      <td>F</td>\n",
       "      <td>154.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Weightlifting Women's Featherweight</td>\n",
       "      <td>F</td>\n",
       "      <td>154.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>Rowing Women's Single Sculls</td>\n",
       "      <td>F</td>\n",
       "      <td>179.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Volleyball Women's Volleyball</td>\n",
       "      <td>F</td>\n",
       "      <td>179.522388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Beach Volleyball Women's Beach Volleyball</td>\n",
       "      <td>F</td>\n",
       "      <td>180.057143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>Rowing Women's Quadruple Sculls</td>\n",
       "      <td>F</td>\n",
       "      <td>180.536082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Basketball Women's Basketball</td>\n",
       "      <td>F</td>\n",
       "      <td>183.012821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Event Sex  avg_height\n",
       "543            Weightlifting Women's Flyweight   F  149.866667\n",
       "293            Gymnastics Women's Balance Beam   F  153.297872\n",
       "546          Weightlifting Women's Lightweight   F  154.600000\n",
       "314             Judo Women's Extra-Lightweight   F  154.777778\n",
       "542        Weightlifting Women's Featherweight   F  154.866667\n",
       "..                                         ...  ..         ...\n",
       "355               Rowing Women's Single Sculls   F  179.375000\n",
       "525              Volleyball Women's Volleyball   F  179.522388\n",
       "108  Beach Volleyball Women's Beach Volleyball   F  180.057143\n",
       "354            Rowing Women's Quadruple Sculls   F  180.536082\n",
       "106              Basketball Women's Basketball   F  183.012821\n",
       "\n",
       "[221 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1 \n",
    "#filter column by F to get the avg height and sort for smallest\n",
    "height_by_sex[(height_by_sex.Sex == 'F')].sort_values(by='avg_height')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "DAkgAFwYXpcg",
    "outputId": "01be7cf4-14fe-40f7-d716-c092cd3f47d8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Event</th>\n",
       "      <th>Sex</th>\n",
       "      <th>avg_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Equestrianism Mixed Dressage, Team</td>\n",
       "      <td>F</td>\n",
       "      <td>35.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Equestrianism Mixed Jumping, Team</td>\n",
       "      <td>F</td>\n",
       "      <td>34.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Equestrianism Mixed Dressage, Individual</td>\n",
       "      <td>F</td>\n",
       "      <td>33.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Shooting Women's Skeet</td>\n",
       "      <td>F</td>\n",
       "      <td>33.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Shooting Mixed Small-Bore Rifle, Three Positio...</td>\n",
       "      <td>F</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Swimming Women's 400 metres Freestyle</td>\n",
       "      <td>F</td>\n",
       "      <td>18.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>Swimming Women's 100 metres Backstroke</td>\n",
       "      <td>F</td>\n",
       "      <td>18.826087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Gymnastics Women's Team All-Around</td>\n",
       "      <td>F</td>\n",
       "      <td>18.812977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Rhythmic Gymnastics Women's Group</td>\n",
       "      <td>F</td>\n",
       "      <td>18.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>Swimming Women's 800 metres Freestyle</td>\n",
       "      <td>F</td>\n",
       "      <td>18.487179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Event Sex    avg_age\n",
       "237                 Equestrianism Mixed Dressage, Team   F  35.187500\n",
       "241                  Equestrianism Mixed Jumping, Team   F  34.047619\n",
       "235           Equestrianism Mixed Dressage, Individual   F  33.500000\n",
       "408                             Shooting Women's Skeet   F  33.066667\n",
       "402  Shooting Mixed Small-Bore Rifle, Three Positio...   F  33.000000\n",
       "..                                                 ...  ..        ...\n",
       "491              Swimming Women's 400 metres Freestyle   F  18.833333\n",
       "479             Swimming Women's 100 metres Backstroke   F  18.826087\n",
       "297                 Gymnastics Women's Team All-Around   F  18.812977\n",
       "335                  Rhythmic Gymnastics Women's Group   F  18.730000\n",
       "494              Swimming Women's 800 metres Freestyle   F  18.487179\n",
       "\n",
       "[221 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2\n",
    "#filter column by F to get the avg age and sort highest avg\n",
    "age_by_sex[(age_by_sex.Sex == 'F')].sort_values(by='avg_age', ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "tngNcIWKY6OV",
    "outputId": "308ac75d-9f03-4d18-b16f-20ff1afc4754"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Event</th>\n",
       "      <th>Sex</th>\n",
       "      <th>avg_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>Weightlifting Men's Super-Heavyweight</td>\n",
       "      <td>M</td>\n",
       "      <td>139.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>Wrestling Men's Super-Heavyweight, Freestyle</td>\n",
       "      <td>M</td>\n",
       "      <td>121.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Judo Men's Heavyweight</td>\n",
       "      <td>M</td>\n",
       "      <td>120.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>Wrestling Men's Super-Heavyweight, Greco-Roman</td>\n",
       "      <td>M</td>\n",
       "      <td>120.710526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Athletics Men's Shot Put</td>\n",
       "      <td>M</td>\n",
       "      <td>114.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>Wrestling Men's Flyweight, Freestyle</td>\n",
       "      <td>M</td>\n",
       "      <td>54.193548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Boxing Men's Flyweight</td>\n",
       "      <td>M</td>\n",
       "      <td>51.721311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>Wrestling Men's Light-Flyweight, Greco-Roman</td>\n",
       "      <td>M</td>\n",
       "      <td>50.619048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>Wrestling Men's Light-Flyweight, Freestyle</td>\n",
       "      <td>M</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Boxing Men's Light-Flyweight</td>\n",
       "      <td>M</td>\n",
       "      <td>48.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Event Sex  avg_weight\n",
       "539           Weightlifting Men's Super-Heavyweight   M  139.542857\n",
       "567    Wrestling Men's Super-Heavyweight, Freestyle   M  121.333333\n",
       "310                          Judo Men's Heavyweight   M  120.900000\n",
       "568  Wrestling Men's Super-Heavyweight, Greco-Roman   M  120.710526\n",
       "64                         Athletics Men's Shot Put   M  114.468750\n",
       "..                                              ...  ..         ...\n",
       "554            Wrestling Men's Flyweight, Freestyle   M   54.193548\n",
       "128                          Boxing Men's Flyweight   M   51.721311\n",
       "559    Wrestling Men's Light-Flyweight, Greco-Roman   M   50.619048\n",
       "558      Wrestling Men's Light-Flyweight, Freestyle   M   50.200000\n",
       "130                    Boxing Men's Light-Flyweight   M   48.720000\n",
       "\n",
       "[357 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3\n",
    "#filter column by M to get the avg weight and sort for highest number\n",
    "\n",
    "weight_by_sex[(weight_by_sex.Sex == 'M')].sort_values(by='avg_weight', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "IZWF1l2hZcH0",
    "outputId": "b1b62f5f-75ef-472f-9aac-946c35541cf3"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Event</th>\n",
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       "      <th>avg_height</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>Weightlifting Men's Flyweight</td>\n",
       "      <td>M</td>\n",
       "      <td>153.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>Weightlifting Men's Bantamweight</td>\n",
       "      <td>M</td>\n",
       "      <td>155.717391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>Wrestling Men's Light-Flyweight, Greco-Roman</td>\n",
       "      <td>M</td>\n",
       "      <td>155.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>Wrestling Men's Light-Flyweight, Freestyle</td>\n",
       "      <td>M</td>\n",
       "      <td>156.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>Weightlifting Men's Featherweight</td>\n",
       "      <td>M</td>\n",
       "      <td>158.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Volleyball Men's Volleyball</td>\n",
       "      <td>M</td>\n",
       "      <td>193.940695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Beach Volleyball Men's Beach Volleyball</td>\n",
       "      <td>M</td>\n",
       "      <td>194.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>Taekwondo Men's Heavyweight</td>\n",
       "      <td>M</td>\n",
       "      <td>194.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>Swimming Men's 50 metres Freestyle</td>\n",
       "      <td>M</td>\n",
       "      <td>195.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>M</td>\n",
       "      <td>197.996721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Event Sex  avg_height\n",
       "531                 Weightlifting Men's Flyweight   M  153.350000\n",
       "529              Weightlifting Men's Bantamweight   M  155.717391\n",
       "559  Wrestling Men's Light-Flyweight, Greco-Roman   M  155.904762\n",
       "558    Wrestling Men's Light-Flyweight, Freestyle   M  156.150000\n",
       "530             Weightlifting Men's Featherweight   M  158.940000\n",
       "..                                            ...  ..         ...\n",
       "524                   Volleyball Men's Volleyball   M  193.940695\n",
       "107       Beach Volleyball Men's Beach Volleyball   M  194.000000\n",
       "506                   Taekwondo Men's Heavyweight   M  194.222222\n",
       "475            Swimming Men's 50 metres Freestyle   M  195.333333\n",
       "105                   Basketball Men's Basketball   M  197.996721\n",
       "\n",
       "[357 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4\n",
    "#filter column by M to get the avg height and sort for highest number\n",
    "height_by_sex[(height_by_sex.Sex == 'M')].sort_values(by='avg_height')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BSl1lkIz3XU_"
   },
   "source": [
    "## Section 2: Winter is Coming\n",
    "\n",
    "\n",
    "*Which countries perform better in the Winter Olympics as compared to the Summer Olympics?*\n",
    "\n",
    "To do this, we will take the following general steps:\n",
    "\n",
    "\n",
    "*   Create a metric to evaluate Olympic performance\n",
    "*   Calculate that metric for every country and season (Summer/Winter)\n",
    "*   For a given country, compare the relative contribution to the metric from the Summer Olympics vs the Winter Olympics\n",
    "*   Rank them by highest relative performance in the Winter Olympics\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y_oEbOni4BXT"
   },
   "source": [
    "### Part 1: Filter Data and Fill NA\n",
    "\n",
    "1.   The Winter Olympics have only been around since 1924 while the Summer Olympics started in 1896.  Let's filter this dataframe to `Year >= 1924` so that we have an even playing field within which to compare Winter performance to Summer performance.\n",
    "2.   The `Medal` column contains many `NaN` values.  Use `fillna` to substitute these values with `\"None\"` (i.e. the string \"None\") instead.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "zw6ba3mzKuqi",
    "outputId": "b81c6912-361e-485a-fee5-262bccf4dc2c"
   },
   "outputs": [
    {
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       "      <th>166713</th>\n",
       "      <td>83740</td>\n",
       "      <td>Dimitar Mutafchiev</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
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       "      <th>131875</th>\n",
       "      <td>66367</td>\n",
       "      <td>Megan Lane</td>\n",
       "      <td>F</td>\n",
       "      <td>25.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
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       "      <td>Equestrianism Mixed Dressage, Individual</td>\n",
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       "      <td>23.0</td>\n",
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       "      <td>Poland</td>\n",
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       "      <td>Weightlifting Women's Light-Heavyweight</td>\n",
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       "      <th>162993</th>\n",
       "      <td>81841</td>\n",
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       "      <td>174.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Czech Republic</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>188461</th>\n",
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       "      <td>Selena Piek</td>\n",
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       "      <td>59.0</td>\n",
       "      <td>Netherlands</td>\n",
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       "      <td>2016 Summer</td>\n",
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       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Badminton</td>\n",
       "      <td>Badminton Mixed Doubles</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177996</th>\n",
       "      <td>89442</td>\n",
       "      <td>Ahmet rken</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>TUR</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>Cycling Men's Road Race, Individual</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254333 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  ...   Medal\n",
       "48017    24745  ...     NaN\n",
       "247373  123859  ...     NaN\n",
       "247374  123859  ...  Bronze\n",
       "27662    14357  ...     NaN\n",
       "166713   83740  ...     NaN\n",
       "...        ...  ...     ...\n",
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       "162993   81841  ...     NaN\n",
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       "177996   89442  ...     NaN\n",
       "\n",
       "[254333 rows x 15 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {
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   ],
   "source": [
    "# Write code below\n",
    "#1\n",
    "\n",
    "#prepare the dataset for summer and winter comparsion and set it to year >= 1924\n",
    "medal_athlete = athlete[(athlete['Year'] >= 1924)].sort_values(by='Year')\n",
    "medal_athlete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IzE9RtVPdlHj"
   },
   "outputs": [],
   "source": [
    "#2\n",
    "# replace NaN with None \n",
    "medal_athlete[\"Medal\"].fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "Ned9As-8ahRh",
    "outputId": "ff02b61d-705e-47e2-c829-ee1ca4ad4e76"
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       "      <td>80.0</td>\n",
       "      <td>France</td>\n",
       "      <td>FRA</td>\n",
       "      <td>1924 Summer</td>\n",
       "      <td>1924</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166713</th>\n",
       "      <td>83740</td>\n",
       "      <td>Dimitar Mutafchiev</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Megan Lane</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Equestrianism</td>\n",
       "      <td>Equestrianism Mixed Dressage, Individual</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188438</th>\n",
       "      <td>94658</td>\n",
       "      <td>Patrycja Piechowiak</td>\n",
       "      <td>F</td>\n",
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       "      <td>162.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Poland</td>\n",
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       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Women's Light-Heavyweight</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
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       "      <td>F</td>\n",
       "      <td>27.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>CZE</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>Swimming Women's 100 metres Breaststroke</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188461</th>\n",
       "      <td>94667</td>\n",
       "      <td>Selena Piek</td>\n",
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       "      <td>59.0</td>\n",
       "      <td>Netherlands</td>\n",
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       "      <td>Rio de Janeiro</td>\n",
       "      <td>Badminton</td>\n",
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       "    <tr>\n",
       "      <th>177996</th>\n",
       "      <td>89442</td>\n",
       "      <td>Ahmet rken</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>TUR</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>Cycling Men's Road Race, Individual</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254333 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  ...   Medal\n",
       "48017    24745  ...    None\n",
       "247373  123859  ...    None\n",
       "247374  123859  ...  Bronze\n",
       "27662    14357  ...    None\n",
       "166713   83740  ...    None\n",
       "...        ...  ...     ...\n",
       "131875   66367  ...    None\n",
       "188438   94658  ...    None\n",
       "162993   81841  ...    None\n",
       "188461   94667  ...    None\n",
       "177996   89442  ...    None\n",
       "\n",
       "[254333 rows x 15 columns]"
      ]
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     "execution_count": 24,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "medal_athlete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "laVFsKMKKyLc"
   },
   "source": [
    "### Part 2: Calculate Weighted Medal Points\n",
    "\n",
    "When evaluating country performance in the Olympics, we don't want to count Gold medals as equivalent to Silver/Bronze medals.  Let's assign \"Medal Points\" to each tier of Olympic medal as follows:\n",
    "\n",
    "*   Gold:  3 points\n",
    "*   Silver: 2 points\n",
    "*   Bronze: 1 point\n",
    "*   No medal: 0 points\n",
    "\n",
    "Write a function `medal_to_points` to transform and create a new column called `MedalPoints`.  `medal_to_points` will accept a string (possible values are `\"Gold\"`, `\"Silver\"`, `\"Bronze\"`, `\"None\"`) and returns the appropriate number of points as an integer.\n",
    "\n",
    "Then add a new column `MedalPoints` to your dataframe using `transform` and the newly defined function `medal_to_points`"
   ]
  },
  {
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       "            ID  ...   Medal\n",
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       "247374  123859  ...  Bronze\n",
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4hPbmDqDKxYp"
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   "outputs": [],
   "source": [
    "# Define medal_to_points below\n",
    "def medal_to_points(medal_name):\n",
    "  medal = str(medal_name)\n",
    "  # Insert logic to convert medal to point value\n",
    "  if medal == 'Gold':\n",
    "    return 3\n",
    "  elif medal == \"Silver\":\n",
    "    return 2\n",
    "  elif medal == \"Bronze\":\n",
    "    return 1\n",
    "  else:\n",
    "    return 0\n",
    "    # Return the converted point value\n",
    "    return medal_name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GA3jHsR6mWEI"
   },
   "outputs": [],
   "source": [
    "#adding a new column with the new metric points\n",
    "medal_athlete['MedalPoints'] = medal_athlete.Medal.transform(medal_to_points)"
   ]
  },
  {
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       "      <td>Megan Lane</td>\n",
       "      <td>F</td>\n",
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       "      <td>178.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Canada</td>\n",
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       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Equestrianism</td>\n",
       "      <td>Equestrianism Mixed Dressage, Individual</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188438</th>\n",
       "      <td>94658</td>\n",
       "      <td>Patrycja Piechowiak</td>\n",
       "      <td>F</td>\n",
       "      <td>23.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>POL</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Women's Light-Heavyweight</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162993</th>\n",
       "      <td>81841</td>\n",
       "      <td>Martina Moravkov</td>\n",
       "      <td>F</td>\n",
       "      <td>27.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>CZE</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>Swimming Women's 100 metres Breaststroke</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188461</th>\n",
       "      <td>94667</td>\n",
       "      <td>Selena Piek</td>\n",
       "      <td>F</td>\n",
       "      <td>24.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Badminton</td>\n",
       "      <td>Badminton Mixed Doubles</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177996</th>\n",
       "      <td>89442</td>\n",
       "      <td>Ahmet rken</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>TUR</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>Cycling Men's Road Race, Individual</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254333 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  ... MedalPoints\n",
       "48017    24745  ...           0\n",
       "247373  123859  ...           0\n",
       "247374  123859  ...           1\n",
       "27662    14357  ...           0\n",
       "166713   83740  ...           0\n",
       "...        ...  ...         ...\n",
       "131875   66367  ...           0\n",
       "188438   94658  ...           0\n",
       "162993   81841  ...           0\n",
       "188461   94667  ...           0\n",
       "177996   89442  ...           0\n",
       "\n",
       "[254333 rows x 16 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medal_athlete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DqjRwITCN3cy"
   },
   "source": [
    "### Part 3: Aggregate MedalPoints by Country and Season\n",
    "\n",
    "We need to figure out how each country performed in the Summer, and how each country performed in the winter.  Now that we have created the performance metric `MedalPoints`, we need to aggregate by Country and Season to figure out how many `MedalPoints` each country earned in each season (Summer and Winter).\n",
    "\n",
    "1.   Using `NOC` for country and `Season` for season, group by these two fields and `sum()` the field `MedalPoints` to determine how many \"Medal Points\" each country earned in each season (Summer vs Winter)\n",
    "2.   Store the results of the above in a new dataframe\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "he3Cco-3Og2K"
   },
   "outputs": [],
   "source": [
    "# Write your code below\n",
    "\n",
    "#create a variable and groupby country and season and then sum all medalpoints\n",
    "medal_by_points = medal_athlete.groupby(by=['NOC', 'Season'], as_index=False )['MedalPoints'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 962
    },
    "id": "Mb_68FAhdmh3",
    "outputId": "cfc9669d-a68e-4b16-e5d9-519e8c34cacc"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NOC</th>\n",
       "      <th>Season</th>\n",
       "      <th>MedalPoints</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Summer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AHO</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AHO</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALB</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALG</td>\n",
       "      <td>Summer</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ALG</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AND</td>\n",
       "      <td>Summer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AND</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ANG</td>\n",
       "      <td>Summer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ANT</td>\n",
       "      <td>Summer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Summer</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ARM</td>\n",
       "      <td>Summer</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ARM</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARU</td>\n",
       "      <td>Summer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ASA</td>\n",
       "      <td>Summer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ASA</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Summer</td>\n",
       "      <td>2398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Winter</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Summer</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Winter</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AZE</td>\n",
       "      <td>Summer</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AZE</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BAH</td>\n",
       "      <td>Summer</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BAN</td>\n",
       "      <td>Summer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>BAR</td>\n",
       "      <td>Summer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BDI</td>\n",
       "      <td>Summer</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Summer</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Winter</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    NOC  Season  MedalPoints\n",
       "0   AFG  Summer            2\n",
       "1   AHO  Summer            2\n",
       "2   AHO  Winter            0\n",
       "3   ALB  Summer            0\n",
       "4   ALB  Winter            0\n",
       "5   ALG  Summer           31\n",
       "6   ALG  Winter            0\n",
       "7   AND  Summer            0\n",
       "8   AND  Winter            0\n",
       "9   ANG  Summer            0\n",
       "10  ANT  Summer            0\n",
       "11  ARG  Summer          548\n",
       "12  ARG  Winter            0\n",
       "13  ARM  Summer           25\n",
       "14  ARM  Winter            0\n",
       "15  ARU  Summer            0\n",
       "16  ASA  Summer            0\n",
       "17  ASA  Winter            0\n",
       "18  AUS  Summer         2398\n",
       "19  AUS  Winter           31\n",
       "20  AUT  Summer          246\n",
       "21  AUT  Winter          536\n",
       "22  AZE  Summer           70\n",
       "23  AZE  Winter            0\n",
       "24  BAH  Summer           79\n",
       "25  BAN  Summer            0\n",
       "26  BAR  Summer            1\n",
       "27  BDI  Summer            5\n",
       "28  BEL  Summer          283\n",
       "29  BEL  Winter           21"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medal_by_points.head(n=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lONv1Jj2OjKA"
   },
   "source": [
    "### Part 4: Calculate the % of Medal Pts earned in each Season for each Country\n",
    "\n",
    "To determine which countries perform better in Winter as compared to Summer, let's rank them by the % of Medal Points earned in the Winter.  In order to do this, we need to calculate a % of total medal points earned by season (Summer vs Winter).\n",
    "\n",
    "1.   Using your new dataframe, group by `NOC` (Country) only and transform `MedalPoints` into a % of total using a function (can be a lambda function). **Note: if stuck here, see lecture and lecture notes where we did a similar transformation** \n",
    "2.   Add the series generated above as a new column `PctTotal` to the dataframe.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "D5F0AAHWMh5o"
   },
   "outputs": [],
   "source": [
    "# groupby country and transform medalpoints to calculte summer/summer+winter\n",
    "medal_by_points['PctTotal'] = medal_by_points.groupby(by='NOC', as_index=False)['MedalPoints'].transform(lambda x: x/x.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
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       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AHO</td>\n",
       "      <td>Summer</td>\n",
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       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AHO</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>0.984088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>YUG</td>\n",
       "      <td>Winter</td>\n",
       "      <td>13</td>\n",
       "      <td>0.015912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>ZAM</td>\n",
       "      <td>Summer</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>ZIM</td>\n",
       "      <td>Summer</td>\n",
       "      <td>60</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>ZIM</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     NOC  Season  MedalPoints  PctTotal\n",
       "0    AFG  Summer            2  1.000000\n",
       "1    AHO  Summer            2  1.000000\n",
       "2    AHO  Winter            0  0.000000\n",
       "3    ALB  Summer            0       NaN\n",
       "4    ALB  Winter            0       NaN\n",
       "..   ...     ...          ...       ...\n",
       "339  YUG  Summer          804  0.984088\n",
       "340  YUG  Winter           13  0.015912\n",
       "341  ZAM  Summer            3  1.000000\n",
       "342  ZIM  Summer           60  1.000000\n",
       "343  ZIM  Winter            0  0.000000\n",
       "\n",
       "[344 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
    }
   ],
   "source": [
    " medal_by_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3aN1xyKZRcBz"
   },
   "source": [
    "### Part 5: Report Results\n",
    "\n",
    "For countries **earning at least 100 MedalPoints in the Winter Olympics**, list the top 3 countries that earned the highest % of their Medal Points in the Winter Olympics.  Also list the percentage of Medal Points earned and total number of Medal Points earned in the Winter Olympics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 718
    },
    "id": "0xLfKkc1Syi7",
    "outputId": "11c4f620-2eb7-4cbf-c539-6a08bb7639dc"
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   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Winter</td>\n",
       "      <td>536</td>\n",
       "      <td>0.685422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Winter</td>\n",
       "      <td>910</td>\n",
       "      <td>0.606667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Winter</td>\n",
       "      <td>1420</td>\n",
       "      <td>0.578883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>FIN</td>\n",
       "      <td>Winter</td>\n",
       "      <td>703</td>\n",
       "      <td>0.546232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>CZE</td>\n",
       "      <td>Winter</td>\n",
       "      <td>139</td>\n",
       "      <td>0.526515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>SUI</td>\n",
       "      <td>Winter</td>\n",
       "      <td>497</td>\n",
       "      <td>0.435965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>SWE</td>\n",
       "      <td>Winter</td>\n",
       "      <td>804</td>\n",
       "      <td>0.397233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>TCH</td>\n",
       "      <td>Winter</td>\n",
       "      <td>243</td>\n",
       "      <td>0.280925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Winter</td>\n",
       "      <td>537</td>\n",
       "      <td>0.234908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>EUN</td>\n",
       "      <td>Winter</td>\n",
       "      <td>139</td>\n",
       "      <td>0.230132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>GER</td>\n",
       "      <td>Winter</td>\n",
       "      <td>828</td>\n",
       "      <td>0.211332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>URS</td>\n",
       "      <td>Winter</td>\n",
       "      <td>1037</td>\n",
       "      <td>0.192073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>KOR</td>\n",
       "      <td>Winter</td>\n",
       "      <td>212</td>\n",
       "      <td>0.161585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>GDR</td>\n",
       "      <td>Winter</td>\n",
       "      <td>328</td>\n",
       "      <td>0.154280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>NED</td>\n",
       "      <td>Winter</td>\n",
       "      <td>244</td>\n",
       "      <td>0.137542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Winter</td>\n",
       "      <td>362</td>\n",
       "      <td>0.125520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>FRG</td>\n",
       "      <td>Winter</td>\n",
       "      <td>134</td>\n",
       "      <td>0.122040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>USA</td>\n",
       "      <td>Winter</td>\n",
       "      <td>1275</td>\n",
       "      <td>0.119304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>FRA</td>\n",
       "      <td>Winter</td>\n",
       "      <td>257</td>\n",
       "      <td>0.105501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>GBR</td>\n",
       "      <td>Winter</td>\n",
       "      <td>177</td>\n",
       "      <td>0.073050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>CHN</td>\n",
       "      <td>Winter</td>\n",
       "      <td>142</td>\n",
       "      <td>0.069745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Winter</td>\n",
       "      <td>119</td>\n",
       "      <td>0.069591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NOC  Season  MedalPoints  PctTotal\n",
       "21   AUT  Winter          536  0.685422\n",
       "227  NOR  Winter          910  0.606667\n",
       "52   CAN  Winter         1420  0.578883\n",
       "101  FIN  Winter          703  0.546232\n",
       "78   CZE  Winter          139  0.526515\n",
       "285  SUI  Winter          497  0.435965\n",
       "290  SWE  Winter          804  0.397233\n",
       "296  TCH  Winter          243  0.280925\n",
       "260  RUS  Winter          537  0.234908\n",
       "97   EUN  Winter          139  0.230132\n",
       "118  GER  Winter          828  0.211332\n",
       "322  URS  Winter         1037  0.192073\n",
       "170  KOR  Winter          212  0.161585\n",
       "113  GDR  Winter          328  0.154280\n",
       "221  NED  Winter          244  0.137542\n",
       "154  ITA  Winter          362  0.125520\n",
       "105  FRG  Winter          134  0.122040\n",
       "326  USA  Winter         1275  0.119304\n",
       "103  FRA  Winter          257  0.105501\n",
       "110  GBR  Winter          177  0.073050\n",
       "60   CHN  Winter          142  0.069745\n",
       "161  JPN  Winter          119  0.069591"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code below\n",
    "# filter the dataframe to only winter\n",
    "winter_ranking = medal_by_points[(medal_by_points.Season == 'Winter')] \n",
    "#filter to only above 100 medalpoints\n",
    "top_winter = winter_ranking[(winter_ranking.MedalPoints >= 100)]\n",
    "# sort the result to highest to get the top 3\n",
    "top_winter.sort_values(by='PctTotal', ascending=False)"
   ]
  }
 ],
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