Assingment4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82f4a9b0-1165-475c-b44a-99576faf3c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fa84d12-236a-4952-8628-f90d6dfdb32e",
   "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>Wins</th>\n",
       "      <th>Top 10</th>\n",
       "      <th>Money</th>\n",
       "      <th>Rounds</th>\n",
       "      <th>Top 10 Ratio</th>\n",
       "      <th>Earnings Per Round</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Henrik Stenson</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2680487</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>44674.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ryan Armour</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2485203</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.027523</td>\n",
       "      <td>22800.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chez Reavie</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2700018</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>29032.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ryan Moore</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1986608</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>25469.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brian Stuard</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1089763</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.029126</td>\n",
       "      <td>10580.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phil Mickelson</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4595187</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>51057.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin Tway</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1379544</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.027273</td>\n",
       "      <td>12541.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ollie Schniederjans</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1303610</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.032967</td>\n",
       "      <td>14325.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameron Tringale</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179459</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2678.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ricky Barnes</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>340591</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>5239.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Year  Wins  Top 10    Money  Rounds  Top 10 Ratio  \\\n",
       "Player Name                                                              \n",
       "Henrik Stenson       2018   0.0     5.0  2680487    60.0      0.083333   \n",
       "Ryan Armour          2018   1.0     3.0  2485203   109.0      0.027523   \n",
       "Chez Reavie          2018   0.0     3.0  2700018    93.0      0.032258   \n",
       "Ryan Moore           2018   0.0     5.0  1986608    78.0      0.064103   \n",
       "Brian Stuard         2018   0.0     3.0  1089763   103.0      0.029126   \n",
       "...                   ...   ...     ...      ...     ...           ...   \n",
       "Phil Mickelson       2018   1.0     6.0  4595187    90.0      0.066667   \n",
       "Kevin Tway           2018   0.0     3.0  1379544   110.0      0.027273   \n",
       "Ollie Schniederjans  2018   0.0     3.0  1303610    91.0      0.032967   \n",
       "Cameron Tringale     2018   0.0     0.0   179459    67.0      0.000000   \n",
       "Ricky Barnes         2018   0.0     1.0   340591    65.0      0.015385   \n",
       "\n",
       "                     Earnings Per Round  \n",
       "Player Name                              \n",
       "Henrik Stenson                 44674.78  \n",
       "Ryan Armour                    22800.03  \n",
       "Chez Reavie                    29032.45  \n",
       "Ryan Moore                     25469.33  \n",
       "Brian Stuard                   10580.22  \n",
       "...                                 ...  \n",
       "Phil Mickelson                 51057.63  \n",
       "Kevin Tway                     12541.31  \n",
       "Ollie Schniederjans            14325.38  \n",
       "Cameron Tringale                2678.49  \n",
       "Ricky Barnes                    5239.86  \n",
       "\n",
       "[193 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file into a DataFrame\n",
    "df = pd.read_csv('pgaTourData.csv')\n",
    "\n",
    "# Fill NaN values with zeros\n",
    "df = df.fillna(0)\n",
    "\n",
    "# Filter the DataFrame based on conditions\n",
    "# Select specific columns\n",
    "clean_df = df.loc[(df['Year'] == 2018) & (df['Rounds'] >= 1), ['Player Name', 'Year', 'Wins', 'Top 10', 'Money', 'Rounds']]\n",
    "\n",
    "# Set 'Player Name' as the index\n",
    "clean_df = clean_df.set_index('Player Name')\n",
    "\n",
    "# Convert 'Money' column to numeric for each player\n",
    "clean_df['Money'] = pd.to_numeric(clean_df['Money'].replace('[\\$,]', '', regex=True), errors='coerce')\n",
    "\n",
    "# Create new columns for each player\n",
    "for player in clean_df.index:\n",
    "    # Convert 'Money' column to numeric for the specific player\n",
    "    clean_df.loc[player, 'Money'] = pd.to_numeric(clean_df.loc[player, 'Money'], errors='coerce')\n",
    "\n",
    "    # Calculate 'Top 10 Ratio'\n",
    "    clean_df.loc[player, 'Top 10 Ratio'] = clean_df.loc[player, 'Top 10'] / clean_df.loc[player, 'Rounds']\n",
    "\n",
    "    # Calculate 'Earnings Per Round'\n",
    "    clean_df.loc[player, 'Earnings Per Round'] = clean_df.loc[player, 'Money'] / clean_df.loc[player, 'Rounds']\n",
    "\n",
    "clean_df['Earnings Per Round'] = clean_df['Earnings Per Round'].round(2)\n",
    "# Display the data frame \n",
    "clean_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f22aebf-c452-4ed1-807b-8865f5cd2ee3",
   "metadata": {},
   "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>Year</th>\n",
       "      <th>Wins</th>\n",
       "      <th>Top 10</th>\n",
       "      <th>Money</th>\n",
       "      <th>Rounds</th>\n",
       "      <th>Top 10 Ratio</th>\n",
       "      <th>Earnings Per Round</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Henrik Stenson</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2680487</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>44674.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tony Finau</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5620138</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.076190</td>\n",
       "      <td>53525.12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameron Smith</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3622607</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>42618.91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xander Schauffele</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4047538</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.052083</td>\n",
       "      <td>42161.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marc Leishman</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3771221</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>40550.76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Landry</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2642179</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.060976</td>\n",
       "      <td>32221.70</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Webb Simpson</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5376417</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.073684</td>\n",
       "      <td>56593.86</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jon Rahm</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3992678</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.070423</td>\n",
       "      <td>56234.90</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patrick Reed</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5006267</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.074468</td>\n",
       "      <td>53258.16</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ian Poulter</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2714450</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.054795</td>\n",
       "      <td>37184.25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Year  Wins  Top 10    Money  Rounds  Top 10 Ratio  \\\n",
       "Player Name                                                            \n",
       "Henrik Stenson     2018   0.0     5.0  2680487    60.0      0.083333   \n",
       "Tony Finau         2018   0.0     8.0  5620138   105.0      0.076190   \n",
       "Cameron Smith      2018   0.0     5.0  3622607    85.0      0.058824   \n",
       "Xander Schauffele  2018   0.0     5.0  4047538    96.0      0.052083   \n",
       "Marc Leishman      2018   0.0     6.0  3771221    93.0      0.064516   \n",
       "...                 ...   ...     ...      ...     ...           ...   \n",
       "Andrew Landry      2018   1.0     5.0  2642179    82.0      0.060976   \n",
       "Webb Simpson       2018   1.0     7.0  5376417    95.0      0.073684   \n",
       "Jon Rahm           2018   1.0     5.0  3992678    71.0      0.070423   \n",
       "Patrick Reed       2018   1.0     7.0  5006267    94.0      0.074468   \n",
       "Ian Poulter        2018   1.0     4.0  2714450    73.0      0.054795   \n",
       "\n",
       "                   Earnings Per Round  Cluster  \n",
       "Player Name                                     \n",
       "Henrik Stenson               44674.78        0  \n",
       "Tony Finau                   53525.12        0  \n",
       "Cameron Smith                42618.91        0  \n",
       "Xander Schauffele            42161.85        0  \n",
       "Marc Leishman                40550.76        0  \n",
       "...                               ...      ...  \n",
       "Andrew Landry                32221.70        9  \n",
       "Webb Simpson                 56593.86        9  \n",
       "Jon Rahm                     56234.90        9  \n",
       "Patrick Reed                 53258.16        9  \n",
       "Ian Poulter                  37184.25        9  \n",
       "\n",
       "[193 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = clean_df[['Top 10 Ratio', 'Earnings Per Round', 'Wins']]\n",
    "scaler = StandardScaler()\n",
    "scaled_features = scaler.fit_transform(features)\n",
    "\n",
    "# Perform KMeans clustering with k=10\n",
    "kmeans = KMeans(n_clusters=10)\n",
    "cluster_labels = kmeans.fit_predict(scaled_features)\n",
    "\n",
    "# Add the cluster labels to the original DataFrame\n",
    "clean_df['Cluster'] = cluster_labels\n",
    "\n",
    "clean_df.sort_values(by='Cluster', inplace=True)\n",
    "# Display the DataFrame with the added 'Cluster' column\n",
    "clean_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3192a6c1-ea1c-4ba6-8ab7-2b06b724eec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = clean_df[['Top 10 Ratio', 'Earnings Per Round']]\n",
    "cluster_labels = clean_df['Cluster']\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Scatter plot points based on the clusters\n",
    "for cluster in range(0,10):\n",
    "    cluster_points = features[cluster_labels == cluster]\n",
    "    plt.scatter(cluster_points['Top 10 Ratio'], cluster_points['Earnings Per Round'], label=f'Cluster {cluster + 1}')\n",
    "\n",
    "plt.xlabel('Top 10 Ratio')\n",
    "plt.ylabel('Earnings Per Round')\n",
    "plt.title('2018 PGA Tour Clusters')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d02afdfc-8c86-44bb-aad6-b198eb13ac9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}