Exploratory_Data_Analysis_Male.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a98e74e2",
   "metadata": {},
   "source": [
    "# Dataset information for WOD Project\n",
    "\n",
    "The column names refer to workouts. For example 17.3, would refer to the 3rd workout in the 2017 competition, defined [here](https://games.crossfit.com/workouts/open/2017/3?division=1). For each workout, the owner has listed three things:\n",
    "\n",
    "- score: This is either reps or time. You'd see the column formatted accordinly\n",
    "- percentile: this is just their ranking in the world for that workout. Someone with the most reps would be 100th percentile. We can calculate this pretty easily from the score\n",
    "- predicted_reps: don't know where this came from, since it doesn't align with any standard practice. My guess is it may have been some prediction the owner ran on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "a754034f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4baf61b3",
   "metadata": {},
   "source": [
    "### Combine datasets and create one big dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "05bd8d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Neel Patel\\AppData\\Local\\Temp\\ipykernel_10772\\2659286706.py:3: DtypeWarning: Columns (13,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  comp_male_2017_data = pd.read_csv(r'C:\\Users\\Neel Patel\\Documents\\Github Repositories\\WOD Project\\WOD-Project-NP\\Sample_Data\\Men_Rx_2017.csv')\n"
     ]
    }
   ],
   "source": [
    "DATA_FILE_PATH = r'C:\\Users\\Neel Patel\\Documents\\Github Repositories\\WOD Project\\WOD-Project-NP\\Sample_Data\\Profile_Men.csv'\n",
    "male_data = pd.read_csv(DATA_FILE_PATH, encoding='latin1', index_col = None)\n",
    "comp_male_2017_data = pd.read_csv(r'C:\\Users\\Neel Patel\\Documents\\Github Repositories\\WOD Project\\WOD-Project-NP\\Sample_Data\\Men_Rx_2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "id": "109da11f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Both of these datasets have a unique userID for each competitor. Let's merge these two dataframes!\n",
    "combined_df = pd.merge(male_data,comp_male_2017_data,on = \"Userid\", how = \"inner\")\n",
    "\n",
    "combined_df.drop(['Unnamed: 0', 'Region', 'Affiliate'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "90bf8a8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 93611 entries, 0 to 93610\n",
      "Data columns (total 46 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Index                93611 non-null  int64  \n",
      " 1   Userid               93611 non-null  int64  \n",
      " 2   Name_x               93611 non-null  object \n",
      " 3   Age_x                93611 non-null  int64  \n",
      " 4   Height               93611 non-null  int64  \n",
      " 5   Weight               93611 non-null  int64  \n",
      " 6   Back Squat           93611 non-null  int64  \n",
      " 7   Clean & Jerk         93611 non-null  int64  \n",
      " 8   Snatch               93611 non-null  int64  \n",
      " 9   Deadlift             93611 non-null  int64  \n",
      " 10  Fight Gone Bad       93611 non-null  int64  \n",
      " 11  Max Pull-ups         93611 non-null  float64\n",
      " 12  Fran                 87190 non-null  object \n",
      " 13  Grace                87190 non-null  object \n",
      " 14  Helen                87190 non-null  object \n",
      " 15  Filthy 50            87190 non-null  object \n",
      " 16  Sprint 400m          87190 non-null  object \n",
      " 17  Run 5K               87190 non-null  object \n",
      " 18  Name_y               93611 non-null  object \n",
      " 19  Height (m)           76337 non-null  float64\n",
      " 20  Weight (kg)          20674 non-null  float64\n",
      " 21  Age_y                93611 non-null  int64  \n",
      " 22  Regionid             93611 non-null  int64  \n",
      " 23  Regionname           93611 non-null  object \n",
      " 24  Affiliateid          93611 non-null  int64  \n",
      " 25  Overallrank          93611 non-null  int64  \n",
      " 26  Overallscore         93611 non-null  int64  \n",
      " 27  Overallpercentile    93611 non-null  float64\n",
      " 28  17.1_rank            93611 non-null  int64  \n",
      " 29  17.1_score           89653 non-null  object \n",
      " 30  17.1_percentile      89653 non-null  float64\n",
      " 31  17.1_predicted_reps  89653 non-null  float64\n",
      " 32  17.2_rank            93611 non-null  int64  \n",
      " 33  17.2_score           83314 non-null  float64\n",
      " 34  17.2_percentile      83314 non-null  float64\n",
      " 35  17.3_rank            93611 non-null  int64  \n",
      " 36  17.3_score           78256 non-null  object \n",
      " 37  17.3_percentile      78256 non-null  float64\n",
      " 38  17.3_predicted_reps  78256 non-null  float64\n",
      " 39  17.4_rank            93611 non-null  int64  \n",
      " 40  17.4_score           77337 non-null  float64\n",
      " 41  17.4_percentile      77337 non-null  float64\n",
      " 42  17.5_rank            93611 non-null  int64  \n",
      " 43  17.5_score           69939 non-null  object \n",
      " 44  17.5_percentile      69939 non-null  float64\n",
      " 45  17.5_predicted_time  69939 non-null  object \n",
      "dtypes: float64(13), int64(20), object(13)\n",
      "memory usage: 33.6+ MB\n"
     ]
    }
   ],
   "source": [
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "0814934c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 93611 entries, 0 to 93610\n",
      "Data columns (total 44 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Index                93611 non-null  int64  \n",
      " 1   Userid               93611 non-null  int64  \n",
      " 2   Name_x               93611 non-null  object \n",
      " 3   Age_x                93611 non-null  int64  \n",
      " 4   Height               93611 non-null  int64  \n",
      " 5   Weight               93611 non-null  int64  \n",
      " 6   Back Squat           93611 non-null  int64  \n",
      " 7   Clean & Jerk         93611 non-null  int64  \n",
      " 8   Snatch               93611 non-null  int64  \n",
      " 9   Deadlift             93611 non-null  int64  \n",
      " 10  Fight Gone Bad       93611 non-null  int64  \n",
      " 11  Max Pull-ups         93611 non-null  float64\n",
      " 12  Fran                 87190 non-null  object \n",
      " 13  Grace                87190 non-null  object \n",
      " 14  Helen                87190 non-null  object \n",
      " 15  Filthy 50            87190 non-null  object \n",
      " 16  Sprint 400m          87190 non-null  object \n",
      " 17  Run 5K               87190 non-null  object \n",
      " 18  Height (m)           76337 non-null  float64\n",
      " 19  Weight (kg)          20674 non-null  float64\n",
      " 20  Regionid             93611 non-null  int64  \n",
      " 21  Regionname           93611 non-null  object \n",
      " 22  Affiliateid          93611 non-null  int64  \n",
      " 23  Overallrank          93611 non-null  int64  \n",
      " 24  Overallscore         93611 non-null  int64  \n",
      " 25  Overallpercentile    93611 non-null  float64\n",
      " 26  17.1_rank            93611 non-null  int64  \n",
      " 27  17.1_score           89653 non-null  object \n",
      " 28  17.1_percentile      89653 non-null  float64\n",
      " 29  17.1_predicted_reps  89653 non-null  float64\n",
      " 30  17.2_rank            93611 non-null  int64  \n",
      " 31  17.2_score           83314 non-null  float64\n",
      " 32  17.2_percentile      83314 non-null  float64\n",
      " 33  17.3_rank            93611 non-null  int64  \n",
      " 34  17.3_score           78256 non-null  object \n",
      " 35  17.3_percentile      78256 non-null  float64\n",
      " 36  17.3_predicted_reps  78256 non-null  float64\n",
      " 37  17.4_rank            93611 non-null  int64  \n",
      " 38  17.4_score           77337 non-null  float64\n",
      " 39  17.4_percentile      77337 non-null  float64\n",
      " 40  17.5_rank            93611 non-null  int64  \n",
      " 41  17.5_score           69939 non-null  object \n",
      " 42  17.5_percentile      69939 non-null  float64\n",
      " 43  17.5_predicted_time  69939 non-null  object \n",
      "dtypes: float64(13), int64(19), object(12)\n",
      "memory usage: 32.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#Let's delete the name_y column and age_y column and the \n",
    "\n",
    "combined_df.drop(['Name_y','Age_y'], axis = 1, inplace = True)\n",
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "e2f3d8f3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
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       "       Height  Weight  Height (m)  Weight (kg)\n",
       "0          77     231        1.96          NaN\n",
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       "4          70     180        1.78          NaN\n",
       "...       ...     ...         ...          ...\n",
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       "93607       0       0        1.68          NaN\n",
       "93608       0       0        1.83          NaN\n",
       "93609       0       0        1.78          NaN\n",
       "93610       0       0         NaN          NaN\n",
       "\n",
       "[93611 rows x 4 columns]"
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df[['Height','Weight', 'Height (m)','Weight (kg)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "1d68a690",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Let's also delete the regionid, regionname, affiliateid columns, Height (m), Weight (kg)\n",
    "\n",
    "combined_df.drop(['Height (m)','Weight (kg)','Regionid','Regionname','Affiliateid'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "34145ad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 93611 entries, 0 to 93610\n",
      "Data columns (total 39 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Index                93611 non-null  int64  \n",
      " 1   Userid               93611 non-null  int64  \n",
      " 2   Name_x               93611 non-null  object \n",
      " 3   Age_x                93611 non-null  int64  \n",
      " 4   Height               93611 non-null  int64  \n",
      " 5   Weight               93611 non-null  int64  \n",
      " 6   Back Squat           93611 non-null  int64  \n",
      " 7   Clean & Jerk         93611 non-null  int64  \n",
      " 8   Snatch               93611 non-null  int64  \n",
      " 9   Deadlift             93611 non-null  int64  \n",
      " 10  Fight Gone Bad       93611 non-null  int64  \n",
      " 11  Max Pull-ups         93611 non-null  float64\n",
      " 12  Fran                 87190 non-null  object \n",
      " 13  Grace                87190 non-null  object \n",
      " 14  Helen                87190 non-null  object \n",
      " 15  Filthy 50            87190 non-null  object \n",
      " 16  Sprint 400m          87190 non-null  object \n",
      " 17  Run 5K               87190 non-null  object \n",
      " 18  Overallrank          93611 non-null  int64  \n",
      " 19  Overallscore         93611 non-null  int64  \n",
      " 20  Overallpercentile    93611 non-null  float64\n",
      " 21  17.1_rank            93611 non-null  int64  \n",
      " 22  17.1_score           89653 non-null  object \n",
      " 23  17.1_percentile      89653 non-null  float64\n",
      " 24  17.1_predicted_reps  89653 non-null  float64\n",
      " 25  17.2_rank            93611 non-null  int64  \n",
      " 26  17.2_score           83314 non-null  float64\n",
      " 27  17.2_percentile      83314 non-null  float64\n",
      " 28  17.3_rank            93611 non-null  int64  \n",
      " 29  17.3_score           78256 non-null  object \n",
      " 30  17.3_percentile      78256 non-null  float64\n",
      " 31  17.3_predicted_reps  78256 non-null  float64\n",
      " 32  17.4_rank            93611 non-null  int64  \n",
      " 33  17.4_score           77337 non-null  float64\n",
      " 34  17.4_percentile      77337 non-null  float64\n",
      " 35  17.5_rank            93611 non-null  int64  \n",
      " 36  17.5_score           69939 non-null  object \n",
      " 37  17.5_percentile      69939 non-null  float64\n",
      " 38  17.5_predicted_time  69939 non-null  object \n",
      "dtypes: float64(11), int64(17), object(11)\n",
      "memory usage: 28.6+ MB\n"
     ]
    }
   ],
   "source": [
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "252f8bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df.replace(0,np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "454d0fae",
   "metadata": {},
   "outputs": [
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       "      <td>94.5979</td>\n",
       "      <td>3078</td>\n",
       "      <td>0 days 00:09:13</td>\n",
       "      <td>97.2820</td>\n",
       "      <td>0 days 00:09:13.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1620</td>\n",
       "      <td>Everette Sweeting</td>\n",
       "      <td>28</td>\n",
       "      <td>72.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>84.0797</td>\n",
       "      <td>88.0</td>\n",
       "      <td>58705</td>\n",
       "      <td>173.0</td>\n",
       "      <td>55.5074</td>\n",
       "      <td>42786</td>\n",
       "      <td>0 days 00:16:08</td>\n",
       "      <td>62.2064</td>\n",
       "      <td>0 days 00:16:08.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1624</td>\n",
       "      <td>Troy Gordon</td>\n",
       "      <td>37</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67</td>\n",
       "      <td>63.2817</td>\n",
       "      <td>67.0</td>\n",
       "      <td>30126</td>\n",
       "      <td>186.0</td>\n",
       "      <td>77.6135</td>\n",
       "      <td>23051</td>\n",
       "      <td>0 days 00:13:14</td>\n",
       "      <td>79.6391</td>\n",
       "      <td>0 days 00:13:14.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1633</td>\n",
       "      <td>Adam Eidson</td>\n",
       "      <td>42</td>\n",
       "      <td>65.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>77.9264</td>\n",
       "      <td>80.0</td>\n",
       "      <td>52974</td>\n",
       "      <td>176.0</td>\n",
       "      <td>61.6191</td>\n",
       "      <td>40468</td>\n",
       "      <td>0 days 00:15:47</td>\n",
       "      <td>64.2540</td>\n",
       "      <td>0 days 00:15:47.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92663</th>\n",
       "      <td>182565</td>\n",
       "      <td>836884</td>\n",
       "      <td>Keith Harpe</td>\n",
       "      <td>29</td>\n",
       "      <td>73.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>51.6339</td>\n",
       "      <td>57.0</td>\n",
       "      <td>84157</td>\n",
       "      <td>165.0</td>\n",
       "      <td>37.1827</td>\n",
       "      <td>106968</td>\n",
       "      <td>359</td>\n",
       "      <td>5.5235</td>\n",
       "      <td>0 days 00:49:02.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92696</th>\n",
       "      <td>182671</td>\n",
       "      <td>837170</td>\n",
       "      <td>Rui Eus?bio</td>\n",
       "      <td>31</td>\n",
       "      <td>68.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>77.9264</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13021</td>\n",
       "      <td>201.0</td>\n",
       "      <td>90.3552</td>\n",
       "      <td>2412</td>\n",
       "      <td>0 days 00:08:56</td>\n",
       "      <td>97.8703</td>\n",
       "      <td>0 days 00:08:56.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93316</th>\n",
       "      <td>184959</td>\n",
       "      <td>843226</td>\n",
       "      <td>Sean ODeay</td>\n",
       "      <td>36</td>\n",
       "      <td>74.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>97.7952</td>\n",
       "      <td>130.0</td>\n",
       "      <td>5722</td>\n",
       "      <td>217.0</td>\n",
       "      <td>95.6639</td>\n",
       "      <td>3638</td>\n",
       "      <td>0 days 00:09:25</td>\n",
       "      <td>96.7873</td>\n",
       "      <td>0 days 00:09:25.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93392</th>\n",
       "      <td>185269</td>\n",
       "      <td>843971</td>\n",
       "      <td>Nick Rakowski</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>77.9264</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16850</td>\n",
       "      <td>197.0</td>\n",
       "      <td>87.8897</td>\n",
       "      <td>12223</td>\n",
       "      <td>0 days 00:11:28</td>\n",
       "      <td>89.2038</td>\n",
       "      <td>0 days 00:11:28.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93582</th>\n",
       "      <td>186069</td>\n",
       "      <td>847964</td>\n",
       "      <td>Jason Garwood</td>\n",
       "      <td>40</td>\n",
       "      <td>69.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>51.6339</td>\n",
       "      <td>57.0</td>\n",
       "      <td>65068</td>\n",
       "      <td>171.0</td>\n",
       "      <td>51.3688</td>\n",
       "      <td>44624</td>\n",
       "      <td>0 days 00:16:25</td>\n",
       "      <td>60.5828</td>\n",
       "      <td>0 days 00:16:25.000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5963 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Index  Userid              Name_x  Age_x  Height  Weight  Back Squat  \\\n",
       "0           1      86       Justin Bergh      34    77.0   231.0       335.0   \n",
       "1           2      88          Cary Hair      32    72.0   191.0       455.0   \n",
       "8           9    1620  Everette Sweeting      28    72.0   240.0       420.0   \n",
       "10         11    1624        Troy Gordon      37    74.0   198.0       297.0   \n",
       "11         12    1633        Adam Eidson      42    65.0   145.0       330.0   \n",
       "...       ...     ...                 ...    ...     ...     ...         ...   \n",
       "92663  182565  836884        Keith Harpe      29    73.0   250.0       325.0   \n",
       "92696  182671  837170       Rui Eus?bio      31    68.0   163.0       282.0   \n",
       "93316  184959  843226         Sean ODeay      36    74.0   212.0       405.0   \n",
       "93392  185269  843971      Nick Rakowski      28    67.0   155.0       315.0   \n",
       "93582  186069  847964      Jason Garwood      40    69.0   189.0       415.0   \n",
       "\n",
       "       Clean & Jerk  Snatch  Deadlift  ...  17.3_score  17.3_percentile  \\\n",
       "0             265.0   210.0     415.0  ...          56          48.9657   \n",
       "1             315.0   265.0     518.0  ...          43          25.6382   \n",
       "8             295.0   225.0     455.0  ...          88          84.0797   \n",
       "10            231.0   169.0     352.0  ...          67          63.2817   \n",
       "11            250.0   198.0     375.0  ...          80          77.9264   \n",
       "...             ...     ...       ...  ...         ...              ...   \n",
       "92663         245.0   175.0     445.0  ...          57          51.6339   \n",
       "92696         187.0   132.0     359.0  ...          80          77.9264   \n",
       "93316         310.0   250.0     465.0  ...         130          97.7952   \n",
       "93392         220.0   165.0     405.0  ...          80          77.9264   \n",
       "93582         185.0   155.0     345.0  ...          57          51.6339   \n",
       "\n",
       "      17.3_predicted_reps 17.4_rank 17.4_score 17.4_percentile 17.5_rank  \\\n",
       "0                    56.0     45858      178.0         65.3368     37463   \n",
       "1                    43.0      7209      212.0         94.5979      3078   \n",
       "8                    88.0     58705      173.0         55.5074     42786   \n",
       "10                   67.0     30126      186.0         77.6135     23051   \n",
       "11                   80.0     52974      176.0         61.6191     40468   \n",
       "...                   ...       ...        ...             ...       ...   \n",
       "92663                57.0     84157      165.0         37.1827    106968   \n",
       "92696                80.0     13021      201.0         90.3552      2412   \n",
       "93316               130.0      5722      217.0         95.6639      3638   \n",
       "93392                80.0     16850      197.0         87.8897     12223   \n",
       "93582                57.0     65068      171.0         51.3688     44624   \n",
       "\n",
       "            17.5_score  17.5_percentile        17.5_predicted_time  \n",
       "0      0 days 00:15:20          66.9084  0 days 00:15:20.000000000  \n",
       "1      0 days 00:09:13          97.2820  0 days 00:09:13.000000000  \n",
       "8      0 days 00:16:08          62.2064  0 days 00:16:08.000000000  \n",
       "10     0 days 00:13:14          79.6391  0 days 00:13:14.000000000  \n",
       "11     0 days 00:15:47          64.2540  0 days 00:15:47.000000000  \n",
       "...                ...              ...                        ...  \n",
       "92663              359           5.5235  0 days 00:49:02.000000000  \n",
       "92696  0 days 00:08:56          97.8703  0 days 00:08:56.000000000  \n",
       "93316  0 days 00:09:25          96.7873  0 days 00:09:25.000000000  \n",
       "93392  0 days 00:11:28          89.2038  0 days 00:11:28.000000000  \n",
       "93582  0 days 00:16:25          60.5828  0 days 00:16:25.000000000  \n",
       "\n",
       "[5963 rows x 39 columns]"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's consider data and people that have all the rows filled with information!\n",
    "\n",
    "combined_df = combined_df.dropna()\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "3b97837a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Userid</th>\n",
       "      <th>Name_x</th>\n",
       "      <th>Age_x</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Back Squat</th>\n",
       "      <th>Clean &amp; Jerk</th>\n",
       "      <th>Snatch</th>\n",
       "      <th>Deadlift</th>\n",
       "      <th>Fight Gone Bad</th>\n",
       "      <th>...</th>\n",
       "      <th>17.2_rank</th>\n",
       "      <th>17.2_score</th>\n",
       "      <th>17.3_rank</th>\n",
       "      <th>17.3_score</th>\n",
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       "      <th>17.4_rank</th>\n",
       "      <th>17.4_score</th>\n",
       "      <th>17.5_rank</th>\n",
       "      <th>17.5_score</th>\n",
       "      <th>17.5_predicted_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>Justin Bergh</td>\n",
       "      <td>34</td>\n",
       "      <td>77.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>...</td>\n",
       "      <td>62648</td>\n",
       "      <td>92.0</td>\n",
       "      <td>66988</td>\n",
       "      <td>56</td>\n",
       "      <td>56.0</td>\n",
       "      <td>45858</td>\n",
       "      <td>178.0</td>\n",
       "      <td>37463</td>\n",
       "      <td>0 days 00:15:20</td>\n",
       "      <td>0 days 00:15:20.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88</td>\n",
       "      <td>Cary Hair</td>\n",
       "      <td>32</td>\n",
       "      <td>72.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>...</td>\n",
       "      <td>79300</td>\n",
       "      <td>85.0</td>\n",
       "      <td>104729</td>\n",
       "      <td>43</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7209</td>\n",
       "      <td>212.0</td>\n",
       "      <td>3078</td>\n",
       "      <td>0 days 00:09:13</td>\n",
       "      <td>0 days 00:09:13.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1620</td>\n",
       "      <td>Everette Sweeting</td>\n",
       "      <td>28</td>\n",
       "      <td>72.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>...</td>\n",
       "      <td>105513</td>\n",
       "      <td>78.0</td>\n",
       "      <td>20490</td>\n",
       "      <td>88</td>\n",
       "      <td>88.0</td>\n",
       "      <td>58705</td>\n",
       "      <td>173.0</td>\n",
       "      <td>42786</td>\n",
       "      <td>0 days 00:16:08</td>\n",
       "      <td>0 days 00:16:08.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1624</td>\n",
       "      <td>Troy Gordon</td>\n",
       "      <td>37</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15903</td>\n",
       "      <td>128.0</td>\n",
       "      <td>47517</td>\n",
       "      <td>67</td>\n",
       "      <td>67.0</td>\n",
       "      <td>30126</td>\n",
       "      <td>186.0</td>\n",
       "      <td>23051</td>\n",
       "      <td>0 days 00:13:14</td>\n",
       "      <td>0 days 00:13:14.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1633</td>\n",
       "      <td>Adam Eidson</td>\n",
       "      <td>42</td>\n",
       "      <td>65.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>...</td>\n",
       "      <td>69402</td>\n",
       "      <td>89.0</td>\n",
       "      <td>37460</td>\n",
       "      <td>80</td>\n",
       "      <td>80.0</td>\n",
       "      <td>52974</td>\n",
       "      <td>176.0</td>\n",
       "      <td>40468</td>\n",
       "      <td>0 days 00:15:47</td>\n",
       "      <td>0 days 00:15:47.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92663</th>\n",
       "      <td>836884</td>\n",
       "      <td>Keith Harpe</td>\n",
       "      <td>29</td>\n",
       "      <td>73.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114462</td>\n",
       "      <td>78.0</td>\n",
       "      <td>64865</td>\n",
       "      <td>57</td>\n",
       "      <td>57.0</td>\n",
       "      <td>84157</td>\n",
       "      <td>165.0</td>\n",
       "      <td>106968</td>\n",
       "      <td>359</td>\n",
       "      <td>0 days 00:49:02.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92696</th>\n",
       "      <td>837170</td>\n",
       "      <td>Rui Eus?bio</td>\n",
       "      <td>31</td>\n",
       "      <td>68.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22107</td>\n",
       "      <td>123.0</td>\n",
       "      <td>35486</td>\n",
       "      <td>80</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13021</td>\n",
       "      <td>201.0</td>\n",
       "      <td>2412</td>\n",
       "      <td>0 days 00:08:56</td>\n",
       "      <td>0 days 00:08:56.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93316</th>\n",
       "      <td>843226</td>\n",
       "      <td>Sean ODeay</td>\n",
       "      <td>36</td>\n",
       "      <td>74.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4000</td>\n",
       "      <td>167.0</td>\n",
       "      <td>3093</td>\n",
       "      <td>130</td>\n",
       "      <td>130.0</td>\n",
       "      <td>5722</td>\n",
       "      <td>217.0</td>\n",
       "      <td>3638</td>\n",
       "      <td>0 days 00:09:25</td>\n",
       "      <td>0 days 00:09:25.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93392</th>\n",
       "      <td>843971</td>\n",
       "      <td>Nick Rakowski</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13637</td>\n",
       "      <td>136.0</td>\n",
       "      <td>30526</td>\n",
       "      <td>80</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16850</td>\n",
       "      <td>197.0</td>\n",
       "      <td>12223</td>\n",
       "      <td>0 days 00:11:28</td>\n",
       "      <td>0 days 00:11:28.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93582</th>\n",
       "      <td>847964</td>\n",
       "      <td>Jason Garwood</td>\n",
       "      <td>40</td>\n",
       "      <td>69.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47699</td>\n",
       "      <td>112.0</td>\n",
       "      <td>62475</td>\n",
       "      <td>57</td>\n",
       "      <td>57.0</td>\n",
       "      <td>65068</td>\n",
       "      <td>171.0</td>\n",
       "      <td>44624</td>\n",
       "      <td>0 days 00:16:25</td>\n",
       "      <td>0 days 00:16:25.000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5963 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Userid              Name_x  Age_x  Height  Weight  Back Squat  \\\n",
       "0          86       Justin Bergh      34    77.0   231.0       335.0   \n",
       "1          88          Cary Hair      32    72.0   191.0       455.0   \n",
       "8        1620  Everette Sweeting      28    72.0   240.0       420.0   \n",
       "10       1624        Troy Gordon      37    74.0   198.0       297.0   \n",
       "11       1633        Adam Eidson      42    65.0   145.0       330.0   \n",
       "...       ...                 ...    ...     ...     ...         ...   \n",
       "92663  836884        Keith Harpe      29    73.0   250.0       325.0   \n",
       "92696  837170       Rui Eus?bio      31    68.0   163.0       282.0   \n",
       "93316  843226         Sean ODeay      36    74.0   212.0       405.0   \n",
       "93392  843971      Nick Rakowski      28    67.0   155.0       315.0   \n",
       "93582  847964      Jason Garwood      40    69.0   189.0       415.0   \n",
       "\n",
       "       Clean & Jerk  Snatch  Deadlift  Fight Gone Bad  ...  17.2_rank  \\\n",
       "0             265.0   210.0     415.0           393.0  ...      62648   \n",
       "1             315.0   265.0     518.0           407.0  ...      79300   \n",
       "8             295.0   225.0     455.0           286.0  ...     105513   \n",
       "10            231.0   169.0     352.0           398.0  ...      15903   \n",
       "11            250.0   198.0     375.0           314.0  ...      69402   \n",
       "...             ...     ...       ...             ...  ...        ...   \n",
       "92663         245.0   175.0     445.0           274.0  ...     114462   \n",
       "92696         187.0   132.0     359.0           282.0  ...      22107   \n",
       "93316         310.0   250.0     465.0           419.0  ...       4000   \n",
       "93392         220.0   165.0     405.0           305.0  ...      13637   \n",
       "93582         185.0   155.0     345.0           277.0  ...      47699   \n",
       "\n",
       "      17.2_score 17.3_rank 17.3_score 17.3_predicted_reps 17.4_rank  \\\n",
       "0           92.0     66988         56                56.0     45858   \n",
       "1           85.0    104729         43                43.0      7209   \n",
       "8           78.0     20490         88                88.0     58705   \n",
       "10         128.0     47517         67                67.0     30126   \n",
       "11          89.0     37460         80                80.0     52974   \n",
       "...          ...       ...        ...                 ...       ...   \n",
       "92663       78.0     64865         57                57.0     84157   \n",
       "92696      123.0     35486         80                80.0     13021   \n",
       "93316      167.0      3093        130               130.0      5722   \n",
       "93392      136.0     30526         80                80.0     16850   \n",
       "93582      112.0     62475         57                57.0     65068   \n",
       "\n",
       "      17.4_score  17.5_rank       17.5_score        17.5_predicted_time  \n",
       "0          178.0      37463  0 days 00:15:20  0 days 00:15:20.000000000  \n",
       "1          212.0       3078  0 days 00:09:13  0 days 00:09:13.000000000  \n",
       "8          173.0      42786  0 days 00:16:08  0 days 00:16:08.000000000  \n",
       "10         186.0      23051  0 days 00:13:14  0 days 00:13:14.000000000  \n",
       "11         176.0      40468  0 days 00:15:47  0 days 00:15:47.000000000  \n",
       "...          ...        ...              ...                        ...  \n",
       "92663      165.0     106968              359  0 days 00:49:02.000000000  \n",
       "92696      201.0       2412  0 days 00:08:56  0 days 00:08:56.000000000  \n",
       "93316      217.0       3638  0 days 00:09:25  0 days 00:09:25.000000000  \n",
       "93392      197.0      12223  0 days 00:11:28  0 days 00:11:28.000000000  \n",
       "93582      171.0      44624  0 days 00:16:25  0 days 00:16:25.000000000  \n",
       "\n",
       "[5963 rows x 32 columns]"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's delete the percentile columns!\n",
    "\n",
    "combined_df.drop(['Overallpercentile','17.1_percentile','17.2_percentile','17.3_percentile','17.4_percentile','17.5_percentile','Index'], axis = 1, inplace = True)\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601ca402",
   "metadata": {},
   "source": [
    "### Let's Convert our times into different data types!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0455d9",
   "metadata": {},
   "source": [
    "### 17.1 Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "id": "bf15c42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df['17.1_score'].isna().sum() #check for NaN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "626e0e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_format(score):\n",
    "    if isinstance(score,str) and ':' in score:\n",
    "        return (pd.to_timedelta(score), 225) #total reps for that exercise!\n",
    "    else:  # It's an integer entry\n",
    "        return (pd.Timedelta(minutes=20), int(score))  # 20 minutes as the cap time\n",
    "\n",
    "combined_df['17.1_time'],combined_df['17.1_reps'] = zip(*combined_df['17.1_score'].apply(score_format))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae0059f",
   "metadata": {},
   "source": [
    "**Below is testing code (IGNORE)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "07cbf7fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "\n",
    "# # Sample data\n",
    "# data = {\n",
    "#     '17.1_score': ['0 days 00:14:29', '215', '0 days 00:18:51', '224']\n",
    "# }\n",
    "# df = pd.DataFrame(data)\n",
    "\n",
    "# # Define the total reps\n",
    "# #total_reps = 225\n",
    "\n",
    "# # Function to convert to timedelta or calculate remaining reps\n",
    "# def convert_score(score):\n",
    "#     if ':' in score:  # It's a time entry\n",
    "#         return (pd.to_timedelta(score), 225)\n",
    "#     else:  # It's an integer entry\n",
    "#         return (pd.Timedelta(minutes=20), int(score))  # return both the time in format of 00:20:ss and the reps\n",
    "\n",
    "# # Apply the function to the '17.1_score' column and create new columns\n",
    "# #df[['time_up', 'reps_up']] = df['17.1_score'].apply(convert_score)\n",
    "\n",
    "# # Convert the 'time' column to total seconds\n",
    "# #df['time_in_seconds_up'] = df['time_up'].dt.total_seconds()\n",
    "\n",
    "# df['time_up'],df['reps_up'] = zip(*df['17.1_score'].apply(convert_score))\n",
    "# # Show the new dataframe\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "id": "cb0b0c50",
   "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>17.1_score</th>\n",
       "      <th>17.1_time</th>\n",
       "      <th>17.1_reps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>218</td>\n",
       "      <td>0 days 00:20:00</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:20:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>221</td>\n",
       "      <td>0 days 00:20:00</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0 days 00:14:38</td>\n",
       "      <td>0 days 00:14:38</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>178</td>\n",
       "      <td>0 days 00:20:00</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92663</th>\n",
       "      <td>195</td>\n",
       "      <td>0 days 00:20:00</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92696</th>\n",
       "      <td>0 days 00:14:20</td>\n",
       "      <td>0 days 00:14:20</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93316</th>\n",
       "      <td>0 days 00:13:24</td>\n",
       "      <td>0 days 00:13:24</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93392</th>\n",
       "      <td>0 days 00:16:35</td>\n",
       "      <td>0 days 00:16:35</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93582</th>\n",
       "      <td>0 days 00:16:49</td>\n",
       "      <td>0 days 00:16:49</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5963 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            17.1_score       17.1_time  17.1_reps\n",
       "0                  218 0 days 00:20:00        218\n",
       "1                    1 0 days 00:20:00          1\n",
       "8                  221 0 days 00:20:00        221\n",
       "10     0 days 00:14:38 0 days 00:14:38        225\n",
       "11                 178 0 days 00:20:00        178\n",
       "...                ...             ...        ...\n",
       "92663              195 0 days 00:20:00        195\n",
       "92696  0 days 00:14:20 0 days 00:14:20        225\n",
       "93316  0 days 00:13:24 0 days 00:13:24        225\n",
       "93392  0 days 00:16:35 0 days 00:16:35        225\n",
       "93582  0 days 00:16:49 0 days 00:16:49        225\n",
       "\n",
       "[5963 rows x 3 columns]"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df[['17.1_score','17.1_time','17.1_reps']] #We got to convert it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "id": "ca035c97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Series name: 17.1_time\n",
      "Non-Null Count  Dtype          \n",
      "--------------  -----          \n",
      "5963 non-null   timedelta64[ns]\n",
      "dtypes: timedelta64[ns](1)\n",
      "memory usage: 93.2 KB\n"
     ]
    }
   ],
   "source": [
    "combined_df['17.1_time'].info() #And it has time delta object!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "835ec9b1",
   "metadata": {},
   "source": [
    "### Repeat above for all!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65baa70b",
   "metadata": {},
   "source": [
    "### 17.2 Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "id": "72a0099d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         92.0\n",
       "1         85.0\n",
       "8         78.0\n",
       "10       128.0\n",
       "11        89.0\n",
       "         ...  \n",
       "92663     78.0\n",
       "92696    123.0\n",
       "93316    167.0\n",
       "93392    136.0\n",
       "93582    112.0\n",
       "Name: 17.2_score, Length: 5963, dtype: float64"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 17.2 ###\n",
    "\n",
    "# Complete as many rounds and reps as possible in 12 minutes of:\n",
    "# 2 rounds of:\n",
    "#   50-ft. weighted walking lunge\n",
    "#   16 toes-to-bars\n",
    "#   8 power cleans\n",
    "# Then, 2 rounds of:\n",
    "#   50-ft. weighted walking lunge\n",
    "#   16 bar muscle-ups\n",
    "#   8 power cleans\n",
    "# Etc., alternating between toes-to-bars and bar muscle-ups every 2 rounds.\n",
    "\n",
    "# Men use 50-lb. dumbbells\n",
    "\n",
    "#This is easy because they all are the same data type which are just numbers!\n",
    "\n",
    "combined_df['17.2_score']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7732f8b",
   "metadata": {},
   "source": [
    "### 17.3 Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "id": "eebf211f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### 17.3 #####\n",
    "\n",
    "# Prior to 8:00, complete:\n",
    "# 3 rounds of:\n",
    "#     6 chest-to-bar pull-ups\n",
    "#     6 squat snatches, 95 lb.\n",
    "# Then, 3 rounds of:\n",
    "#     7 chest-to-bar pull-ups\n",
    "#     5 squat snatches, 135 lb.\n",
    "# *Prior to 12:00, complete 3 rounds of:\n",
    "#     8 chest-to-bar pull-ups\n",
    "#     4 squat snatches, 185 lb.\n",
    "# *Prior to 16:00, complete 3 rounds of:\n",
    "#     9 chest-to-bar pull-ups\n",
    "#     3 squat snatches, 225 lb.\n",
    "# *Prior to 20:00, complete 3 rounds of:\n",
    "#     10 chest-to-bar pull-ups\n",
    "#     2 squat snatches, 245 lb.\n",
    "# Prior to 24:00, complete 3 rounds of:\n",
    "#     11 chest-to-bar pull-ups\n",
    "#     1 squat snatch 265 lb.\n",
    "\n",
    "# *If all reps are completed, time cap extends by 4 minutes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "id": "be146c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         56\n",
       "1         43\n",
       "8         88\n",
       "10        67\n",
       "11        80\n",
       "        ... \n",
       "92663     57\n",
       "92696     80\n",
       "93316    130\n",
       "93392     80\n",
       "93582     57\n",
       "Name: 17.3_score, Length: 5963, dtype: object"
      ]
     },
     "execution_count": 547,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df['17.3_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "id": "62ee68f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's create a function that will make this for us\n",
    "\n",
    "def _17_3_convert_score(score):\n",
    "    if isinstance(score,str) and \":\" in score:\n",
    "        return (pd.Timedelta(score), 216)\n",
    "    else:\n",
    "        return (pd.Timedelta(minutes=24),int(score))\n",
    "\n",
    "combined_df['17.3_time'],combined_df['17.3_reps'] = zip(*combined_df['17.3_score'].apply(_17_3_convert_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "id": "9cc108c0",
   "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>17.3_score</th>\n",
       "      <th>17.3_time</th>\n",
       "      <th>17.3_reps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>88</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>67</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>80</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92663</th>\n",
       "      <td>57</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92696</th>\n",
       "      <td>80</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93316</th>\n",
       "      <td>130</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93392</th>\n",
       "      <td>80</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93582</th>\n",
       "      <td>57</td>\n",
       "      <td>0 days 00:24:00</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5963 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      17.3_score       17.3_time  17.3_reps\n",
       "0             56 0 days 00:24:00         56\n",
       "1             43 0 days 00:24:00         43\n",
       "8             88 0 days 00:24:00         88\n",
       "10            67 0 days 00:24:00         67\n",
       "11            80 0 days 00:24:00         80\n",
       "...          ...             ...        ...\n",
       "92663         57 0 days 00:24:00         57\n",
       "92696         80 0 days 00:24:00         80\n",
       "93316        130 0 days 00:24:00        130\n",
       "93392         80 0 days 00:24:00         80\n",
       "93582         57 0 days 00:24:00         57\n",
       "\n",
       "[5963 rows x 3 columns]"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df[['17.3_score','17.3_time','17.3_reps']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f0a7ab",
   "metadata": {},
   "source": [
    "### 17.4 Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "id": "944e93d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Complete as many rounds and reps as possible in 13 minutes of:\n",
    "# 55 deadlifts, 225 lb.\n",
    "# 55 wall-ball shots, 20-lb. ball to 10-ft. target\n",
    "# 55-calorie row\n",
    "# 55 handstand push-ups\n",
    "\n",
    "## This one is easy since all the data is consistent!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5212520f",
   "metadata": {},
   "source": [
    "### 17.5 Section\n",
    "\n",
    "Just convert to timedelta object!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "id": "77b856b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df['17.5_score'] = combined_df['17.5_score'].apply(lambda x: pd.Timedelta(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "id": "255f13f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Series name: 17.5_score\n",
      "Non-Null Count  Dtype          \n",
      "--------------  -----          \n",
      "5963 non-null   timedelta64[ns]\n",
      "dtypes: timedelta64[ns](1)\n",
      "memory usage: 93.2 KB\n"
     ]
    }
   ],
   "source": [
    "combined_df['17.5_score'].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df96875b",
   "metadata": {},
   "source": [
    "## Convert everytime delta into seconds!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "id": "b629015e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype          \n",
      "---  ------      --------------  -----          \n",
      " 0   17.1_time   5963 non-null   timedelta64[ns]\n",
      " 1   17.3_time   5963 non-null   timedelta64[ns]\n",
      " 2   17.5_score  5963 non-null   timedelta64[ns]\n",
      "dtypes: timedelta64[ns](3)\n",
      "memory usage: 186.3 KB\n"
     ]
    }
   ],
   "source": [
    "combined_df[['17.1_time','17.3_time','17.5_score']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "f7fb6718",
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in ['17.1_time', '17.3_time', '17.5_score']:\n",
    "    combined_df[column] = combined_df[column].apply(lambda x: x.total_seconds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "4ed65447",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   17.1_time   5963 non-null   float64\n",
      " 1   17.3_time   5963 non-null   float64\n",
      " 2   17.5_score  5963 non-null   float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 186.3 KB\n"
     ]
    }
   ],
   "source": [
    "combined_df[['17.1_time','17.3_time','17.5_score']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "3a861546",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   17.1_reps   5963 non-null   int64  \n",
      " 1   17.3_reps   5963 non-null   int64  \n",
      " 2   17.5_score  5963 non-null   float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 186.3 KB\n"
     ]
    }
   ],
   "source": [
    "combined_df[['17.1_reps','17.3_reps','17.5_score']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "e8756cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert all the reps and time in seconds to int!\n",
    "combined_df = combined_df.astype({\"17.1_reps\": 'int64',\n",
    "                                  \"17.3_reps\": 'int64',\n",
    "                                  \"17.5_score\": 'int64'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "da77509e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   17.1_reps   5963 non-null   int64\n",
      " 1   17.3_reps   5963 non-null   int64\n",
      " 2   17.5_score  5963 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 186.3 KB\n"
     ]
    }
   ],
   "source": [
    "combined_df[['17.1_reps','17.3_reps','17.5_score']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "a7d5bf35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now let's get rid of the score column for 17.1 and 17.3 and ranks and predicted reps and overall rank\n",
    "\n",
    "combined_df.drop(['17.1_score','17.3_score','17.1_rank','17.2_rank','17.3_rank','17.4_rank','17.5_rank','17.1_predicted_reps','17.3_predicted_reps','17.5_predicted_time','Overallrank','Overallscore'],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "9c926893",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Data columns (total 24 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Userid          5963 non-null   int64  \n",
      " 1   Name_x          5963 non-null   object \n",
      " 2   Age_x           5963 non-null   int64  \n",
      " 3   Height          5963 non-null   float64\n",
      " 4   Weight          5963 non-null   float64\n",
      " 5   Back Squat      5963 non-null   float64\n",
      " 6   Clean & Jerk    5963 non-null   float64\n",
      " 7   Snatch          5963 non-null   float64\n",
      " 8   Deadlift        5963 non-null   float64\n",
      " 9   Fight Gone Bad  5963 non-null   float64\n",
      " 10  Max Pull-ups    5963 non-null   float64\n",
      " 11  Fran            5963 non-null   object \n",
      " 12  Grace           5963 non-null   object \n",
      " 13  Helen           5963 non-null   object \n",
      " 14  Filthy 50       5963 non-null   object \n",
      " 15  Sprint 400m     5963 non-null   object \n",
      " 16  Run 5K          5963 non-null   object \n",
      " 17  17.2_score      5963 non-null   float64\n",
      " 18  17.4_score      5963 non-null   float64\n",
      " 19  17.5_score      5963 non-null   int64  \n",
      " 20  17.1_time       5963 non-null   float64\n",
      " 21  17.1_reps       5963 non-null   int64  \n",
      " 22  17.3_time       5963 non-null   float64\n",
      " 23  17.3_reps       5963 non-null   int64  \n",
      "dtypes: float64(12), int64(5), object(7)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2111265",
   "metadata": {},
   "source": [
    "### Delete the ['Fran','Grace','Helen','Filthy 50','Sprint 400m', 'Run 5K'] columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "id": "8483888c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the columns!\n",
    "combined_df.rename(columns = {'Height':'Height_inches',\n",
    "                             'Weight':'Weight_lbs',\n",
    "                             'Name_x':'Name',\n",
    "                             'Age_x':'Age',\n",
    "                             'Clean & Jerk':'Clean_and_Jerk'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "id": "13596efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Data columns (total 24 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Userid          5963 non-null   int64  \n",
      " 1   Name            5963 non-null   object \n",
      " 2   Age             5963 non-null   int64  \n",
      " 3   Height_inches   5963 non-null   float64\n",
      " 4   Weight_lbs      5963 non-null   float64\n",
      " 5   Back Squat      5963 non-null   float64\n",
      " 6   Clean_and_Jerk  5963 non-null   float64\n",
      " 7   Snatch          5963 non-null   float64\n",
      " 8   Deadlift        5963 non-null   float64\n",
      " 9   Fight Gone Bad  5963 non-null   float64\n",
      " 10  Max Pull-ups    5963 non-null   float64\n",
      " 11  Fran            5963 non-null   object \n",
      " 12  Grace           5963 non-null   object \n",
      " 13  Helen           5963 non-null   object \n",
      " 14  Filthy 50       5963 non-null   object \n",
      " 15  Sprint 400m     5963 non-null   object \n",
      " 16  Run 5K          5963 non-null   object \n",
      " 17  17.2_score      5963 non-null   float64\n",
      " 18  17.4_score      5963 non-null   float64\n",
      " 19  17.5_score      5963 non-null   int64  \n",
      " 20  17.1_time       5963 non-null   float64\n",
      " 21  17.1_reps       5963 non-null   int64  \n",
      " 22  17.3_time       5963 non-null   float64\n",
      " 23  17.3_reps       5963 non-null   int64  \n",
      "dtypes: float64(12), int64(5), object(7)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "021ce53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_columns = ['Fran','Grace','Helen','Filthy 50','Sprint 400m', 'Run 5K']\n",
    "combined_df.drop(['Fran','Grace','Helen','Filthy 50','Sprint 400m', 'Run 5K'], axis =1 ,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "id": "53510f68",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Data columns (total 18 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Userid          5963 non-null   int64  \n",
      " 1   Name            5963 non-null   object \n",
      " 2   Age             5963 non-null   int64  \n",
      " 3   Height_inches   5963 non-null   float64\n",
      " 4   Weight_lbs      5963 non-null   float64\n",
      " 5   Back Squat      5963 non-null   float64\n",
      " 6   Clean_and_Jerk  5963 non-null   float64\n",
      " 7   Snatch          5963 non-null   float64\n",
      " 8   Deadlift        5963 non-null   float64\n",
      " 9   Fight Gone Bad  5963 non-null   float64\n",
      " 10  Max Pull-ups    5963 non-null   float64\n",
      " 11  17.2_score      5963 non-null   float64\n",
      " 12  17.4_score      5963 non-null   float64\n",
      " 13  17.5_score      5963 non-null   int64  \n",
      " 14  17.1_time       5963 non-null   float64\n",
      " 15  17.1_reps       5963 non-null   int64  \n",
      " 16  17.3_time       5963 non-null   float64\n",
      " 17  17.3_reps       5963 non-null   int64  \n",
      "dtypes: float64(12), int64(5), object(1)\n",
      "memory usage: 885.1+ KB\n"
     ]
    }
   ],
   "source": [
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "id": "5b2ee3e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Userid',\n",
       " 'Name',\n",
       " 'Age',\n",
       " 'Height_inches',\n",
       " 'Weight_lbs',\n",
       " 'Back Squat',\n",
       " 'Clean_and_Jerk',\n",
       " 'Snatch',\n",
       " 'Deadlift',\n",
       " 'Fight Gone Bad']"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = combined_df.columns.tolist()\n",
    "columns[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "id": "f60d6ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cols = columns[:10]+['17.1_time','17.1_reps','17.2_score','17.3_time','17.3_reps','17.4_score','17.5_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "id": "9804a0c6",
   "metadata": {
    "scrolled": true
   },
   "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>Userid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_inches</th>\n",
       "      <th>Weight_lbs</th>\n",
       "      <th>Back Squat</th>\n",
       "      <th>Clean_and_Jerk</th>\n",
       "      <th>Snatch</th>\n",
       "      <th>Deadlift</th>\n",
       "      <th>Fight Gone Bad</th>\n",
       "      <th>17.1_time</th>\n",
       "      <th>17.1_reps</th>\n",
       "      <th>17.2_score</th>\n",
       "      <th>17.3_time</th>\n",
       "      <th>17.3_reps</th>\n",
       "      <th>17.4_score</th>\n",
       "      <th>17.5_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>Justin Bergh</td>\n",
       "      <td>34</td>\n",
       "      <td>77.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>218</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>56</td>\n",
       "      <td>178.0</td>\n",
       "      <td>920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88</td>\n",
       "      <td>Cary Hair</td>\n",
       "      <td>32</td>\n",
       "      <td>72.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>43</td>\n",
       "      <td>212.0</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1620</td>\n",
       "      <td>Everette Sweeting</td>\n",
       "      <td>28</td>\n",
       "      <td>72.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>221</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>88</td>\n",
       "      <td>173.0</td>\n",
       "      <td>968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1624</td>\n",
       "      <td>Troy Gordon</td>\n",
       "      <td>37</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>878.0</td>\n",
       "      <td>225</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>67</td>\n",
       "      <td>186.0</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1633</td>\n",
       "      <td>Adam Eidson</td>\n",
       "      <td>42</td>\n",
       "      <td>65.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>178</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>80</td>\n",
       "      <td>176.0</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>92663</th>\n",
       "      <td>836884</td>\n",
       "      <td>Keith Harpe</td>\n",
       "      <td>29</td>\n",
       "      <td>73.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>195</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>57</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92696</th>\n",
       "      <td>837170</td>\n",
       "      <td>Rui Eus?bio</td>\n",
       "      <td>31</td>\n",
       "      <td>68.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>225</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>80</td>\n",
       "      <td>201.0</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93316</th>\n",
       "      <td>843226</td>\n",
       "      <td>Sean ODeay</td>\n",
       "      <td>36</td>\n",
       "      <td>74.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>225</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>130</td>\n",
       "      <td>217.0</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93392</th>\n",
       "      <td>843971</td>\n",
       "      <td>Nick Rakowski</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>225</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>80</td>\n",
       "      <td>197.0</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93582</th>\n",
       "      <td>847964</td>\n",
       "      <td>Jason Garwood</td>\n",
       "      <td>40</td>\n",
       "      <td>69.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>225</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>57</td>\n",
       "      <td>171.0</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5963 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Userid                Name  Age  Height_inches  Weight_lbs  Back Squat  \\\n",
       "0          86       Justin Bergh    34           77.0       231.0       335.0   \n",
       "1          88          Cary Hair    32           72.0       191.0       455.0   \n",
       "8        1620  Everette Sweeting    28           72.0       240.0       420.0   \n",
       "10       1624        Troy Gordon    37           74.0       198.0       297.0   \n",
       "11       1633        Adam Eidson    42           65.0       145.0       330.0   \n",
       "...       ...                 ...  ...            ...         ...         ...   \n",
       "92663  836884        Keith Harpe    29           73.0       250.0       325.0   \n",
       "92696  837170       Rui Eus?bio    31           68.0       163.0       282.0   \n",
       "93316  843226         Sean ODeay    36           74.0       212.0       405.0   \n",
       "93392  843971      Nick Rakowski    28           67.0       155.0       315.0   \n",
       "93582  847964      Jason Garwood    40           69.0       189.0       415.0   \n",
       "\n",
       "       Clean_and_Jerk  Snatch  Deadlift  Fight Gone Bad  17.1_time  17.1_reps  \\\n",
       "0               265.0   210.0     415.0           393.0     1200.0        218   \n",
       "1               315.0   265.0     518.0           407.0     1200.0          1   \n",
       "8               295.0   225.0     455.0           286.0     1200.0        221   \n",
       "10              231.0   169.0     352.0           398.0      878.0        225   \n",
       "11              250.0   198.0     375.0           314.0     1200.0        178   \n",
       "...               ...     ...       ...             ...        ...        ...   \n",
       "92663           245.0   175.0     445.0           274.0     1200.0        195   \n",
       "92696           187.0   132.0     359.0           282.0      860.0        225   \n",
       "93316           310.0   250.0     465.0           419.0      804.0        225   \n",
       "93392           220.0   165.0     405.0           305.0      995.0        225   \n",
       "93582           185.0   155.0     345.0           277.0     1009.0        225   \n",
       "\n",
       "       17.2_score  17.3_time  17.3_reps  17.4_score  17.5_score  \n",
       "0            92.0     1440.0         56       178.0         920  \n",
       "1            85.0     1440.0         43       212.0         553  \n",
       "8            78.0     1440.0         88       173.0         968  \n",
       "10          128.0     1440.0         67       186.0         794  \n",
       "11           89.0     1440.0         80       176.0         947  \n",
       "...           ...        ...        ...         ...         ...  \n",
       "92663        78.0     1440.0         57       165.0           0  \n",
       "92696       123.0     1440.0         80       201.0         536  \n",
       "93316       167.0     1440.0        130       217.0         565  \n",
       "93392       136.0     1440.0         80       197.0         688  \n",
       "93582       112.0     1440.0         57       171.0         985  \n",
       "\n",
       "[5963 rows x 17 columns]"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rearrange the data to format nicely!\n",
    "\n",
    "combined_df = combined_df[new_cols]\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "id": "82356991",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Userid', 'Name', 'Age', 'Height_inches', 'Weight_lbs', 'Back Squat',\n",
       "       'Clean_and_Jerk', 'Snatch', 'Deadlift', 'Fight Gone Bad', '17.1_time',\n",
       "       '17.1_reps', '17.2_score', '17.3_time', '17.3_reps', '17.4_score',\n",
       "       '17.5_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "id": "0d2466de",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5963 entries, 0 to 93582\n",
      "Data columns (total 17 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Userid          5963 non-null   int64  \n",
      " 1   Name            5963 non-null   object \n",
      " 2   Age             5963 non-null   int64  \n",
      " 3   Height_inches   5963 non-null   float64\n",
      " 4   Weight_lbs      5963 non-null   float64\n",
      " 5   Back Squat      5963 non-null   float64\n",
      " 6   Clean_and_Jerk  5963 non-null   float64\n",
      " 7   Snatch          5963 non-null   float64\n",
      " 8   Deadlift        5963 non-null   float64\n",
      " 9   Fight Gone Bad  5963 non-null   float64\n",
      " 10  17.1_time       5963 non-null   float64\n",
      " 11  17.1_reps       5963 non-null   int64  \n",
      " 12  17.2_score      5963 non-null   float64\n",
      " 13  17.3_time       5963 non-null   float64\n",
      " 14  17.3_reps       5963 non-null   int64  \n",
      " 15  17.4_score      5963 non-null   float64\n",
      " 16  17.5_score      5963 non-null   int64  \n",
      "dtypes: float64(11), int64(5), object(1)\n",
      "memory usage: 838.5+ KB\n"
     ]
    }
   ],
   "source": [
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "id": "153523d8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Userid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_inches</th>\n",
       "      <th>Weight_lbs</th>\n",
       "      <th>Back Squat</th>\n",
       "      <th>Clean_and_Jerk</th>\n",
       "      <th>Snatch</th>\n",
       "      <th>Deadlift</th>\n",
       "      <th>Fight Gone Bad</th>\n",
       "      <th>17.1_time</th>\n",
       "      <th>17.1_reps</th>\n",
       "      <th>17.2_score</th>\n",
       "      <th>17.3_time</th>\n",
       "      <th>17.3_reps</th>\n",
       "      <th>17.4_score</th>\n",
       "      <th>17.5_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>Justin Bergh</td>\n",
       "      <td>34</td>\n",
       "      <td>77.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>218</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>56</td>\n",
       "      <td>178.0</td>\n",
       "      <td>920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88</td>\n",
       "      <td>Cary Hair</td>\n",
       "      <td>32</td>\n",
       "      <td>72.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>43</td>\n",
       "      <td>212.0</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1620</td>\n",
       "      <td>Everette Sweeting</td>\n",
       "      <td>28</td>\n",
       "      <td>72.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>221</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>88</td>\n",
       "      <td>173.0</td>\n",
       "      <td>968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1624</td>\n",
       "      <td>Troy Gordon</td>\n",
       "      <td>37</td>\n",
       "      <td>74.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>878.0</td>\n",
       "      <td>225</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>67</td>\n",
       "      <td>186.0</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1633</td>\n",
       "      <td>Adam Eidson</td>\n",
       "      <td>42</td>\n",
       "      <td>65.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>178</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>80</td>\n",
       "      <td>176.0</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>92663</th>\n",
       "      <td>836884</td>\n",
       "      <td>Keith Harpe</td>\n",
       "      <td>29</td>\n",
       "      <td>73.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>195</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>57</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92696</th>\n",
       "      <td>837170</td>\n",
       "      <td>Rui Eus?bio</td>\n",
       "      <td>31</td>\n",
       "      <td>68.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>225</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>80</td>\n",
       "      <td>201.0</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93316</th>\n",
       "      <td>843226</td>\n",
       "      <td>Sean ODeay</td>\n",
       "      <td>36</td>\n",
       "      <td>74.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>225</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>130</td>\n",
       "      <td>217.0</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93392</th>\n",
       "      <td>843971</td>\n",
       "      <td>Nick Rakowski</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>225</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>80</td>\n",
       "      <td>197.0</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93582</th>\n",
       "      <td>847964</td>\n",
       "      <td>Jason Garwood</td>\n",
       "      <td>40</td>\n",
       "      <td>69.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>225</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>57</td>\n",
       "      <td>171.0</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5963 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Userid                Name  Age  Height_inches  Weight_lbs  Back Squat  \\\n",
       "0          86       Justin Bergh    34           77.0       231.0       335.0   \n",
       "1          88          Cary Hair    32           72.0       191.0       455.0   \n",
       "8        1620  Everette Sweeting    28           72.0       240.0       420.0   \n",
       "10       1624        Troy Gordon    37           74.0       198.0       297.0   \n",
       "11       1633        Adam Eidson    42           65.0       145.0       330.0   \n",
       "...       ...                 ...  ...            ...         ...         ...   \n",
       "92663  836884        Keith Harpe    29           73.0       250.0       325.0   \n",
       "92696  837170       Rui Eus?bio    31           68.0       163.0       282.0   \n",
       "93316  843226         Sean ODeay    36           74.0       212.0       405.0   \n",
       "93392  843971      Nick Rakowski    28           67.0       155.0       315.0   \n",
       "93582  847964      Jason Garwood    40           69.0       189.0       415.0   \n",
       "\n",
       "       Clean_and_Jerk  Snatch  Deadlift  Fight Gone Bad  17.1_time  17.1_reps  \\\n",
       "0               265.0   210.0     415.0           393.0     1200.0        218   \n",
       "1               315.0   265.0     518.0           407.0     1200.0          1   \n",
       "8               295.0   225.0     455.0           286.0     1200.0        221   \n",
       "10              231.0   169.0     352.0           398.0      878.0        225   \n",
       "11              250.0   198.0     375.0           314.0     1200.0        178   \n",
       "...               ...     ...       ...             ...        ...        ...   \n",
       "92663           245.0   175.0     445.0           274.0     1200.0        195   \n",
       "92696           187.0   132.0     359.0           282.0      860.0        225   \n",
       "93316           310.0   250.0     465.0           419.0      804.0        225   \n",
       "93392           220.0   165.0     405.0           305.0      995.0        225   \n",
       "93582           185.0   155.0     345.0           277.0     1009.0        225   \n",
       "\n",
       "       17.2_score  17.3_time  17.3_reps  17.4_score  17.5_score  \n",
       "0            92.0     1440.0         56       178.0         920  \n",
       "1            85.0     1440.0         43       212.0         553  \n",
       "8            78.0     1440.0         88       173.0         968  \n",
       "10          128.0     1440.0         67       186.0         794  \n",
       "11           89.0     1440.0         80       176.0         947  \n",
       "...           ...        ...        ...         ...         ...  \n",
       "92663        78.0     1440.0         57       165.0           0  \n",
       "92696       123.0     1440.0         80       201.0         536  \n",
       "93316       167.0     1440.0        130       217.0         565  \n",
       "93392       136.0     1440.0         80       197.0         688  \n",
       "93582       112.0     1440.0         57       171.0         985  \n",
       "\n",
       "[5963 rows x 17 columns]"
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e60ba1",
   "metadata": {},
   "source": [
    "### Implement Outlier analysis here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "id": "d9c43875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Userid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_inches</th>\n",
       "      <th>Weight_lbs</th>\n",
       "      <th>Back Squat</th>\n",
       "      <th>Clean_and_Jerk</th>\n",
       "      <th>Snatch</th>\n",
       "      <th>Deadlift</th>\n",
       "      <th>Fight Gone Bad</th>\n",
       "      <th>17.1_time</th>\n",
       "      <th>17.1_reps</th>\n",
       "      <th>17.2_score</th>\n",
       "      <th>17.3_time</th>\n",
       "      <th>17.3_reps</th>\n",
       "      <th>17.4_score</th>\n",
       "      <th>17.5_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>Justin Bergh</td>\n",
       "      <td>34</td>\n",
       "      <td>0.389501</td>\n",
       "      <td>0.633006</td>\n",
       "      <td>0.092805</td>\n",
       "      <td>0.196615</td>\n",
       "      <td>0.236411</td>\n",
       "      <td>0.080497</td>\n",
       "      <td>0.876709</td>\n",
       "      <td>0.956492</td>\n",
       "      <td>0.140451</td>\n",
       "      <td>0.817371</td>\n",
       "      <td>0.073159</td>\n",
       "      <td>0.849116</td>\n",
       "      <td>0.380378</td>\n",
       "      <td>0.098019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88</td>\n",
       "      <td>Cary Hair</td>\n",
       "      <td>32</td>\n",
       "      <td>0.079756</td>\n",
       "      <td>0.006626</td>\n",
       "      <td>0.523166</td>\n",
       "      <td>0.850740</td>\n",
       "      <td>1.055675</td>\n",
       "      <td>0.488711</td>\n",
       "      <td>1.075705</td>\n",
       "      <td>0.956492</td>\n",
       "      <td>9.644667</td>\n",
       "      <td>1.026479</td>\n",
       "      <td>0.073159</td>\n",
       "      <td>1.229451</td>\n",
       "      <td>0.644426</td>\n",
       "      <td>0.926868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1620</td>\n",
       "      <td>Everette Sweeting</td>\n",
       "      <td>28</td>\n",
       "      <td>0.079756</td>\n",
       "      <td>0.773942</td>\n",
       "      <td>0.343508</td>\n",
       "      <td>0.589090</td>\n",
       "      <td>0.459846</td>\n",
       "      <td>0.140555</td>\n",
       "      <td>0.644187</td>\n",
       "      <td>0.956492</td>\n",
       "      <td>0.275729</td>\n",
       "      <td>1.235587</td>\n",
       "      <td>0.073159</td>\n",
       "      <td>0.087093</td>\n",
       "      <td>0.531084</td>\n",
       "      <td>0.232064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1624</td>\n",
       "      <td>Troy Gordon</td>\n",
       "      <td>37</td>\n",
       "      <td>0.203654</td>\n",
       "      <td>0.116242</td>\n",
       "      <td>0.287863</td>\n",
       "      <td>0.248190</td>\n",
       "      <td>0.374314</td>\n",
       "      <td>0.428653</td>\n",
       "      <td>0.947779</td>\n",
       "      <td>1.157879</td>\n",
       "      <td>0.456100</td>\n",
       "      <td>0.258043</td>\n",
       "      <td>0.073159</td>\n",
       "      <td>0.527295</td>\n",
       "      <td>0.139247</td>\n",
       "      <td>0.253849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1633</td>\n",
       "      <td>Adam Eidson</td>\n",
       "      <td>42</td>\n",
       "      <td>0.353888</td>\n",
       "      <td>0.713712</td>\n",
       "      <td>0.118471</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0.057662</td>\n",
       "      <td>0.301548</td>\n",
       "      <td>0.246195</td>\n",
       "      <td>0.956492</td>\n",
       "      <td>1.663258</td>\n",
       "      <td>0.906989</td>\n",
       "      <td>0.073159</td>\n",
       "      <td>0.146960</td>\n",
       "      <td>0.440661</td>\n",
       "      <td>0.173419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Userid                Name  Age  Height_inches  Weight_lbs  Back Squat  \\\n",
       "0       86       Justin Bergh    34       0.389501    0.633006    0.092805   \n",
       "1       88          Cary Hair    32       0.079756    0.006626    0.523166   \n",
       "8     1620  Everette Sweeting    28       0.079756    0.773942    0.343508   \n",
       "10    1624        Troy Gordon    37       0.203654    0.116242    0.287863   \n",
       "11    1633        Adam Eidson    42       0.353888    0.713712    0.118471   \n",
       "\n",
       "    Clean_and_Jerk    Snatch  Deadlift  Fight Gone Bad  17.1_time  17.1_reps  \\\n",
       "0         0.196615  0.236411  0.080497        0.876709   0.956492   0.140451   \n",
       "1         0.850740  1.055675  0.488711        1.075705   0.956492   9.644667   \n",
       "8         0.589090  0.459846  0.140555        0.644187   0.956492   0.275729   \n",
       "10        0.248190  0.374314  0.428653        0.947779   1.157879   0.456100   \n",
       "11        0.000377  0.057662  0.301548        0.246195   0.956492   1.663258   \n",
       "\n",
       "    17.2_score  17.3_time  17.3_reps  17.4_score  17.5_score  \n",
       "0     0.817371   0.073159   0.849116    0.380378    0.098019  \n",
       "1     1.026479   0.073159   1.229451    0.644426    0.926868  \n",
       "8     1.235587   0.073159   0.087093    0.531084    0.232064  \n",
       "10    0.258043   0.073159   0.527295    0.139247    0.253849  \n",
       "11    0.906989   0.073159   0.146960    0.440661    0.173419  "
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "z_score_df = combined_df.copy()\n",
    "numerical_columns = ['Height_inches', 'Weight_lbs', 'Back Squat',\n",
    "       'Clean_and_Jerk', 'Snatch', 'Deadlift', 'Fight Gone Bad', '17.1_time',\n",
    "       '17.1_reps', '17.2_score', '17.3_time', '17.3_reps', '17.4_score',\n",
    "       '17.5_score']\n",
    "\n",
    "for cols in numerical_columns:\n",
    "    z_score_df[cols] = np.abs(stats.zscore(combined_df[cols]))\n",
    "    \n",
    "z_score_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "id": "e6162771",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = z_score_df[numerical_columns].applymap(lambda x: 1 if x < 3 else 0)\n",
    "combined_df = combined_df[(mask!=0).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "id": "17525eb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Userid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height_inches</th>\n",
       "      <th>Weight_lbs</th>\n",
       "      <th>Back Squat</th>\n",
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       "      <th>Snatch</th>\n",
       "      <th>Deadlift</th>\n",
       "      <th>Fight Gone Bad</th>\n",
       "      <th>17.1_time</th>\n",
       "      <th>17.1_reps</th>\n",
       "      <th>17.2_score</th>\n",
       "      <th>17.3_time</th>\n",
       "      <th>17.3_reps</th>\n",
       "      <th>17.4_score</th>\n",
       "      <th>17.5_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92036</th>\n",
       "      <td>831217</td>\n",
       "      <td>James Buchanan</td>\n",
       "      <td>48</td>\n",
       "      <td>72.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>186</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92696</th>\n",
       "      <td>837170</td>\n",
       "      <td>Rui Eus?bio</td>\n",
       "      <td>31</td>\n",
       "      <td>68.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>225</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>80</td>\n",
       "      <td>201.0</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93316</th>\n",
       "      <td>843226</td>\n",
       "      <td>Sean ODeay</td>\n",
       "      <td>36</td>\n",
       "      <td>74.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>225</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>130</td>\n",
       "      <td>217.0</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93392</th>\n",
       "      <td>843971</td>\n",
       "      <td>Nick Rakowski</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>225</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>80</td>\n",
       "      <td>197.0</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93582</th>\n",
       "      <td>847964</td>\n",
       "      <td>Jason Garwood</td>\n",
       "      <td>40</td>\n",
       "      <td>69.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>225</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>57</td>\n",
       "      <td>171.0</td>\n",
       "      <td>985</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Userid             Name  Age  Height_inches  Weight_lbs  Back Squat  \\\n",
       "92036  831217  James Buchanan    48           72.0       207.0       220.0   \n",
       "92696  837170    Rui Eus?bio    31           68.0       163.0       282.0   \n",
       "93316  843226      Sean ODeay    36           74.0       212.0       405.0   \n",
       "93392  843971   Nick Rakowski    28           67.0       155.0       315.0   \n",
       "93582  847964   Jason Garwood    40           69.0       189.0       415.0   \n",
       "\n",
       "       Clean_and_Jerk  Snatch  Deadlift  Fight Gone Bad  17.1_time  17.1_reps  \\\n",
       "92036           176.0   132.0     220.0           230.0     1200.0        186   \n",
       "92696           187.0   132.0     359.0           282.0      860.0        225   \n",
       "93316           310.0   250.0     465.0           419.0      804.0        225   \n",
       "93392           220.0   165.0     405.0           305.0      995.0        225   \n",
       "93582           185.0   155.0     345.0           277.0     1009.0        225   \n",
       "\n",
       "       17.2_score  17.3_time  17.3_reps  17.4_score  17.5_score  \n",
       "92036        78.0     1440.0         31       160.0        1624  \n",
       "92696       123.0     1440.0         80       201.0         536  \n",
       "93316       167.0     1440.0        130       217.0         565  \n",
       "93392       136.0     1440.0         80       197.0         688  \n",
       "93582       112.0     1440.0         57       171.0         985  "
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get rid of any rows with 0 in them\n",
    "\n",
    "combined_df = combined_df[(combined_df != 0).all(axis = 1)]\n",
    "combined_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "id": "0d03a55b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save as a csv file for later use!\n",
    "combined_df.to_csv('Mens_Crossfit_data_cleaned.csv',index = False)"
   ]
  }
 ],
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