Adams-Project2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "#Raymond Adams\n",
    "#Project 2\n",
    "\n",
    "#Pandas is to work with the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#Matplot is to plot charts\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is to make the charts look prettier\n",
    "import seaborn as sns; sns.set()\n",
    "%matplotlib inline\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.style.use('classic')\n",
    "\n",
    "pd.options.display.max_rows = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#how do we use seaborn\n",
    "sns.set(style=\"ticks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>#</th>\n",
       "      <th>Time</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Date</th>\n",
       "      <th>Meet</th>\n",
       "      <th>Location</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>23.86</td>\n",
       "      <td>Male</td>\n",
       "      <td>Jonty Skinner</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>1976</td>\n",
       "      <td>AAU Nationals</td>\n",
       "      <td>Philadelphia, PA, United States</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>23.74</td>\n",
       "      <td>Male</td>\n",
       "      <td>Joe Bottom</td>\n",
       "      <td>United States</td>\n",
       "      <td>1977</td>\n",
       "      <td>Canada Cup</td>\n",
       "      <td>Etobicoke, Canada</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23.72</td>\n",
       "      <td>Male</td>\n",
       "      <td>Ron Manganiello</td>\n",
       "      <td>United States</td>\n",
       "      <td>1978</td>\n",
       "      <td>-</td>\n",
       "      <td>Miami, FL, United States</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>23.70</td>\n",
       "      <td>Male</td>\n",
       "      <td>Klaus Steinbach</td>\n",
       "      <td>West Germany</td>\n",
       "      <td>1979</td>\n",
       "      <td>-</td>\n",
       "      <td>Freiburg, West Germany</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>23.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>Chris Cavanaugh</td>\n",
       "      <td>United States</td>\n",
       "      <td>1980</td>\n",
       "      <td>International Invitational</td>\n",
       "      <td>Amersfoort, Netherlands</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>23.12</td>\n",
       "      <td>Male</td>\n",
       "      <td>Chris Cavanaugh</td>\n",
       "      <td>United States</td>\n",
       "      <td>1980</td>\n",
       "      <td>USA Spring Nationals</td>\n",
       "      <td>Austin, TX, United States</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>22.96</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rowdy Gaines</td>\n",
       "      <td>United States</td>\n",
       "      <td>1980</td>\n",
       "      <td>USA Spring Nationals</td>\n",
       "      <td>Austin, TX, United States</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>22.83</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bruce Stahl</td>\n",
       "      <td>United States</td>\n",
       "      <td>1980</td>\n",
       "      <td>USA Spring Nationals</td>\n",
       "      <td>Austin, TX, United States</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>22.83</td>\n",
       "      <td>Male</td>\n",
       "      <td>Joe Bottom</td>\n",
       "      <td>United States</td>\n",
       "      <td>1980</td>\n",
       "      <td>Hawaiian Invitational</td>\n",
       "      <td>Honolulu, HI, United States</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>22.71</td>\n",
       "      <td>Male</td>\n",
       "      <td>Joe Bottom</td>\n",
       "      <td>United States</td>\n",
       "      <td>1980</td>\n",
       "      <td>Hawaiian Invitational</td>\n",
       "      <td>Honolulu, HI, United States</td>\n",
       "      <td>Freestyle</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>9</td>\n",
       "      <td>28.09</td>\n",
       "      <td>Female</td>\n",
       "      <td>Li Yang</td>\n",
       "      <td>China</td>\n",
       "      <td>2007</td>\n",
       "      <td>World Military Games</td>\n",
       "      <td>Hyderabad, India</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>10</td>\n",
       "      <td>28.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Hayley McGregory</td>\n",
       "      <td>United States</td>\n",
       "      <td>2008</td>\n",
       "      <td>Texas Senior Circuit Championships</td>\n",
       "      <td>Austin, United States</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>11</td>\n",
       "      <td>27.95</td>\n",
       "      <td>Female</td>\n",
       "      <td>Emily Seebohm</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2008</td>\n",
       "      <td>Australia Championships</td>\n",
       "      <td>Sydney, Australia</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>12</td>\n",
       "      <td>27.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sophie Edington</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2008</td>\n",
       "      <td>Australia Championships</td>\n",
       "      <td>Sydney, Australia</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>12</td>\n",
       "      <td>27.67</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zhao Jing</td>\n",
       "      <td>China</td>\n",
       "      <td>2009</td>\n",
       "      <td>China Championships</td>\n",
       "      <td>Shaoxing, China</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>13</td>\n",
       "      <td>27.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>Daniela Samulski</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2009</td>\n",
       "      <td>Germany Championships</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>14</td>\n",
       "      <td>27.39</td>\n",
       "      <td>Female</td>\n",
       "      <td>Daniela Samulski</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2009</td>\n",
       "      <td>World Championships</td>\n",
       "      <td>Rome, Italy</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>15</td>\n",
       "      <td>27.38</td>\n",
       "      <td>Female</td>\n",
       "      <td>Anastasia Zuyeva</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2009</td>\n",
       "      <td>World Championships</td>\n",
       "      <td>Rome, Italy</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>16</td>\n",
       "      <td>27.06</td>\n",
       "      <td>Female</td>\n",
       "      <td>Zhao Jing</td>\n",
       "      <td>China</td>\n",
       "      <td>2009</td>\n",
       "      <td>World Championships</td>\n",
       "      <td>Rome, Italy</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>17</td>\n",
       "      <td>26.98</td>\n",
       "      <td>Female</td>\n",
       "      <td>Liu Xiang</td>\n",
       "      <td>China</td>\n",
       "      <td>2018</td>\n",
       "      <td>Asian Games</td>\n",
       "      <td>Jakarta, Indonesia</td>\n",
       "      <td>Backstroke</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      #   Time  Gender              Name     Nationality  Date  \\\n",
       "0     1  23.86    Male     Jonty Skinner    South Africa  1976   \n",
       "1     2  23.74    Male        Joe Bottom   United States  1977   \n",
       "2     3  23.72    Male   Ron Manganiello   United States  1978   \n",
       "3     4  23.70    Male   Klaus Steinbach    West Germany  1979   \n",
       "4     5  23.66    Male   Chris Cavanaugh   United States  1980   \n",
       "5     6  23.12    Male   Chris Cavanaugh   United States  1980   \n",
       "6     7  22.96    Male      Rowdy Gaines   United States  1980   \n",
       "7     8  22.83    Male       Bruce Stahl   United States  1980   \n",
       "8     8  22.83    Male        Joe Bottom   United States  1980   \n",
       "9     9  22.71    Male        Joe Bottom   United States  1980   \n",
       "..   ..    ...     ...               ...             ...   ...   \n",
       "118   9  28.09  Female           Li Yang           China  2007   \n",
       "119  10  28.00  Female  Hayley McGregory   United States  2008   \n",
       "120  11  27.95  Female     Emily Seebohm       Australia  2008   \n",
       "121  12  27.67  Female   Sophie Edington       Australia  2008   \n",
       "122  12  27.67  Female         Zhao Jing           China  2009   \n",
       "123  13  27.61  Female  Daniela Samulski         Germany  2009   \n",
       "124  14  27.39  Female  Daniela Samulski         Germany  2009   \n",
       "125  15  27.38  Female  Anastasia Zuyeva          Russia  2009   \n",
       "126  16  27.06  Female         Zhao Jing           China  2009   \n",
       "127  17  26.98  Female         Liu Xiang           China  2018   \n",
       "\n",
       "                                   Meet                         Location  \\\n",
       "0                         AAU Nationals  Philadelphia, PA, United States   \n",
       "1                            Canada Cup                Etobicoke, Canada   \n",
       "2                                     -         Miami, FL, United States   \n",
       "3                                     -           Freiburg, West Germany   \n",
       "4            International Invitational          Amersfoort, Netherlands   \n",
       "5                  USA Spring Nationals        Austin, TX, United States   \n",
       "6                  USA Spring Nationals        Austin, TX, United States   \n",
       "7                  USA Spring Nationals        Austin, TX, United States   \n",
       "8                 Hawaiian Invitational      Honolulu, HI, United States   \n",
       "9                 Hawaiian Invitational      Honolulu, HI, United States   \n",
       "..                                  ...                              ...   \n",
       "118                World Military Games                 Hyderabad, India   \n",
       "119  Texas Senior Circuit Championships            Austin, United States   \n",
       "120             Australia Championships                Sydney, Australia   \n",
       "121             Australia Championships                Sydney, Australia   \n",
       "122                 China Championships                  Shaoxing, China   \n",
       "123               Germany Championships                  Berlin, Germany   \n",
       "124                 World Championships                      Rome, Italy   \n",
       "125                 World Championships                      Rome, Italy   \n",
       "126                 World Championships                      Rome, Italy   \n",
       "127                         Asian Games               Jakarta, Indonesia   \n",
       "\n",
       "         Stroke   Age  \n",
       "0     Freestyle  22.0  \n",
       "1     Freestyle  22.0  \n",
       "2     Freestyle   NaN  \n",
       "3     Freestyle  26.0  \n",
       "4     Freestyle  18.0  \n",
       "5     Freestyle  18.0  \n",
       "6     Freestyle  21.0  \n",
       "7     Freestyle   NaN  \n",
       "8     Freestyle  25.0  \n",
       "9     Freestyle  25.0  \n",
       "..          ...   ...  \n",
       "118  Backstroke   NaN  \n",
       "119  Backstroke  22.0  \n",
       "120  Backstroke  16.0  \n",
       "121  Backstroke  24.0  \n",
       "122  Backstroke  19.0  \n",
       "123  Backstroke  25.0  \n",
       "124  Backstroke  25.0  \n",
       "125  Backstroke  19.0  \n",
       "126  Backstroke  19.0  \n",
       "127  Backstroke  22.0  \n",
       "\n",
       "[128 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://en.wikipedia.org/wiki/World_record_progression_50_metres_freestyle\n",
    "df = pd.read_csv('Swimming_WRP.csv')\n",
    "df = df.rename(index=str, columns = {\"Age \" : \"Age\"})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sport_gender</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Gender</th>\n",
       "      <th>avg_age</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Diving_Female</td>\n",
       "      <td>Diving</td>\n",
       "      <td>Female</td>\n",
       "      <td>23.1778</td>\n",
       "      <td>Aquatics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Diving_Male</td>\n",
       "      <td>Diving</td>\n",
       "      <td>Male</td>\n",
       "      <td>22.4250</td>\n",
       "      <td>Aquatics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Swimming_Female</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>Female</td>\n",
       "      <td>21.6738</td>\n",
       "      <td>Aquatics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Swimming_Male</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>Male</td>\n",
       "      <td>23.4697</td>\n",
       "      <td>Aquatics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Synchronized Swimming_Female</td>\n",
       "      <td>Synchronized Swimming</td>\n",
       "      <td>Female</td>\n",
       "      <td>23.1132</td>\n",
       "      <td>Aquatics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water Polo_Female</td>\n",
       "      <td>Water Polo</td>\n",
       "      <td>Female</td>\n",
       "      <td>24.6364</td>\n",
       "      <td>Aquatics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Water Polo_Male</td>\n",
       "      <td>Water Polo\\t</td>\n",
       "      <td>Male</td>\n",
       "      <td>27.3269</td>\n",
       "      <td>Aquatics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Football_Female</td>\n",
       "      <td>Football</td>\n",
       "      <td>Female</td>\n",
       "      <td>26.7500</td>\n",
       "      <td>Games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Football_Male</td>\n",
       "      <td>Football</td>\n",
       "      <td>Male</td>\n",
       "      <td>22.8077</td>\n",
       "      <td>Games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Handball_Female</td>\n",
       "      <td>Handball</td>\n",
       "      <td>Female</td>\n",
       "      <td>26.1228</td>\n",
       "      <td>Games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Trampolining_Male</td>\n",
       "      <td>Trampolining</td>\n",
       "      <td>Male</td>\n",
       "      <td>25.3333</td>\n",
       "      <td>Gymnastics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Rhythmic Gymnastics_Female</td>\n",
       "      <td>Rhythmic Gymnastics</td>\n",
       "      <td>Female</td>\n",
       "      <td>19.5319</td>\n",
       "      <td>Gymnastics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Shooting_Female</td>\n",
       "      <td>Shooting</td>\n",
       "      <td>Female</td>\n",
       "      <td>29.4688</td>\n",
       "      <td>Technical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Shooting_Male</td>\n",
       "      <td>Shooting</td>\n",
       "      <td>Male</td>\n",
       "      <td>32.5743</td>\n",
       "      <td>Technical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Archery_Female</td>\n",
       "      <td>Archery</td>\n",
       "      <td>Female</td>\n",
       "      <td>25.7407</td>\n",
       "      <td>Technical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Archery_Male</td>\n",
       "      <td>Archery</td>\n",
       "      <td>Male</td>\n",
       "      <td>26.1111</td>\n",
       "      <td>Technical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Athletics_Female</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Female</td>\n",
       "      <td>26.5533</td>\n",
       "      <td>Track_and_Field</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Athletics_Male</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Male</td>\n",
       "      <td>26.7606</td>\n",
       "      <td>Track_and_Field</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Weightlifting_Female</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Female</td>\n",
       "      <td>23.8462</td>\n",
       "      <td>Weightlifting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Weightlifting_Male</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Male</td>\n",
       "      <td>25.4412</td>\n",
       "      <td>Weightlifting</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Sport_gender                  Sport  Gender  avg_age  \\\n",
       "0                  Diving_Female                 Diving  Female  23.1778   \n",
       "1                    Diving_Male                 Diving    Male  22.4250   \n",
       "2                Swimming_Female               Swimming  Female  21.6738   \n",
       "3                  Swimming_Male               Swimming    Male  23.4697   \n",
       "4   Synchronized Swimming_Female  Synchronized Swimming  Female  23.1132   \n",
       "5              Water Polo_Female             Water Polo  Female  24.6364   \n",
       "6                Water Polo_Male           Water Polo\\t    Male  27.3269   \n",
       "7                Football_Female               Football  Female  26.7500   \n",
       "8                  Football_Male               Football    Male  22.8077   \n",
       "9                Handball_Female               Handball  Female  26.1228   \n",
       "..                           ...                    ...     ...      ...   \n",
       "52             Trampolining_Male           Trampolining    Male  25.3333   \n",
       "53    Rhythmic Gymnastics_Female    Rhythmic Gymnastics  Female  19.5319   \n",
       "54               Shooting_Female               Shooting  Female  29.4688   \n",
       "55                 Shooting_Male               Shooting    Male  32.5743   \n",
       "56                Archery_Female                Archery  Female  25.7407   \n",
       "57                  Archery_Male                Archery    Male  26.1111   \n",
       "58              Athletics_Female              Athletics  Female  26.5533   \n",
       "59                Athletics_Male              Athletics    Male  26.7606   \n",
       "60          Weightlifting_Female          Weightlifting  Female  23.8462   \n",
       "61            Weightlifting_Male          Weightlifting    Male  25.4412   \n",
       "\n",
       "               Type  \n",
       "0          Aquatics  \n",
       "1          Aquatics  \n",
       "2          Aquatics  \n",
       "3          Aquatics  \n",
       "4          Aquatics  \n",
       "5          Aquatics  \n",
       "6          Aquatics  \n",
       "7             Games  \n",
       "8             Games  \n",
       "9             Games  \n",
       "..              ...  \n",
       "52       Gymnastics  \n",
       "53       Gymnastics  \n",
       "54        Technical  \n",
       "55        Technical  \n",
       "56        Technical  \n",
       "57        Technical  \n",
       "58  Track_and_Field  \n",
       "59  Track_and_Field  \n",
       "60    Weightlifting  \n",
       "61    Weightlifting  \n",
       "\n",
       "[62 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " This data was collected from Wikipedia. Every stroke was initially seperated into its own data table so I used excel to combine it all into one big table. The columns \"Gender\" and \"Stroke\" and \"Country were all added to the table table using excel. This allowed me to better represent the data by gender, event, and country.The column \"Location\" has the name of the of the city and country separated by a comma. For my purposes I only wanted the name of the country for each record set. I know that it is possible to loop through each row and only take the value after the comma but I could not get the code to work properly. So, instead I made a new column named \"Country\" and inserted the name of the country that each world record holder represented for. After cleaning the data I presented it as shown above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qbAAAIB+FKvKkpCTX1yEhIVqyZInlgQAAQMGxshsAADZGkQMAYGMUOQAANkaRAwBgYxQ5AAA2RpEDAGBjFDkAADbm1oIwKDxnTpaWToi2dDwAAChyDynh669D8e0tG6/6awstGwsAYF+8tQ4AgI1R5AAA2BhFDgCAjVHkAADYGEUOAICN3VCRJyYmKiIiQhEREXrzzTetygQAAArI7SLPyMhQfHy8PvroIyUmJmrLli1av369ldkAAEA+3C5yh8Mhp9OpjIwM5eTkKCcnR6VKlbIyGwAAyIfbC8IEBATo5ZdfVqtWrVSmTBn97W9/U4MGDazMBgAA8uH2GfmPP/6ohQsX6quvvtLatWtVokQJzZw508psAAAgH26fkX/77bcKCQlRpUqVJEkxMTH6+OOPFRsbW+AxKlUKcHfzRSIoKND1tTMnSyV8/Ysxjeddvv/ufN+TvCmLJ3nznPEG5Mmbt+WBNdwu8tq1a+utt95Senq6ypQpo6SkJD3wwAOFGuPs2TQ5ncbdCJYKCgpUSkpqrtu32trol+//la48PsXJm7JInv3l6M1zpriRJ2/emAfWcLvImzRpoj179igmJkZ+fn564IEH1Lt3byuzAQCAfNzQ1c969+5NeQMAUIxY2Q0AABujyAEAsDGKHAAAG6PIAQCwMYocAAAbo8gBALAxihwAABu7ob8jR/Fx5mRp6YRoS8cDANgPRW5TJXz9i2AJ2UzLxgMAeAZvrQMAYGMUOQAANkaRAwBgYxQ5AAA2RpEDAGBjN1TkSUlJiomJUatWrTRq1CirMgEAgAJyu8iPHj2q4cOHa+rUqVqyZIn27NmjNWvWWJkNAADkw+2/I1+1apVat26tO+64Q5KUkJCgUqVKWRYMAADkz+0z8sOHD8vhcKhPnz6Kjo7Wxx9/rAoVKliZDQAA5MPtInc4HNqwYYNGjx6tuXPnaufOnVq8eLGV2QAAQD7cfmv9T3/6k0JCQlSxYkVJ0qOPPqqdO3cqJiamwGNUqhTg7uaLRFBQYHFHKFb57b83HR9vyuJJzJm8kSdv3pYH1nC7yENDQzVo0CBdvHhR5cqV09q1a/XII48UaoyzZ9PkdBp3I1gqKChQKSmpuW7fai7f/ytdeXyKkzdlkTz7WvHmOVPcyJM3b8wDa7hd5PXq1VNsbKy6dOmi7OxsPfzww2rf3rqLeAAAgPzd0NXPOnTooA4dOliVBQAAFBIruwEAYGMUOQAANkaRAwBgYxQ5AAA2RpEDAGBjFDkAADZGkQMAYGMUOQAANnZDC8IAQFG5vYK/fP0Ld2nkvJb9dGZnqYSf/43GcrmUmaPUixmWjQe4iyIH4JV8/UvpULx1yz5Xf22hol5NtGy8pROi5T0rl+NWxlvrAADYGEUOAICNUeQAANgYRQ4AgI1R5AAA2JglRf7mm29q8ODBVgwFAAAK4YaLfMOGDVq8eLEVWQAAQCHdUJGfP39eCQkJ6tOnj1V5AABAIdxQkQ8bNkz9+/dX+fLlrcoDAAAKwe2V3ebPn68777xTISEhWrRokVtjVKoU4O7mi0ReyzveCvLbf286Pt6UxZOYM97FTnNG8r48sIbbRb58+XKlpKQoOjpaFy5cUHp6ukaPHq2hQ4cWeIyzZ9PkdBp3I1gqKChQKSmpuW7fai7f/ytdeXyKkzdlkTz7WvHmOVMU43s7u8wZyTvzwBpuF/msWbNcXy9atEjfffddoUocAADcOP6OHAAAG7Pk6mcxMTGKiYmxYigAAFAInJEDAGBjFDkAADZGkQMAYGMUOQAANkaRAwBgYxQ5AAA2RpEDAGBjlvwdOQDcapw5WZautZ6TlalfL2TdaCzcgihyAHBDCV9/HYpvb9l41V9bKIkiR+Hx1joAADZGkQMAYGMUOQAANkaRAwBgYxQ5AAA2dkOfWp88ebJWrFghSWrevLkGDhxoSSgAAFAwbp+Rr1+/Xt9++60WL16szz77TD/88INWrVplZTYAAJAPt8/Ig4KCNHjwYPn7+0uSatSooePHj1sWDAAA5M/tIr/vvvtcXycnJ2vFihX65JNPLAkFAAAK5oY/7LZ//3716tVLAwcO1L333mtBJAAAUFA39GG3rVu3qm/fvho6dKgiIiIK/fxKlQJuZPOWK8y6yDcbq9eNdmZnqYSf/43GsiTLzYQ54z5nTpaWTogu7hh5KurjaaefFwrO7SI/ceKEXnjhBSUkJCgkJMStMc6eTZPTadyNYKmgoEClpKTmun0rKYp1oy8/nla68mdV3Dz5WvHmOVMU41upaNZGt1ZRH89bdd7c7Nwu8pkzZyozM1Njx4513depUyd17tzZkmAAACB/bhd5XFyc4uLirMwCAAAKiZXdAACwMYocAAAbo8gBALAxihwAABujyAEAsDGKHAAAG6PIAQCwMYocAAAbo8gBALAxihwAABujyAEAsDGKHAAAG6PIAQCwMYocAAAbu6EiX7p0qVq3bq2wsDDNmTPHqkwAAKCA3L4e+alTp5SQkKBFixbJ399fnTp1UnBwsGrWrGllPgAAkAe3z8jXr1+vRo0a6bbbblPZsmXVsmVLff7551ZmAwAA+XC7yE+fPq2goCDX7cqVK+vUqVOWhAIAAAXj9lvrTqdTPj4+rtvGmFy3C6JEicI9vqhdmce3QtB1HumeW228ovz5ettrx1O8bb+LOo+3v8btNGc8MT6Kh48xxrjzxMWLF2vLli2Kj4+XJE2ZMkXGGL344ouWBgQAANfn9lvrjRs31oYNG3Tu3DllZGToiy++ULNmzazMBgAA8uH2W+tVqlRR//791b17d2VnZ6tDhw6qW7euldkAAEA+3H5rHQAAFD9WdgMAwMYocgAAbIwiBwDAxihyAMBN58KFCxoyZIhatmypxx57TE8//bQOHz58Q2PGxMTo2LFjFiW0DkUOALipGGPUp08fNWzYUCtXrtSqVav0xBNPqG/fvsUdrUi4/ednAAB4o82bN6tChQrq0KGD676wsDClpKQoLS1N48aN0+7du1WiRAkNGTJEDRs2VEREhJo2bar169erSpUqeuedd1SuXDm98847WrFihe655x6lpaVJkrKzszVy5MirxmjdurXuvvtupaen66OPPvLY/nJGDgC4qezevVsNGjS46v6uXbsqMTFR1apV06JFizR58mT961//kiRlZGSobt26WrJkicqUKaOkpCRt375dGzdu1LJlyzR06FAdP35ckjRv3rxrjnHp0iV16dLFoyUucUYOALjJXHntjz59+ujEiRNKTU1V1apVdfr0aX322WeSpLS0NJ07d06SFBISIkmqWbOmLl68qK1btyo0NFR+fn6699579Ze//EWStHHjRv3000/XHOOvf/2rx/bzDxQ5AOCm8pe//EVz5sxx3Z4+fbokqW/fvlq/fr2mTZumv/3tb5KkU6dO6fbbb5ck+fv7S5J8fHx0rbXSSpYsKUlyOBwaNWrUNccoXbp0Ee3V9fHWOgDgphIcHKxz585p4cKFrkI+cuSIDhw4oKeeekoLFiyQJP3444/q3LnzNUtbkh566CF9+eWXysrK0tGjR/XDDz9Ikho0aFDgMTyBM3IAwE2lRIkS+ve//63x48frv//9ry5duqTy5curd+/eCg8P1+uvv67IyEj5+Pho3LhxKlHi2ue09erVU4sWLRQVFaW77rpLNWrUkCQ9+eSTBR7DE1hrHQAAG+OtdQAAbIwiBwDAxihyAABs7JYu8u3bt6tbt26KiopSZGSkYmNjtX//ftf3e/Xq5frbwILatGmTIiMjrY56TYMHD1bTpk0VHR2tNm3aKDw8XCNHjlROTk6Rbzs1NVXdu3fP93GTJ0/Wl19+Wejx77///kIfe3gecyi3Y8eO6cEHHyzUc+Li4rR79+4i3w5uXrfsp9azsrL07LPP6v3331edOnUkSYmJiXrmmWe0evVqlSxZUuvWrSvmlPnr2bOnnn76aUlSZmamOnXqpOXLl6tNmzZFut0LFy5o165d+T5u06ZNqlmzZpFmQfFgDllj/fr1euKJJzyyLdycbtkiz8jIUGpqqtLT0133tWnTRgEBAXI4HIqLi5Mk9ejRQzNmzFDXrl1Vt25d7du3T6+88oruvfdejRgxQufPn5ePj4969eqltm3b5trGli1bNGDAAL399ttq0KCBkpKSNG3aNGVnZ6t06dIaNGjQVf9XfeDAAb366qtX5e3evbvat2+f5z6lp6crKytLQUFBkqRu3bqpQoUKOnTokDp37qy2bdsqPj5eP/30k7KzsxUSEqKBAwfK19dXCxYs0Ny5c5Wdna0LFy7omWeeUZcuXZSSkqJBgwbp119/lSQ1b95c/fr105AhQ3Tp0iVFR0dr0aJFmjJlilatWiU/Pz/dfvvtGjNmjFatWqXdu3dr3LhxKlmypFavXq3z58/r6NGj+sc//qE+ffrojTfe0I8//igfHx81bdpUr7zyinx9//+XZUpKip566il17txZXbt21cGDBxUfH6/z58/L4XCoW7duudZThufcCnNo+/bteuutt5SVlaWUlBQ1btxYo0ePliR99dVXeuedd+R0OlW2bFm98cYbCggIcI118OBBPfPMMxoyZIhCQ0M1cuRIff/99/Lz89Ndd92lMWPGaMaMGTp9+rQGDBigcePGafz48bnm7GOPPabXX39dv/zyi4wxatu2rWJjY3Nlvnw7jz32mL7//nuNHz9eGRkZKlGihF588UWFhobm9+OEnZlb2Pvvv2/q1q1rWrRoYQYMGGDmz59v0tPTXd+vVauWOXv2rDHGmNDQUDN58mRjjDHZ2dnmkUceMStXrjTGGHPy5EnTtGlT8/3335uNGzeaiIgIs2HDBvPoo4+avXv3GmOM+fnnn01kZKQ5d+6cMcaYn376yTz88MPmt99+czv/oEGDTJMmTUybNm1MZGSkqVu3runRo4fJzMw0xhjz5JNPmiFDhrgeP3jwYPPhhx8aY4zJyckxAwYMMDNmzDBpaWnm8ccfd2Xbtm2bqV+/vjHGmMmTJ5t//etfxhhjfvvtN9OvXz9z8eJFc/ToUddjjh8/bho0aODa7syZM82qVatcGVasWOHK26NHD1eegQMHmpEjRxqn02kyMzNNr169zL///W/Xsd+zZ49p3bq1SUxMdB331q1bm927dxtjjLl48aJp1aqV2bZtm9vHEDfmZp9D/fv3Nxs3bjTGGJOWlmaCg4PNrl27TEpKimnYsKH54YcfjDHGrFy50jz99NOuebFv3z7z6KOPmvXr1xtjjNm8ebMJDw83TqfTGGPMuHHjzNatW13HZefOncaYq+ds165dzfvvv2+M+f31HhUVZZYtW3bd7Zw/f96EhYWZo0ePuo5rs2bNzC+//OL2MfK0zKycYhv36NGjplatWq7feX/Ys2ePqVWrllm4cOF1nxsaGuo67p52y56RS9JTTz2ljh07avPmzdq8ebPee+89vffee1qwYIECAwOvevxDDz0kSUpOTlZmZqbCwsIkSVWqVFFYWJjWrl2r4OBgnTx5Un369FHnzp1Vu3ZtSdK6det0+vRp9ezZ0zWej4+Pjhw54nqMVPizicvfFkxPT1f//v01atQojRgxIldmSfr666+1a9cu14pEly5dkiSVK1dO06dP15o1a5ScnKwff/zRdZbVtGlT9e7dWydOnFDjxo316quvKjAwUBcuXHCNW6VKFdWuXVvt2rVTs2bN1KxZM9eaxVdq2LCh6+tvvvlGn3zyiXx8fOTv769OnTrpv//9r3r37i1JeuaZZ3THHXcoKirKddyPHDmioUOHusa4dOmS9uzZo/r1619zeyhaN/scGjt2rL755htNnz5dhw4dUmZmptLT0/X999/rvvvuc629HRYWprCwMB07dkxZWVnq3r27/v73v7vmQa1atVSyZEl17NhRTZo0UcuWLVW3bt1rHtM/jtEf23n//fclSYGBgYqJidE333yjevXqXXM727dvV0pKil544YVcx2jfvn36v//7v2tuz9v4+5VU1KuJlo+7dEJ0gR532223ae3atXI4HK4lWZcvX66KFStanskqt2yRb926Vdu2bVNsbKxCQ0MVGhqqV155RZGRkVq3bp3Cw8Ovek7ZsmUl/b7O7uUL8ktmyrQZAAAcNUlEQVS/L9L/xwdkSpYsqRkzZuj5559XeHi46tWrJ6fTqZCQEL3zzjuu55w4cUKVK1fONU7NmjWVmOjei7hs2bLq2LGjJkyYcFVmSXI6nXr33XddqxNdvHhRPj4+OnnypJ544gk9/vjjatiwocLDw/XVV19JkurWravVq1drw4YN2rhxozp27Kj33ntPt912m2vcEiVKaPbs2dq1a5c2bNig0aNHq2nTpho4cOB1j+EfeS4/jk6nM9eHjEaMGKHp06dr1qxZ6tWrlxwOhwIDA3MdnzNnzlyzMFD0boU59OSTT+r+++9X06ZN1apVK+3YsUPGGJUsWTJXfmOM9u3b53prfcqUKRo4cKBWrlypli1bqnz58kpMTNT333+vjRs3ql+/fnr66afVtWvX6x4jp9N51bKfV86RK7fjcDhUo0YNzZ8/3/WYU6dOeXUJeZty5cqpdu3a2rx5sxo1aiTp9/+JbNy4sSRp9uzZSkxMVEZGhvz8/DRhwgRVr17d9XyHw6Fx48bpu+++k8PhUExMTK7/+SwKt+yn1itWrKhp06Zpy5Ytrvv+uFZtrVq1JP3+y+Ran16tXr26fH199cUXX0j6faKsXLnS9YMOCgpSgwYNNGjQIA0cOFAZGRkKCQnRunXrdPDgQUnSmjVr1KZNG9dZsRWcTqe++eab6/6ffpMmTfTBBx/IGKOsrCw999xzmj17tnbv3q2KFSvq+eefV5MmTVwl7nA4NH78eE2dOlWPPvqoXnvtNdWsWVP79++Xr6+vHA6HjDH68ccfFRkZqRo1aujZZ59Vz549XR+Eu94x/CPP7NmzXXnmzZvnOoaSVL9+fY0dO1bTpk3TTz/9pGrVqql06dKuX9InTpxQZGRkoT/xC2vc7HPo4sWL2rVrlwYMGKCwsDCdPHlSR44ckdPpVL169XTw4EHXJ/RXr16tf/7zn5J+v/BGw4YNNXr0aL3++utKSUnRV199pZ49e+rBBx/USy+9pLZt27pet9c7RgEBAapXr57r4h+pqan67LPPXMfoWtupX7++Dh8+rM2bN0uS9u7dq5YtW+rUqVOWHaNbQatWrbRy5UpJ0s6dO3X//ffLz89PaWlp+vLLL/XRRx9p2bJl+sc//pHr4izS75c4laTFixdrwYIFWr16da45UhRu2TPyatWqacqUKUpISNDJkydVqlQpBQYGavTo0a7/uwoPD1e3bt00adKkXM/18/PT1KlTNWrUKE2aNEkOh0MvvPCCGjVqpE2bNrke165dO61cuVJjx47VG2+8oREjRuiVV16RMUa+vr6aNm2aypUrd0P78cEHH2jJkiXy8fFRRkaG6tSpo+HDh1/zsa+99pri4+MVFRWl7OxsNW7cWLGxscrJydGCBQsUHh4uHx8f/f3vf1fFihV1+PBh9ejRQ4MHD1ZkZKT8/f11//33KyIiQiVLllTdunUVERGhOXPmqFWrVmrfvr3Kli2r0qVLuz7o1KJFC7399tvKzs6+Kk9cXJxGjRrlytO0aVP16dMn12OqV6+u559/Xv/85z81f/58TZ06VfHx8frPf/6jnJwcvfzyy7nerofn3Oxz6I+1udu1a6eyZcuqSpUqatCggQ4fPqyQkBCNHz9egwYNksPhUEBAgBISEnKNGxwcrIiICA0dOlTTp0/XN998o8jISJUtW1YVKlTQyJEjJUmPPfaY/vnPf+r111+/Ktv48eM1YsQILVq0SFlZWYqKilJMTIx++eWXa27nvffe08SJEzVu3DhlZmbKGKNx48bprrvuuqFjdKtp0aKF64OMK1asUKtWrbR8+XIFBARowoQJ+t///qfk5GStXbtWf/7zn3M9d8OGDdq7d682btwo6fd/Itm3b1+uf+a0WoHWWk9LS1OnTp00ffr0XC+I2bNna+XKlR6/iDoA4OZVXP9GfuzYMXXv3l1JSUl64YUX1LNnT40ePVrz589XXFyc7r77bi1evFhPPvmk6tSpoz179mjv3r0aO3asWrRooQ8//FBjxoxRdHS06/Mf586dU7ly5VSqVCnL9+kP+b61vmPHDnXu3FnJycm57j9w4IBmzJhRVLkAACg2rVq10oQJE/TXv/7V9SexZcuW1T333KOePXvqgQce0JdffimHw5HreY0aNdK8efOUnZ2t3377TV26dNH27duLNGu+RT5v3jwNHz481wdKsrKyNGzYMPXt27dIwwEAUBxCQ0O1d+9etW7d2nWfn5+fnE6nWrdurXbt2qlatWo6duxYrud16tRJ9957r9q1a6f27dsrJiZGwcHBRZq1wJcx/eNtgz8WMqhVq5buuusuTZ48mbfWAQCWyMp2yN+vpG3G9QaF/tT6unXrdOLEiXxXSAIAoLCKqmxv1hKX3PjU+rJly7R//35FR0crPT1dZ86cUb9+/XL9bScAAPCMQhf5mDFjXF9v2rRJkydPdrvEz55Nk9NZoHf2i1xQUKBSUlKLO4YLea7Pm7JIv+fxFObM9ZEnb96YB9a4ZReEAQDgZlDgM/KkpKSr7gsODi7yT+MBAIDr44wcAAAbo8gBALCxW3atdQCA93HmZKmEr3+xjHvs2DGFh4e7rhD5h+nTp+vOO++0NM/ly8HeKIocAOA1Svj661C89euUVH9tYYEeV7lyZbcvg1tcKHIAAPJw5swZDRs2TCdPnpSPj49effVVNW7cWJMmTdLx48eVnJysc+fO6bnnntOGDRu0Y8cO1a5dWwkJCXI4HHr99de1f/9+nTlzRvfff7/efvvtAo1fUBQ5AAD/z+nTpxUd/f9fKS0qKko//PCD2rdvr0ceeUSnT59Wly5d9Nlnn0mSfvrpJ82dO1fff/+9evTooaVLl+ree+9V69attW/fPqWmpsrPz09z586V0+lUjx49tGbNGtWpU8e1jfj4+GuOHxAQUKDMFDkAAP/Ptd5aDw4O1qFDhzRx4kRJUk5Ojo4ePSpJevjhh+Xr66v/+7//U1BQkGrWrClJqlKlii5cuKDg4GDddtttmjNnjg4dOqTk5GSlp6fnGn/9+vXXHP/Ka51fD0UOAEAenE6n/vvf/+q2226T9PtZe6VKlfTll1/Kz8/P9bg/Lnd6udWrV2vixInq3r27YmJi9Ouvv+rKa5Vdb/yC4s/PAADIQ6NGjfTxxx9Lkg4cOKCoqChlZGQU6LkbNmxQq1at1L59e5UvX16bNm265jXM3R1f4owcAOBFnDlZBf6EeWHHdffP2uLi4jRs2DBFRUVJksaNG1fgf7/u2LGjBgwYoP/973/y8/NTgwYNrrqG+Y2MLxXieuRFgQtAXB95rs+bskhcNMVbkCdv3pgH1uCtdQAAbIwiBwDAxihyAABsjCIHAMDGClzkaWlpioyMdH3abu7cuYqMjFRUVJSGDBmirKysIgsJAACurUBFvmPHDnXu3FnJycmSpJ9//lkzZ87Up59+qiVLlsjpdLr+Bg4AAHhOgYp83rx5Gj58uCpXrixJ8vf31/DhwxUQECAfHx/VqlVLx48fL9KgAADgagVaECY+Pj7X7apVq6pq1aqSpHPnzmnOnDkaM2aM9ekAAECebmhlt1OnTik2Nlbt27dXcHBwoZ9fqVLBV67xBG9boIA81+dNWTyJOZM38uTN2/LAGm4X+cGDBxUbG6tu3bqpV69ebo3BKlXXR57r86YsEiu7eQvy5M0b88AabhV5Wlqann76afXr109t27a1OhMAACggt/6OfMGCBTpz5oxmzZql6OhoRUdH691337U6GwAAyEehzsiTkpIkST179lTPnj2LIg8AACgEVnYDAMDGKHIAAGyMIgcAwMYocgAAbIwiBwDAxihyAABsjCIHAMDGKHIAAGyMIgcAwMYocgAAbIwiBwDAxihyAABsjCIHAMDGKHIAAGyswEWelpamyMhIHTt2TJK0fv16RUVFKSwsTAkJCUUWEAAAXF+BinzHjh3q3LmzkpOTJUmXLl3S0KFDNXXqVC1fvly7d+/WmjVrijInAAC4hgIV+bx58zR8+HBVrlxZkrRz507dc889uvvuu+Xr66uoqCh9/vnnRRoUAABczbcgD4qPj891+/Tp0woKCnLdrly5sk6dOmVtMgAAkK8CFfmVnE6nfHx8XLeNMbluF1SlSgHubL7IBAUFFneEXMhzfd6UxZOYM3kjT968LQ+s4VaR33HHHUpJSXHdTklJcb3tXhhnz6bJ6TTuRLBcUFCgUlJSizuGC3muz5uySJ795cicuT7y5M0b88Aabv35Wb169fTzzz/r8OHDcjgcWrZsmZo1a2Z1NgAAkA+3zshLlSqlsWPH6qWXXlJmZqaaN2+u8PBwq7MBAIB8FKrIk5KSXF+HhIRoyZIllgcCAAAFx8puAADYGEUOAICNUeQAANgYRQ4AgI1R5AAA2BhFDgCAjVHkAADYmFsLwqDwAsuXUelShTvceS1heCkzR6kXM240FnDLuL2Cv3z9S1k2Xk5Wpn69kGXZeIC7KHIPKV3KV1GvJlo23tIJ0fKeVZMB7+frX0qH4ttbNl711xZKoshR/HhrHQAAG6PIAQCwMYocAAAbo8gBALAxihwAABu7oSJPTExURESEIiIi9Oabb1qVCQAAFJDbRZ6RkaH4+Hh99NFHSkxM1JYtW7R+/XorswEAgHy4XeQOh0NOp1MZGRnKyclRTk6OSpWybrEFAACQP7cXhAkICNDLL7+sVq1aqUyZMvrb3/6mBg0aWJkNAADkw+0z8h9//FELFy7UV199pbVr16pEiRKaOXOmldkAAEA+3D4j//bbbxUSEqJKlSpJkmJiYvTxxx8rNja2wGNUqhTg7uaLRF5rm3sjT+f1puPjTVk86VaaM86cLJXw9S/Uc7xtTnjb69Tb8sAabhd57dq19dZbbyk9PV1lypRRUlKSHnjggUKNcfZsmpxO424ESwUFBSolpehWLy+KCVSUea9U1MenMLwpi+TZX4632pyxfm10a+W1/974OvW2PLCG20XepEkT7dmzRzExMfLz89MDDzyg3r17W5kNAADk44aufta7d2/KGwCAYsTKbgAA2BhFDgCAjVHkAADYGEUOAICNUeQAANgYRQ4AgI1R5AAA2BhFDgCAjVHkAADYGEUOAICNUeQAANgYRQ4AgI1R5AAA2BhFDgCAjd1QkSclJSkmJkatWrXSqFGjrMoEAAAKyO0iP3r0qIYPH66pU6dqyZIl2rNnj9asWWNlNgAAkA9fd5+4atUqtW7dWnfccYckKSEhQaVKlbIsGAAAyJ/bZ+SHDx+Ww+FQnz59FB0drY8//lgVKlSwMhsAAMiH20XucDi0YcMGjR49WnPnztXOnTu1ePFiK7MBAIB8uP3W+p/+9CeFhISoYsWKkqRHH31UO3fuVExMTIHHqFQpwN3NF4mgoMDijlAons7rTcfHm7J4EnPGu+S3/952fLwtD6zhdpGHhoZq0KBBunjxosqVK6e1a9fqkUceKdQYZ8+myek07kawVFBQoFJSUot0fKsVZd4rFfXxKQxvyiJ59pcjc8a75LX/3vg69bY8sIbbRV6vXj3FxsaqS5cuys7O1sMPP6z27dtbmQ0AAOTD7SKXpA4dOqhDhw5WZQEAAIXEym4AANgYRQ4AgI1R5AAA2BhFDgCAjVHkAADYGEUOAICNUeQAANgYRQ4AgI3d0IIwKDhnTpaWToi2dDwAAChyDynh669D8dYtYVv9tYWSMi0bDwBgT7y1DgCAjVHkAADYGEUOAICNUeQAANgYRQ4AgI1ZUuRvvvmmBg8ebMVQAACgEG64yDds2KDFixdbkQUAABTSDRX5+fPnlZCQoD59+liVBwAAFMINFfmwYcPUv39/lS9f3qo8AACgENxe2W3+/Pm68847FRISokWLFrk1RqVKAe5uvkgEBQUWd4QCc+ZkWZrXmZOlEr7+eT7Gm46PN2XxJOaMd8lv/73t+HhbHljD7SJfvny5UlJSFB0drQsXLig9PV2jR4/W0KFDCzzG2bNpcjqNuxEsFRQUqJSU1CId30pFseRrXvtf1MenMLwpi+TZX47MGe9ilzkjeWceWMPtIp81a5br60WLFum7774rVIkDAIAbx9+RAwBgY5Zc/SwmJkYxMTFWDAUAAAqBM3IAAGyMIgcAwMYocgAAbIwiBwDAxihyAABsjCIHAMDGKHIAAGyMIgcAwMYocgAAbIwiBwDAxihyAABsjCIHAMDGKHIAAGyMIgcAwMZu6DKmkydP1ooVKyRJzZs318CBAy0JBQAACsbtM/L169fr22+/1eLFi/XZZ5/phx9+0KpVq6zMBgAA8uH2GXlQUJAGDx4sf39/SVKNGjV0/Phxy4IBAID8uV3k9913n+vr5ORkrVixQp988okloQAAQMHc8Ifd9u/fr169emngwIG69957LYgEAAAK6oY+7LZ161b17dtXQ4cOVURERKGfX6lSwI1s3nJBQYGur505WSrh61+MaTzv8v135/ue5E1ZPMmb58ytyE5zRvK+PLCG20V+4sQJvfDCC0pISFBISIhbY5w9myan07gbwVJBQYFKSUnNdftQfHvLxq/+2kLLxioql+//la48PsXJm7JInv3l6M1zpijG93Z2mTOSd+aBNdwu8pkzZyozM1Njx4513depUyd17tzZkmAAACB/bhd5XFyc4uLirMwCAAAKiZXdAACwMYocAAAbo8gBALAxihwAABujyAEAsDGKHAAAG6PIAQCwsRtaohUA/nB7BX/5+pcq7hgeZeUSrZcyc5R6MeNGI+EWRJEDsISvf6lbblnjqFcTLRtr6YRoec8CqrAT3loHAMDGKHIAAGyMIgcAwMYocgAAbIwiBwDAxm6oyJcuXarWrVsrLCxMc+bMsSoTAAAoILf//OzUqVNKSEjQokWL5O/vr06dOik4OFg1a9a0Mh8AAMiD22fk69evV6NGjXTbbbepbNmyatmypT7//HMrswEAgHy4XeSnT59WUFCQ63blypV16tQpS0IBAICCcfutdafTKR8fH9dtY0yu2wVRokThHl/UrszjWyHoOo90j7ePl9/Pw5t+Xt6UxZO8bb9v9TlT+fYylo5X1D9fb3v9wBo+xhjjzhMXL16sLVu2KD4+XpI0ZcoUGWP04osvWhoQAABcn9tvrTdu3FgbNmzQuXPnlJGRoS+++ELNmjWzMhsAAMiH22+tV6lSRf3791f37t2VnZ2tDh06qG7dulZmAwAA+XD7rXUAAFD8WNkNAAAbo8gBALAxihwAABujyAEAsDGKHAAAG6PIAQCwMY8Xubdc+jQtLU2RkZE6duyYpN8vAhMVFaWwsDAlJCR4NMvkyZMVERGhiIgIjRs3rtjzvPvuu2rdurUiIiI0a9asYs8jSW+++aYGDx4sSdq7d69iYmLUsmVLvfbaa8rJyfFolm7duikiIkLR0dGKjo7Wjh07ivR1zZy5GnOmYLxl3nh6ztxyjAedPHnShIaGml9//dX89ttvJioqyuzfv9+TEYwxxmzfvt1ERkaaOnXqmKNHj5qMjAzTvHlzc+TIEZOdnW169eplvv76a49kWbdunXniiSdMZmamycrKMt27dzdLly4ttjybNm0ynTp1MtnZ2SYjI8OEhoaavXv3FlseY4xZv369CQ4ONoMGDTLGGBMREWG2bdtmjDFmyJAhZs6cOR7L4nQ6TZMmTUx2drbrvqJ8XTNnrsacKRhvmTeenjO3Io+ekXvLpU/nzZun4cOHq3LlypKknTt36p577tHdd98tX19fRUVFeSxXUFCQBg8eLH9/f/n5+alGjRpKTk4utjx///vf9eGHH8rX11dnz56Vw+HQxYsXiy3P+fPnlZCQoD59+kiSfvnlF126dEn169eXJMXExHj0NXTo0CFJUq9evdSmTRvNnj27SF/XzJmrMWfy503zxtNz5lbk0SL3lkufxsfH66GHHvKKXPfdd59rciUnJ2vFihXy8fEp1uPk5+eniRMnKiIiQiEhIcV6fIYNG6b+/furfPnykq7+WQUFBXn02Fy8eFEhISGaMmWKPvjgA3366ac6fvx4kR0f5szVmDP586Z54+k5cyvyaJFbcenTouANufbv369evXpp4MCBuvvuu4s9T9++fbVhwwadOHFCycnJxZJn/vz5uvPOOxUSEuK6r7h/Vg8++KDGjRunwMBAVaxYUR06dNDEiROLLFNx7+/1eEMu5sy1edu88fScuRW5fdEUd9xxxx3asmWL63ZKSorrrbridMcddyglJcV129O5tm7dqr59+2ro0KGKiIjQd999V2x5Dh48qKysLP35z39WmTJlFBYWps8//1wlS5b0eJ7ly5crJSVF0dHRunDhgtLT0+Xj45Pr2Jw5c8ajP6stW7YoOzvb9UvSGKOqVasW2c+LOXNtzJnr87Z54+k5cyvy6Bm5t176tF69evr55591+PBhORwOLVu2zGO5Tpw4oRdeeEHjx49XREREsec5duyY4uLilJWVpaysLK1evVqdOnUqljyzZs3SsmXLlJiYqL59+6pFixYaM2aMSpUqpa1bt0qSEhMTPfoaSk1N1bhx45SZmam0tDQtXrxYb731VpG9rpkzV2PO5M3b5o2n58ytyKNn5N566dNSpUpp7Nixeumll5SZmanmzZsrPDzcI9ueOXOmMjMzNXbsWNd9nTp1KrY8zZs3186dO9W2bVuVLFlSYWFhioiIUMWKFYslz7WMHz9ecXFxSktLU506ddS9e3ePbTs0NFQ7duxQ27Zt5XQ61aVLFzVs2LDIXtfMmasxZ9xTXPPG03PmVsRlTAEAsDFWdgMAwMYocgAAbIwiBwDAxihyAABsjCIHAMDGKHIAAGyMIre57OxsNWnSRLGxscUdBbAF5gxuNhS5za1atUq1a9fW7t27dfDgweKOA3g95gxuNiwIY3PdunVT69attX//fuXk5GjEiBGSpBkzZmjBggUqV66cHnroIa1evVpJSUnKysrS+PHjtXnzZjkcDv3lL39RXFycAgICinlPAM9gzuBmwxm5jR04cEDbtm1TeHi42rZtq8TERP36669au3atFi1apAULFmjRokX67bffXM+ZMWOGSpYsqUWLFmnJkiWqXLmyxo8fX4x7AXgOcwY3I4+utQ5rffLJJwoNDdXtt9+u22+/XXfddZfmzZunlJQUhYeHu65F3LVrV23cuFGS9PXXXys1NVXr16+X9Pu/F1aqVKnY9gHwJOYMbkYUuU2lp6crMTFR/v7+atGihSQpLS1Ns2fPVkREhC7/F5PLL6fodDo1dOhQNW/eXJL022+/KTMz07PhgWLAnMHNirfWbWrp0qW67bbbtHbtWiUlJSkpKUlffvml0tPTVadOHX3xxRdKTU2VJC1YsMD1vCZNmmjOnDnKysqS0+nUv/71L7399tvFtRuAxzBncLOiyG3qk08+0VNPPZXrzKF8+fLq1q2bPvjgAz3++ON64oknFBMTo9TUVJUpU0aS9Pzzz6tq1apq166dWrduLWOMBg8eXFy7AXgMcwY3Kz61fhPatWuXtm3b5rre8KxZs7Rjxw698847xZwM8E7MGdgZRX4TSktL09ChQ3Xo0CH5+Pjozjvv1MiRI1WlSpXijgZ4JeYM7IwiBwDAxvg3cgAAbIwiBwDAxihyAABsjCIHAMDGKHIAAGyMIgcAwMb+P0i2nmAw6QLJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 508.35x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CHANGE FEMALE COLOR\n",
    "#Displays the Age range and amount of athletes at that range for each stroke\n",
    "sns.set(style=\"darkgrid\")\n",
    "#creates a small multiple by using FacetGrid\n",
    "g = sns.FacetGrid(df, hue=\"Gender\", col=\"Stroke\",col_wrap=2, height = 3)\n",
    "\n",
    "bins = np.linspace(0, 50, 10)\n",
    "g.map(plt.hist, \"Age\", bins=bins)\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 619.875x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This shows the rate at which swimmers are improving each year\n",
    "df = df.dropna()\n",
    "g = sns.FacetGrid(df,col=\"Gender\", hue=\"Stroke\", aspect=1.8, height=4, col_wrap=1)\n",
    "g.map(plt.plot, \"Date\", \"Time\")\n",
    "plt.subplots_adjust(top=0.9)\n",
    "g.fig.suptitle('Figure 2: World Record Progression')\n",
    "g.axes[0].get_xaxis().get_major_formatter().set_useOffset(False)\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Radam\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "C:\\Users\\Radam\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:720: UserWarning: Using the barplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b5bfa0add8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Displays the average age for each Category of sport in the Olympics\n",
    "grid = sns.FacetGrid(df2, col=\"Type\", col_wrap=5)\n",
    "bp = grid.map(sns.barplot,\"avg_age\",\"Sport\",\"Gender\")\n",
    "grid.axes[0].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the Regression for Time vs Age\n",
    "#Now make small multiple for each stroke\n",
    "df = df.dropna()\n",
    "g = sns.regplot(x=df[\"Age\"], y=df[\"Time\"], color=\"r\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   Time   R-squared:                       0.958\n",
      "Model:                            OLS   Adj. R-squared:                  0.958\n",
      "Method:                 Least Squares   F-statistic:                     2735.\n",
      "Date:                Fri, 14 Dec 2018   Prob (F-statistic):           5.83e-84\n",
      "Time:                        21:40:20   Log-Likelihood:                -368.30\n",
      "No. Observations:                 120   AIC:                             738.6\n",
      "Df Residuals:                     119   BIC:                             741.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Age            1.1057      0.021     52.300      0.000       1.064       1.148\n",
      "==============================================================================\n",
      "Omnibus:                        4.666   Durbin-Watson:                   0.636\n",
      "Prob(Omnibus):                  0.097   Jarque-Bera (JB):                2.512\n",
      "Skew:                          -0.060   Prob(JB):                        0.285\n",
      "Kurtosis:                       2.301   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "df = df.dropna()\n",
    "\n",
    "X = df[\"Age\"]\n",
    "Y = df[\"Time\"]\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "print(model.summary())\n"
   ]
  }
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