FinalProject_group002.ipynb

{
 "cells": [
  {
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   },
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<html> \n",
    "    <img src = \"Cover_photo.jpeg\"> \n",
    "</html>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp; Our project explores the athletes from Olympic Dataset from 1912 to 2016, where we attempt to identify the optical age range for athletes being sucessful. Specifically, we define being sucessful as whether or not an athlete won a medal. We split the original athletes dataset into two groups: athletes with medals and those without and subsequently plot data and perform statistical testing to try to determine the optimal age range. Next, we further define more specific optical age ranges for athletes with respect to sex and season using similar approach. Finally, we obtain multiple optical age ranges, where some are more general and others are more specific."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   },
   "source": [
    "# Names\n",
    "\n",
    "- Ruoyu Liu\n",
    "- Ting Yang Hung\n",
    "- Yijun Liu\n",
    "- Yiluo Qin\n",
    "- Yu-chieh Chen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "What is the optimal age range for being successful, for example getting medals, at Olympic Games?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp; All the members on our team are genuinely interested in sports. For example, I am particularly a big fan of soccer, and my favourite soccer team is FC Barcelona, a spanish soccer team with super stars like Lionel Messi. Another team member was a promising swimmer, and his idol has always been Michael Phelps, who is considered to be one the greatest athletes in Olympic history. Therefore, we came together and decided we want to discover something interesting regarding sports. We then decided to explore Olympic datasets because the Olympics symbolizes the pinnacle of almost all kinds of sports events. The Olympics also is considered a global celebration for all the athletes from the globe to compete and to achieve higher standards. We were very excited to further explore this dataset and find interesting results.<br>\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; Our hypothesis is that the optimal age to be successful at the Olympics is between 20 to 25 years old. Historically, according to many other popular sports such as basketball, soccer, swimming, and football. The blooming age of an athlete is around age 20. For example, most of the world records in swimming were set by swimmers in their early 20s. Additionally, most players who won their Ballon D’or(FIFA Best Player of the Year) were around 25 years old. As a result, we hypothesized that the most likely age range for an athlete to succeed at the Olympics is from 20 to 25 years old.<br>\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; Testing our hypothesis is important because we could predict how long we possibly need to wait to see a given athlete to succeed at the Olympics. For those who have already won medals in the past Olympics, we could approximately predict how many years left for him/her to maintain at the highest competitive level. Furthermore, assuming our hypothesis is correct, we could even tell our friends to train harder if they want to compete at the highest sports events! In general, our hypothesis would serve as an indicator predicting a lot of useful information in many fields of sports.<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp; From National Geographic Magazine<sup>[1]</sup>, I noticed that although the article agrees that the majority of athletes won gold medals in their early and mid 20s, there were some great exceptions that simply prove age really is just a number. The article uses Michael Phelps as a great example to show that even people who retire from the Olympics can make a great comeback. As the article writes: “Thirty-one-year-old Michael Phelps won his 20th and 21st gold medals as part of this year's Olympics in Rio de Janeiro—impressive feats for a swimmer his age.” What really determines an athlete’s success is not age but really is his/her talent and how much commitment he/she put into the games and training.<br>\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; According to TIME<sup>[2]</sup>, there is “no specific age limit to compete in the Olympic Games.” Age restriction varies according to sports; however, we actually see a surge of young athletes compete in recent Olympics games. For example, American snowboarder, Chloe Kim took home a gold medalist in the 2018 Winter Olympics at age 17! Being a young athlete and has achieved such high performance, no doubt she would shine in the next decade. Definitely she has a bright future in front of her. Therefore, based on such statistics, it is actually possible the optimal age is getting younger and younger. Or we might witness a longer span of optimalgolden age. Instead of only 5 years like we hypothesize, the actual optimal age span might be larger. <br>\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; With everything researched, we are very excited to delve into our dataset and find out the results ourselves.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- 1) Brady, Heather. “Meet the Olympians Who Prove Age Really Is Just a Number.” National Geographic, 11 Aug. 2016, www.nationalgeographic.com/news/2016/08/oldest-olympians-compete-games-2016-rio/#close.\n",
    "- 2) Quackenbush, Casey. “How Old Do You Have to Be to Compete in the Olympics?” Time, Time, 13 Feb. 2018, time.com/5154982/age-requirement-olympics-2018/.\n",
    "- 3) Rgriffin. “120 Years of Olympic History: Athletes and Results.” Kaggle, 15 June 2018, www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Under majority conditions, athletes get enough practice and have the physical conditions to achieve their peak. Therefore, we hypothesize the optimal age range is between 20 - 25 for athletes to be successful in Olympics games. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Dataset Name: athelete_events\n",
    "- Link to the dataset: https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results\n",
    "- Number of observations: 271116 observations, with a total of 15 attributes\n",
    "- athlete_events dataset:\n",
    "This dataset includes a comprehensive documentation of all the atheletes' records in the competition. The dataset includes crucial attributes for answering our question such as ID, Name, Age, Event, and Medal. From this information, we are able to investigate the golden age for the atheletes.\n",
    "\n",
    "\n",
    "- Metadata: "
   ]
  },
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   "source": [
    "| Column Name | Type | Description | \n",
    "|---|---|---|\n",
    "| `ID` | int | Unique number (id) for each athlete | \n",
    "| `Name` | str | The name of the athlete's |\n",
    "| `Sex` | str | M or F; The gender of the athlete | \n",
    "| `Age` | int | The age of the athlete (depend on the participated year) |\n",
    "| `Height` | float | The height of the athlete in centimeters |\n",
    "| `Weight` | float |  The weight of the athlete in kilograms |\n",
    "| `Team` | str |  Team name |\n",
    "| `NOC` | str |  National Olympic Committee in 3-letter code |\n",
    "| `Games` | str |  Year and season of the Olympics |\n",
    "| `Year` | int | The year of the olympics |\n",
    "| `Season` | str | Summer or Winter; Season type of the Olympics game |\n",
    "| `City` | str | Host city |\n",
    "| `Sport` | str | Sport type of the game that the athlete participated in |\n",
    "| `Event` | str | The game event | \n",
    "| `Medal` | str | Gold, Silver, Bronze, or NA; the prize that the athlete earned | "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from matplotlib.patches import Polygon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Describe your data cleaning steps here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight            Team  \\\n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0           China   \n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0           China   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN         Denmark   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN  Denmark/Sweden   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
       "\n",
       "   NOC        Games  Year  Season       City          Sport  \\\n",
       "0  CHN  1992 Summer  1992  Summer  Barcelona     Basketball   \n",
       "1  CHN  2012 Summer  2012  Summer     London           Judo   \n",
       "2  DEN  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  DEN  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  NED  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event Medal  \n",
       "0       Basketball Men's Basketball   NaN  \n",
       "1      Judo Men's Extra-Lightweight   NaN  \n",
       "2           Football Men's Football   NaN  \n",
       "3       Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4  Speed Skating Women's 500 metres   NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read csv\n",
    "athlete = pd.read_csv('athlete_events.csv')\n",
    "athlete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "# replace medal to integer (Ordinal Encoding)\n",
    "athlete['Medal'] = athlete['Medal'].replace({'Gold':3,'Silver':2,'Bronze':1,None:0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "# drop duplicate row\n",
    "athlete = athlete[athlete.duplicated()==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "# drop weight, height, games, team, city, id, and name columns\n",
    "athlete.drop(columns=['Weight','Height','Games','Team','City','ID','Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "# add a column called got_medal ( 0 means the person doesn't get the medal)\n",
    "athlete['got_medal']= athlete['Medal'].apply(lambda x: 0 if x == 0 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "# Get event title\n",
    "athlete['Event'] = athlete.apply(lambda x: ''.join([i+' ' \n",
    "                                        for i in x['Event'].split(' ')[len(x['Sport'].split(' '))+1:]]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex           object\n",
       "Age          float64\n",
       "NOC           object\n",
       "Year           int64\n",
       "Season        object\n",
       "Sport         object\n",
       "Event         object\n",
       "Medal          int64\n",
       "got_medal      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check type\n",
    "athlete.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1896    0.428947\n",
       "1900    0.405690\n",
       "1904    0.210607\n",
       "1906    0.428736\n",
       "1908    0.207559\n",
       "1912    0.038614\n",
       "1920    0.196878\n",
       "1924    0.198025\n",
       "1928    0.176403\n",
       "1932    0.108271\n",
       "1936    0.027848\n",
       "1948    0.157930\n",
       "1952    0.029600\n",
       "1956    0.099161\n",
       "1960    0.023931\n",
       "1964    0.005907\n",
       "1968    0.011261\n",
       "1972    0.008027\n",
       "1976    0.004951\n",
       "1980    0.020924\n",
       "1984    0.018640\n",
       "1988    0.007495\n",
       "1992    0.002681\n",
       "1994    0.000633\n",
       "1996    0.000581\n",
       "1998    0.000555\n",
       "2000    0.000072\n",
       "2002    0.000000\n",
       "2004    0.000000\n",
       "2006    0.000000\n",
       "2008    0.000147\n",
       "2010    0.000000\n",
       "2012    0.000000\n",
       "2014    0.000000\n",
       "2016    0.000000\n",
       "Name: isNull, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the means of the age in different years that are null\n",
    "athlete.assign(isNull = athlete['Age'].isna()).groupby('Year')['isNull'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# only keeps the year that is after 1908\n",
    "athlete = athlete[athlete['Year']> 1908]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# drop the columns that does not have age (age is np.NaN)\n",
    "athlete.dropna(subset=['Age'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "        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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "      <th>got_medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Extra-Lightweight</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>500 metres</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>1,000 metres</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex   Age  NOC  Year  Season          Sport               Event  Medal  \\\n",
       "0   M  24.0  CHN  1992  Summer     Basketball         Basketball       0   \n",
       "1   M  23.0  CHN  2012  Summer           Judo  Extra-Lightweight       0   \n",
       "2   M  24.0  DEN  1920  Summer       Football           Football       0   \n",
       "4   F  21.0  NED  1988  Winter  Speed Skating         500 metres       0   \n",
       "5   F  21.0  NED  1988  Winter  Speed Skating       1,000 metres       0   \n",
       "\n",
       "   got_medal  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "4          0  \n",
       "5          0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athlete.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Optimal Age Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'People')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig3, ax1 = plt.subplots(figsize=(12,4))\n",
    "ax1.boxplot(athlete['Age'], vert=False, flierprops=green_diamond)\n",
    "ax1.xaxis.grid(True, linestyle='-', which='major', color='lightgrey',alpha=0.5)\n",
    "ax1.set_title('Age Distribution')\n",
    "ax1.set_xlabel('Age')\n",
    "ax1.set_ylabel('People')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age 37.5 percentile: 23.0\n",
      "Age 62.5 percentile: 26.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Age 37.5 percentile:\", np.percentile(athlete['Age'], 37.5, axis = 0))\n",
    "print(\"Age 62.5 percentile:\", np.percentile(athlete['Age'], 62.5, axis = 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<b> Majority of participants in Olympics have age range from 23 - 26. <b>\n",
    "    <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "got_medal\n",
       "0    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: Age, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "athlete.groupby('got_medal')['Age'].plot(kind='kde',legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25.84854547420785, 25.34127480081237, 0.5072706733954782)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "got = athlete[athlete['got_medal']==1]['Age'].mean()\n",
    "no_got = athlete[athlete['got_medal']==0]['Age'].mean()\n",
    "got,no_got, got - no_got"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> We find that the distributions of athletes who get medals and those don't get medals look very similar. However, the average age still have a difference about 0.5. Thus, we want to check if the 0.5 difference is a significant difference. We use t-test package and permutation test to double check if 0.5 is a significant difference. <b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Overview of Age Distribution between medals and no medals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Medal data**: plot the comparison of the data and a normal distribution\n",
    "\n",
    "This plots a histogram, with the hypothetical normal distribution (with same mean and variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "athlete_age_medals = athlete[athlete['got_medal']==1]['Age']\n",
    "xs = np.arange(athlete_age_medals.min(), athlete_age_medals.max(), 0.1)\n",
    "fit = stats.norm.pdf(xs, np.mean(athlete_age_medals), np.std(athlete_age_medals))\n",
    "plt.plot(xs, fit, label = 'Normal Dist.', lw = 4)\n",
    "plt.hist(athlete_age_medals, density = True, label = 'Actual Data');\n",
    "plt.title('Age With Medals')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**No medal data**: plot the comparison of the data and a normal distribution\n",
    "\n",
    "This plots a histogram, with the hypothetical normal distribution (with same mean and variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "athlete_age_no_medals = athlete[athlete['got_medal']==0]['Age']\n",
    "xs = np.arange(athlete_age_no_medals.min(), athlete_age_no_medals.max(), 0.1)\n",
    "fit = stats.norm.pdf(xs, np.mean(athlete_age_no_medals), np.std(athlete_age_no_medals))\n",
    "plt.plot(xs, fit, label = 'Normal Dist.', lw = 4)\n",
    "plt.hist(athlete_age_no_medals, density = True, label = 'Actual Data');\n",
    "plt.title('Age Without Medals')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average age of people who get medal is \t\t\t 25.85 inches\n",
      "Average age of people who does not get medal is \t 25.34 inches\n"
     ]
    }
   ],
   "source": [
    "# Print out the average age for the two groups \n",
    "print('Average age of people who get medal is \\t\\t\\t {:2.2f} inches'.format(athlete_age_medals.mean()))\n",
    "print('Average age of people who does not get medal is \\t {:2.2f} inches'.format(athlete_age_no_medals.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> From the above plots, we found that both athletes' ages with medals won and those with no medals won have a normal distribution. Therefore, for the later t-test, we can be assure that the resulting p value will deterministically tell us to either reject our null hypothesis. Later, we printed the means for both ditributions and figured out that there was a roughly 0.5 age difference. We will later perform the t-test to test if the age difference is significant or not. <b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Permutation Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=14.789012546725141, pvalue=1.8083165169764167e-49)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(athlete_age_medals, athlete_age_no_medals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> In addition, we did a t-test to see whether there is a difference in age distribution between athletes who won medals and those who did not. In this case, our null hypothesis stating that there is no difference in age distribution between athletes who won medals and athletes who did not. What we got for our p-value is close to zero, which suggests us to reject our null hypothesis and turns for our alternative hypothesis that there is a difference between the age distribution be between athletes who won medals and athletes who did not. <b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#### Hypothesis testingn for observed mean\n",
    "n_repetitions = 500\n",
    "\n",
    "means = []\n",
    "for i in range(n_repetitions):\n",
    "    # shuffle the gender column\n",
    "    shuffled_col = (\n",
    "        athlete['Age']\n",
    "        .sample(replace=False, frac=1)\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "    \n",
    "    # put them in a table\n",
    "    shuffled = (\n",
    "        athlete\n",
    "        .assign(**{\n",
    "            'Age': shuffled_col,\n",
    "            'medal_not': athlete['Medal'] == 0\n",
    "        })\n",
    "    )\n",
    "    \n",
    "    # compute the differences in means\n",
    "    mean = shuffled.groupby('medal_not')['Age'].mean().diff().abs().iloc[-1]\n",
    "    \n",
    "    means.append(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs = (athlete.loc[athlete.Medal!= 0].Age.mean() - athlete.loc[athlete.Medal == 0].Age.mean())\n",
    "pval = np.mean(np.array(means) > obs)\n",
    "pd.Series(means).plot(kind='hist', density=True, alpha=0.8, title='p-value: %f' % pval)\n",
    "plt.scatter(obs, 0, color='red', s=40);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Permutation Test Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "After we getting our data, we observed that there is an approximately 0.6 years old differences beween athletes who won medals and athletes who did not. Therefore, we want to discover whether there is a difference in age between these two groups, or more specifically, the 0.6 years old difference is meaningful.\n",
    "\n",
    "* Null hypothesis -- there is no difference in age between athletes who won medals and athletes who did not.\n",
    "* Alternative hypothesis -- there is a difference in age between athletes who won medals and athletes who did not.\n",
    "\n",
    "\n",
    "After deciding our Null Hypothesis and Alternative Hypothesis, we perform a permutation test, choosing a significance level = 0.05. This permutation test gives us a p-value = 0.0, which is smaller than our significance level, meaning we need to reject our null hypothesis, and concluding that there is a difference in ahe between athletes who won medals and athletes who did not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Optimal Age Ranges for Medal vs No Medal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "age_medal = athlete[athlete['got_medal'] == 1]['Age']\n",
    "age_no_medal = athlete[athlete['got_medal'] == 0]['Age']\n",
    "data = [age_medal, age_no_medal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1a2482f510>,\n",
       "  <matplotlib.axis.YTick at 0x181ed7b750>],\n",
       " <a list of 2 Text yticklabel objects>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig3, ax1 = plt.subplots(figsize=(12,4))\n",
    "ax1.boxplot(data, vert=False, flierprops=green_diamond)\n",
    "ax1.xaxis.grid(True, linestyle='-', which='major', color='lightgrey',alpha=0.5)\n",
    "ax1.set_title('Medal vs. Age')\n",
    "ax1.set_xlabel('Age Distribution')\n",
    "ax1.set_ylabel('medal vs. no medal')\n",
    "plt.yticks(np.arange(1,3), ('has_medal','no_medal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "medal's 37.5 percentile: 24.0\n",
      "medal's 62.5 percentile: 27.0\n",
      "no medal's 37.5 percentile: 23.0\n",
      "no medal's 62.5 percentile: 26.0\n"
     ]
    }
   ],
   "source": [
    "print(\"medal's 37.5 percentile:\", np.percentile(age_medal, 37.5, axis = 0))\n",
    "print(\"medal's 62.5 percentile:\", np.percentile(age_medal, 62.5, axis = 0))\n",
    "\n",
    "print(\"no medal's 37.5 percentile:\", np.percentile(age_no_medal, 37.5, axis = 0))\n",
    "print(\"no medal's 62.5 percentile:\", np.percentile(age_no_medal, 62.5, axis = 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The optimal age range for getting `medals` is 24 - 27."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "athletes who do not get medals\n"
     ]
    },
    {
     "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>F</td>\n",
       "      <td>23.545034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>26.068144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age\n",
       "Sex           \n",
       "F    23.545034\n",
       "M    26.068144"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "athletes who get medals\n"
     ]
    },
    {
     "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>F</td>\n",
       "      <td>24.473167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>26.445810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age\n",
       "Sex           \n",
       "F    24.473167\n",
       "M    26.445810"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender = athlete[athlete['got_medal']==False].groupby(['Sex'])['Age'].mean().reset_index()\n",
    "gender_medal = athlete[athlete['got_medal']==True].groupby(['Sex'])['Age'].mean().reset_index()\n",
    "print('athletes who do not get medals')\n",
    "display(gender.set_index('Sex'))\n",
    "print('athletes who get medals')\n",
    "display(gender_medal.set_index('Sex'))\n",
    "fig, axes = plt.subplots(1,2)\n",
    "axes[0].bar(gender['Sex'],gender['Age'])\n",
    "axes[1].bar(gender_medal['Sex'],gender_medal['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Here, the bar chart indicates that on average, within those two groups -- athletes with medals and athletes without, we found that males tend to have a higher average age than females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-30.791569409443806, pvalue=1.3531323792160763e-205)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## for with medals\n",
    "with_medals = athlete[athlete['got_medal']==1]\n",
    "stats.ttest_ind(with_medals.loc[with_medals['Sex'] == 'F']['Age'].dropna(),\n",
    "                with_medals.loc[with_medals['Sex'] == 'M']['Age'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-88.08056048165984, pvalue=0.0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## for without medals\n",
    "without_medals = athlete[athlete['got_medal']==0]\n",
    "stats.ttest_ind(without_medals.loc[without_medals['Sex'] == 'F']['Age'].dropna(),\n",
    "                without_medals.loc[without_medals['Sex'] == 'M']['Age'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> From the t-test, we notice that, no matter for athletes with or without medals, the age distribution between Sex has a difference. In this case, our null hypothesis states that there is no difference in age distribution between females and males. What we got for our p-value are both close to 0, no matter for athletes with medals or those without medals. Therefore, in both groups (athletes with medals or athletes without medals), we conclude that there is a differences in age distribution between females and males. <b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1fb43350>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z5P7y2AXg4W2uwAH8a0G728zShplpxT8XCeBl12mceUO42mDm/uWw6DloNhQGve7s1hSu0z1mubo/X6yYi0YLxzsSCcrTdKFcLryPGay8OCAX1P++fACzQL0O5oq+qIyqXT+aCT+9n4YzSRC3qPj27V0AYdeYC5wC3R40x9j6dfHPRQJ42dUIN0uHbf0a/jrsGt0BqXHwwz1Qq4Wp7e2q9WY8POD6D8y0/j9edHZrREVwZAPUaQO+Vf/+2Pl+8IvSCRMiwdMH6rYv/Hj1OkJ+tplmX5itM03+eL8XIKC+SZ8tSmqcKVFb0H1SoE4b07ZNX5R4BS8B/Ep0exBOH4UP28G7zWHOWFj3kUlXys1ybFsy02D27aZ75445hb9RXUlwS7jmcdg5x31ntgr3kJ9r+pgv7/8uULmGWcjk4n7whE1Qt0PRJa/rdjBfC+tGObbdLPzQaZwZMO18j1nA5uT+wo+1d7752ryQevzdHzIJE3t/L/y5VhLAr0SzwfDgehj+H5OOlLwblvwTZgyB/wuBL/rDoufNFPJTCfa7Ss/Lge/vMgtljJ5tJie4g95Pm0qF8580kxiEsIeknZB3rvD+7wLhfU03S26W+Xs6tu3v6YMXq9EIfKsVHsC3zjQls9vdZr7vdDd4eBU9mBm70FzRV6v/98eaDYHq4SWmFDpxdoebq93a3Lreb77PSDEDJgmbzG3LVxBp/eFXrWvKUob3Mf+VPb2v/vxaw4In4fBauPmL4t90rsbbz2TJfH0DrJoCA19xdotEeVSwgENoMQE8rDds+MSkuHpXNt0jxf0tKWUdyLysJkrOWdj5I7S6ESpVN9uq1jHdI9tnQf+XLl0w/MxxEy+ufanw83h4mglwi58rNvVWrsBtxT/Y/LIGvQYTFsELCXD/Chg2xSyekLQdFj4N/xsIqbFXf74Nn5iB1D7PQLvbr/54jtaoL7S/w9RpSY52dmtEeXRkg8nlDqhb9D4Ne5pBzoOrL0zgKSwD5WL1OloHMrMvbIueBzlnoPO4S/eNuNcsyxb9y6XbY62Dm5f3f1+swx0mbXFj0SmFEsDtxdMb6ncyyzHdOh0e3wWjvjWrgUztY34pV1qeNnYx/PlPaDUS+v3Dtu12pMH/At8AU6/FXUv1CtektbWAVRH93wX8AkxALgjggQ2KD/hgrsAtuZdeeGyZafLKLz9feB9TCOvybpS9C0yZi+CWxbet010X0hILIQHckVpeDw9uMP1ui5+Db28qez758d3w873mTXTj5yazw11VqQlD/m0+vm4pxaQHIUor7YCZIFNc/3eB8D6mm+LQuqLTBy92+YzMlBjzHu50998n/ygFERPM48et0/azz5jMlxYjSl7SsOvEYkt3uPFfv5uqWtvUJxnxgVmy6b89zCLDpZGRAt+NNpkmo78Dn8r2basjtB9t/qEtfc29Kj0K13Z+AYcSrsDBBHBLHmSeKF0AD2xo+rkLaoNv/dpMxmk/pvD9248xg5sFKYXxyyA/p/Dsk8vVCIeRnxT5sARwZ1DK1FV5YA0ENTdX1D9NKH7mVW6WSVc8ewLGfFfyxzx3oZQZ0MzPgUXPOrs1orw4stEsSBzUrOR9Q7uZ3G8wyQYlUcpchR/bZv4ud3wHLUdAlaDC969cA9rcAjt/MDM19y4w6wuU5p8FmNnfRZAA7kw1G8P4RdD/n7DnV/hvz8Jzo7WG3x4xH8NunnbhI1x5UbOxGYyN+e3C4I4QV6NgAYfSdDH6VDYDl96VzSSc0qjbwXSd7P7JDFKWVLoi4l5TMnb7LNj3h5kxbYMSzxLAnc3TC/o8DfctMwN639wEC5+FnMwL+6z+j5mi2/+f0OoG57XVnno+BrVawoKnzUxNIYqjNSx4CuY9YAYIk/dcGAg/ewJO7itd/3eBAS/D9R8WGlRPZRYybb5eR9Ptsux106US3q/449fvZGZ3Lv+XKbBVXPZJGUgAdxX1OsCkVWYG1qapJlPl6FaTnrTiX9BuNPR+ytmttB8vH/MHdDoRVrzp7NYIV3d4vVmGLOZ3mP+EGUt6Owy+uRkWP2/2KU3/d4EG3QpNx91z7DSd/7WUVXGplz5Q8Ck4I9lkipR0pa+UuQrPyQCvSmaVLBuQAO5KvCvB0P+Du381xWymD4K5k0xf2Q0flTxi7e4adDMj9pGfm39eQhRl42dQqQY8vQ8e3Qo3TYW2t5iAuusnM1uyYNr7VVi0O4l8i2bhzssG2KuFQOUgk0Peoeg+6ku0vdUUrWoywGYJCDIT0xU16mem6i96zuSajppl1uWsCAa8AnsXwu+Pwf0rnbsUnHBNaQfNQGDvJ00grNnY3NqPNo9nnTYr5xRVz6QMluwxizKsiE3BYtF4eFgvopQyA5OW3NInFPhUgQl/mkFNG5G/DldVKRBuroBLkFUKhGFvw4/jTCmCno86u0XC1Wz6wkw173Jf4Y/7BQABV32ahLRM9h4/Q5v6Aew+eproY6dpG3JR2dfhU8p+0OAWV92ui0kXinA9rUaaUfoV/zYle4UokHXa5F23vgkC6tn1VH9ar75fH9kGpWDZ3lIskeZgEsCF61HKVHpEmfoxrlBzXbiG7bNNzZFuD9r9VEv2HKdZbX86NahOh9BAVuxNsfs5y0oCuHBNgaHQ/0XY96fJNhDCkm8GuEO7QUhnu57qVGYOmw/9xcCWtQEY0CKYHYnppJxxcL3/EkgAF66r6yTTlbLwmdKXGxDlV9wf8NdB6G7/q+8VsSnkWzSDWpkAfm2LYABWxqYW9zSHkwAuXJenF9z2lcnnnTcJ9i11douEM238DAJCoMX1dj/Vkj3JBFf1pX1IIACt6gZQJ8CP5TGu1Y1SYgBXSoUqpVYopWKUUtFKqckXPfaoUirWuv0KhmSFKIF3JbNUXHBL+P5Os3qKqHiO74JDa6DbRLunlmbn5bMqNpUBLWufTxtUStG/ZTBr9qWSnVd0dUBHK80VeB7wlNa6JdAdeFgp1UopdS0wEmintW4N/MeO7RQVmV81uHOeyTqYfZspqSsqlo2fm1olJdUcsYH1+09yNiefwdbukwL9mwdzNiefzQf/Kvb5Ww6ncf3HazmQav+SECUGcK11ktZ6q/X+GSAGqA88CLyltc62PuZany1E+eJfC+7+BbyrmHoxaQec3SLhKBmpsOsHs0JNwXJldrR0TzKVfTzp0bjmJdt7NQnC18uj2HRCi0Xz8q/R7Dqazsu/RqPtnEFVpj5wpVQY0BGIBJoBvZVSkUqpVUqpQuswKqUmKqWilFJRqamuNQAg3ExgA7hrnpn99vWNUj+8ooiabsoNd3vA7qeyWDRLY5Lp07QWft6elzxWyRrUl+9NKTIwz9t2lOhjp+ndNIi18SdYsMu+79FSB3CllD/wM/C41vo0ZhZndUy3yjPAD0r9vViH1nqa1jpCax1Rq1YtGzVbVFjBLWDsz6bi3Lc3F19DXbi/vGyTRtp0MAQ1tfvpdh1NJ/l09vnsk8sNaBHM4ZOZHDhx9m+PncvJ5z9/xtIupBoz7ulC63oBvDF/DxnZeXZrb6kCuFLKGxO8Z2mtCxZoSwTmamMTYAGKqGguhA2FdIYxs+FkPMweZVYEv1IJm83g6JRG8P1dsGOO/FNwJdu+MUuj2SB18PDJs4yZtpFftxe9jOGSPcl4eij6W9MGL1eQTljYpJ4Z6w6SlJ7FP4a3xNvTg3/d2IaUM9l8uDTuqttelNJkoShgOhCjtX7vood+Afpb92kG+AAn7NFIIf6mUT+4ZTocjTIB+OIVwktisZhiSNOHwPSBcHCNOV7iZpOu+E4T+PI6WP8JnNxvn/aLkp1JhqWvQ1jvqy6/ui/5DLd9voENB07yxPfb+X3HsUL3WxqTTETD6lSv4lPo4yHVK9O8dlWWXZZOmHomm89WxDOoVW26NzJ95x0bVGd0l1BmrDvE3uOnr6jdf53N4YFvthT5eGmuwHsBdwH9lVLbrbfhwAygkVJqNzAHGKft3WMvxMVa3QDXf2RWMZpb/OKvgFn+KupL+LQLzLkDzhyDoW/DE9Fw6wx4Yg/cv8JUucs6BX++CB93gk+6wtJXIWHThUUDhP0tft5UFRzxwVWVUt59NJ1R0zaigXkP9SSiYQ0e/347i3cfv2S/guJVRXWfFOjfMpjNh9I4nZV7ftuHy+LIzrPwwrBLi1U9O6QFAX5evPxL2Qc018efYOiHq4sdNC1NFsparbXSWrfTWnew3hZqrXO01ndqrdtorTtprQtZC0wIO+t0Fwx6A/b8AgueLLxuSmYarJoCH7SB+Y+Dj78J2I9ug+4PgK+/2c/Dw6yc0v8leHAdTN5hAnzV2rDuI1Of/d1m8OsjpuTtxasmCdvatwSi55rVqoKaXPFhthxOY8wXG6nk7cmPk3rQsUF1ZozvQruQajz63VaWxVwIjgXFq0oM4C2CybNo1sSZDof4lDN8tymBsd0a0KiW/yX7Vq/iw/PDWrDpUBpztxbddXOxnDwLby3ay9jpkVTx8WLeQ72K3FfKyQr31+sxOJcGa983Rf4HvmK2px00s/e2fWsWyGg62CzdFnZN6a7oqoeZAN/9AbPu4b6lELvQrF+67RuTl9xsiKmM12SQzYr0V3g5Z2H+k2ZB4l6TS96/COviT3DfzCjqVPPj2/u6UT+wEgD+vl58Nb4rd/4vkge/3cr/xkXQp1mt88WrGtasUuxxO4YGEljZm2V7k7muXV3+b+FeKnt78tiAwgdZb+scyvebE/j3whgGtqxNtcreRR774ImzTJ6zjZ2J6YzpGso/R7Sisk/RYVoCuCgfBrxiguza98xahaeOmEWSlSe0GwU9HzGzOa9UperQ7jZzy8uBw2vNcl57fjPL3nlXgeZDodWN0HSQmUEqrszK/4P0I2bB7ytcyGTpnmQemr2V8JpV+Oa+rgRXvXRxh2qVvPnm3q6M+SKS+7+O4oNRHdh86C8e6NuoxGN7eXrQt1ktVsWmsnbfCZbtTeH5YS2o6V94Wz08FG/c2IbrP17Lf/6M5Y0b/75wssWi+WlrIq/+Fo23pwef39mJoW1KXihCObLbOiIiQkdFRTnsfKKCseTDTxNMd4pvNYgYb3KHS7tiypXIzzPBPPoX8w8j86Tpomk21HplPtAmK8NUGEk7YVo/6DgWbvj4ig4xd2siz/60k1b1Apg5vmuRA5IAJzOyGfPFRuKSzazJeQ/1pGODkicL/br9KJPnbCfI3wdfL0+WPdX3b3njl3v1t2hmbjjEs0NakJGdy7FTWRw7dY5j6ec4np5Fbr6me6MavD+qA3WrXXoBoJTaorWOuPyYEsBF+ZKXA/FLIbw3+FZ17Lnz80y9juh55ur8XBr4VIXmw6D1jdB4gATz4ljy4X8DID0RHtlc5lmXFovm3SWxfLpiP90b1eCLuyOo6ld0d0WB1DPZjJq2gZw8C6ufufbCsmnFOJWZQ6c3lmDR8OHoDozsUL/E55zOymXQe6tIPp2Nl4eidoAf9QMrUTfQj3qBlWheuyrXt6+HZyHnlwAuhCPl514WzP8ywbztLdDvBahax9ktdD0bP4fFz5n00La3lumpmTl5PPn9DhZHH2d0l1BeH9kGH6/STzQ/l5PP2Zw8goroBinM3TM2cTY7jx8n9ShV0AcT+LNyLdSq6ltooC6KBHAhnCU/Fw6ugt3zYOf34OljUhV7PCx95QXSE+HTbmaxhjt/LlPa4PH0LO77ejPRx07z4vCW3HtNOIVMCre5rFyTtlpS14ktFBXApR64EPbm6W36wm/8FB6OhMbXwvI3TH757rmyZFxeDvz2qOlCGfFemYL3zsRT3PDJWg6mnmX6uAju693IIcEbTOB2RPAujgRwIRypZmMYPQvG/W7K5P40HmYMhaNbnd0y58g9B9+PNZOxhr1lUjdLaeGuJG77fAM+Xh7MfagX/VsUn79dHkkAF8IZwvvApFVmJmnafvjiWpj3AJwufIp3uZSdAbNvN5N2rv8QOt9T6qcmpGUyec42WtcL4JeHe9G8joMHrF2EBHAhnMXDEzqPg0e3Qq/HYffP8HFnM2u0vM/yzEqHb2+BQ+vgpqllCt4A7y+Jw0MpPhvbuUwDj+WNBHAhnM0vAAa9Bg9vMpOAVrwJn3QxCzmXx/7xzDT4etqDVAkAACAASURBVKQpRHbbl9B+VJmeHnv8DPO2H+WenmHUqVax0zIlgAvhKmqEw+1fwz0LoXIN+PlemD7Y1F1xxRK3aQfhxD5IP2rSJPNzS35ORirMvB6S98Do2dBqZJlP+84fsfj7evFgv8ZX0OjyRabSC+FqwnrBxJWw4ztY9jrMGWO2BzUzaXYFt6CmV1Wl74olboGV/zYTpi7n4W1qwvj4m1oxl99P2gkZyXDH9yYbp4y2HE5jaUwyzwxpTmDlomdYVhQSwIVwRR6e0PFOaHOr6WpIiDTlbPfON4W0wMxUDO0GoV0htDvU62jfglrHtps6JXGLTdGw/v+EwIaQe9YUoMrJtN7PNN9ffD/zBJzKBJ8qJs+7Yc8yn15rzduLYwny92V8rzDbvz43JAFcCFfm7WeqJ4ZdY763WMxKRAmRF25xi81jHl5Qp50J6o36Qnhf2wT047tg5Vvmn4dfIAx4GbpOdHipglVxqWw6mMbrI1sXW6GvIpGZmEK4u8w0s5pQQiQciYSjWyDvHHj5mXTFZkOg6RAIDC3bcVNizBX3nl9NcbCej0C3SSZ/3cEsFs2Ij9dyJjuXZU/2K9M0+fKgqJmY8m9MCHdXuYYJ0s2GmO/zcuDIeoj7w1ydL/gTeAqCW1v3GwohEaabpjCpcbDqLTNL1Mcf+jwLPR4qc3EpW1qwK4k9Sad5f1T7Che8iyNX4EKUdyfiTSCPWwxHNph66ZVqmAUumg2Bxv2hUqBZ/3PVFNj1A3hVMlfbPR81/yDs7KctiSSdOsdNneoTUv3Sbp/cfAuD3luFn7cnCx7rXaYiUOWFXIELUVEFNYGgR0wXyLlTZtp63B+w70/YOcfad97WZIh4+kCPR8xKOFWCHNK8vHwLr/4WTUZ2Hu8tjeOaJkHc2jmEIa3r4OftyQ9RCRw6mcn/7o6okMG7OBLAhahIKgVCm5vNzZJv+svjFsPBNeaK+5onwD/YoU3akXiKjOw8XrquJRnZefwYlcjkOdsJ8PPihg71WLInmc4NqzOgpWPb5Q4kgAtRUXl4WlMQuzq1GWv3nUQpuKVTCNWr+PBY/6ZsPHCSH6IS+DEqkew8Cx+N7uiwKoPuRAK4EMKp1sWfoG39aueXPvPwUPRsEkTPJkG8di6XhLRM2tR3fOaLO5DhXCGE02Rk57H1yF/0alJ4f3u1St4SvIshAVwI4TSbDp4kz6K5pogALoonAVwI4TRr953E18uDzg2dl2PuziSACyGcZm18Kl3Dazh9aTJ3JYOYosyy8/K5+bP1/HU2h7Yh1WgXEki7kGq0rV9NKsSJUks5nUVccgY3dwpxdlPclgRwUWZfrz9M9LHTDGgRTFxyBn9EJ59/rEGNyrQLqWYN6IG0qR9AVT9vJ7ZWuKp1+08ASP/3VZAALsrkZEY2Hy3fR7/mtZh+TxcA0jNz2X0snZ2J6ew6eoptR04xf2cSYMpVNwqqcv4qfVSXUKkkJwBYs+8E1St706pugLOb4rbkL0mUyQdL95GZk89L17U8v61aZW96NQm6JBXsZEY2u46msysxnR2J6azff4J5244Sn5LBmze1dUbThQvRWrMu/gQ9mwThIdPjr5gEcFFqcclnmBV5mDu7N6RJcPG1oGv6+9KveTD9ml+Y/vzsTzv4eWuirKYi2J+aQfLpbOk+uUqShSJK7V8LYvD39eLxgc2u6PkTrgknK9fCd5sSbNwy4W7W7JP+b1uQAC5KZUVsCqvjUnlsQFNqVLmyq+cWdQLo1aQmX284RG6+xbYNFG5lXfwJGtasTGgNOy4BVwFIABclys238OaCGMKDqnB3j7CrOtaEXuEkpWexePdx2zROuJ3cfAsbD6QVOX1elF6JAVwpFaqUWqGUilFKRSulJl/2+NNKKa2Ukt9GOTU78gjxKRn8Y3jLq14N5drmwYTVrMyMdQdt1DrhbnYkmPKx0n1y9Urz15gHPKW1bgl0Bx5WSrUCE9yBQcAR+zVROFN6Zi7vL42jZ+OaDLRBPWYPD8X4XuFsO3KKrUf+skELhbtZG38CpaBn45rOborbKzGAa62TtNZbrffPADFAfevD7wPPAo5bl0041EfL95F+LpeXrmtls3rMt3YOoaqfFzPWylV4RVRQPlYyka5emT4PK6XCgI5ApFLqBuCo1npHCc+ZqJSKUkpFpaamXnFDheMdSM1g5vpDjIoIpVU92022qOLrxeguoSzafZxjp87Z7LjC9WVk57HtyCnp/7aRUgdwpZQ/8DPwOKZb5UXg5ZKep7WeprWO0FpH1KpV64obKhzv3wv34uftyVODm9v82ON6hqG15usNh21+bOG6Ig+Y8rG9JYDbRKkCuFLKGxO8Z2mt5wKNgXBgh1LqEBACbFVK1bFXQ4VjrYs/wdKYZB66tjG1qvra/Pgh1SsztE0dvtt0hMycPJsfX7imtfEn8PXyoJOUj7WJ0mShKGA6EKO1fg9Aa71Lax2stQ7TWocBiUAnrbXkhpUD+RbNG/P3EFK9EhN6hdvtPBN6hZN+Lpe5W4/a7RzCtayLPyHlY22oNFfgvYC7gP5Kqe3W23A7t0s40Q9RCew9foYXhrW06x9a54bVaRdSjS/XHcRikXHw8i4p/RxxyRnS/21DpclCWau1VlrrdlrrDtbbwsv2CdNan7BfM4WjnMnK5d0/Y+kSVp3hbe3bI6aUYkKvcPannmX1PhngLs/+OpvD/V9H4e2pGNiytrObU27ITExxic9W7udERo5N0waLM7xtXYKr+jJdUgrLrRMZ2Yz5YiNxyRlMvaszTYL9nd2kckMCuDgvIS2T6WsOcnOn+rQPDXTIOX28PLi7R0PW7DtBXPIZh5xTOE7K6SxGT9vIoZNnmT4ugv4t5OrbliSAi/PeWrQXTw/Fs0NaOPS8d3RriK+XB1+uO+TQ8wr7OnbqHLdP3cCxU+f4anxXejeVNGJbkwAuANh8KI0Fu5KY1LcRdar5OfTcNar4cHOn+szdmshfZ3Mcem5hHwlpmdw+dQMnM3L45t6udG8k0+btQQK4wGLRvP77HuoE+DGxTyOntGF8r3Cy8yzM3iRlddzdoRNnGTV1A2ey8ph1fzc6N6zh7CaVWxLABfO2HWXX0XSeG9bcaetVNqtdld5Ng6RWuJuLT8ng9qkbyMqzMPv+brQLccxYSkUlAbyCy8zJY8ofe2kfUo2R7euX/AQ7mtArnOTT2SzcleTUdogrs/f4aUZP24BFw3f3d6d1vWrOblK5V64C+JGTmbwxf49kM5TB1FUHSD6dzT9HtHL64rJ9m9WiUVAVZqw9iNYysced7D6azuhpG/H0UHw/qTvN6xS/ZqqwjXIRwE9l5vCv+XsY8N5Kpq89yKu/RTu7SW4hKf0cU1fvZ0S7ukSEOb+f0tQKD2NHYrrUCncj2xNOcccXG6ni48UPk3rQuJbkeTuKWwfw7Lx8/rfmAH3fWcn0dQe5qWN9HuzXmPX7TxJ1KM3ZzXN5UxbHYtHw/DDHpg0W5+ZOIQT4eTFj7SFnN0WUQtShNO78XySBlX34flJ3Gtas4uwmVSjOGbG6SlprFu46ztuL93IkLZPeTYP4x/CWtKwbwLmcfH7YnMBHy+P5ekJXZzfVZW1POMW8bUd5qF9jQqq7zsKyVXy9GNOtAV+sPkDiX5ku1TZxqfX7T3DfzCjqBPgx+/7uDk8/FW54Bb7lcBo3/3c9D8/eSmUfT2ZO6Mo393ajZV2z4EAlH0/u692I1XGpbE845eTWuiatTbXBIH9fHrq2ibOb8zd39whDKcU3Uivc5aSczmJW5GHGzdjE3dM3UT+wEnMmSfB2Fre5Aj904ixvL97Lot3HCa7qy5Rb2nFL5xA8Cxl4u6tHQ6au3s/Hy/Yx/Z4uTmita5u/M4kth//i7Vva4u/rem+B+oGVztcKf2xAU6q4YBsrkv2pGfwZncyfe46z7Yi5KGpYszITrgnngb6NqVFFlkZzFpf/y0g7m8NHy/bx7cbD+Hh58MTAZtzfJ7zYfGV/Xy/u7RXOu0vi2H00nTb1JZ2pQFZuPm8t2kurugHc2jnU2c0p0oRe4SzYmcTcrYnc1SPM2c2pUCwWzY7EU/y5J5k/o4+zP/UsAO1CqvH04GYMbl2HpsH+Dil2JornsgE8KzefmesP8cmKeM5m5zGqSwOeGNSU4Kql+6g2rlcY09Yc4JPl8Xx+V2c7t9Z9TF97kKOnzvHObe0K/fTiKjo1CKR9aCBfrjvE2G4NnZ7iWN7l5FnYcOAkf0YfZ8meZFLOZOPloejeqCbjeoYxsGVt6gVWcnYzxWVcLoBbLJrfdx5jyuJYjp46x7XNa/HC8JY0q122vNIAP2/G9wzjo+XxxB4/I3mpQMqZLD5bEc/gVrXp2di1i+qbWuFhTJ6znefn7uTpwc0JDpB+VlvTWvPz1qP838IYTp7NobKPJ/2a12Jwqzpc2zyYapW9nd1EUQyXCuCRB07y74Ux7EhMp1XdAKbc2u6qVu+YcE0409ce5OPl+/jkjk42bKl7evePOHLyLfxjeEtnN6VUrmtbl21HTvHtxsP8tuMYE3qFM6lvY6pVkqBiC/EpZ3hx3m4iD6bRuWH1839vstyZ+3CJAL4/NYO3Fu1lyZ5k6gT48e5t7bmpY/2r/tgcWNmHu3uG8fmq/TyeklGhC8lHH0vnhy0J3NsrnLAg98jV9fL04NUbWjO+VxjvLYnjs5X7mRV5hAf7NeaenmESaK5QVm4+nyyPZ+rq/VT28eKtm9tye0SodFO5IeXIKcsRERE6Kirq/PcnM7L5cNk+ZkUewc/Lg4eubcKEXuFU8rHdH+bJjGyueXsFw9rU4b1RHWx2XHeitWbMFxuJPX6Glc9c67ZXsNHH0nnnj1hWxqZSO8CXxwc247bOIXh5ul02rNOsjE3h5V+jOZKWyc2d6vOP4S0J8vd1drNECZRSW7TWEZdvd8oVeFZuPjPWHeSzFfs5l5vPmK6hTB7QjFpVbf9Gqunvy9huDZix7iCPDWjqNleftvTnnmQ2HkjjjZGt3TZ4A7SuV42vxndl44GTTFm8lxfm7uKL1Qd4anBzhrWpI1eQxUg+ncXrv+9hwa4kGtWqwuz7u7n8OIgomcOvwP8x7Rf+80csx9KzGNiyNs8Pa2H3ro2U01n0nrKCkR3qMeXW9nY9l6vJzstn8Pur8fH0YNHk3uXmalVrzdKYFN75Yy9xyRm0rV+NZ4c2l1VfLpNv0Xyz4RD/+dOMfzx6bRMm9m2Er5d0P7kTl7gCj0/J4MkfdtC2fjXevb0DPRo7ZpWO4AA/xnRtwLcbD/No/6aE1qg407O/Xn+Ywycz+Wp8l3ITvMFkqQxqVZv+LYKZt+0o7y+J467pm+jVpCaTBzQjuKoveRYLOXmaPIuF3HxNXr6FPIsmN99CXr7ZnmPdXr2yD/2a1ypXuc07E0/x4rzd7DqaTu+mQbwxsk2F/ARanjn0Ctw/pLmeNX8517er5/CPu0np5+g7ZSW3RoTw75vaOvTcznIyI5t+/1lJ54bV+Wp8+a4Lk52Xz6yNR/hkRTxpV7gs2+0RIbx5U1u83fwf3emsXN79I5avNx4myN+XV65vxXVt65arf06llZubS2JiIllZWc5uSqn4+fkREhKCt/elXZ0ucQXevHZVRnZwzqIBdatV4taIEH6KSuTR/k2oW638T0r4YOk+MnPyeek690gbvBq+Xp5MuCac27uEsiwmmXyLxsvTA28PZb56Krw9PfC66HsvD+tXTw/mbU3ko+XxJKVn8dnYTlT1c6+xgrSzOazYm8KyvcmsjjvB2Zw87u7ekKeGNCfAzV6LLSUmJlK1alXCwsJc/h+Y1pqTJ0+SmJhIeHh4qZ7j0ADu7J/fg30b88PmBKauOsCrN7R2bmPsLC75DLM3HWFstwY0Ca44k5j8fb2u6CLhycHNCalemX/M28Vtn2/gy/FdXPqfvNaa+JQMlsaksCwmma1H/sKiIbiqL9e3r8uYrg1kOTMgKyvLLYI3mG7BmjVrkpqaWurnuEQeuKOE1qjMzZ3q892mIzzUr3G5ntn3rwUxVPHx5PGBzZzdFLdxe5dQ6lTz46FZW7np0/V8Ob7L+SqXriA338Kmg2ksjUlmWUwKR9IyAWhdL4BH+jdlYMtg2tSrJtk4l3GH4F2grG2tUAEc4KF+TfhpSyLTVh/gpRGtnN0cu1gRm8LquFReuq6lVIoroz7NavHDpB5M+Gozt32+gc/GdqJPs6vPbLFYNH9EH2dHYjo+Xh74Wm8+l3z1xMfTA19vD+tX831s8mmWxqSwOjaVM9l5+Hh50KtxTSb2acSAlsEu/UlB2FeFC+BhQVUY2aH++Rl9NcvZJIbcfAtvLoghPKgKd0sVvyvSql4A8x7uyfgvNzPhq8382zpT8UrkWzQLdyXx8fJ9xCVn4OWhyLOUPXEgyN+X4W3rMqBlMNc0DSq2GqewjTfffJPZs2fj6emJh4cHU6dOpVu3bs5u1iUq5Lvg4Wub8Mv2o/xv7UGeG+o6y4nZwuzII8SnZDDtrs74eLl3NoUz1a1WiR8f6MFDs7by7E87SUzL5IlBzUr9ETffolmwK4mPl+1jX0oGTYP9+XhMR4a3rYuHgpx8Czl5FrLzLv+af+n9XAt1AyvRrr50jTjShg0bmD9/Plu3bsXX15cTJ06Qk3Nl2U32VCEDeJNgf65rW5ev1x9iYu9GVC8n3Qzpmbm8vzSOno1rMqhVbWc3x+1V9fNmxj1d+MfcXXy0PJ7Ev87x1i3tiv3HmG/RzN95jI+XxxNvDdyf3NGR4W3qXhKAfb088fXypOIML7uXpKQkgoKC8PU1n9CDgsys1S1btvDkk0+SkZFBUFAQX331FbVq1aJHjx6888479OvXjxdeeAEPDw/efPNNu7ezwl6iPdK/CWdz8vly3UFnN8VmPlq+j/Rzubx0XSu3GrhxZd6eHky5tR1PDmrG3G1HuefLTaSfy/3bfvkWza/bjzL4/VVMnrMdDwWf3tGJPx7vwwgnzHsQV2fw4MEkJCTQrFkzHnroIVatWkVubi6PPvooP/30E1u2bGHChAm8+OKLeHl58dVXX/Hggw+yZMkSFi9ezCuvvOKQdlbIK3CAFnUCGNq6Dl+uP8S9vRu5dY0QgAOpGcxcf4hREaG0quc6mRPlgVKKxwY0pX5gJZ77eSe3fb6eL8d3pX5gJfItmt93HOOj5fs4kHqW5rWr8tnYTgxtLbVZ3Jm/vz9btmxhzZo1rFixglGjRvHSSy+xe/duBg0aBEB+fj5169YFoHXr1tx1111cf/31bNiwAR8fx3yqr7ABHMxV+OLo48xcf4jHBjR1dnOuyr8X7sXP25OnBjd3dlPKrVs6h1Cnmh8PfLOFmz5dx6S+jZkVeZgDqWdpUacq/x3biSESuMsNT09P+vXrR79+/Wjbti2ffvoprVu3ZsOGDYXuv2vXLgIDA0lOTnZYGytsFwpAm/rVGNAimBnrDpKRnefs5lyx9fEnWBqTzEPXNrZLRUdxQa8mQfz0YE+8PBRvzN+Dj6cHn9/ZiYWP9WZY27oSvMuJ2NhY9u3bd/777du307JlS1JTU88H8NzcXKKjowGYO3cuJ0+eZPXq1Tz22GOcOnXKIe0s8QpcKRUKfA3UASzANK31h0qpd4DrgRxgPzBea+2YVtvQowOacuOn65i6ar9bXr3mWzSvz99DSPVKTOhVuum34uo0r1OV3x69hr1JZ+jZuKYE7XIoIyODRx99lFOnTuHl5UWTJk2YNm0aEydO5LHHHiM9PZ28vDwef/xxateuzfPPP8+yZcsIDQ3lkUceYfLkycycOdPu7SyxmJVSqi5QV2u9VSlVFdgC3AiEAMu11nlKqbcBtNbPFXesyxd0cBWT52xj0e7jLH2iLw1qulelwu82HeGFubv49I5OXNeurrObI4RLiYmJoWVL96oFVFibiypmVWIXitY6SWu91Xr/DBAD1Nda/6m1Luh32IgJ6G7phWEtzUfiBXuc3ZQyOZOVy7t/xtIlrDrD29ZxdnOEEA5Wpj5wpVQY0BGIvOyhCcCiIp4zUSkVpZSKKkuRFkeqU82PR/o3YcmeZFbFuWYbC/PZyv2cyMiRtEEhKqhSB3CllD/wM/C41vr0RdtfBPKAWYU9T2s9TWsdobWOqFXLdVdLufeacMJqVua136PJybM4uzklSkjLZPrag9zcsT7tQ6XqnBAVUakCuFLKGxO8Z2mt5160fRwwAhirHbkyhB34enny8vWtOJB6lq/Wu/7knrcW7cVTKZ4Z6n4Dr0II2ygxgCvz2Xw6EKO1fu+i7UOB54AbtNaZ9mui4/RvYZbo+nDpPlJOu+4KHpsPpbFgVxKT+jaSSnRCVGCluQLvBdwF9FdKbbfehgOfAFWBJdZtn9uzoY7y8ohW5OZr3lq819lNKZTFonlj/h7qBPgxsU8jZzdHCOFEJeaBa63XAoWNkC20fXOcLyyoCvf1DuezlfsZ260BnRvWcHaTLjFv21F2Jqbz/qj2UlJUCDfg6elJ27YX1uH95ZdfCAsLs8mxJQIU4uFrmzB361Fe/W0PvzzcC08XmaiRmZPHlD/20j6kGiPbO2dtUSFE2VSqVInt27fb5dgSwAtRxdeLF4a3YPKc7fwQlcCYrg2c3SQApq46QPLpbD69o5PM/hOijF77PZo9x06XvGMZtKoXwCvXO2993QpdC6U4N7SvR9ewGrzzRyzpmX8vH+poSennmLp6P9e1q0tEmGt16wghinbu3Dk6dOhAhw4duOmmm2x6bLkCL4JSildvaM2Ij9fw/tI4p69iP2VxLBYNz5ezFYSEcBRnXSnbswtFrsCL0apeAGO7NeSbjYfZe9y2H73KYnvCKeZtO8p914QTWsO9arUIIexHAngJnhrcjAA/L175NRpnzFXS2qQNBvn78tC1TRx+fiGE65IAXoLAyj48PaQ5kQfN5BlHm78ziS2H/+KZIc3w95UeLyHEBRLAS2F0lwa0rhfAmwtiyMxx3MIPWbn5vLVoL63qBnBr51CHnVcIYTsZGRl2O7YE8FLw9FC8dkNrktKz+GzFfoecU2vNlMWxHD11jpdGtHSZXHQhhOuQAF5KEWE1uKljfaatPsDhk2ftei6tNe/+GceMdQcZ16MhPRsH2fV8Qgj3JAG8DJ4f1gJvT8Ub82Psep4Pl+3jkxXxjOka6tRJAkII1yYBvAxqB/jx6ICmLI1JZmVsil3O8fGyfXywdB+3dQ7hzRvbyoxLIUSRJICX0YRe4TQKqsLrv++x+Ur2n62M590lcdzcsT5v3dJOgrcQolgSwMvIx8uD10a25tDJswx5fzXr4k/Y5LhfrD7AlMWxjOxQj3duay+DlkKIEkkAvwK9m9bixwd64uvlwdj/RfLSL7s4exVX4zPWHuTNhTFc164u70rwFqJcUUpx1113nf8+Ly+PWrVqMWLEiKs+tgTwK9S5YXUWTu7NfdeEMyvyCEM/XM2G/SfLfJyvNxzi9fl7GNq6Dh+M6oCXp/xKhChPqlSpwu7duzl37hwAS5YsoX5925SDlql9V8HP25OXRrRiSJs6PPPjDsZ8sZFxPRry3LAWpVpsYVbkYV7+NZpBrWrz0ZiOeEvwFsJ+Fj0Px3fZ9ph12sKwt0rcbdiwYSxYsIBbb72V7777jjFjxrBmzZqrPr1EDBvoElaDRZP7ML5XGF9vPMzQD9aw6WBasc/5fvMRXpy3m/4tgvnkjo74eMmvQojyavTo0cyZM4esrCx27txJt27dbHJcuQK3kUo+nrxyfWuGtq7DMz/tZNS0DYzvGc4zQ5pTycfzkn1/jErg+bm76NusFp+N7YSvl2cRRxVC2EwprpTtpV27dhw6dIjvvvuO4cOH2+y4ctlnY90a1WTx4725u3tDZqw7yPCP1hB16MLV+LxtiTz78056NQ5i6l2d8fOW4C1ERXDDDTfw9NNPM2bMGJsdU67A7aCyjxevjWzDkDZ1ePanndw2dQP39gqneZ2qPPfzTrqH1+SLuyMkeAtRgUyYMIFq1arRtm1bVq5caZNjSgC3o56Ng1j8eB/+b2EM/1t7EICu4TWYfk/E37pVhBDlW0hICJMnT7bpMSWA25m/rxdv3tSWYW3qsiI2hScGNStVhooQonworJxsv3796Nev31UfWyKJg1zTNIhrmkpVQSGE7cggphBCuCkJ4EKIcs0Za9leqbK2VQK4EKLc8vPz4+TJk24RxLXWnDx5Ej8/v1I/R/rAhRDlVkhICImJiaSmpjq7KaXi5+dHSEhIqfeXAC6EKLe8vb0JDw93djPsRrpQhBDCTUkAF0IINyUBXAgh3JRy5OisUioVOOyAUwUBtlnrzD3J66/Yrx/kZ1DeXn9DrXWtyzc6NIA7ilIqSmsd4ex2OIu8/or9+kF+BhXl9UsXihBCuCkJ4EII4abKawCf5uwGOJm8flHRfwYV4vWXyz5wIYSoCMrrFbgQQpR7EsCFEMJNuUUAV0rNUEqlKKV2X7StvVJqg1Jql1Lqd6VUgHX7IKXUFuv2LUqp/hc9p7N1e7xS6iOllHLG67kSZfkZXPR4A6VUhlLq6Yu2DVVKxVp/Bs878jVcjbK+fqVUO+tj0dbH/azb3fI9UMa/AW+l1Ezr9hil1AsXPcddf/+hSqkV1tcTrZSabN1eQym1RCm1z/q1unW7sv5+45VSO5VSnS461jjr/vuUUuOc9ZpsQmvt8jegD9AJ2H3Rts1AX+v9CcAb1vsdgXrW+22Aoxc9ZxPQA1DAImCYs1+bPX4GFz3+M/Aj8LT1e09gP9AI8AF2AK2c/drs8B7wAnYC7a3f1wQ83fk9UMbXfwcwx3q/MnAICHPz339doJP1flUgDmgFTAGet25/Hnjben+49fergO5ApHV7DeCA9Wt16/3qzn59V3pziytwrfVqIO2yzc2B1db7S4BbrPtu01ofs26PBvyUUr5KqbpAgNZ6gza/ya+BG+3fXGNXxQAABLhJREFUetsoy88AQCl1I+bNGX3R/l2BeK31Aa11DjAHGGm3RttQGV//YGCn1nqH9bkntdb57vweKOPr10AVpZQXUAnIAU7j3r//JK31Vuv9M0AMUB/T/pnW3WZy4fc5EvhaGxuBQOvvfwiwRGudprX+C/NzG+rAl2JTbhHAi7AbuMF6/zYgtJB9bgG2aa2zMb/sxIseS7Ruc2eF/gyUUlWA54DXLtu/PpBw0ffu/jMo6j3QDNBKqT+UUluVUs9at5e390BRr/8n4CyQBBwB/qO1TqOc/P6VUmGYT9qRQG2tdRKYIA8EW3cr6rWWi59BAXcO4BOAh5VSWzAfqXIuflAp1Rp4G5hUsKmQY7h7DmVRP4PXgPe11pcvh13efgZFvX4v4BpgrPXrTUqpAVSc198VyAfqAeHAU0qpRpSD16+U8sd0DT6utT5d3K6FbNPFbHdLbrugg9Z6L+ajMkqpZsB1BY8ppUKAecDdWuv91s2JwMVLXYQAx3BjxfwMugG3KqWmAIGARSmVBWzh0k8qbv0zKOb1JwKrtNYnrI8txPQff0s5eg8U8/rvABZrrXOBFKXUOiACc+Xptr9/pZQ3JnjP0lrPtW5OVkrV1VonWbtIUqzbEyn8tSYC/S7bvtKe7bYnt70CV0oFW796AC8Bn1u/DwQWAC9ordcV7G/9eHVGKdXdmnlwN/CrwxtuQ0X9DLTWvbXWYVrrMOAD4N9a608wg15NlVLhSikfYDTwm1MabwNFvX7gD6CdUqqytR+4L7CnvL0Hinn9R4D+1kyMKphBvL248e/f+vuaDsRord+76KHfgIJMknFc+H3+Btxt/Rl0B9Ktv/8/gMFKqerWjJXB1m3uydmjqKW5Ad9h+vNyMf9B7wUmY0ai44C3uDCr9CVM/9/2i27B1sciMP2G+4FPCp7jDrey/Awue96rWLNQrN8Pt+6/H3jR2a/LXq8fuBMzgLsbmHLRdrd8D5Txb8Afk30UDewBnikHv/9rMF0dOy/6ux6OyTBaBuyzfq1h3V8Bn1pf5y4g4qJjTQDirbfxzn5tV3OTqfRCCOGm3LYLRQghKjoJ4EII4aYkgAshhJuSAC6EEG5KArgQQrgpCeCi3LLmAK9VSg27aNvtSqnFzmyXELYiaYSiXFNKtcHkRHfEVOPbDgzVF2boXskxvbTWeTZqohBXTAK4KPesJQXOAlWAM1rrN6x1oB/GlFVdDzyitbYopaZhpt1XAr7XWr9uPUYiMBVTue4DrfWPTngpQlzCbWuhCFEGrwFbMcWeIqxX5TcBPbXWedagPRqYjaktnWadgr9CKfWT1nqP9Thntda9nPEChCiMBHBR7mmtzyqlvgcytNbZSqmBQBcgypTYoBIXSoyOUUrdi/nbqIdZNKAggH/v2JYLUTwJ4KKisFhvYOpkzNBa//PiHZRSTTH1RbpqrU8ppb4F/C7a5axDWipEKUkWiqiIlgK3K6WCAJRSNZVSDYAA4Axw+qLVW4RwWXIFLiocrfUupdRrwFJrKdZc4AEgCtNdshuzHN26oo8ihPNJFooQQrgp6UIRQgg3JQFcCCHclARwIYRwUxLAhRDCTUkAF0IINyUBXAgh3JQEcCGEcFP/DzLQIg/ozWMUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender = athlete[athlete['got_medal']==True].pivot_table(columns = 'Sex',index='Year', values='Age',aggfunc = 'mean')\n",
    "gender.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a201c1190>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender = athlete[athlete['got_medal'] == False].pivot_table(columns = 'Sex',index='Year', values='Age',aggfunc = 'mean')\n",
    "gender.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> From the above plots, we observed that as the years pass by, the average age differences between female athletes and male athletes are shrinking, no matter for the athletes won medals or not. <b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ffeead0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_diff = athlete.groupby(['Year','Sex'])['Age'].mean().reset_index()\n",
    "age_diff.groupby('Year')['Age'].diff().dropna().to_frame().set_index(age_diff.Year.unique()).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a202006d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_diff = athlete[athlete['got_medal']==False].groupby(['Year','Sex'])['Age'].mean().reset_index()\n",
    "age_diff.groupby('Year')['Age'].diff().dropna().to_frame().set_index(age_diff.Year.unique()).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a254b2310>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_diff = athlete[athlete['got_medal']==True].groupby(['Year','Sex'])['Age'].mean().reset_index()\n",
    "age_diff.groupby('Year')['Age'].diff().dropna().to_frame().set_index(age_diff.Year.unique()).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "These two plots further justify the age difference between sex is decreasing in the 2 groups (athletes with medals and athletes without medals)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Optimal Age Ranges for Female vs Male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "age_female = athlete[(athlete['got_medal'] == 1)&(athlete['Sex']=='F')]['Age']\n",
    "age_male = athlete[(athlete['got_medal'] == 1)&(athlete['Sex']=='M')]['Age']\n",
    "data = [age_female, age_male]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1a24820510>,\n",
       "  <matplotlib.axis.YTick at 0x1a2494aa50>],\n",
       " <a list of 2 Text yticklabel objects>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dKQf+AnhpZt4BEBEvBrYAx0/XwCRJkqRuUTeUPyMz9w1vZOanIuKaaRqTJEmS1FXqXuh5VER8MCI+BxARTwROm75hSZIkSd2jbii/BPg88AvV9p3AudMxIEmSJKnb1D5TnpkfB34GkJl7gX2tHyJJkiSpjrqh/McRcSSQABHxdOAH0zYqSZIkqYvUvdDzD4ArgF+MiC8DC4Ezpm1UkiRJUhdpeaY8Ip4aEYsz86vAs4HXAw8CVwF3z8D4JEmSpIPeRMtXPgD8tLq/Gvg/wF/S+KueF03juCRJkqSuMdHylZ7M3Fnd/1Xgosy8HLg8Im6e3qFJkiRJ3WGiM+U9ETEc3J9L4694Dqu7Hl2SJElSCxMF60uBayLifuAnwBcBIuJx+OkrkiRJUlu0DOWZ+daI+AKNPxp0VWZmdWgO8L+ne3CSJElSN5hwCUpmfqXJvjunZziSJElS96n7x4MkSZIkTRNDuSRJklSYoVySJEkqzFAuSZIkFWYolyRJkgozlEuSJEmFGcolSZKkwgzlkiRJUmGGckmSJKkwQ7kkSZJUmKFckiRJKsxQLkmSJBVmKJckSZIKM5RLkiRJhRnKJUmSpMIM5ZIkSVJhhnJJkiSpMEO5JEmSVJihXJIkSSrMUC5JkiQVZiiXJEmSCjOUS5IkSYUZyiVJkqTCDOWSJElSYYZySZIkqTBDuSRJklSYoVySJEkqzFAuSZIkFWYolyRJkgozlEuSJEmFGcolSZKkwgzlkiRJUmGGckmSJKkwQ7kkSZJUmKFcXW9gYICImPEbGxYU6bfObWBgoPS0SJLUVXpLD0AqbdeuXWTmzHe8YUGZfmuIiNJDkCSpq3imXJIkSSrMUC5JkiQVZigvyCUCklqp8x4REURfTFgbERy5+MhadXXamzNnDtEXzJkz/o+R3t5eoi/o7W29UnJgYIDom/hahjVr1hB9wZo1a1rWHXbYYURfcNhhh7WlvaGhIQaPHWRoaGjcmk2bNtE7r5dNmzZN2NYJTz2hZVuTae/ss88mDgnOPvvslnUHgzrzoM7hfE1BZk7LDdgH3DzqNjiNfZ0FvHeqj1+5cmW2044dO2rVNV5+Tac6c1FsHi44vEy/NbT7Nan7PaH9TTQPQDKX5PnV13Hq210XEfvVRcQjanp6evar6enpadpWf3//fnX9/f1N61avXr1f3erVq5vWzZ8/f7+6+fPnH1B7W7ZsyXkL5iXPJ+ctmJdbtmx5RM3GjRv3a2vjxo1Tbmsy7a1fv36/uvXr1zetOxjUfe0mw/el6TPZ+eqmuQC25nh5drwDB3oDdk9X2036MpSrKUP51BjKO0OreRgJ0GeSbKi+NgnS7a4bCeRj6kYH85FAPqZmbDAfCeRj6sYG85EAPaZubJAeCeRj6sYG87rtjQSLUXVjA8ZIgB7T1tggXaetybQ3EsjH1B2MwbzuazdZvi9Nj6nMVzfNRatQPqPLVyKiJyL+PCJuiIhbIuJ3qv0nR8Q1EfHxiLgzIt4eEa+IiOsj4taI+MWq7n9GxHURcVNE/FNELGrSx8KIuLzq44aIaP17SUmaZSIC5gIvB5ZVO5dV23MfXvbS7ro5c+aQvdm0LnuTOXPm0Nvby745+5rW7Juzb2Qpy8DAALt272pat2v3rpGlLGvWrOHaG65tWnftDdeOLD057LDD2P3g7qZ1ux/cPbKUpW57Q0NDnHr6qew5bc9+dXtO28Opp5/K0NAQmzZt4rzzz2va1nnnnzey9KROW0Dt9s4++2zed/H7mta97+L3HVRLWeq+duoMzteBiUZon4aGI/YBt1abd2Xm6RHxauDnM/MtEXEI8GXgpcBS4DPAE4CdwLeAizPzgog4B1iWmedGRD/wQGZmRLwKeEJmnhcRZwGrMvM1EfFR4K8y80sRcQzw+cx8QpPxvRp4NcAxxxyzcvv27W177jt37qz1Oc+uKe8c0/V90NKGBbDhBzPfbw3+2+wczf5tRl/AyUCzUw5fBq6G/GkWqQM6dmyTqRs8dpDty7aPW7f0rqXc/Z272XfSvnFrer7Uw949e2u1te0b2+id11urvTgk4NktnsM1kA925setTlbd124q6v6sVn1Tna9umouIuDEzVzU7Np1nyn+SmSdWt9OrfS8AfjMibgauA44Ejq2O3ZCZ92Tmg8A3gauq/bcCg9X9JcDnI+JW4LXA8ib9Pg94b9XHFcDhEfGIq30y86LMXJWZqxYuXHjAT3aqxvsVhrf23Hbs2FFn+ZOamOl58DaJf5sP0QjAd43Zf1e1/6HpqYu90bIu9gY9P+tpWdPzsx4A+uf3t6zrn98PwOqnrm5Zt/qpqwGYf8j8lnXzD5k/qfY2X7SZedfPa1o37/p5bL5oMxe+5cKWbV34lgtrtwXUbm/9K9e3rFv/yvUcLOq+duoMztcBmsYfJo9YUw5cDrywyf6TgStHbV9N48z3fseq/WtH7b+6un8W1Zpy4H7g0MmM1TXlBy/XlE9Nu1+Tblov2E6t5gHXlLum3DXlB9Su70vTwzXlrdEpF3rSWC7yGWButf144FGTCOU3ASur+5vHCeUfBV47qq0TJxqrofzgZSifGkN5Z5hoHkaCtJ++MsJPXzk4+ekrs4ufvjK+Tgrlc4C30ViSchswBCyYRChfR2O9+ReBPx8nlB8FfAy4BbgDeP9EYzWUH7wM5VNjKO8MdeZhJEjXDfBtqhsO5s0C+bDhYD5eIB82HMzHC+TDhoP0eAF62HAwHy+QT7a9LVu25NLHLW0ZLDZu3Jg9h/aMG6BHt7XksUsmDCl121u/fn3Sd3AH8mF15mEyfF+aXpOZr26ai1ahfNou9JxNVq1alVu3bm1be5O50NPXf3rVmYti89DhF3q28zXppot42sl5ODg5D53Beegc3TQXpS701AQM5JJa8T1CkrqHoVySJEkqzFAuSZIkFdZbegBSJyjxx3LygsM79o/09Pf3lx6CJEldxVCurldy3W5uKNa1JEnqIC5fkSRJkgozlEuSJEmFGcolSZKkwgzlkiRJUmGGckmSJKkwQ7kkSZJUmKFckiRJKsxQLkmSJBVmKJckSZIKM5RLkiRJhRnKJUmSpMIM5ZIkSVJhhnJJkiSpMEO5JEmSVJihXJIkSSrMUC5JkiQVZiiXJEmSCjOUS5IkSYUZyiVJkqTCDOWSJElSYYZySZIkqTBDuSRJklSYoVySJEkqzFAuSZIkFWYolyRJkgozlEuSJEmFGcolSZKkwgzlkiRJUmGGckmSJKkwQ7kkSZJUmKFckiRJKsxQLkmSJBVmKJckSZIKM5RLkiRJhRnKJUmSpMIM5ZIkSVJhhnJJkiSpMEO5JEmSVJihXJIkSSrMUC5JkiQVZiiXJEmSCjOUS5IkSYUZyiVJkqTCDOWSJElSYZGZpcdQXER8H9jexiaPAu5vY3uaOueiMzgPncF56AzOQ2dwHjpHN83F0sxc2OyAoXwaRMTWzFxVehxyLjqF89AZnIfO4Dx0BuehczgXDS5fkSRJkgozlEuSJEmFGcqnx0WlB6ARzkVncB46g/PQGZyHzuA8dA7nAteUS5IkScV5plySJEkqzFAuSZIkFWYoP0AR8aGIuC8ibhu1byAi/jEivlF97S85xm4QEY+JiKGI+FpE3B4R51T7nYsZFBE/FxHXR8S/VvPwxmr/soi4rpqHj0VEX+mxdoOI6ImImyLiymrbeSggIrZFxK0RcXNEbK32+d40wyLiiIj4ZET8W/Wz4hnOw8yKiOOq74Ph2w8j4lznocFQfuAuAV40Zt/5wBcy81jgC9W2ptde4LzMfALwdOB3I+KJOBcz7UHgOZl5AnAi8KKIeDrwZ8A7qnnYBbyy4Bi7yTnA10ZtOw/l/PfMPHHUZzH73jTz3gV8LjOPB06g8b3hPMygzPx69X1wIrAS2AN8GucBMJQfsMz8Z2DnmN3rgA9X9z8MnDajg+pCmXlPZn61uv8jGm+2R+NczKhs2F1tzq1uCTwH+GS133mYARGxBPhl4OJqO3AeOonvTTMoIg4HngV8ECAzf5qZD+A8lPRc4JuZuR3nATCUT5dFmXkPNMIi8POFx9NVImIQeDJwHc7FjKuWTNwM3Af8I/BN4IHM3FuV3E3jP0yaXu8E/gj4WbV9JM5DKQlcFRE3RsSrq32+N82sxwLfBzZXS7oujohH4TyU9GvApdV95wFDuQ4yETEfuBw4NzN/WHo83Sgz91W/mlwCPA14QrOymR1Vd4mIU4H7MvPG0bublDoPM2NNZj4FOIXG0rpnlR5QF+oFngK8LzOfDPyYLl0i0Qmq61nWAp8oPZZOYiifHvdGxC8AVF/vKzyerhARc2kE8o9k5qeq3c5FIdWvhq+mscb/iIjorQ4tAb5balxdYg2wNiK2AZfRWLbyTpyHIjLzu9XX+2isn30avjfNtLuBuzPzumr7kzRCuvNQxinAVzPz3mrbecBQPl2uAM6s7p8J/F3BsXSFar3sB4GvZeamUYecixkUEQsj4ojq/qHA82is7x8CzqjKnIdplpmvy8wlmTlI41fEWzLzFTgPMy4iHhURhw3fB14A3IbvTTMqM78HfDsijqt2PRe4A+ehlJfx8NIVcB4A/6LnAYuIS4GTgaOAe4ELgM8AHweOAf4DeGlmjr0YVG0UEScBXwRu5eE1tK+nsa7cuZghEfEkGhfp9ND4T//HM/NNEfFYGmdsB4CbgF/PzAfLjbR7RMTJwB9m5qnOw8yrXvNPV5u9wEcz860RcSS+N82oiDiRxoXPfcC3gN+iep/CeZgxETEP+Dbw2Mz8QbXP7wcM5ZIkSVJxLl+RJEmSCjOUS5IkSYUZyiVJkqTCDOWSJElSYYZySZIkqTBDuSR1sIg4PSIyIo5vY5sbIuI7EXFzRHwjIj4VEU8cdfzi0dtNHn9WRDy6xfE3RcTzqvvbIuKoSYxtMCJePmp7VUS8u+7jJWm2MpRLUmd7GfAlGn8EqJ3ekZknZuaxwMeALRGxECAzX5WZd7R47FlA01AeET2Z+YbM/KcpjmsQGAnlmbk1M39vim1J0qxhKJekDhUR84E1wCsZFcojYk5E/FVE3B4RV0bEP0TEGdWxlRFxTUTcGBGfH/7T1a1k5seAq6jCcERcXZ2h7omISyLitoi4NSJ+v+pnFfCR6kz7odXZ8DdExJeAl1aPOWNUF6+NiOur2+OqPvariYjd1d23A8+s2v79iDg5Iq6sagYi4jMRcUtEfKX6Y1XDZ/4/VI37WxFhiJc06xjKJalznQZ8LjPvBHZGxFOq/S+mcUb5l4BXAc8AiIi5wHuAMzJzJfAh4K01+/oqMHaJzInA0Zm5IjN/CdicmZ8EtgKvqM60/6Sq/c/MPCkzL2vS9g8z82nAe4F3TjCO84EvVm2/Y8yxNwI3ZeaTaPzF3r8Zdex44IXA04ALqtdCkmYNQ7kkda6XAcMh97JqG+Ak4BOZ+bPM/B4wVO0/DlgB/GNE3Az8CbCkZl/RZN+3gMdGxHsi4kXAD1s8/mMtjl066uszao6nmZOAvwXIzC3AkRGxoDr295n5YGbeD9wHLDqAfiRpxvWWHoAk6ZEi4kjgOcCKiEigB8iI+COaB2iq/bdn5lSC75NpnAEfkZm7IuIEGmegfxf4FeC3x3n8j1u0nU3u76U6MRQRAfTVGGOz5z3c3oOj9u3Dn2+SZhnPlEtSZzoD+JvMXJqZg5n5GOAuGmeLvwS8pFpbvgg4uXrM14GFETGynCUilk/UUUS8BHgBD5/RHt5/FDAnMy8H/hQYXj7zI+CwSTyXXx319V+q+9uAldX9dcDwcpNWbf8z8IpqbCcD92dmq7P3kjRreCZBkjrTy2hc9Dja5TQuxvxd4LnAbcCdwHXADzLzp9XFk++ulnX00ljDfXuT9n8/In4deFTVznMy8/tjao4GNkfE8Amc11VfLwHeHxE/od5ylEMi4joaJ4KGl+D8NfB3EXE98AUePtN+C7A3Iv616uemUe1sqMZzC7AHOLNG35I0K0RmTlwlSeooETE/M3dXy1yuB9ZU68slSbOQZ8olaXa6MiKOoLEW+80Gckma3TxTLkmSJBXmhZ6SJElSYYZySZIkqTBDuSRJklSYoVySJEkqzFAuSZIkFfZfVIjUfcCKvwEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig3, ax1 = plt.subplots(figsize=(12,4))\n",
    "ax1.boxplot(data, vert=False, flierprops=green_diamond)\n",
    "# Add a horizontal grid to the plot, but make it very light in color\n",
    "# so we can use it for reading data values but not be distracting\n",
    "ax1.xaxis.grid(True, linestyle='-', which='major', color='lightgrey',alpha=0.5)\n",
    "# Hide these grid behind plot objects\n",
    "ax1.set_title('Sex vs Age')\n",
    "ax1.set_xlabel('Age Distribution')\n",
    "ax1.set_ylabel('Sex')\n",
    "plt.yticks(np.arange(1,3), ('Female', 'Male'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female's 37.5 percentile: 23.0\n",
      "Female's 62.5 percentile: 26.0\n",
      "Male's 37.5 percentile: 24.0\n",
      "Male's 62.5 percentile: 27.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Female's 37.5 percentile:\", np.percentile(age_female, 37.5, axis = 0))\n",
    "print(\"Female's 62.5 percentile:\", np.percentile(age_female, 62.5, axis = 0))\n",
    "\n",
    "print(\"Male's 37.5 percentile:\", np.percentile(age_male, 37.5, axis = 0))\n",
    "print(\"Male's 62.5 percentile:\", np.percentile(age_male, 62.5, axis = 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<b>Findings:<b>\n",
    "* The optimal age for `female` getting medals is 23-26\n",
    "* The optimal age for `male` getting medals is 24-27"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### a) Age & Season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> In this part, we try to discover the average age differences between Summer and Winter Olympics and try to test whether the age distributions between Summer and Winter Olympics have significant differences. <b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season\n",
       "Summer    25.502896\n",
       "Winter    25.039147\n",
       "Name: Age, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24a0f790>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "season_df = athlete.groupby('Season')['Age'].mean()\n",
    "display(season_df)\n",
    "season_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> This bar chart suggests that the average ages for both summer and winter are very similar. There only exists an approximately 0.5 age difference between summer and winter groups, slightly suggesting there is a difference in age distribution between these two groups. To further discover whether there is a difference, we perform following t-test. <b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=15.040707692283027, pvalue=4.179255122632089e-51)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## for general age distribution between seasons\n",
    "stats.ttest_ind(athlete.loc[athlete['Season'] == 'Summer']['Age'].dropna(),\n",
    "                athlete.loc[athlete['Season'] == 'Winter']['Age'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> This t-test justifies that we <i>reject</i> our null hypothesis that there is no difference between age distributions in summer and winter Olympic seasons. We are in favor of our alternative hypothesis, which states that the age distribution between summer and winter Olympic seasons is different. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "To dive deeper into athletes who won medals in Olympics with perspective to Summer and Winter Seasons, we did following exploration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### b) Age & Seasons for Atheltes with Medals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Under the group of athletes with Medals, we try to discover whether there is difference in age distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season\n",
       "Summer    25.809481\n",
       "Winter    26.063688\n",
       "Name: Age, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2abd7850>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "season_df = athlete[athlete['got_medal']==1].groupby(['Season'])['Age'].mean()\n",
    "display(season_df)\n",
    "season_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> This bar chart suggests that the differences in average age between Summer and Winter seasons is small, and approximately equal to 2 years old. To further discover whether there exists a differences in age distribution, we did following t-test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.075735419606377, pvalue=0.00210137255599842)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## for summer, t-test for athletes got medal or not\n",
    "stats.ttest_ind(athlete[athlete['got_medal']==1].loc[with_medals['Season'] == 'Summer']['Age'],\n",
    "                athlete[athlete['got_medal']==1].loc[with_medals['Season'] == 'Winter']['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> In this case, our t-test gives back a pvalue = 0.002, which is smaller than the general significance level =0.05, therefore, we <i>reject</i> our Null hypothesis that there is no differences in age distribution between Summer and Winter Olympic seasons for athletes who won medals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### c) Age & Seasons & Sex for Atheltes with Medals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "In this section, we are trying to see whether there is a difference between age distribution with perspective to `Sex` and `Season` for athletes with medals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Season\n",
       "Summer    24.335535\n",
       "Winter    25.186637\n",
       "Name: Age, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2aa6af10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Female')\n",
    "season_df = athlete[(athlete['got_medal']==1) & (athlete['Sex']=='F')].groupby(['Season'])['Age'].mean()\n",
    "display(season_df)\n",
    "season_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Season\n",
       "Summer    26.440865\n",
       "Winter    26.473793\n",
       "Name: Age, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a27dac490>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Male')\n",
    "season_df = athlete[(athlete['got_medal']==1) & (athlete['Sex']=='M')].groupby(['Season'])['Age'].mean()\n",
    "display(season_df)\n",
    "season_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> The above 2 bar chart indicate that, for females, there exists an approximately 1 year difference in average age between Summer and Winter seasons for atheltes who won medals. For males, there exists an approximately 0.03 year difference. Therefore, we are curious about whether this specific difference suggests there are differences in age distributions for these two groups. Therefore, we perform following t-tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-6.402427599851293, pvalue=1.5904466084555113e-10)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Female')\n",
    "females_w_medals = athlete[(athlete['got_medal'] == 1)&(athlete['Sex']=='F')]\n",
    "stats.ttest_ind(females_w_medals.loc[females_w_medals['Season'] == 'Summer']['Age'],\n",
    "                females_w_medals.loc[females_w_medals['Season'] == 'Winter']['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.32268241640130196, pvalue=0.7469383861973562)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Male')\n",
    "males_w_medals = athlete[(athlete['got_medal'] == 1)&(athlete['Sex']=='M')]\n",
    "stats.ttest_ind(males_w_medals.loc[males_w_medals['Season'] == 'Summer']['Age'],\n",
    "                males_w_medals.loc[males_w_medals['Season'] == 'Winter']['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> Interesting findings here: \n",
    "* For females, we <i>reject</i> our Null hypothesis and conclude that there has differences in age distribution with perspective to Summer or Winter seasons for female athletes who won medals, as the pvalue is approximately to 0; \n",
    "* However, for males, there is no difference in age distribution with perspective to Summer or Winter seasons for athletes who won medals since the p-value we got is 0.74, which is far larger than the genral significance level 0.05. Therefore, in the male case, we <i> cannot reject</i> our Null hypothesis, and conclude that, there is no difference in age distribution with perspective to Summer and Winter seasons for male athletes who won medals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b>Such interesting findings lead us to wonder: why when combining genders, the t-tests show different result. So, we consider one specific sport, gymnastics, to discover whether the age for participating in sports, for example, gymnastics which only hold in summer, will result such differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season\n",
       "Summer    1969\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athlete.loc[(athlete['got_medal'] == 1) & (athlete['Sport'] == 'Gymnastics')].groupby('Season').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "F    20.200000\n",
       "M    25.022763\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "athlete.loc[(athlete['got_medal'] == 1) & (athlete['Sport'] == 'Gymnastics')].groupby('Sex')['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<b> The above statistic indicates that, for gymnastics, female athletes who won medals tends to had a smaller age than male athletes who won medals. As the data shows, female athletes who won medals is around `20`, which is much smaller than the average age `24.4` for females who won medals. However, male athletes who won medals is around `25`, which is much closer to `26.4` than female's data. Thus, we understand that the reason why female get different optimal age ranges for different season is due to the fact that some sports like gymnastics which only hold in summer have a smaller average age than female overall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Optimal Age Ranges for Winter vs Summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "summer_f = athlete[(athlete['got_medal'] == 1)&(athlete['Sex']=='F')&(athlete['Season']=='Summer')]['Age']\n",
    "winter_f = athlete[(athlete['got_medal'] == 1)&(athlete['Sex']=='F')&(athlete['Season']=='Winter')]['Age']\n",
    "data = [summer_f, winter_f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1a2535c650>,\n",
       "  <matplotlib.axis.YTick at 0x1a25357d90>],\n",
       " <a list of 2 Text yticklabel objects>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig3, ax1 = plt.subplots(figsize=(12,4))\n",
    "ax1.boxplot(data, vert=False, flierprops=green_diamond)\n",
    "# Add a horizontal grid to the plot, but make it very light in color\n",
    "# so we can use it for reading data values but not be distracting\n",
    "ax1.xaxis.grid(True, linestyle='-', which='major', color='lightgrey',alpha=0.5)\n",
    "# Hide these grid behind plot objects\n",
    "ax1.set_title('Season vs Age')\n",
    "ax1.set_xlabel('Age Distribution')\n",
    "ax1.set_ylabel('Season')\n",
    "plt.yticks(np.arange(1,3), ('summer', 'winter'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summer female's 37.5 percentile: 22.0\n",
      "Summer female's 62.5 percentile: 26.0\n",
      "Winter female's 37.5 percentile: 23.0\n",
      "Winter female's 62.5 percentile: 26.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Summer female's 37.5 percentile:\", np.percentile(summer_f, 37.5, axis = 0))\n",
    "print(\"Summer female's 62.5 percentile:\", np.percentile(summer_f, 62.5, axis = 0))\n",
    "\n",
    "print(\"Winter female's 37.5 percentile:\", np.percentile(winter_f, 37.5, axis = 0))\n",
    "print(\"Winter female's 62.5 percentile:\", np.percentile(winter_f, 62.5, axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "summer_m = athlete[(athlete['got_medal'] == 1)&(athlete['Sex']=='M')&(athlete['Season']=='Summer')]['Age']\n",
    "winter_m = athlete[(athlete['got_medal'] == 1)&(athlete['Sex']=='M')&(athlete['Season']=='Winter')]['Age']\n",
    "data = [summer_m, winter_m]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1a27f0aad0>,\n",
       "  <matplotlib.axis.YTick at 0x1a27f0a250>],\n",
       " <a list of 2 Text yticklabel objects>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig3, ax1 = plt.subplots(figsize=(12,4))\n",
    "ax1.boxplot(data, vert=False, flierprops=green_diamond)\n",
    "# Add a horizontal grid to the plot, but make it very light in color\n",
    "# so we can use it for reading data values but not be distracting\n",
    "ax1.xaxis.grid(True, linestyle='-', which='major', color='lightgrey',alpha=0.5)\n",
    "# Hide these grid behind plot objects\n",
    "ax1.set_title('Season vs Age')\n",
    "ax1.set_xlabel('Age Distribution')\n",
    "ax1.set_ylabel('Season')\n",
    "plt.yticks(np.arange(1,3), ('Summer', 'Winter'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summer male's 37.5 percentile: 24.0\n",
      "Summer male's 62.5 percentile: 27.0\n",
      "Winter male's 37.5 percentile: 24.0\n",
      "Winter male's 62.5 percentile: 27.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Summer male's 37.5 percentile:\", np.percentile(summer_m, 37.5, axis = 0))\n",
    "print(\"Summer male's 62.5 percentile:\", np.percentile(summer_m, 62.5, axis = 0))\n",
    "\n",
    "print(\"Winter male's 37.5 percentile:\", np.percentile(winter_m, 37.5, axis = 0))\n",
    "print(\"Winter male's 62.5 percentile:\", np.percentile(winter_m, 62.5, axis = 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* The optimal age range for `Female` participating in `Summer` Olympic is 22-26\n",
    "* The optimal age range for `Female` participating in `Winter` Olympic is 23-26\n",
    "* The optimal age range for `Male` participating in `Summer` Olympic is 24-27\n",
    "* The optimal age range for `Male` participating in `Winter` Olympic is 24-27"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp; The data is from Kaggle, which is an open-source platform. Therefore, we have the permission to use this dataset and there is no privacy concern to deal with. Also, the Olympic Games are internationalized and the information of the athletes are shared to the public, there are no privacy issues about recording and analyzing these open data, including events they participated in, medals they earned, or the countries they represented.\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; However, since the date for this dataset varies from 1896 to 2016, the data for weight and height from the earlier years may not be accurate due to the malfunctioness of machines in those years. Also, the name for athletes might not be accurate and our goal has nothing to deal with names of athletes. So, we would like to drop `name`, `weight`, and `height` columns from our dataset. \n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; What's more, the dataset might have a gender bias in earlier years. For instance, in 1896, women represented 0% of the dataset. Women began to appear in 1900. Until 1996, the percentage of women in those data entries reached about 50%. Thus, the dataset has a clear gender bias before 1996. Therefore, to be more accurate in analysis, we decide to separate females from males and to discover the “optimal age” for each group.\n",
    "\n",
    "If our analysis justifies that athletes between age 20 and 25 could win the most medals, this will possibly lead to the discriminations on athletes who were not in this age range between age 20 and 25. Also, this research might cause talented athletes outside this age range being overlooked by sponsers and failed in their paths as athletes on Olympics Games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Optimal Age Range Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src='tree.jpg'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "&nbsp;&nbsp;&nbsp;&nbsp;Our project focuses on finding the optimal age of athletes in the Olympic games. We attempt to verify the correctness of our hypothesis: The optimal age is between 20 - 25 for athletes to get successful in the Olympic games. We find a dataset on Kaggle and use it as our dataset for this analysis. Our leading question is “What is the optimal age for athletes to be successful”. In our analysis, we clean the dataset by dropping the duplicate rows and columns that are not related to our hypothesis. In addition, we check the number of NaN values in the ‘Age’ column and decide to drop the years that have a high proportion of NaN values. \n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;For data analysis, we first use the “Age” column to find out the optimal age range. We then separate the dataset into ‘medal’ and ‘no medal’ to define what we mean by successful. We find out a 0.5 age difference and then using a permutation test and t-test to prove that 0.5 is a significant difference. Thus, we decide that getting a medal means being successful. Later on, we develop data analysis in the ‘Gender’ and ‘Season’ category. We decide to use the middle 25% athletes as our optimal age range since there are many outliners. As a result, the overall optimal age is around 24 - 27. However, the female’s optimal age range is smaller than the male’s, which is 23 - 26 and 24 - 27 accordingly. In addition, we further calculate a more specific optical age ranges with respect to Sex and Season.\n",
    "\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;Our analysis has some limitations. First of all, it is hard to do a deeper analysis of the ‘Sport’ or ‘NOC’ column since they have too many unique values. Secondly, our data is not completed. We decide to drop the NaN in the ‘Age’ column, which reduces the size of our dataset. In addition, since most of the data are categorical data, it is not suitable  to use the regression model like linear regression. Lastly,  since the Olympics only happen once every four years, it is extremely hard to define the true optimal age range because of the gap . In the upcoming Tokyo Olympics game, our predicted optimal age range might be a helpful resource to determine the athletes who can be successful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- `Rouyu Liu`: She offers a significant amount of contributions on the conceptual framework. For instance, She helped to define ways of approaching our hypothesis through statistics.\n",
    "- `Ting-Yang Hung`: He contributed on data visualizations and statistical testings (t-test, permutation-test) as well as explaining the outcome obtained from each testing in a simplified manner.\n",
    "- `Yijun Liu`: She contributed mostly on visualization the data through plotting as well as explaining what each plot represents. In addition, she had written codes for most of the hypothesis/permutation tests. \n",
    "- `Yiluo Qin`: He contributed mostly on plotting the data as well as offering suggestions on which plot option can best convey that ideas that we are trying to deliver across to the audiances.\n",
    "- `Yu-Chieh Chen`: She did most of the data cleaning. For instance, she dropped the unncessary columns that are not related to the hypothesis and replace the null values to a for meaningful value(such as ordinal encoding). Also, she plotted many plots for data as well as polishing the plots."
   ]
  }
 ],
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