notebooks/relative_age_effect_and_others_cn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 足球运动员的相对年龄效应以及其他\n",
    "——Kaggle欧洲足球数据库的探索性分析\n",
    "\n",
    "数据来源：https://www.kaggle.com/hugomathien/soccer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 主要发现\n",
    "\n",
    "* 欧洲职业联赛球员中出现了“相对年龄效应”，或者叫做对于球员出生月份的偏见：一月份出生的球员最多，然后随着月份的增大而减少，十二月份出生的球员最少。\n",
    "* 欧洲职业足球运动员的平均身高为1.82米，平均体重为76.4公斤。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 详细过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先导入需要的python模块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sqlite3, pickle, os\n",
    "from scipy.stats import norm # 正态分布\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context('poster')\n",
    "sns.set_style({'font.sans-serif':['simhei','Arial']}) #图中显示中文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据实际情况修改数据库文件路径，然后连接数据库。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在数据库中找到了以下数据表：\n",
      "sqlite_sequence\n",
      "Player_Attributes\n",
      "Player\n",
      "Match\n",
      "League\n",
      "Country\n",
      "Team\n",
      "Team_Attributes\n"
     ]
    }
   ],
   "source": [
    "# 数据库文件名称\n",
    "db_filename = 'database.sqlite'\n",
    "\n",
    "# 数据库文件路径\n",
    "db_filedir = os.path.join(os.path.pardir, 'data_source', 'kaggle', db_filename)\n",
    "\n",
    "con = sqlite3.connect(db_filedir)\n",
    "cursor = con.execute(\"select name from sqlite_master where type='table'\")\n",
    "\n",
    "print('在数据库中找到了以下数据表：')\n",
    "for i in cursor.fetchall():\n",
    "    print('{}'.format(i[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "解释：\n",
    "\n",
    "* sqlite_sequence: 与数据库自身相关信息\n",
    "* Player_Attributes：球员属性\n",
    "* Player：球员信息\n",
    "* Match：比赛信息\n",
    "* League：联赛信息\n",
    "* Country：国家信息\n",
    "* Team：球队信息\n",
    "* Team_Attributes：球队属性\n",
    "\n",
    "下面的分析只限于：球员、比赛、联赛、国家和球队信息数据表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取球员信息数据表\n",
    "player = pd.read_sql_query('select * from Player', con)\n",
    "# 读取联赛信息数据表\n",
    "league = pd.read_sql_query('select * from League', con)\n",
    "# 读取国家信息数据表\n",
    "country = pd.read_sql_query('select * from Country', con)\n",
    "# 读取球队信息数据表\n",
    "team = pd.read_sql_query('select * from Team', con)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='Country'></a>1. 国家"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们来看看国家这个数据表里有什么内容。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id         name\n",
       "0       1      Belgium\n",
       "1    1729      England\n",
       "2    4769       France\n",
       "3    7809      Germany\n",
       "4   10257        Italy\n",
       "5   13274  Netherlands\n",
       "6   15722       Poland\n",
       "7   17642     Portugal\n",
       "8   19694     Scotland\n",
       "9   21518        Spain\n",
       "10  24558  Switzerland"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "阅读数据表的方法为：首先阅读表的表头，看看每一列代表什么含义。其次看一下数据表的大小是是多少，有多少行和多少列。最后再节选开头几个具体的数据进行查看。因为国家这个数据表很小，所以可以不用节选而进行完整地阅读。\n",
    "\n",
    "可以看到这一数据表中包括了十一个国家（地区）以及其对应的国家ID。这十一个国家分别是：比利时、英格兰、法国、德国、意大利、荷兰、波兰、葡萄牙、苏格兰、西班牙和瑞士。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='League'></a>2. 联赛"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面看一下联赛数据表中有什么内容。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Belgium Jupiler League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1729</td>\n",
       "      <td>1729</td>\n",
       "      <td>England Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4769</td>\n",
       "      <td>4769</td>\n",
       "      <td>France Ligue 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7809</td>\n",
       "      <td>7809</td>\n",
       "      <td>Germany 1. Bundesliga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10257</td>\n",
       "      <td>10257</td>\n",
       "      <td>Italy Serie A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13274</td>\n",
       "      <td>13274</td>\n",
       "      <td>Netherlands Eredivisie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15722</td>\n",
       "      <td>15722</td>\n",
       "      <td>Poland Ekstraklasa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17642</td>\n",
       "      <td>17642</td>\n",
       "      <td>Portugal Liga ZON Sagres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19694</td>\n",
       "      <td>19694</td>\n",
       "      <td>Scotland Premier League</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21518</td>\n",
       "      <td>21518</td>\n",
       "      <td>Spain LIGA BBVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24558</td>\n",
       "      <td>24558</td>\n",
       "      <td>Switzerland Super League</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  country_id                      name\n",
       "0       1           1    Belgium Jupiler League\n",
       "1    1729        1729    England Premier League\n",
       "2    4769        4769            France Ligue 1\n",
       "3    7809        7809     Germany 1. Bundesliga\n",
       "4   10257       10257             Italy Serie A\n",
       "5   13274       13274    Netherlands Eredivisie\n",
       "6   15722       15722        Poland Ekstraklasa\n",
       "7   17642       17642  Portugal Liga ZON Sagres\n",
       "8   19694       19694   Scotland Premier League\n",
       "9   21518       21518           Spain LIGA BBVA\n",
       "10  24558       24558  Switzerland Super League"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "league"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "联赛数据表中有联赛名称，以及对应的联赛ID和国家ID。可以看到，这十一个联赛都是该国的顶级联赛。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='Team'></a>3. 球队"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面是球队数字表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 5 columns):\n",
      "id                  299 non-null int64\n",
      "team_api_id         299 non-null int64\n",
      "team_fifa_api_id    288 non-null float64\n",
      "team_long_name      299 non-null object\n",
      "team_short_name     299 non-null object\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 11.8+ KB\n",
      "None\n"
     ]
    },
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>team_api_id</th>\n",
       "      <th>team_fifa_api_id</th>\n",
       "      <th>team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9987</td>\n",
       "      <td>673.0</td>\n",
       "      <td>KRC Genk</td>\n",
       "      <td>GEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9993</td>\n",
       "      <td>675.0</td>\n",
       "      <td>Beerschot AC</td>\n",
       "      <td>BAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10000</td>\n",
       "      <td>15005.0</td>\n",
       "      <td>SV Zulte-Waregem</td>\n",
       "      <td>ZUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9994</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Sporting Lokeren</td>\n",
       "      <td>LOK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9984</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>KSV Cercle Brugge</td>\n",
       "      <td>CEB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  team_api_id  team_fifa_api_id     team_long_name team_short_name\n",
       "0   1         9987             673.0           KRC Genk             GEN\n",
       "1   2         9993             675.0       Beerschot AC             BAC\n",
       "2   3        10000           15005.0   SV Zulte-Waregem             ZUL\n",
       "3   4         9994            2007.0   Sporting Lokeren             LOK\n",
       "4   5         9984            1750.0  KSV Cercle Brugge             CEB"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(team.info())\n",
    "team.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "球队数字表中有球队的全称、简称、球队的team_api_id和team_fifa_api_id。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                   0\n",
      "team_api_id          0\n",
      "team_fifa_api_id    11\n",
      "team_long_name       0\n",
      "team_short_name      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(team.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "表中一共有299只球队，其中有288只球队有team_fifa_api_id，这就意味着剩下11只球队没有team_fifa_api_id。\n",
    "\n",
    "这十一支球队是："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>team_api_id</th>\n",
       "      <th>team_fifa_api_id</th>\n",
       "      <th>team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>7947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCV Dender EH</td>\n",
       "      <td>DEN</td>\n",
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       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>4049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tubize</td>\n",
       "      <td>TUB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>26561</td>\n",
       "      <td>6601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FC Volendam</td>\n",
       "      <td>VOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>34816</td>\n",
       "      <td>177361</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Termalica Bruk-Bet Nieciecza</td>\n",
       "      <td>TBN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>35286</td>\n",
       "      <td>7992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Trofense</td>\n",
       "      <td>TRO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>35291</td>\n",
       "      <td>10213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Amadora</td>\n",
       "      <td>AMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>36248</td>\n",
       "      <td>9765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Portimonense</td>\n",
       "      <td>POR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>36723</td>\n",
       "      <td>4064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Feirense</td>\n",
       "      <td>FEI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>38789</td>\n",
       "      <td>6367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Uniao da Madeira</td>\n",
       "      <td>MAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>38791</td>\n",
       "      <td>188163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tondela</td>\n",
       "      <td>TON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>51606</td>\n",
       "      <td>7896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lugano</td>\n",
       "      <td>LUG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  team_api_id  team_fifa_api_id                team_long_name  \\\n",
       "8        9         7947               NaN                 FCV Dender EH   \n",
       "14      15         4049               NaN                        Tubize   \n",
       "170  26561         6601               NaN                   FC Volendam   \n",
       "204  34816       177361               NaN  Termalica Bruk-Bet Nieciecza   \n",
       "208  35286         7992               NaN                      Trofense   \n",
       "213  35291        10213               NaN                       Amadora   \n",
       "223  36248         9765               NaN                  Portimonense   \n",
       "225  36723         4064               NaN                      Feirense   \n",
       "232  38789         6367               NaN              Uniao da Madeira   \n",
       "233  38791       188163               NaN                       Tondela   \n",
       "298  51606         7896               NaN                        Lugano   \n",
       "\n",
       "    team_short_name  \n",
       "8               DEN  \n",
       "14              TUB  \n",
       "170             VOL  \n",
       "204             TBN  \n",
       "208             TRO  \n",
       "213             AMA  \n",
       "223             POR  \n",
       "225             FEI  \n",
       "232             MAD  \n",
       "233             TON  \n",
       "298             LUG  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team[team['team_fifa_api_id'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有299个不同的team_api_id。\n"
     ]
    }
   ],
   "source": [
    "print('有{}个不同的team_api_id。'.format(team.team_api_id.unique().shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的输出表明，每只球队都有一个单独的team_api_id。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有285个不同的team_fifa_api_id。\n"
     ]
    }
   ],
   "source": [
    "print('有{}个不同的team_fifa_api_id。'.format(team[team.team_fifa_api_id.notnull()].team_fifa_api_id.unique().shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的输出表明，总共288个team_fifa_api_id，其中有285个不同的team_fifa_api_id。这就意味着有些球队的team_fifa_api_id相同。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重复使用的team_fifa_ids： [   301. 111429. 111560.]\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>id</th>\n",
       "      <th>team_api_id</th>\n",
       "      <th>team_fifa_api_id</th>\n",
       "      <th>team_long_name</th>\n",
       "      <th>team_short_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>9996</td>\n",
       "      <td>111560.0</td>\n",
       "      <td>Royal Excel Mouscron</td>\n",
       "      <td>MOU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2510</td>\n",
       "      <td>274581</td>\n",
       "      <td>111560.0</td>\n",
       "      <td>Royal Excel Mouscron</td>\n",
       "      <td>MOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>31444</td>\n",
       "      <td>8031</td>\n",
       "      <td>111429.0</td>\n",
       "      <td>Polonia Bytom</td>\n",
       "      <td>POB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>31445</td>\n",
       "      <td>8020</td>\n",
       "      <td>111429.0</td>\n",
       "      <td>Polonia Bytom</td>\n",
       "      <td>GOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>31451</td>\n",
       "      <td>8244</td>\n",
       "      <td>301.0</td>\n",
       "      <td>Widzew Łódź</td>\n",
       "      <td>LOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>32409</td>\n",
       "      <td>8024</td>\n",
       "      <td>301.0</td>\n",
       "      <td>Widzew Łódź</td>\n",
       "      <td>WID</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  team_api_id  team_fifa_api_id        team_long_name  \\\n",
       "15      16         9996          111560.0  Royal Excel Mouscron   \n",
       "24    2510       274581          111560.0  Royal Excel Mouscron   \n",
       "182  31444         8031          111429.0         Polonia Bytom   \n",
       "183  31445         8020          111429.0         Polonia Bytom   \n",
       "189  31451         8244             301.0           Widzew Łódź   \n",
       "199  32409         8024             301.0           Widzew Łódź   \n",
       "\n",
       "    team_short_name  \n",
       "15              MOU  \n",
       "24              MOP  \n",
       "182             POB  \n",
       "183             GOR  \n",
       "189             LOD  \n",
       "199             WID  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_fifa_id = team.groupby('team_fifa_api_id',as_index=False).count()\n",
    "shared_fifa_id = counts_fifa_id[counts_fifa_id['id']!=1]['team_fifa_api_id'].values\n",
    "print('重复使用的team_fifa_ids： {}'.format(shared_fifa_id))\n",
    "team[team['team_fifa_api_id'].isin(shared_fifa_id)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的输出表明，有三组球队的team_fifa_api_id相同。但是从球队的全称来看，同一组的“两”只球队其实就是一只球队，只是不知道为何会有不同的简称和team_api_id。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有296个不同的球队全称。\n",
      "有259个不同的球队简称。\n"
     ]
    }
   ],
   "source": [
    "print('有{}个不同的球队全称。'.format(team.team_long_name.unique().shape[0]))\n",
    "print('有{}个不同的球队简称。'.format(team.team_short_name.unique().shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的输出表明，有不少球队的简称相同。\n",
    "\n",
    "对于球队信息数据表有以下总结：\n",
    "    1. 这一表中实际有296只不同的球队；\n",
    "    2. 可以通过team_api_id或者球队全称来查找球队。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <a id='Player'></a>4. 球员\n",
    "最后是球员数据表。这一表中包含了球员名字、生日、身高、体重和球员ID的信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11060 entries, 0 to 11059\n",
      "Data columns (total 7 columns):\n",
      "id                    11060 non-null int64\n",
      "player_api_id         11060 non-null int64\n",
      "player_name           11060 non-null object\n",
      "player_fifa_api_id    11060 non-null int64\n",
      "birthday              11060 non-null object\n",
      "height                11060 non-null float64\n",
      "weight                11060 non-null int64\n",
      "dtypes: float64(1), int64(4), object(2)\n",
      "memory usage: 605.0+ KB\n",
      "None\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>id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>birthday</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>505942</td>\n",
       "      <td>Aaron Appindangoye</td>\n",
       "      <td>218353</td>\n",
       "      <td>1992-02-29 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>155782</td>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>189615</td>\n",
       "      <td>1989-12-15 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>162549</td>\n",
       "      <td>Aaron Doran</td>\n",
       "      <td>186170</td>\n",
       "      <td>1991-05-13 00:00:00</td>\n",
       "      <td>170.18</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>30572</td>\n",
       "      <td>Aaron Galindo</td>\n",
       "      <td>140161</td>\n",
       "      <td>1982-05-08 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>23780</td>\n",
       "      <td>Aaron Hughes</td>\n",
       "      <td>17725</td>\n",
       "      <td>1979-11-08 00:00:00</td>\n",
       "      <td>182.88</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  player_api_id         player_name  player_fifa_api_id  \\\n",
       "0   1         505942  Aaron Appindangoye              218353   \n",
       "1   2         155782     Aaron Cresswell              189615   \n",
       "2   3         162549         Aaron Doran              186170   \n",
       "3   4          30572       Aaron Galindo              140161   \n",
       "4   5          23780        Aaron Hughes               17725   \n",
       "\n",
       "              birthday  height  weight  \n",
       "0  1992-02-29 00:00:00  182.88     187  \n",
       "1  1989-12-15 00:00:00  170.18     146  \n",
       "2  1991-05-13 00:00:00  170.18     163  \n",
       "3  1982-05-08 00:00:00  182.88     198  \n",
       "4  1979-11-08 00:00:00  182.88     154  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(player.info())\n",
    "player.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有11060个不同的player_api_id。\n",
      "有11060个不同的player_fifa_api_id。\n"
     ]
    }
   ],
   "source": [
    "print('有{}个不同的player_api_id。'.format(player.player_api_id.unique().shape[0]))\n",
    "print('有{}个不同的player_fifa_api_id。'.format(player.player_fifa_api_id.unique().shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "球员数据表中共有11060名球员。每名球员都有一个单独的player_api_id和player_fifa_api_id。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 球员名字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有10848个不同的球员名字。\n"
     ]
    }
   ],
   "source": [
    "print('有{}个不同的球员名字。'.format(player.player_name.unique().shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11060名球员有10848个名字，这表明有些球员的名字相同。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 列出数量排名前十的球员名字\n",
    "top_num = 10\n",
    "\n",
    "top_counts_name = player.groupby('player_name',as_index=False).count().sort_values('player_api_id',ascending=False).head(top_num)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "ax.barh(np.arange(top_num), top_counts_name['player_api_id'].values, align = 'center')\n",
    "ax.set_yticks(np.arange(top_num))\n",
    "ax.set_yticklabels(top_counts_name['player_name'].values)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xticklabels(ax.get_xticks())\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
    "ax.set_xlabel('球员人数')\n",
    "ax.set_title('{}大最常见的球员名字'.format(top_num));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中叫Danilo（达尼诺）的球员最多，一共有7位；叫Paulinho（保利尼奥）的排第二，一共有6位；叫Ricardo（里卡多）的排第三。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 球员出生日期\n",
    "出生日期为1989年3月2日的球员最多，有10名球员。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 列出数量排名前十的球员生日\n",
    "top_num = 10\n",
    "\n",
    "top_counts_birthday = player.groupby('birthday',as_index=False).count().sort_values('player_api_id',ascending=False).head(top_num)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "ax.barh(np.arange(top_num), top_counts_birthday['player_api_id'].values, align = 'center')\n",
    "ax.set_yticks(np.arange(top_num))\n",
    "ax.set_yticklabels([i[:-9] for i in top_counts_birthday['birthday'].values])\n",
    "ax.invert_yaxis()\n",
    "ax.set_xticklabels(ax.get_xticks())\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
    "ax.set_xlabel('球员个数')\n",
    "ax.set_title('{}大最常见球员生日'.format(top_num));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 相对年龄效应（出生月份偏见）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "player['birthday'] = pd.to_datetime(player['birthday'], yearfirst=True)\n",
    "player['month'] = [i.month for i in player['birthday']]\n",
    "\n",
    "month_name = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月']\n",
    "month_gby = player.groupby(by='month', as_index=False).count()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax.bar(month_gby['month'], month_gby['birthday'], align = 'center')\n",
    "ax.set_xticks(month_gby['month'])\n",
    "ax.set_xticklabels(month_name)\n",
    "ax.set_ylabel('球员人数')\n",
    "ax.set_title('球员出生月份分布'.format(top_num));"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这一结果验证了[相对年龄效应](https://en.wikipedia.org/wiki/Relative_age_effect)。这一效应说的是在职业球员中某些月份出生的球员更多，而其他月份出生的球员较少。\n",
    "\n",
    "青少年体育比赛通常以年为单位，把球员划分成不同的年龄段。但是即使是在同一个年龄段中，最小的球员和最大的球员也有可能相差接近1岁。因此，年龄较大的球员在同年龄段中会因为身体上的优势而获得更多的比赛机会，这有助于其成长为职业球员。而年龄较小的球员会因为身体上的劣势导致比赛机会较少，从而更难脱颖而出。\n",
    "\n",
    "国际青少年足球比赛通常以1月1日来划分年龄段，因此1月份出生的小孩在同年龄段中属于年龄较大的球员，更有可能成长为职业球员。而十二月份出生的球员在同年龄段中属于年龄较小的球员，因此成长道路更为艰辛。这样就造成了在职业球员中一月份出生的球员最多，十二月份出生的球员最少，如上面的数据统计所示。\n",
    "\n",
    "这也造成了表面上的对球员出生月份的偏见：人们会误认为是因为1月份出生的小孩更适合踢球才导致了1月份出生的球员多。\n",
    "\n",
    "那么如何说明是1月份出生的小孩并没有更适合踢球呢？\n",
    "\n",
    "事实上，不同的年龄段划分日期会导致不同的效应。比如，英国足球以9月1日来划分年龄段，因此造成的结果就是九、十月份出生的球员就最多，而不是一月份。（参见下图和[The Science of Soccer](https://www.amazon.com/Science-Soccer-John-Wesson/dp/0750308133)）。这就是人为划分年龄段造成人为选择的一个证据。\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 球员身高\n",
    "球员身高服从高斯分布，平均身高为1.82米。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "欧洲职业球员的平均身高为1.82米。\n",
      "最矮的球员是Juan Quero，身高为1.57米。\n",
      "最高的球员是Kristof van Hout，身高为2.08米。\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.hist(player['height']/100, bins=10)\n",
    "plt.title('球员身高分布')\n",
    "plt.xlabel('身高（米）')\n",
    "plt.ylabel('球员人数')\n",
    "\n",
    "print('欧洲职业球员的平均身高为{:.2f}米。'.format(player['height'].mean()/100))\n",
    "print('最矮的球员是{}，身高为{:.2f}米。'.format(player[player.height==np.min(player.height)].player_name.values[0],\n",
    "                                    np.min(player.height)/100))\n",
    "print('最高的球员是{}，身高为{:.2f}米。'.format(player[player.height==np.max(player.height)].player_name.values[0],\n",
    "                                    np.max(player.height)/100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 球员体重\n",
    "\n",
    "球员体重也服从高斯分布，平均体重为76.4公斤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "欧洲职业球员的平均体重为76.4公斤。\n",
      "最轻的球员是Juan Quero，体重为53.1公斤。\n",
      "最重的球员是Kristof van Hout，体重为110.2公斤。\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.hist(player['weight']*0.4536, bins=20)\n",
    "plt.title('球员体重分布')\n",
    "plt.xlabel('体重（公斤）')\n",
    "plt.ylabel('球员人数')\n",
    "\n",
    "print('欧洲职业球员的平均体重为{:.1f}公斤。'.format(player['weight'].mean()*0.4536))\n",
    "print('最轻的球员是{}，体重为{:.1f}公斤。'.format(player[player.weight==np.min(player.weight)].player_name.values[0],\n",
    "                                     np.min(player.weight)*0.4536))\n",
    "print('最重的球员是{}，体重为{:.1f}公斤。'.format(player[player.weight==np.max(player.weight)].player_name.values[0],\n",
    "                                     np.max(player.weight)*0.4536))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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