CS411_BackEnd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymysql\n",
    "import calendar\n",
    "from pymongo import MongoClient\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from operator import itemgetter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#User can insert their comments and ratings into our database, the avgrating in Player_Bio will also update\n",
    "def user_comments(PlayerName, UserName, ratings, Comments):\n",
    "    #insert new entry in User_ratings\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    #Get correct ratingID\n",
    "    my_cursor = conn.cursor()\n",
    "    my_cursor.execute(\"SELECT MAX(ratingID) FROM User_ratings\")\n",
    "    my_result = my_cursor.fetchall()\n",
    "    if not my_result[0][0]:\n",
    "        new_ID = 1\n",
    "    else:\n",
    "        new_ID = int(my_result[0][0]) + 1\n",
    "    \n",
    "    my_cursor = conn.cursor()\n",
    "    sqlFormula = \"INSERT INTO User_ratings (ratings, PlayerName, UserName, UserComments, ratingID) VALUES (%s, %s, %s, %s, %s)\"\n",
    "    records = (ratings, PlayerName, UserName, Comments, new_ID)\n",
    "    my_cursor.execute(sqlFormula, records)\n",
    "    conn.commit()\n",
    "    \n",
    "    #update in Player_Bio\n",
    "    myplayer = PlayerName\n",
    "    formula_1 = \"SELECT Avg(ratings) as AvgRating FROM User_ratings WHERE PlayerName = %s\"\n",
    "    my_cursor.execute(formula_1, myplayer)\n",
    "    my_result = my_cursor.fetchall()\n",
    "    new_avgRatings = float(my_result[0][0])\n",
    "    my_cursor = conn.cursor()\n",
    "    formula_2 = \"UPDATE Player_Bio SET AvgRating = %s WHERE PlayerName = %s\"\n",
    "    my_cursor.execute(formula_2, (new_avgRatings, myplayer))\n",
    "    conn.commit()\n",
    "    conn.close()\n",
    "    print(\"Successfully commented, your commentID is %d.\" % new_ID)\n",
    "\n",
    "#query Player_Bio table by PlayerName\n",
    "def return_playerBio(PlayerName):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    my_cursor = conn.cursor()\n",
    "    formula_1 = \"SELECT * FROM Player_Bio WHERE PlayerName = '%s'\"\n",
    "    sqlformula = formula_1 % PlayerName\n",
    "    df = pd.read_sql(sqlformula, conn)\n",
    "    print(df.to_string())\n",
    "    conn.close()\n",
    "\n",
    "#新加的\n",
    "#query Player_Bio table by PlayerName and Season\n",
    "def return_playerBio_season(PlayerName, season):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    my_cursor = conn.cursor()\n",
    "    formula_1 = \"SELECT * FROM Player_Bio WHERE PlayerName = '%s' and Season = '%s'\"\n",
    "    sqlformula = formula_1 % (PlayerName,season)\n",
    "    df = pd.read_sql(sqlformula, conn)\n",
    "    print(df.to_string())\n",
    "    conn.close()\n",
    "\n",
    "#delete user rating and comments, then update average rating\n",
    "def delete_ratings(deleteID):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    \n",
    "    #get playerName before deleting\n",
    "    my_cursor = conn.cursor()\n",
    "    formula_1 = \"SELECT PlayerName FROM User_ratings WHERE ratingID = %s\"\n",
    "    my_cursor.execute(formula_1, deleteID)\n",
    "    my_result = my_cursor.fetchall()\n",
    "    Player_name = my_result[0][0]\n",
    "    \n",
    "    #delete record\n",
    "    my_cursor = conn.cursor()\n",
    "    sqlformula = \"DELETE FROM User_ratings WHERE ratingID = %s\"\n",
    "    my_cursor.execute(sqlformula, deleteID)\n",
    "    \n",
    "    #update avgrating of the player in Player_Bio\n",
    "    myplayer = Player_name\n",
    "    formula_1 = \"SELECT Avg(ratings) as AvgRating FROM User_ratings WHERE PlayerName = %s\"\n",
    "    my_cursor.execute(formula_1, myplayer)\n",
    "    my_result = my_cursor.fetchall()\n",
    "    new_avgRatings = 0\n",
    "    if my_result[0][0]:\n",
    "        new_avgRatings = float(my_result[0][0])\n",
    "    my_cursor = conn.cursor()\n",
    "    formula_2 = \"UPDATE Player_Bio SET AvgRating = %s WHERE PlayerName = %s\"\n",
    "    my_cursor.execute(formula_2, (new_avgRatings, myplayer))\n",
    "    conn.commit()\n",
    "    print(\"Successfully deleted, the average rating of %s has been adjusted.\" % myplayer)\n",
    "    conn.close()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully commented, your commentID is 3.\n"
     ]
    }
   ],
   "source": [
    "user_comments(\"James Harden\", \"Xiao Ming\", 3, \"I hate seeing him scoring 40 while is 2/16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully commented, your commentID is 4.\n"
     ]
    }
   ],
   "source": [
    "user_comments(\"James Harden\", \"Xiao Ming\", 4, \"I hate seeing him scoring 50 while shooting 3/16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PlayerName TeamName  AvgPoints  AvgRebounds  AvgAssists  AvgSteals  AvgRating   Season      SeasonType\n",
      "0  James Harden      HOU       36.1          6.6         7.5        2.0        3.5  2018-19  Regular_Season\n",
      "1  James Harden      HOU       31.6          6.8         6.6        2.2        3.5  2018-19         Playoff\n"
     ]
    }
   ],
   "source": [
    "return_playerBio_season('James Harden', '2018-19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully deleted, the average rating of James Harden has been adjusted.\n",
      "     PlayerName TeamName  AvgPoints  AvgRebounds  AvgAssists  AvgSteals  AvgRating   Season      SeasonType\n",
      "0  James Harden      HOU       36.1          6.6         7.5        2.0        0.0  2018-19  Regular_Season\n",
      "1  James Harden      HOU       31.6          6.8         6.6        2.2        0.0  2018-19         Playoff\n"
     ]
    }
   ],
   "source": [
    "delete_ratings(4)\n",
    "return_playerBio_season('James Harden', '2018-19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully commented, your commentID is 1.\n"
     ]
    }
   ],
   "source": [
    "user_comments(\"James Harden\", \"Xiao Ming\", 3, \"I hate seeing him scoring 40 while is 2/16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query player stats by playerName and Month, show results in past five years\n",
    "def query_stats_by_month(PlayerName, Month):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    \n",
    "    Months = {}\n",
    "    Months[\"Jan\"] = \"01\"\n",
    "    Months[\"Feb\"] = \"02\"\n",
    "    Months[\"Mar\"] = \"03\"\n",
    "    Months[\"Apr\"] = \"04\"\n",
    "    Months[\"May\"] = \"05\"\n",
    "    Months[\"Jun\"] = \"06\"\n",
    "    Months[\"Jul\"] = \"07\"\n",
    "    Months[\"Aug\"] = \"08\"\n",
    "    Months[\"Sep\"] = \"09\"\n",
    "    Months[\"Oct\"] = \"10\"\n",
    "    Months[\"Nov\"] = \"11\"\n",
    "    Months[\"Dec\"] = \"12\"\n",
    "\n",
    "    my_cursor = conn.cursor()\n",
    "    my_month = Months[Month]\n",
    "    # “有改动”\n",
    "    formula_1 = \"SELECT PlayerName, Avg(Points) as Points, Avg(Rebounds) as Rebounds, Avg(Assists) as Assists, Avg(Steals) as Steals, count(*) as Game_Played, Season FROM Game_Stats WHERE PlayerName = '%s' and CAST(Date AS CHAR) LIKE '____%s__' group by Season\"\n",
    "    sqlformula = formula_1 % (PlayerName, my_month)\n",
    "    df = pd.read_sql(sqlformula, conn)\n",
    "    print(df.to_string())\n",
    "    conn.close()\n",
    "\n",
    "def query_stats_by_opponent(PlayerName, Opponent):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "\n",
    "    my_cursor = conn.cursor()\n",
    "    #有改动\n",
    "    formula_1 = \"SELECT PlayerName, OpponentTeam, Avg(Points) as Points, Avg(Rebounds) as Rebounds, Avg(Assists) as Assists, Avg(Steals) as Steals, count(*) as Game_Played, Season FROM Game_Stats WHERE PlayerName = '%s' and OpponentTeam = '%s' group by Season\"\n",
    "    sqlformula = formula_1 % (PlayerName, Opponent)\n",
    "    df = pd.read_sql(sqlformula, conn)\n",
    "    print(df.to_string())\n",
    "    conn.close()\n",
    "    \n",
    "#return a player's performance against each opponent team in a given season\n",
    "#有改动\n",
    "def query_all_results_groupby_opponent(PlayerName, season):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "\n",
    "    my_cursor = conn.cursor()\n",
    "    formula_1 = \"SELECT PlayerName, OpponentTeam, Avg(Points) as Points, Avg(Rebounds) as Rebounds, Avg(Assists) as Assists, Avg(Steals) as Steals, count(*) as Game_Played, Season FROM Game_Stats WHERE PlayerName = '%s' and Season = '%s' Group By OpponentTeam\"\n",
    "    sqlformula = formula_1 % (PlayerName,season)\n",
    "    df = pd.read_sql(sqlformula, conn)\n",
    "    print(df.to_string())\n",
    "    conn.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PlayerName OpponentTeam   Points  Rebounds  Assists  Steals  Game_Played   Season\n",
      "0  LeBron James          GSW  17.0000   13.0000   5.0000  1.0000            1  2018-19\n",
      "1  LeBron James          GSW  31.3333    8.0000   8.6667  1.3333            6  2017-18\n",
      "2  LeBron James          GSW  31.2857   11.5714   8.0000  1.4286            7  2016-17\n",
      "3  LeBron James          GSW  27.6667   10.3333   7.6667  2.2222            9  2015-16\n",
      "4  LeBron James          GSW  36.7143   13.0000   8.2857  1.5714            7  2014-15\n"
     ]
    }
   ],
   "source": [
    "query_stats_by_opponent('LeBron James','GSW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      PlayerName OpponentTeam   Points  Rebounds  Assists  Steals  Game_Played   Season\n",
      "0   LeBron James          CHA  25.5000    7.5000  10.0000  0.5000            2  2018-19\n",
      "1   LeBron James          WAS  18.0000    6.5000   8.5000  0.5000            2  2018-19\n",
      "2   LeBron James          SAC  27.0000    9.0000   6.5000  1.5000            2  2018-19\n",
      "3   LeBron James          BKN  30.5000   11.0000  11.0000  1.0000            2  2018-19\n",
      "4   LeBron James          NYK  33.0000    6.0000   8.0000  0.0000            1  2018-19\n",
      "5   LeBron James          TOR  23.5000    3.0000   6.0000  0.5000            2  2018-19\n",
      "6   LeBron James          CHI  36.0000   10.0000   4.0000  2.0000            1  2018-19\n",
      "7   LeBron James          BOS  29.0000   11.0000  12.0000  1.5000            2  2018-19\n",
      "8   LeBron James          DEN  24.3333    8.3333   6.6667  2.0000            3  2018-19\n",
      "9   LeBron James          LAC  25.5000   11.0000   7.5000  1.0000            2  2018-19\n",
      "10  LeBron James          PHX  22.6667    7.3333  11.3333  1.0000            3  2018-19\n",
      "11  LeBron James          MIL  31.0000    7.0000  10.0000  1.0000            1  2018-19\n",
      "12  LeBron James          NOP  27.3333    8.3333  12.0000  2.6667            3  2018-19\n",
      "13  LeBron James          MEM  22.0000   11.3333   9.0000  2.0000            3  2018-19\n",
      "14  LeBron James          HOU  27.3333    7.3333   5.0000  1.6667            3  2018-19\n",
      "15  LeBron James          ATL  27.0000    9.0000  10.0000  2.5000            2  2018-19\n",
      "16  LeBron James          PHI  18.0000   10.0000   9.0000  0.0000            1  2018-19\n",
      "17  LeBron James          IND  28.0000    8.0000   8.0000  1.5000            2  2018-19\n",
      "18  LeBron James          GSW  17.0000   13.0000   5.0000  1.0000            1  2018-19\n",
      "19  LeBron James          MIA  39.5000    8.5000   7.5000  1.5000            2  2018-19\n",
      "20  LeBron James          SAS  36.0000    8.0000   8.7500  1.7500            4  2018-19\n",
      "21  LeBron James          DAL  28.5000    5.0000   5.0000  1.5000            2  2018-19\n",
      "22  LeBron James          ORL  23.0000    4.0000   7.0000  0.0000            2  2018-19\n",
      "23  LeBron James          UTA  22.0000   10.0000   7.0000  2.0000            1  2018-19\n",
      "24  LeBron James          CLE  32.0000   14.0000   7.0000  0.0000            1  2018-19\n",
      "25  LeBron James          POR  32.6667    9.0000   7.3333  0.6667            3  2018-19\n",
      "26  LeBron James          MIN  26.5000   10.5000   8.5000  1.0000            2  2018-19\n"
     ]
    }
   ],
   "source": [
    "query_all_results_groupby_opponent(\"LeBron James\", \"2018-19\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PlayerName   Points  Rebounds  Assists  Steals  Game_Played   Season\n",
      "0  James Harden  43.5714    8.7143   7.5714  2.0714           14  2018-19\n",
      "1  James Harden  27.8571    5.1429   9.0000  2.1429            7  2017-18\n",
      "2  James Harden  28.2941    8.1765  10.4118  1.5294           17  2016-17\n",
      "3  James Harden  26.8125    6.7500   7.2500  1.1875           16  2015-16\n",
      "4  James Harden  25.8235    4.7059   6.7059  2.1765           17  2014-15\n"
     ]
    }
   ],
   "source": [
    "query_stats_by_month(\"James Harden\", 'Jan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who is the best clutch free throw shooter in team?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# More complicated query functions\n",
    "# Find best free throw shooter, who has played more than 10 min in clutch moments\n",
    "# for selected team and SeasonType, decision made by previous season：\n",
    "def find_best_clutch_FT_Shooter(TeamName, SeasonType):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    my_cursor = conn.cursor()\n",
    "\n",
    "    my_cursor.execute('''Create view Jointed_Table as \n",
    "     select * from (select * from Player_Clutch_Stats where Season = '2018-19') as short_clutch \n",
    "     natural join (select distinct PlayerName, TeamName from Player_Bio where Season = '2018-19') as short_bio;''')\n",
    "\n",
    "    my_cursor.execute('''Create view BestFT_Shooters as\n",
    "    Select distinct PlayerName, Free_Throw_P, J.TeamName, J.Minutes_Played, J.SeasonType from Jointed_Table J, \n",
    "    (Select max(Free_Throw_P) as maxFT, TeamName, SeasonType from Jointed_Table where Minutes_Played > 10 group by TeamName, SeasonType) as bestFT\n",
    "    where J.Free_Throw_P = bestFT.maxFT and J.TeamName = bestFT.TeamName and J.Minutes_Played > 10;''')\n",
    "\n",
    "    formula_1 = \"select PlayerName, Free_Throw_P, TeamName, Minutes_Played, SeasonType from BestFT_Shooters where TeamName = '%s' and SeasonType = '%s'\"\n",
    "    sqlformula = formula_1 % (TeamName, SeasonType)\n",
    "    df = pd.read_sql(sqlformula, conn)\n",
    "    print(df.to_string())\n",
    "    my_cursor.execute(\"Drop view Jointed_Table;\")\n",
    "    my_cursor.execute(\"Drop view BestFT_Shooters;\")\n",
    "    conn.close()\n",
    "\n",
    "# More complicated query functions\n",
    "# Find best Three Pointer shooter, who has played more than 10 min in clutch moments\n",
    "# for selected team and SeasonType, decision made by previous season：\n",
    "def find_best_clutch_3pointer_Shooter(TeamName, SeasonType):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    my_cursor = conn.cursor()\n",
    "\n",
    "    my_cursor.execute('''Create view Jointed_Table as \n",
    "     select * from (select * from Player_Clutch_Stats where Season = '2018-19') as short_clutch \n",
    "     natural join (select distinct PlayerName, TeamName from Player_Bio where Season = '2018-19') as short_bio;''')\n",
    "\n",
    "    my_cursor.execute('''Create view BestFT_Shooters as\n",
    "    Select distinct PlayerName, 3pointer_P, J.TeamName, J.Minutes_Played, J.SeasonType from Jointed_Table J, \n",
    "    (Select max(3pointer_P) as maxFT, TeamName, SeasonType from Jointed_Table where Minutes_Played > 10 group by TeamName, SeasonType) as bestFT\n",
    "    where J.3pointer_P = bestFT.maxFT and J.TeamName = bestFT.TeamName and J.Minutes_Played > 10;''')\n",
    "\n",
    "    formula_1 = \"select PlayerName, 3pointer_P, TeamName, Minutes_Played, SeasonType from BestFT_Shooters where TeamName = '%s' and SeasonType = '%s'\"\n",
    "    sqlformula = formula_1 % (TeamName, SeasonType)\n",
    "    df = pd.read_sql(sqlformula, conn)\n",
    "    print(df.to_string())\n",
    "    my_cursor.execute(\"Drop view Jointed_Table;\")\n",
    "    my_cursor.execute(\"Drop view BestFT_Shooters;\")\n",
    "    conn.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       PlayerName  3pointer_P TeamName  Minutes_Played      SeasonType\n",
      "0  Draymond Green        50.0      GSW             101  Regular_Season\n"
     ]
    }
   ],
   "source": [
    "find_best_clutch_3pointer_Shooter('GSW','Regular_Season')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just WoW! So suprising!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      PlayerName  Free_Throw_P TeamName  Minutes_Played      SeasonType\n",
      "0  Klay Thompson         100.0      GSW             125  Regular_Season\n"
     ]
    }
   ],
   "source": [
    "find_best_clutch_FT_Shooter('GSW','Regular_Season')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PlayerName  Free_Throw_P TeamName  Minutes_Played SeasonType\n",
      "0  Jamal Murray         100.0      DEN              43    Playoff\n",
      "1   Gary Harris         100.0      DEN              38    Playoff\n",
      "2  Paul Millsap         100.0      DEN              43    Playoff\n"
     ]
    }
   ],
   "source": [
    "find_best_clutch_FT_Shooter('DEN','Playoff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which team likes 3 pointer most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_team_3PTA_ranking(TeamName, Season):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    \n",
    "    nbaTeams = {}\n",
    "    nbaTeams['Atlanta Hawks'] = 'ATL'\n",
    "    nbaTeams['Brooklyn Nets'] = 'BKN'\n",
    "    nbaTeams['Boston Celtics'] = 'BOS'\n",
    "    nbaTeams['Charlotte Hornets'] = 'CHA'\n",
    "    nbaTeams['Chicago Bulls'] = 'CHI'\n",
    "    nbaTeams['Cleveland Cavaliers'] = 'CLE'\n",
    "    nbaTeams['Dallas Mavericks'] = 'DAL'\n",
    "    nbaTeams['Denver Nuggets'] = 'DEN'\n",
    "    nbaTeams['Detroit Pistons'] = 'DET'\n",
    "    nbaTeams['Golden State Warriors'] = 'GSW'\n",
    "    nbaTeams['Houston Rockets'] = 'HOU'\n",
    "    nbaTeams['Indiana Pacers'] = 'IND'\n",
    "    nbaTeams['LA Clippers'] = 'LAC'\n",
    "    nbaTeams['Los Angeles Lakers'] = 'LAL'\n",
    "    nbaTeams['Memphis Grizzlies'] = 'MEM'\n",
    "    nbaTeams['Miami Heat'] = 'MIA'\n",
    "    nbaTeams['Milwaukee Bucks'] = 'MIL'\n",
    "    nbaTeams['Minnesota Timberwolves'] = 'MIN'\n",
    "    nbaTeams['New Orleans Pelicans'] = 'NOP'\n",
    "    nbaTeams['New York Knicks'] = 'NYK'\n",
    "    nbaTeams['Oklahoma City Thunder'] = 'OKC'\n",
    "    nbaTeams['Orlando Magic'] = 'ORL'\n",
    "    nbaTeams['Philadelphia 76ers'] = 'PHI'\n",
    "    nbaTeams['Phoenix Suns'] = 'PHX'\n",
    "    nbaTeams['Portland Trail Blazers'] = 'POR'\n",
    "    nbaTeams['Sacramento Kings'] = 'SAC'\n",
    "    nbaTeams['San Antonio Spurs'] = 'SAS'\n",
    "    nbaTeams['Toronto Raptors'] = 'TOR'\n",
    "    nbaTeams['Utah Jazz'] = 'UTA'\n",
    "    nbaTeams['Washington Wizards'] = 'WAS'\n",
    "    nbaTeams_2 = {v: k for k, v in nbaTeams.items()}\n",
    "    \n",
    "    my_cursor = conn.cursor()\n",
    "    sqlformula1 = '''Create view Jointed_Table as \n",
    "    select * from (select * from Shot_Selection where Season = '%s') as \n",
    "    short_shot natural join (select PlayerName, TeamName from Player_Bio where season = '%s') as short_bio;''' % (Season,Season)\n",
    "    \n",
    "    my_cursor.execute(sqlformula1)\n",
    "\n",
    "    my_cursor.execute('''Create view Team_Shooting as\n",
    "    select TeamName, sum(3FGA) as Total_3pt_attempt, sum(2FGA) as Total_2pt_attempt from Jointed_Table group by TeamName;''')\n",
    "\n",
    "    \n",
    "    sqlformula = \"SELECT Total_3pt_attempt FROM Team_Shooting where TeamName = %s\"\n",
    "    my_cursor.execute(sqlformula, TeamName)\n",
    "    my_result = my_cursor.fetchall()\n",
    "    Three_ptA = int(my_result[0][0])\n",
    "    \n",
    "    sqlformula = '''select count(*)+1 from Team_Shooting \n",
    "    where Total_3pt_attempt > %s;'''\n",
    "\n",
    "    my_cursor.execute(sqlformula, Three_ptA)\n",
    "    my_result = my_cursor.fetchall()\n",
    "    ranking = my_result[0][0]\n",
    "    \n",
    "    print(\"The team %s attempted %s three pointers during %s season, which ranked No.%d in the league.\" % (nbaTeams_2[TeamName], Three_ptA, Season, ranking))\n",
    "    \n",
    "    my_cursor.execute(\"drop view Jointed_Table;\")\n",
    "    my_cursor.execute(\"drop view Team_Shooting\")\n",
    "    conn.close()\n",
    "\n",
    "def find_team_2PTA_ranking(TeamName, Season):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    \n",
    "    nbaTeams = {}\n",
    "    nbaTeams['Atlanta Hawks'] = 'ATL'\n",
    "    nbaTeams['Brooklyn Nets'] = 'BKN'\n",
    "    nbaTeams['Boston Celtics'] = 'BOS'\n",
    "    nbaTeams['Charlotte Hornets'] = 'CHA'\n",
    "    nbaTeams['Chicago Bulls'] = 'CHI'\n",
    "    nbaTeams['Cleveland Cavaliers'] = 'CLE'\n",
    "    nbaTeams['Dallas Mavericks'] = 'DAL'\n",
    "    nbaTeams['Denver Nuggets'] = 'DEN'\n",
    "    nbaTeams['Detroit Pistons'] = 'DET'\n",
    "    nbaTeams['Golden State Warriors'] = 'GSW'\n",
    "    nbaTeams['Houston Rockets'] = 'HOU'\n",
    "    nbaTeams['Indiana Pacers'] = 'IND'\n",
    "    nbaTeams['LA Clippers'] = 'LAC'\n",
    "    nbaTeams['Los Angeles Lakers'] = 'LAL'\n",
    "    nbaTeams['Memphis Grizzlies'] = 'MEM'\n",
    "    nbaTeams['Miami Heat'] = 'MIA'\n",
    "    nbaTeams['Milwaukee Bucks'] = 'MIL'\n",
    "    nbaTeams['Minnesota Timberwolves'] = 'MIN'\n",
    "    nbaTeams['New Orleans Pelicans'] = 'NOP'\n",
    "    nbaTeams['New York Knicks'] = 'NYK'\n",
    "    nbaTeams['Oklahoma City Thunder'] = 'OKC'\n",
    "    nbaTeams['Orlando Magic'] = 'ORL'\n",
    "    nbaTeams['Philadelphia 76ers'] = 'PHI'\n",
    "    nbaTeams['Phoenix Suns'] = 'PHX'\n",
    "    nbaTeams['Portland Trail Blazers'] = 'POR'\n",
    "    nbaTeams['Sacramento Kings'] = 'SAC'\n",
    "    nbaTeams['San Antonio Spurs'] = 'SAS'\n",
    "    nbaTeams['Toronto Raptors'] = 'TOR'\n",
    "    nbaTeams['Utah Jazz'] = 'UTA'\n",
    "    nbaTeams['Washington Wizards'] = 'WAS'\n",
    "    nbaTeams_2 = {v: k for k, v in nbaTeams.items()}\n",
    "    \n",
    "    my_cursor = conn.cursor()\n",
    "    sqlformula1 = '''Create view Jointed_Table as \n",
    "    select * from (select * from Shot_Selection where Season = '%s') as \n",
    "    short_shot natural join (select PlayerName, TeamName from Player_Bio where season = '%s') as short_bio;''' % (Season,Season)\n",
    "    \n",
    "    my_cursor.execute(sqlformula1)\n",
    "\n",
    "    my_cursor.execute('''Create view Team_Shooting as\n",
    "    select TeamName, sum(3FGA) as Total_3pt_attempt, sum(2FGA) as Total_2pt_attempt from Jointed_Table group by TeamName;''')\n",
    "\n",
    "    \n",
    "    sqlformula = \"SELECT Total_2pt_attempt FROM Team_Shooting where TeamName = %s\"\n",
    "    my_cursor.execute(sqlformula, TeamName)\n",
    "    my_result = my_cursor.fetchall()\n",
    "    Two_ptA = int(my_result[0][0])\n",
    "    \n",
    "    sqlformula = '''select count(*)+1 from Team_Shooting \n",
    "    where Total_2pt_attempt > %s;'''\n",
    "\n",
    "    my_cursor.execute(sqlformula, Two_ptA)\n",
    "    my_result = my_cursor.fetchall()\n",
    "    ranking = my_result[0][0]\n",
    "    \n",
    "    print(\"The team %s attempted %s two pointers during %s season, which ranked No.%d in the league.\" % (nbaTeams_2[TeamName], Two_ptA, Season, ranking))\n",
    "    \n",
    "    my_cursor.execute(\"drop view Jointed_Table;\")\n",
    "    my_cursor.execute(\"drop view Team_Shooting\")\n",
    "    conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The team Houston Rockets attempted 7617 three pointers during 2018-19 season, which ranked No.1 in the league.\n"
     ]
    }
   ],
   "source": [
    "find_team_3PTA_ranking(\"HOU\",'2018-19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The team Houston Rockets attempted 6509 two pointers during 2017-18 season, which ranked No.16 in the league.\n"
     ]
    }
   ],
   "source": [
    "find_team_2PTA_ranking(\"HOU\",'2017-18')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advance Function:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AF1: Predict a player's game stat line based on his past games vs the selected opponent team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call Stored procedure in mysql server:\n",
    "'''\n",
    "    DROP PROCEDURE IF EXISTS I_Hate_Your_Team;\n",
    "    DELIMITER //\n",
    "    CREATE PROCEDURE I_Hate_Your_Team(IN player_name varchar(255), In Opponent_Team VARCHAR(255))\n",
    "    BEGIN\n",
    "   declare Done int default 0;\n",
    "   declare temp char(10) default 0;\n",
    "   declare currDate INT;\n",
    "   declare DateCurr cursor for select distinct Date from Game_Stats where PlayerName = player_name and OpponentTeam = Opponent_Team;\n",
    "   declare continue handler for NOT FOUND set Done = 1;\n",
    "   \n",
    "   Drop table if exists new_table;\n",
    "   Create table new_table(\n",
    "      Date    Int primary key,\n",
    "      Month   char(10),\n",
    "      PlayerName VARCHAR(255),\n",
    "      Points  Int,\n",
    "      Rebounds Int,\n",
    "      Assists Int,\n",
    "      Steals Int,\n",
    "      OpponentTeam VARCHAR(255)\n",
    "   );\n",
    "   \n",
    "   open DateCurr;\n",
    "   Repeat \n",
    "       fetch DateCurr into currDate;\n",
    "       Insert ignore into new_table (Date, Month, PlayerName, Points, Rebounds, Assists, Steals, OpponentTeam)\n",
    "       (SELECT Date, Substring(CAST(Date AS CHAR),-4,2), PlayerName, Points, Rebounds, Assists, Steals, OpponentTeam\n",
    "        from Game_Stats where PlayerName = player_name and OpponentTeam = Opponent_Team);\n",
    "\t   set temp = (select Month from new_table where Date = currDate);\n",
    "       if temp = '01' then\n",
    "          Update new_table\n",
    "          Set Month = \"Jan\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '02' then\n",
    "          Update new_table\n",
    "          Set Month = \"Feb\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '03' then\n",
    "          Update new_table\n",
    "          Set Month = \"Mar\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '04' then\n",
    "          Update new_table\n",
    "          Set Month = \"Apr\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '05' then\n",
    "          Update new_table\n",
    "          Set Month = \"May\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '06' then\n",
    "          Update new_table\n",
    "          Set Month = \"Jun\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '07' then\n",
    "          Update new_table\n",
    "          Set Month = \"Jul\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '08' then\n",
    "          Update new_table\n",
    "          Set Month = \"Aug\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '09' then\n",
    "          Update new_table\n",
    "          Set Month = \"Sep\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '10' then\n",
    "          Update new_table\n",
    "          Set Month = \"Oct\"\n",
    "          where Date = currDate;\n",
    "\t   elseif temp = '11' then\n",
    "          Update new_table\n",
    "          Set Month = \"Nov\"\n",
    "          where Date = currDate;\n",
    "\t   else\n",
    "          Update new_table\n",
    "          Set Month = \"Dec\"\n",
    "          where Date = currDate;\n",
    "       end if;\n",
    "       \n",
    "\t   \n",
    "   UNTIL Done\n",
    "   End Repeat;\n",
    "   close DateCurr;\n",
    "   \n",
    "   select Month, PlayerName, OpponentTeam, AVG(Points) as AVG_Points, AVG(Rebounds) as AVG_Rebounds, AVG(Assists) as AVG_Assists, AVG(Steals) as AVG_Steals from new_table group by Month, PlayerName, OpponentTeam;\n",
    "    END//\n",
    "    DELIMITER ;\n",
    "'''\n",
    "    \n",
    "def stat_predictor(Player_Name, Opponent_Team, Month):\n",
    "    conn = pymysql.connect(\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd= \"12345678\",\n",
    "    database = \"NBA_DB\")\n",
    "    \n",
    "    Months = {}\n",
    "    Months[\"Jan\"] = 1\n",
    "    Months[\"Feb\"] = 2\n",
    "    Months[\"Mar\"] = 3\n",
    "    Months[\"Apr\"] = 4\n",
    "    Months[\"May\"] = 5\n",
    "    Months[\"Jun\"] = 6\n",
    "    Months[\"Jul\"] = 7\n",
    "    Months[\"Aug\"] = 8\n",
    "    Months[\"Sep\"] = 9\n",
    "    Months[\"Oct\"] = 10\n",
    "    Months[\"Nov\"] = 11\n",
    "    Months[\"Dec\"] = 12\n",
    "    \n",
    "    my_cursor = conn.cursor()\n",
    "    sqlformula = 'call I_Hate_Your_Team(\"%s\", \"%s\")' % (Player_Name, Opponent_Team)\n",
    "    df = pd.read_sql(sqlformula, conn)\n",
    "    conn.close()\n",
    "    \n",
    "    Month_dict = dict((v,k) for k,v in enumerate(calendar.month_abbr))\n",
    "    Month_digit = [Month_dict[key] for key in df.Month.tolist()]\n",
    "    df.Month = Month_digit\n",
    "    df = df.sort_values(by=['Month'])\n",
    "\n",
    "    X = [[value] for value in df.Month.tolist()]\n",
    "    point = [value for value in df.AVG_Points.tolist()]\n",
    "    rebound = [value for value in df.AVG_Rebounds.tolist()]\n",
    "    assist = [value for value in df.AVG_Assists.tolist()]\n",
    "    steals = [value for value in df.AVG_Steals.tolist()]\n",
    "\n",
    "    poly_reg = PolynomialFeatures(degree=2)\n",
    "    X_poly = poly_reg.fit_transform(X)\n",
    "    pol_reg_point = LinearRegression()\n",
    "    pol_reg_point.fit(X_poly, point)\n",
    "    pol_reg_rebound = LinearRegression()\n",
    "    pol_reg_rebound.fit(X_poly, rebound)\n",
    "    pol_reg_assist = LinearRegression()\n",
    "    pol_reg_assist.fit(X_poly, assist)\n",
    "    pol_reg_steal = LinearRegression()\n",
    "    pol_reg_steal.fit(X_poly, steals)\n",
    "\n",
    "    plt.plot(X, pol_reg_point.predict(poly_reg.fit_transform(X)), color='blue',label = \"Points\")\n",
    "    plt.plot(X, pol_reg_rebound.predict(poly_reg.fit_transform(X)), color='red',label = \"Rebounds\")\n",
    "    plt.plot(X, pol_reg_assist.predict(poly_reg.fit_transform(X)), color='green',label = \"Assists\")\n",
    "    plt.plot(X, pol_reg_steal.predict(poly_reg.fit_transform(X)), color='yellow',label = \"Steals\")\n",
    "    plt.title('Stat_Prediction: %s vs %s' %(Player_Name, Opponent_Team))\n",
    "    plt.xlabel('Month')\n",
    "    plt.ylabel('Stats')\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    plt.show()\n",
    "    \n",
    "    predicted_point = pol_reg_point.predict(poly_reg.fit_transform([[Months[Month]]]))\n",
    "    predicted_rebound = pol_reg_rebound.predict(poly_reg.fit_transform([[Months[Month]]]))\n",
    "    predicted_assist = pol_reg_assist.predict(poly_reg.fit_transform([[Months[Month]]]))\n",
    "    predicted_steal = pol_reg_steal.predict(poly_reg.fit_transform([[Months[Month]]]))\n",
    "    \n",
    "    predicted_stat_line = \"%s will have %d points, %d rebounds, %d assists, %d steals against %s.\" %(Player_Name, int(predicted_point[0]), int(predicted_rebound[0]), int(predicted_assist[0]), int(predicted_steal[0]),Opponent_Team)\n",
    "    print(predicted_stat_line)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jeremy Lamb will have 12 points, 5 rebounds, 2 assists, 0 steals against OKC.\n"
     ]
    }
   ],
   "source": [
    "stat_predictor(\"Jeremy Lamb\", \"OKC\",\"Dec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jimmy Butler will have 27 points, 6 rebounds, 2 assists, 1 steals against BOS.\n"
     ]
    }
   ],
   "source": [
    "# Predict Player's game stat by Opponent Team and game month.\n",
    "stat_predictor(\"Jimmy Butler\", \"BOS\", \"Dec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Myles Turner will have 13 points, 4 rebounds, 1 assists, 0 steals against OKC.\n"
     ]
    }
   ],
   "source": [
    "stat_predictor(\"Myles Turner\", \"OKC\", 'Dec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kemba Walker will have 21 points, 5 rebounds, 4 assists, 2 steals against MIA.\n"
     ]
    }
   ],
   "source": [
    "stat_predictor(\"Kemba Walker\", \"MIA\", 'Dec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
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