LOL_Data_Creation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "gpzaILWi_CCA",
        "-WZjcR0WasWg",
        "6cfp_ogWa9gt",
        "yMj6-OtUbLSL",
        "ej_MdNPRbVYl",
        "yeM2KLmRbc8r"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Imports&Installs"
      ],
      "metadata": {
        "id": "gpzaILWi_CCA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install riotwatcher"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6stbQSHv6_e",
        "outputId": "caf87391-2009-4381-c021-68532a72ca0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting riotwatcher\n",
            "  Downloading riotwatcher-3.2.4-py3-none-any.whl (59 kB)\n",
            "\u001b[K     |████████████████████████████████| 59 kB 5.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from riotwatcher) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->riotwatcher) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->riotwatcher) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->riotwatcher) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->riotwatcher) (1.24.3)\n",
            "Installing collected packages: riotwatcher\n",
            "Successfully installed riotwatcher-3.2.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from riotwatcher import LolWatcher, ApiError\n",
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import requests\n",
        "import json\n",
        "import time\n",
        "import pandas as pd\n",
        "import time\n",
        "import random\n",
        "from threading import Thread\n",
        "from google.colab import files"
      ],
      "metadata": {
        "id": "28m8IcWKz7gk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/MyDrive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwrKpW35xHhp",
        "outputId": "8c04f70a-f0d0-4359-ac76-715e1e5e1d2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Collection"
      ],
      "metadata": {
        "id": "-WZjcR0WasWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# original Code Pulled From https://github.com/fattorib/LeagueMatchScraper\n",
        "# and adapted for our current desires and needs.\n",
        "# wee need keys to spam faster\n",
        "key1='RGAPI-df80d259-9dd8-4089-809a-ca32e370dcb6' # we need a new api after 24 hours\n",
        "key2='RGAPI-7a0eb59a-15b2-4500-a57f-1038db5b2195' # we need a new api after 24 hours\n",
        "key3='RGAPI-c775d65b-43a8-4a27-bfee-53ebd27bc51a' # we need a new api after 24 hours\n",
        "key4='RGAPI-9e4214bb-462f-4851-81e2-59499cde330d' # we need a new api after 24 hours\n",
        "key5='RGAPI-c1209d6d-9ada-4c56-8354-9d896c276286' # we need a new api after 24 hours\n",
        "key6='RGAPI-7051448c-0416-459f-936a-ea83d77d36af' # we need a new api after 24 hours\n",
        "\n",
        "keys=[key1,key2,key3,key4,key5,key6,key1,key2,key3,key4,key5,key6]\n",
        "Ws=[LolWatcher(key) for key in keys]\n",
        "region = 'eun1' # region where we will collect our data"
      ],
      "metadata": {
        "id": "pfbkxhdfzA26"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Collect summonerID From IRon, Silver, Gold, Platinum And Diamond Divisions"
      ],
      "metadata": {
        "id": "6cfp_ogWa9gt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Code for pulling list of summoner IDs\n",
        "#Function to pull a single page of summoner IDs for given rank and tier\n",
        "summID_list = []\n",
        "def summ_ID_puller(division,tier,page,key):\n",
        "    url_pull = \"https://{}.api.riotgames.com/lol/league/v4/entries/RANKED_SOLO_5x5/{}/{}?page={}&api_key={}\".format(region,division,tier,page,key)\n",
        "    profile_list = requests.get(url_pull).json()\n",
        "    num_profiles = len(profile_list)\n",
        "    for profile in range(0,num_profiles):\n",
        "        summID_list.append(profile_list[profile]['summonerId'])\n",
        "\n",
        "\n",
        "for tier in [\"I\",\"II\",\"III\",\"IV\"]:\n",
        "  for page in range(1,24,len(keys)):\n",
        "    print(len(summID_list))\n",
        "    time.sleep(1.5)\n",
        "    for j in range(len(keys)):\n",
        "      summ_ID_puller(\"IRON\",tier,page+j,keys[j])\n",
        "      if len(summID_list)%500==0:\n",
        "            df = pd.DataFrame(summID_list,columns = [\"Summoner ID\"])\n",
        "            df.to_csv('summID.csv',mode = 'w')\n",
        "\n",
        "for tier in [\"I\",\"II\",\"III\",\"IV\"]:\n",
        "  for page in range(1,24,len(keys)):\n",
        "    print(len(summID_list))\n",
        "    time.sleep(1.5)\n",
        "    for j in range(len(keys)):\n",
        "      summ_ID_puller(\"BRONZE\",tier,page+j,keys[j])\n",
        "      if len(summID_list)%500==0:\n",
        "            df = pd.DataFrame(summID_list,columns = [\"Summoner ID\"])\n",
        "            df.to_csv('summID.csv',mode = 'w')\n",
        "\n",
        "for tier in [\"I\",\"II\",\"III\",\"IV\"]:\n",
        "  for page in range(1,24,len(keys)):\n",
        "    print(len(summID_list))\n",
        "    time.sleep(1.5)\n",
        "    for j in range(len(keys)):\n",
        "      summ_ID_puller(\"SILVER\",tier,page+j,keys[j])\n",
        "      if len(summID_list)%500==0:\n",
        "            df = pd.DataFrame(summID_list,columns = [\"Summoner ID\"])\n",
        "            df.to_csv('summID.csv',mode = 'w')\n",
        "\n",
        "for tier in [\"I\",\"II\",\"III\",\"IV\"]:\n",
        "  for page in range(1,24,len(keys)):\n",
        "    print(len(summID_list))\n",
        "    time.sleep(1.5)\n",
        "    for j in range(len(keys)):\n",
        "      summ_ID_puller(\"GOLD\",tier,page+j,keys[j])\n",
        "      if len(summID_list)%500==0:\n",
        "            df = pd.DataFrame(summID_list,columns = [\"Summoner ID\"])\n",
        "            df.to_csv('summID.csv',mode = 'w')\n",
        "\n",
        "for tier in [\"I\",\"II\",\"III\",\"IV\"]:\n",
        "  for page in range(1,24,len(keys)):\n",
        "    print(len(summID_list))\n",
        "    time.sleep(1.5)\n",
        "    for j in range(len(keys)):\n",
        "      summ_ID_puller(\"PLATINUM\",tier,page+j,keys[j])\n",
        "      if len(summID_list)%500==0:\n",
        "            df = pd.DataFrame(summID_list,columns = [\"Summoner ID\"])\n",
        "            df.to_csv('summID.csv',mode = 'w')\n",
        "\n",
        "for tier in [\"I\",\"II\",\"III\",\"IV\"]:\n",
        "  for page in range(1,24,len(keys)):\n",
        "    print(len(summID_list))\n",
        "    time.sleep(1.5)\n",
        "    for j in range(len(keys)):\n",
        "      summ_ID_puller(\"DIAMOND\",tier,page+j,keys[j])\n",
        "      if len(summID_list)%500==0:\n",
        "            df = pd.DataFrame(summID_list,columns = [\"Summoner ID\"])\n",
        "            df.to_csv('summID.csv',mode = 'w')"
      ],
      "metadata": {
        "id": "UVv7gJZvVor9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#shuffle the IDs\n",
        "random.shuffle(summID_list)\n",
        "df = pd.DataFrame(summID_list,columns = [\"Summoner ID\"])\n",
        "df.to_csv('summID.csv',mode = 'w')"
      ],
      "metadata": {
        "id": "17JAbyuwmYZF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Collect PUUIDs From Gathered Summoner IDs"
      ],
      "metadata": {
        "id": "yMj6-OtUbLSL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Code for pulling list of account IDs from summoner IDs\n",
        "summoner_IDs = pd.read_csv(\"summID.csv\")\n",
        "#PUUIDs_list = []\n",
        "\n",
        "summID_list = summoner_IDs[\"Summoner ID\"]\n",
        "\n",
        "#Function to get the PUUID from summoner ID\n",
        "def PUUID_puller(summID,k):\n",
        "    url_acct_pull = \"https://{}.api.riotgames.com/lol/summoner/v4/summoners/{}?api_key={}\".format(region,summID,k)\n",
        "    account_info = requests.get(url_acct_pull).json()\n",
        "    PUUIDs_list.append(account_info[\"puuid\"])\n",
        "\n",
        "for i in range(10000,20000,len(keys)):\n",
        "  time.sleep(1.5)\n",
        "  print(len(PUUIDs_list))\n",
        "  for j in range(0,len(keys)):\n",
        "    try:\n",
        "        PUUID_puller(summID_list[i+j],keys[j])\n",
        "    except KeyError:\n",
        "        print(\"keyerror\")\n",
        "    if len(PUUIDs_list)%500==0:\n",
        "      #Create the PUUID CSV\n",
        "      df = pd.DataFrame(PUUIDs_list, columns = [\"PUUID\"])\n",
        "      df.to_csv('PUUIDs.csv',mode = 'w')"
      ],
      "metadata": {
        "id": "238ew8OUzAyJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d93541c0-9baa-441b-c896-c3284ceb6448"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-e8c892f3453b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3318\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m2445\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m281\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m   \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPUUIDs_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(PUUIDs_list, columns = [\"PUUID\"])\n",
        "df.to_csv('PUUIDs.csv',mode = 'w')\n",
        "print(\"Done pulling PUUIDs!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEu2c2lt4v5A",
        "outputId": "9236045e-bc8e-4178-f569-e55b8751fa4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done pulling PUUIDs!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Get Matches For Every Collected PUUID"
      ],
      "metadata": {
        "id": "ej_MdNPRbVYl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 3: Pulling all matches for each player\n",
        "PUUIDs = pd.read_csv(\"PUUIDs.csv\")\n",
        "PUUIDs_list = PUUIDs[\"PUUID\"]\n",
        "\n",
        "#This is the list of MatchIDs we are creating\n",
        "matchID_list = []\n",
        "\n",
        "#Function to pull all matches for a given account ID\n",
        "#Queue 420 is for 5x5 Ranked SOLO\n",
        "def match_ID_puller(puuid,W):\n",
        "  match_history = W.match.matchlist_by_puuid(region,puuid,queue=420)\n",
        "  NumberOfMatches=len(match_history)\n",
        "  if NumberOfMatches>0:\n",
        "    for i in range(0,NumberOfMatches):\n",
        "        try:\n",
        "            match_id = match_history[i]\n",
        "            matchID_list.append(match_id)\n",
        "        except KeyError:\n",
        "            print(\"KeyError occured with :\",puuid)\n",
        "\n",
        "for i in range(1,len(PUUIDs_list),len(keys)):\n",
        "  time.sleep(1.5)\n",
        "  for j in range(0,len(Ws)):\n",
        "    match_ID_puller(PUUIDs_list[i+j],Ws[j])\n",
        "    if len(matchID_list)%500==0:\n",
        "      #Create the PUUID CSV\n",
        "      df = pd.DataFrame(matchID_list, columns = [\"MatchId\"])\n",
        "      df.to_csv('MatchId.csv',mode = 'w')\n",
        "  print(len(matchID_list),i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5ug9O3-WO7k",
        "outputId": "4fdf8f35-f4c0-4ba7-9680-2f5fcb294034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create the MatchId CSV\n",
        "df = pd.DataFrame(matchID_list, columns = [\"MatchId\"])\n",
        "df.to_csv('MatchId.csv',mode = 'w')"
      ],
      "metadata": {
        "id": "x8z7W9RQXMZq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create The Dataset Of Match Data from Match IDs"
      ],
      "metadata": {
        "id": "yeM2KLmRbc8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#our dataframe design\n",
        "#####################################################\n",
        "#         TeamWon                   TeamLost\n",
        "#####################################################\n",
        "#list of Bans\n",
        "#['champion','baron','dragon','riftHerald','tower'] kills\n",
        "#gameDuration in mins\n",
        "#gameVersion (used to determine the batch)"
      ],
      "metadata": {
        "id": "9gtR8xvR5yT8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Step 4: extracting Match data for every match id\n",
        "MatchIds = pd.read_csv(\"/content/MatchId.csv\")\n",
        "MatchIds_list = MatchIds[\"MatchId\"]\n",
        "#Data = pd.read_csv(\"Data.csv\")\n",
        "#matchData_list = df.values.tolist()\n",
        "#matchData_list=[]\n",
        "\n",
        "def getDataFromMatchByID(region, match_id,W):\n",
        "  #Request Game Data from Api\n",
        "  Game = W.match.by_id(region, match_id)\n",
        "  InfoDto = Game['info']\n",
        "  #Create Teams to collect champion and items\n",
        "  TeamWon = []\n",
        "  TeamLost = []\n",
        "  #select the data collected for every player in the game\n",
        "  PlayerStatsToAppend=['championId','item0', 'item1', 'item2', 'item3', 'item4', 'item5']\n",
        "  #loop over all players\n",
        "  for row in InfoDto['participants']:\n",
        "    participants_row = [row[statName] for statName in PlayerStatsToAppend]\n",
        "    winFlag=row['win']\n",
        "    #if this player belongs to the winning team\n",
        "    if winFlag:\n",
        "      TeamWon.append(participants_row)\n",
        "    #else this player belongs to the losing team\n",
        "    else:\n",
        "      TeamLost.append(participants_row)\n",
        "\n",
        "  #Extract Meta Data for each team\n",
        "  DataNames=['champion','baron','dragon','riftHerald','tower']\n",
        "  for teamData in InfoDto['teams']:\n",
        "    winFlag = teamData['win']\n",
        "    objs = teamData['objectives']\n",
        "    if winFlag:\n",
        "      TeamWon.extend([[ban['championId'] for ban in teamData['bans']]])\n",
        "      TeamWon.extend([[objs[DataName]['kills'] for DataName in DataNames]])\n",
        "    else:\n",
        "      TeamLost.extend([[ban['championId'] for ban in teamData['bans']]])\n",
        "      TeamLost.extend([[objs[DataName]['kills'] for DataName in DataNames]])\n",
        "\n",
        "  # create the follwing list Team won + its Data then team lost + its Data\n",
        "  TeamWon.extend(TeamLost)\n",
        "  # Add Game Duration in mins\n",
        "  TeamWon.append([round(InfoDto['gameDuration']/(60),2)])\n",
        "  # Add Game Version To Determin Batch at which the match occured\n",
        "  TeamWon.append(InfoDto['gameVersion'][:5])\n",
        "  #append a list of Strings\n",
        "  matchData_list.append([','.join([str(elem) for elem in Data]) for Data in TeamWon])\n",
        "\n",
        "\n",
        "columnNames=[\"Champ1Team1\",\"Champ2Team1\",\"Champ3Team1\",\"Champ4Team1\",\"Champ5Team1\",\"BansTeam1\",\"MetaTeam1\",\n",
        "      \"Champ1Team2\",\"Champ2Team2\",\"Champ3Team2\",\"Champ4Team2\",\"Champ5Team2\",\"BansTeam2\",\"MetaTeam2\",\n",
        "      'gameDuration (Mins)','gameVersion']\n",
        "for i in range(85001+2904,100015,len(keys)):\n",
        "  time.sleep(1.5)\n",
        "  print(len(matchData_list))\n",
        "  for j in range(0,len(Ws)):\n",
        "    getDataFromMatchByID(region, MatchIds_list[i+j], Ws[j])\n",
        "    #check if data increased by 500 rows to save a back up\n",
        "    if len(matchData_list)%500==0:\n",
        "      df = pd.DataFrame(matchData_list, columns = columnNames)\n",
        "      #save data in drive\n",
        "      df.to_csv('Data1.csv',mode = 'w')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P11jbK4iuGAu",
        "outputId": "1fd20251-3b24-4dbf-bace-d7771bf0ee98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "14725\n",
            "14737\n",
            "14749\n",
            "14761\n",
            "14773\n",
            "14785\n",
            "14797\n",
            "14809\n",
            "14821\n",
            "14833\n",
            "14845\n",
            "14857\n",
            "14869\n",
            "14881\n",
            "14893\n",
            "14905\n",
            "14917\n",
            "14929\n",
            "14941\n",
            "14953\n",
            "14965\n",
            "14977\n",
            "14989\n",
            "15001\n",
            "15013\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    384\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 100015 is not in range",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-dd974d202472>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     55\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatchData_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mgetDataFromMatchByID\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMatchIds_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0;31m#check if data increased by 500 rows to save a back up\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatchData_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1049\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1050\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    385\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    388\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 100015"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('Data1.csv',mode = 'w')"
      ],
      "metadata": {
        "id": "52FG_m24PL1h"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}