GW_result.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "# Pre-fetch general information and cache it to avoid repeated API requests\n",
    "general_info = requests.get(\"https://fantasy.premierleague.com/api/bootstrap-static/\").json()\n",
    "teams_info = {team[\"id\"]: team[\"name\"] for team in general_info[\"teams\"]}\n",
    "positions_info = {position[\"id\"]: position[\"singular_name_short\"] for position in general_info[\"element_types\"]}\n",
    "\n",
    "def get_team_name(team_id):\n",
    "    \"\"\"Retrieve the team name by team ID from cached data.\"\"\"\n",
    "    return teams_info.get(team_id)\n",
    "\n",
    "def get_player_position(element_type):\n",
    "    \"\"\"Retrieve the player's position shorthand from cached data based on element type.\"\"\"\n",
    "    return positions_info.get(element_type)\n",
    "\n",
    "def get_player_fixture_info(player_id):\n",
    "    \"\"\"\n",
    "    Fetch the first upcoming fixture information for a given player.\n",
    "    Utilizes cached team names for efficiency.\n",
    "    \"\"\"\n",
    "    player_info = requests.get(f\"https://fantasy.premierleague.com/api/element-summary/{player_id}/\").json()\n",
    "    fixture = player_info[\"fixtures\"][0]  # Assuming we always want the first fixture\n",
    "    home_team = get_team_name(fixture[\"team_h\"])\n",
    "    away_team = get_team_name(fixture[\"team_a\"])\n",
    "    return home_team, away_team, fixture[\"kickoff_time\"], fixture[\"is_home\"]\n",
    "\n",
    "def get_gameweek_result(player_id, gameweek):\n",
    "    \"\"\"\n",
    "    Fetch and return a dictionary of the player's statistics for a specific gameweek.\n",
    "    \"\"\"\n",
    "    gameweek_results = requests.get(f\"https://fantasy.premierleague.com/api/element-summary/{player_id}/\").json()\n",
    "    return gameweek_results[\"history\"][gameweek - 1]\n",
    "\n",
    "# Load the gameweek fixture from a CSV file\n",
    "gameweek = 26\n",
    "gameweek_fixture = pd.read_csv(f\"C:\\\\Users\\\\prane\\\\Downloads\\\\FPL\\\\GW_PointsPredictor\\\\Default_data\\\\2023-24\\\\fixtures\\\\GW{gameweek}.csv\")\n",
    "\n",
    "def get_fixture(name):\n",
    "    \"\"\"\n",
    "    Retrieve fixture information for a given player name from the pre-loaded DataFrame.\n",
    "    \"\"\"\n",
    "    fixture_row = gameweek_fixture[gameweek_fixture[\"name\"] == name].iloc[0]\n",
    "    return fixture_row[\"home_team\"], fixture_row[\"away_team\"], fixture_row[\"kickoff_time\"], fixture_row[\"is_home\"]\n",
    "\n",
    "# Process and gather information for all players\n",
    "all_players = []\n",
    "for player_info in general_info[\"elements\"]:\n",
    "    try:\n",
    "        player_id = player_info[\"id\"]\n",
    "        print(player_id)\n",
    "        name = f\"{player_info['first_name']} {player_info['second_name']}\"\n",
    "        team_id = player_info[\"team\"]\n",
    "        cost = player_info[\"now_cost\"]\n",
    "        element_type = player_info[\"element_type\"]\n",
    "\n",
    "        # Utilize cached functions to fetch team name and player position\n",
    "        my_team = get_team_name(team_id)\n",
    "        position = get_player_position(element_type)\n",
    "\n",
    "        # Get gameweek result and fixture info using optimized functions\n",
    "        gameweek_result = get_gameweek_result(player_id, gameweek)\n",
    "        home_team, away_team, kickoff_time, is_home = get_fixture(name)\n",
    "\n",
    "        # Update the gameweek result with additional player details\n",
    "        gameweek_result.update({\n",
    "            \"id\": player_id,\n",
    "            \"name\": name,\n",
    "            \"cost\": cost,\n",
    "            \"position\": position,\n",
    "            \"home_team\": home_team,\n",
    "            \"away_team\": away_team,\n",
    "            \"team_x\": my_team,\n",
    "            \"opponent_team\": get_team_name(gameweek_result[\"opponent_team\"])\n",
    "        })\n",
    "\n",
    "        all_players.append(gameweek_result)\n",
    "    except IndexError:  # Catch cases where specific data may be missing\n",
    "        continue\n",
    "\n",
    "# Create and display a DataFrame with all collected player data\n",
    "df = pd.DataFrame(all_players)\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(f\"C:\\\\Users\\\\prane\\\\Downloads\\\\FPL\\\\GW_PointsPredictor\\\\Default_data\\\\2023-24\\\\results\\\\GW{gameweek}.csv\")"
   ]
  }
 ],
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  "language_info": {
   "name": "python"
  }
 },
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