transform_player_attributes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "player_attributes = pd.read_csv('Player_Attributes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       int64\n",
       "player_fifa_api_id       int64\n",
       "player_api_id            int64\n",
       "date                    object\n",
       "overall_rating         float64\n",
       "potential              float64\n",
       "preferred_foot          object\n",
       "attacking_work_rate     object\n",
       "defensive_work_rate     object\n",
       "crossing               float64\n",
       "finishing              float64\n",
       "heading_accuracy       float64\n",
       "short_passing          float64\n",
       "volleys                float64\n",
       "dribbling              float64\n",
       "curve                  float64\n",
       "free_kick_accuracy     float64\n",
       "long_passing           float64\n",
       "ball_control           float64\n",
       "acceleration           float64\n",
       "sprint_speed           float64\n",
       "agility                float64\n",
       "reactions              float64\n",
       "balance                float64\n",
       "shot_power             float64\n",
       "jumping                float64\n",
       "stamina                float64\n",
       "strength               float64\n",
       "long_shots             float64\n",
       "aggression             float64\n",
       "interceptions          float64\n",
       "positioning            float64\n",
       "vision                 float64\n",
       "penalties              float64\n",
       "marking                float64\n",
       "standing_tackle        float64\n",
       "sliding_tackle         float64\n",
       "gk_diving              float64\n",
       "gk_handling            float64\n",
       "gk_kicking             float64\n",
       "gk_positioning         float64\n",
       "gk_reflexes            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_attributes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_attributes['date'] = pd.to_datetime(player_attributes['date'], format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = player_attributes.groupby(['player_api_id',player_attributes.date.dt.year]).mean().reset_index()\n",
    "new_df = new_df[['player_api_id','date','player_fifa_api_id','overall_rating']]\n",
    "new_df['player_api_id'] = new_df['player_api_id'].astype(int)\n",
    "new_df['player_fifa_api_id'] = new_df['player_fifa_api_id'].astype(int)\n",
    "new_df['date'] = new_df['date'].astype(int)\n",
    "new_df = new_df.rename(columns={\"date\":\"year\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.to_csv('data/player_attributes_transformed.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player_api_id           int32\n",
       "year                    int32\n",
       "player_fifa_api_id      int32\n",
       "overall_rating        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}