advisor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fantasy Premier League (FPL) Advisor\n",
    "\n",
    "# Purpose\n",
    "The purpose of this Jupyter notebook is to help with the selection of team members for the [Fantasy Premier League](https://fantasy.premierleague.com/) (FPL) by forecasting how many points players will earn. It uses the [fpl-data](https://github.com/177arc/fpl-data) generated estimate points and other stats. It provides:\n",
    "- a visual tool for analysing the performance of each player and understanding their potential to earn points\n",
    "- a optimiser to recommend a team with the maximum expected points to improve the performance of your current team\n",
    "- tools for selecting the best game weeks play your chips\n",
    "- visual tools to understand the re-liability of the data\n",
    "\n",
    "If you are not familar with the Fantasy Permier League, you can watch this introduction:\n",
    "\n",
    "<a href=\"http://www.youtube.com/watch?v=SV_F-cL8fC0\" target=\"_blank\"><img src=\"http://img.youtube.com/vi/SV_F-cL8fC0/0.jpg\" \n",
    "alt=\"How to play FPL\" width=\"600\" height=\"400\"/></a>\n",
    "\n",
    "# Installation\n",
    "To get started, run the following command to install all required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-21T19:30:38.173024Z",
     "start_time": "2021-04-21T19:30:38.150018Z"
    }
   },
   "outputs": [],
   "source": [
    "#!pip install -q -r ./requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import requirements\n",
    "Here we import all external and local modulues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-24T09:35:45.359392Z",
     "start_time": "2021-07-24T09:35:45.350393Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os, sys\n",
    "\n",
    "# Load local modules\n",
    "sys.path.append(os.getcwd())\n",
    "from data import get_df, get_next_gw_counts, get_next_gw_name\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "# Define type aliases\n",
    "DF = pd.DataFrame\n",
    "S = pd.Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set variables\n",
    "This section sets all important global variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-24T09:35:45.666396Z",
     "start_time": "2021-07-24T09:35:45.648389Z"
    }
   },
   "outputs": [],
   "source": [
    "CREDS_FILE = 'fpl_credentials.csv' # Location of file holding the FPL user name and password. These are only required for the personalised recommendations in the second half of this notebook.\n",
    "DATA_URL = 'https://s3.eu-west-2.amazonaws.com/fpl.177arc.net/v1/latest/'\n",
    "LAST_SEASON = '2020-21'\n",
    "CURRENT_SEASON = '2021-22'\n",
    "FIXTURES_LOOK_BACK = 38  # Limit of how many fixtures to look back for calculating rolling team stats\n",
    "PLAYER_FIXTURES_LOOK_BACK = 8 # Limit of how many fixture to look back for calcating rolling player stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load pre-processed data\n",
    "This section loads data sets generated by the [fpl-data](https://github.com/177arc/fpl-data) lambda function and made available via the S3 bucket specified in the `DATA_URL` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-24T09:35:52.806393Z",
     "start_time": "2021-07-24T09:35:45.950391Z"
    }
   },
   "outputs": [],
   "source": [
    "gws = get_df(url=f'{DATA_URL}gws.csv', index='GW ID')\n",
    "teams = get_df(url=f'{DATA_URL}teams.csv', index='Team Code')\n",
    "players_ext = get_df(url=f'{DATA_URL}players_ext.csv', index='Player Code')\n",
    "player_teams = get_df(url=f'{DATA_URL}player_teams.csv', index='Player Code')\n",
    "players_gw_team_eps_ext = get_df(url=f'{DATA_URL}players_gw_team_eps_ext.csv', index=['Player Code', 'Season', 'Game Week'])\n",
    "player_gw_next_eps_ext = get_df(url=f'{DATA_URL}player_gw_next_eps_ext.csv', index=['Player Code'])\n",
    "team_fixture_strength_ext = get_df(url=f'{DATA_URL}team_fixture_stats_ext.csv', index='Fixture Code')\n",
    "dd = get_df(url=f'{DATA_URL}data_dictionary.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configure context\n",
    "This section we configure important setting for this notebook including the data dictionary. The data dictionary contains default ordering of fields, for each field a description, default format and mapping of API field names to more readable ones. It is used to show data in a more user-friendly way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-24T09:35:52.836390Z",
     "start_time": "2021-07-24T09:35:52.809392Z"
    }
   },
   "outputs": [],
   "source": [
    "from common import Context\n",
    "from datadict.jupyter import DataDict\n",
    "\n",
    "ctx = Context()\n",
    "ctx.fixtures_look_back = FIXTURES_LOOK_BACK\n",
    "ctx.player_fixtures_look_back = PLAYER_FIXTURES_LOOK_BACK\n",
    "ctx.last_season = LAST_SEASON\n",
    "ctx.current_season = CURRENT_SEASON\n",
    "ctx.dd = DataDict(data_dict=dd)\n",
    "ctx.total_gws = gws.shape[0]\n",
    "ctx.next_gw = gws[lambda df: df['Is Next GW?']].index.values[0]\n",
    "ctx.def_next_gws = get_next_gw_name(min(ctx.total_gws-ctx.next_gw+1, 8), ctx)\n",
    "ctx.next_gw_counts = get_next_gw_counts(ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise players' cost vs their expected points\n",
    "The chart below shows expected points and cost for each player. The expected points are calculated hourly using the [fpl-data](https://github.com/177arc/fpl-data) lambda function. Use filters to focus on a particular segment and click on a dot to view more details about the player."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-24T09:35:52.898394Z",
     "start_time": "2021-07-24T09:35:52.838391Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from player_chart import show_eps_vs_cost\n",
    "\n",
    "show_eps_vs_cost(player_gw_next_eps_ext, players_gw_team_eps_ext, teams, ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get best team for wildcard or season start\n",
    "You can use the code below to get the best team for a wildcard or at the start of the season. It uses the [PuLP linear optimiser](https://pythonhosted.org/PuLP/) to find the team combination within the current money available with the highest total expected points of the over the next game weeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-24T09:35:53.053392Z",
     "start_time": "2021-07-24T09:35:52.900391Z"
    }
   },
   "outputs": [],
   "source": [
    "from team import show_opt_team\n",
    "\n",
    "show_opt_team(player_gw_next_eps_ext, def_budget=100, max_budget=115, ctx=ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load user team data\n",
    "This section loads the data of the user's team. \n",
    "\n",
    "**Note this requires your user credentials to be saved in fpl_credentials.csv in the same directory as this notebook. Use fpl_credentials_template.csv as template.** Alternatively, you can set the fpl_email and fpl_password variables below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-22T21:28:32.617921Z",
     "start_time": "2021-05-22T21:28:32.466923Z"
    }
   },
   "outputs": [],
   "source": [
    "from fplpandas import FPLPandas\n",
    "\n",
    "# Enter your FPL credentials here.\n",
    "fpl_email = ''\n",
    "fpl_password = ''\n",
    "\n",
    "if not os.path.exists(CREDS_FILE):\n",
    "    fpl_cred = {'email': fpl_email, 'password': fpl_password}\n",
    "else:\n",
    "    fpl_cred = pd.read_csv('fpl_credentials.csv').iloc[0].to_dict()\n",
    "    \n",
    "assert len(fpl_cred['email']) > 0 and len(fpl_cred['password']) > 0, 'FPL credentials not set. Please provide your email and password.'\n",
    "\n",
    "fpl = FPLPandas(**fpl_cred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-22T21:28:35.156724Z",
     "start_time": "2021-05-22T21:28:32.619923Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    user_team_raw, _, user_trans_info_raw = fpl.get_user_team()\n",
    "except aiohttp.ClientResponseError as e:\n",
    "    if e.status == 404:\n",
    "        print('Your team cannot be found. Have you created it? You can only optimise your team once you have created it.')\n",
    "    else:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-22T21:28:35.188005Z",
     "start_time": "2021-05-22T21:28:35.158727Z"
    }
   },
   "outputs": [],
   "source": [
    "from data import get_players_id_code_map\n",
    "\n",
    "players_id_code_map = (players_ext\n",
    "                       [lambda df: df['Season'] == ctx.current_season]\n",
    "                       .pipe(get_players_id_code_map))\n",
    "\n",
    "user_team = (user_team_raw\n",
    "    .pipe(ctx.dd.remap, data_set='player')\n",
    "    .assign(**{'In Team?': True})\n",
    "    .assign(**{'Selling Price': lambda df: df['Selling Price']/10})\n",
    "    .assign(**{'Purchase Price': lambda df: df['Purchase Price']/10})\n",
    "    .assign(**{'Selected?': lambda df: df['Team Position'].map(lambda x: x <= 11)}) \n",
    "    .rename_axis('Player ID')\n",
    "    .reset_index()\n",
    "    .merge(players_id_code_map, left_on='Player ID', right_index=True, suffixes=(None, None))\n",
    "    .drop(columns='Player ID')\n",
    "    .set_index('Player Code')\n",
    "    )\n",
    "\n",
    "user_trans_info = user_trans_info_raw.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Current team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-22T21:28:35.468726Z",
     "start_time": "2021-05-22T21:28:35.189726Z"
    }
   },
   "outputs": [],
   "source": [
    "from team import display_team\n",
    "\n",
    "player_user_team = (user_team\n",
    "                    .merge(player_gw_next_eps_ext, left_on='Player Code', right_on='Player Code', how='left', suffixes=(None, None)))\n",
    "display_team(player_user_team, None, ctx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-22T21:28:35.499724Z",
     "start_time": "2021-05-22T21:28:35.479727Z"
    }
   },
   "outputs": [],
   "source": [
    "total_budget = (user_trans_info['bank']/10+player_user_team['Selling Price'].sum())\n",
    "total_budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-22T21:28:35.562727Z",
     "start_time": "2021-05-22T21:28:35.500725Z"
    }
   },
   "outputs": [],
   "source": [
    "show_eps_vs_cost(user_team.merge(player_gw_next_eps_ext, left_on='Player Code', right_on='Player Code', how='outer'), players_gw_team_eps_ext, teams, ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommend team selection and transfers\n",
    "Use this section to get a recommendation on what players to select to optimise the expected points of your team and to improve it by making transfers. You need to have provided your FPL credentials for this to work.\n",
    "\n",
    "It uses the PuLP linear optimiser to find the team combination within the current budget available with the highest total expected points of the over the next five game weeks while taking your current team into account for a user defined number of transfers. Note that when executing more than one transfer on the FPL website, 4 points will be deducted from your balance for every transfer.\n",
    "\n",
    "It uses the same PuLP linear optimiser to find the selection with the highest expected points for the next game week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-22T21:28:38.321756Z",
     "start_time": "2021-05-22T21:28:38.212755Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from team import show_opt_team\n",
    "\n",
    "player_team_eps_user = (user_team\n",
    "    .merge(player_gw_next_eps_ext, left_on='Player Code', right_on='Player Code', how='right', suffixes=(None, None))\n",
    "    .assign(**{'Current Cost': lambda df: df['Selling Price'].fillna(df['Current Cost'])}))\n",
    "\n",
    "show_opt_team(player_team_eps_user, total_budget, ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select a good week to play the free hit chip\n",
    "The idea here is to use the expected points for each player to determine the expected points of the optimal team (selected players only) for each game week. The game week with the highest expected points is the best for a free hit. **Be aware that towards the end of the season, double game weeks get scheduled and therefore it is advisable to wait till early March.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-17T19:19:16.315927Z",
     "start_time": "2021-05-17T19:19:13.829343Z"
    }
   },
   "outputs": [],
   "source": [
    "from common import log_progress\n",
    "from backtest import pred_free_hit_gw\n",
    "\n",
    "if ctx.next_gw > 1:\n",
    "    free_hist_eps = DF()\n",
    "    for gw in log_progress(range(ctx.next_gw, ctx.total_gws+1), name='Game Week'):\n",
    "        free_hist_eps = free_hist_eps.append(\n",
    "            pred_free_hit_gw(players_gw_team_eps_ext, player_teams, total_budget, gw, ctx), \n",
    "            ignore_index=True)\n",
    "\n",
    "    display(free_hist_eps\n",
    "            .sort_values('Expected Points', ascending=False)\n",
    "            [['Game Week', 'Expected Points']]\n",
    "            .pipe(ctx.dd.display, descriptions=False, index=False, head=None))\n",
    "else:\n",
    "    print('This simulation relies on data that will only be available after game week 1 and will only become reliable later in the season.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-01T20:18:17.598143Z",
     "start_time": "2020-03-01T20:18:17.594155Z"
    }
   },
   "source": [
    "# Select a good week for playing the bench boost chip\n",
    "Here we use the expected points for each player to determine the expected points of the user team (incl. non-selected players) for each game week. The game week with the highest expected points is the best for a bench boost. **Be aware that towards the end of the season, double game weeks get scheduled and therefore it is advisable to wait till early March.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:09:09.416550Z",
     "start_time": "2021-04-10T11:08:53.820551Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from backtest import pred_bench_boost_gw\n",
    "from common import log_progress\n",
    "\n",
    "if ctx.next_gw > 1:\n",
    "    player_user_team_eps = (user_team\n",
    "        .merge(players_gw_team_eps_ext.reset_index(), left_on='Player Code', right_on='Player Code', how='right', suffixes=(None, None))\n",
    "        .assign(**{'Current Cost': lambda df: df['Selling Price'].fillna(df['Current Cost'])}))\n",
    "\n",
    "    bench_boost_eps = DF()\n",
    "\n",
    "    for gw in log_progress(range(ctx.next_gw, ctx.total_gws+1), name='Game Week'):\n",
    "        bench_boost_eps = bench_boost_eps.append(\n",
    "            pred_bench_boost_gw(player_user_team_eps, player_teams, total_budget, gw, ctx), \n",
    "            ignore_index=True)\n",
    "\n",
    "    display(bench_boost_eps\n",
    "        .sort_values('Expected Points', ascending=False)\n",
    "        [['Game Week', 'Expected Points']]\n",
    "        .pipe(ctx.dd.display, descriptions=False, index=False, head=None))\n",
    "else:\n",
    "    print('This simulation relies on data that will only be available after game week 1 and will only become reliable later in the season.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Fixture Difficulty Rating (FDR) with Fixture Difficulty Factor (FDF)\n",
    "This section provides a visual comparison between the official FPL Fixture Difficulty Rating (FDR) and the fixture difficulty factor (FDF) that is used to adjust the average expected points in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:09:09.558547Z",
     "start_time": "2021-04-10T11:09:09.419548Z"
    }
   },
   "outputs": [],
   "source": [
    "from data import get_team_fixtures_by_gw\n",
    "\n",
    "fdr_by_team_gw = get_team_fixtures_by_gw(team_fixture_strength_ext, 'Team FDR', ctx)\n",
    "fdr_labels_by_team_gw = get_team_fixtures_by_gw(team_fixture_strength_ext, 'Label', ctx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:09:09.778549Z",
     "start_time": "2021-04-10T11:09:09.561549Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from fdr_chart import get_fdr_chart\n",
    "\n",
    "get_fdr_chart(fdr_by_team_gw, fdr_labels_by_team_gw, 'FDR', True).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:09:09.874546Z",
     "start_time": "2021-04-10T11:09:09.781547Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "goal_strength_by_team_gw = get_team_fixtures_by_gw(\n",
    "    team_fixture_strength_ext.assign(**{'MDR': lambda df: 1/df['Rel Def Fixture Strength']}), 'MDR', ctx)\n",
    "\n",
    "get_fdr_chart(goal_strength_by_team_gw, fdr_labels_by_team_gw, 'Defensive Fixture Difficulty Factor').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:09:10.017548Z",
     "start_time": "2021-04-10T11:09:09.892548Z"
    }
   },
   "outputs": [],
   "source": [
    "goal_strength_by_team_gw = get_team_fixtures_by_gw(\n",
    "    team_fixture_strength_ext.assign(**{'MDR': lambda df: 1/df['Rel Att Fixture Strength']}), 'MDR', ctx)\n",
    "\n",
    "get_fdr_chart(goal_strength_by_team_gw, fdr_labels_by_team_gw, 'Attacking Fixture Difficulty Factor').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Back test expected points for all players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:09:10.239545Z",
     "start_time": "2021-04-10T11:09:10.024547Z"
    }
   },
   "outputs": [],
   "source": [
    "from backtest import get_gw_points_backtest\n",
    "\n",
    "gw_points_backtest = get_gw_points_backtest(players_gw_team_eps_ext, ctx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:09:20.491243Z",
     "start_time": "2021-04-10T11:09:20.466240Z"
    }
   },
   "outputs": [],
   "source": [
    "gw_points_backtest[['Error', 'Error Simple']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:09:20.805759Z",
     "start_time": "2021-04-10T11:09:20.495239Z"
    }
   },
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "px.line(gw_points_backtest, x='Season Game Week', y=['Avg Expected Points', 'Avg Fixture Total Points', 'Error']).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Back test the expected points only for players in a team\n",
    "The basic idea of testing the predictions is to look at each past game week, predict the expected points for the game week (both adjusted for relative team strengths and not adjusted), optimise the team based on the expected points and then calculate the total expected points for the optimised team (only for the selected player). For validation, we calculate the actual points of the players of the optimised team. We also calculate the points of the dream team, i.e. the total points of the team with highest actual points for each game week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:14:33.944863Z",
     "start_time": "2021-04-10T11:09:20.809761Z"
    }
   },
   "outputs": [],
   "source": [
    "from backtest import back_test_gw\n",
    "\n",
    "if ctx.next_gw > 1:\n",
    "    backtest_results = DF()\n",
    "    for gw in log_progress(range(2, ctx.next_gw), name='Game Week'):\n",
    "        backtest_results = backtest_results.append(back_test_gw(players_gw_team_eps_ext.reset_index(), gw, player_teams, ctx), ignore_index=True)\n",
    "else:\n",
    "    print('This simulation relies on data that will only be available after game week 1 and will only become reliable later in the season.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-10T11:14:33.954862Z",
     "start_time": "2021-04-10T11:08:44.282Z"
    }
   },
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "if ctx.next_gw > 1:\n",
    "    px.line(backtest_results, x='Game Week', y=['Actual Points Dream Team', 'Calc Actual Points', 'Calc Expected Points']).show()"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.6.8"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}