INSTALLATION

$ pip3 install -r requirements.txt

OR

$ pip3 install requests urllib3 datetime bs4 numpy pandas

USAGE

from hltv import [class or function name]

Parse matches

>>> get_results_url(filename=None, pages_with_results=[0])

type: func
Params:

• filename - for saving pandas frame
  
• pages_with_results - array with numbers of pages with results
  

Return pandas DataFrame with columns:

• match_url
  

Add main common info from match page

>>> MatchPageParams(df, start_index=None, finish_index=None)

type: class
Params:

• df - pandas frame with match urls
• start_index - start index
• finish_index - last index

>>> MatchPageParams.add_all_params()

type: func
Modify pandas frame, add columns:

• match_url - link to the match
• event_url - link to the tournament
• players_url_1 - links to players of the 1st team
• players_url_2 - links to players of the second team
• maps_url - links to statistics of played maps
• maps_name - map names
• score1_maps - score on each map of team 1
• score2_maps - score on each map of team 2
• picks - peaks of teams; 1 - the first team, -1 - the second team; if the array is None, then the maps are < 2
• date - match date
• total_maps - in total it was planned to play cards (usually 1, 3 or 5)
• maps_played - how many cards were played as a result
• score1 - score of the 1st team
• score2 - score of the 2nd team
• h2h_wins1 - history of victories of the 1st team over the 2nd
• h2h_wins2 - the history of victories of the 2nd team over the 1st
• rank1 - rank of the 1st team
• rank2 - rank of the 2nd team
• 5last_match[match_id]_total_maps[team_id] - the last 5 matches of the [team_id] (1 or 2); [match_id] - serial number of the match; total maps played
• 5last_match[match_id]_score[team_id] - the last 5 matches of the [team_id] (1 or 2); [match_id] - serial number of the match; score [team_id] in this match
• 5last_match[match_id]_opponent_score[team_id] - the last 5 matches of the [team_id] (1 or 2); [match_id] - serial number of the match; opponent score in this match

Add last played maps each team

>>> LastMaps(df, last_maps=20, months=3, start_index=None, finish_index=None)

type: class
Params:

• df - pandas frame from previous step
• last_maps - last X played maps
• months - time period
• start_index - start index
• finish_index - last index

>>> LastMaps.add_all_params()

type: func
Modify pandas frame, add columns:

• last_maps [id] _score [team_id] - team*s score; id - map number, team_id - team number (team1 or team2)
• last_maps [id] _opponent_score [team_id] - opponent’s score; id - map number, team_id - team number (team1 or team2)

Add info about tournament

>>> Tour(df, start_index=None, finish_index=None)

type: class
Params:

• df - pandas frame from previous step
• start_index - start index
• finish_index - last index

>>> Tour.add_all_params()

type: func
Modify pandas frame, add columns:

• event_type - type of tournament (Lan or Online)
• event_teams - the number of teams in the tournament
• prize_pool - prize pool of the tournament

Add played time in teams each player

>>> PlStatInTeam(df, start_index=None, finish_index=None)

type: class
Params:

• df - pandas frame from previous step
• start_index - start index
• finish_index - last index

>>> PlStatInTeam.add_all_params()

type: func
Modify pandas frame, add columns:
playerID - player number (from 1 to 5); team_id - team number (1 or 2)

• player [playerID]_days_in_current_team[team_id] - days how long player is in the team (0 or more)
• player[playerID]_days_in_all_team[team_id] - days how long player is in all teams (0 or more)
• player[playerID]_teams_all_team[team_id] - the number of teams the player was in (0 or more)

Add all stats each player

>>> PlStatAll(df, months=3, start_index=None, finish_index=None)

type: class
Params:

• df - pandas frame from previous step
• months - time period
• start_index - start index
• finish_index - last index

>>> LastMaps.add_all_params()

type: func
Modify pandas frame, add columns:

• [param] _player [player] _team [team_id] - player statistics
• [param] _maps_player [player_id] _team {team_id} - played maps for calculating statistics (only some parameters)
• age_player [player_id] _team [team_id] - player age

Add stats on all maps each team

>>> AllMapsStat(df, last_maps=20, months=3, start_index=None, finish_index=None)

type: class
Params:

• df - pandas frame from previous step
• last_maps - last X played maps on map
• months - time period
• start_index - start index
• finish_index - last index

>>> LastMaps.add_all_params()

type: func
Modify pandas frame, add columns:

• [param][map]_team[team_id]
• map_played[id]_team[team_id] - score; [id] - serial number of played map
• map_played[id]_opponent_team[team_id] - opponent score; [id] - serial number of played map

Split DataFrame on maps and save stats on played map each team

>>> MapsStatTeamFull(df, last_maps=20, months=3, start_index=None, finish_index=None)

type: class
Params:

• df - pandas frame from previous step
• last_maps - last X played maps on map
• months - time period
• start_index - start index
• finish_index - last index

>>> MapsStatTeamFull.add_all_params()

type: func
Modify pandas frame, add columns:

• [param]_team[team_id]
• current_mapplayed[id]_team[team_id] - score; [id] - serial number of played map
• current_mapplayed[id]_opponent_team[team_id] - opponent score; [id] - serial number of played map

Return new pandas DataFrame with all columns