Python库 | baseball-9.7.tar.gz

**Table of Contents** - [Baseball](#baseball) - [Installing from pypi](#installing-from-pypi) - [Installing from source](#installing-from-source) - [Fetch individual MLB game](#fetch-individual-mlb-game) - [Fetch list of MLB games](#fetch-list-of-mlb-games) - [Get Game generator given target directory and date range](#get-game-generator-given-target-directory-and-date-range) - [Get raw XML files for an individual MLB game](#get-raw-xml-files-for-an-individual-mlb-game) - [Convert XML documents into Game object](#convert-xml-documents-into-game-object) - [Write scorecard SVGs for all MLB games on a given date](#write-scorecard-svgs-for-all-mlb-games-on-a-given-date) - [Game Class Structure](#game-class-structure) - [Game](#game) - [Team](#team) - [Inning](#inning) - [PlateAppearance](#plateappearance) - [Player](#player) - [PlayerAppearance](#playerappearance) - [Pitch](#pitch) - [Pickoff](#pickoff) - [RunnerAdvance](#runneradvance) - [Substitution](#substitution) - [Switch](#switch) - [Analyze a game: 2017 World Series - Game 7](#analyze-a-game-2017-world-series---game-7) - [Analyze a player's season: R.A. Dickey - 2017](#analyze-a-players-season-ra-dickey---2017) - [Analyze a lineup of pitchers: Atlanta Braves - 2017 Regular Season](#analyze-a-lineup-of-pitchers-atlanta-braves---2017-regular-season) # Baseball This package fetches and parses event data for Major League Baseball games. [Game](#game) objects generated via the **\_from\_url** methods pull data from MLB endpoints where events are published within about 30 seconds of occurring. This [XML source data zip file](https://spaces-host.nyc3.digitaloceanspaces.com/livebaseballscorecards-artifacts/baseball_files_2008-2017.zip) contains event data from MLB games 2008-2017. ## Installing from pypi ``` pip3 install baseball ``` ## Installing from source ``` git clone git@github.com:benjamincrom/baseball.git cd baseball/ python3 setup.py install ``` ## Fetch individual MLB game * __get_game_from_url(__*date_str, away_code, home_code, game_number*__)__ Fetch an object which contains metadata and events for a single MLB game. ```python import baseball game_id, game = baseball.get_game_from_url('2017-11-1', 'HOU', 'LAD', 1) game_dict = game._asdict() game_json_str = game.json() ``` Write scorecard as SVG image: ```python with open(game_id + '.svg', 'w') as fh: fh.write(game.get_svg_str()) ``` 2017-11-01-HOU-LAD-1.svg  ## Fetch list of MLB games * __get_game_list_from_file_range(__*start_date_str, end_date_str, input_dir*__)__ Fetch a list of game objects which each contain metadata and events for a single MLB game. First, download and unzip the [source data zip file](https://spaces-host.nyc3.digitaloceanspaces.com/livebaseballscorecards-artifacts/baseball_files_2008-2017.zip): ```shell wget https://spaces-host.nyc3.digitaloceanspaces.com/livebaseballscorecards-artifacts/baseball_files_2008-2017.zip unzip baseball_files_2008-2017.zip -d ./baseball_files_2008-2017 ``` Then import the files in Python using this library: ```python import baseball game_tuple_list = baseball.get_game_list_from_file_range('1-1-2017', '12-31-2017', 'baseball_files_2008-2017') ``` ## Get Game generator given target directory and date range * __get_game_generator_from_file_range(__*start_date_str, end_date_str, input_dir*__)__ Returns generator which yields (game_id, [Game](#game)) tuples ## Get raw XML files for an individual MLB game * __get_game_xml_from_url(__*date_str, away_code, home_code, game_number*__)__ Returns game_id and three strings containing XML documents: (game_id, boxscore_raw_xml, players_raw_xml, inning_raw_xml) ## Convert XML documents into Game object * __get_game_from_xml_strings(__*boxscore_raw_xml, players_raw_xml, inning_raw_xml*__)__ Returns [Game](#game) object if enough information to create one is provided. Otherwise returns None. ## Write scorecard SVGs for all MLB games on a given date * __write_games_for_date(__*this_datetime, output_dir*__)__ Writes SVG files for all games played on the given date ## Game Class Structure #### Game - away_batter_box_score_dict - away_pitcher_box_score_dict - away_team ([Team](#team)) - away_team_stats - start_datetime - expected_start_datetime - game_date_str - home_batter_box_score_dict - home_pitcher_box_score_dict - home_team ([Team](#team)) - home_team_stats - inning_list ([Inning](#inning) list) - end_datetime - location - attendance - weather - temp - timezone_str - get_svg_str() - json() - \_asdict() #### Team - abbreviation - batting_order_list_list (list of nine [PlayerAppearance](#playerappearance) lists) - name - pitcher_list ([PlayerAppearance](#playerappearance) list) - player_id_dict - player_last_name_dict - player_name_dict - \_asdict() #### Inning - bottom_half_appearance_list ([PlateAppearance](#plateappearance) list) - bottom_half_inning_stats - top_half_appearance_list ([PlateAppearance](#plateappearance) list) - top_half_inning_stats - \_asdict() #### PlateAppearance - start_datetime - end_datetime - batter ([Player](#player)) - batting_team ([Team](#team)) - error_str - event_list (list of [Pitch](#pitch), [Pickoff](#pickoff), [RunnerAdvance](#runneradvance), [Substitution](#substitution), [Switch](#switch) objects) - got_on_base - hit_location - inning_outs - out_runners_list ([Player](#player) list) - pitcher ([Player](#player)) - plate_appearance_description - plate_appearance_summary - runners_batted_in_list ([Player](#player) list) - scorecard_summary - scoring_runners_list ([Player](#player) list) - \_asdict() #### Player - era - first_name - last_name - mlb_id - number - obp - slg - \_asdict() #### PlayerAppearance - start_inning_batter_num - start_inning_half - start_inning_num - end_inning_batter_num - end_inning_half - end_inning_num - pitcher_credit_code - player_obj ([Player](#player)) - position - \_asdict() #### Pitch - pitch_datetime - pitch_description - pitch_position - pitch_speed - pitch_type - \_asdict() #### Pickoff - pickoff_description - pickoff_base - pickoff_was_successful - \_asdict() #### RunnerAdvance - runner_advance_datetime - run_description - runner ([Player](#player)) - start_base - end_base - runner_scored - run_earned - is_rbi - \_asdict() #### Substitution - substitution_datetime - incoming_player ([Player](#player)) - outgoing_player ([Player](#player)) - batting_order - position - \_asdict() #### Switch - switch_datetime - player ([Player](#player)) - old_position_num - new_position_num - new_batting_order - \_asdict() ## Analyze a game: 2017 World Series - Game 7 ```python import matplotlib import matplotlib.pyplot as plt import pandas as pd import baseball %matplotlib inline game_id, game = baseball.get_game_from_url('11-1-2017', 'HOU', 'LAD', 1) pitch_tuple_list = [] for inning in game.inning_list: for appearance in inning.top_half_appearance_list: for event in appearance.event_list: if isinstance(event, baseball.Pitch): pitch_tuple_list.append( (str(appearance.pitcher), event.pitch_description, event.pitch_position, event.pitch_speed, event.pitch_type) ) data = pd.DataFrame(data=pitch_tuple_list, columns=['Pitcher', 'Pitch Description', 'Pitch Coordinate', 'Pitch Speed', 'Pitch Type']) data.head() ``` <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>Pitcher</th> <th>Pitch Description</th> <th>Pitch Coordinate</th> <th>Pitch Speed</th> <th>Pitch Type</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>21 Yu Darvish</td> <td>Ball</td> <td>(155.47, 160.83)</td> <td>96.0</td> <td>FF</td>