NFL data scraper
3
$begingroup$
I made a Python 3 class that scrapes data from Pro Football Reference. It uses requests and beautifulsoup4 to gather the data and places it into a pandas data frame. All you need to do is create an object and use the get_data() method to get the data frame. This method needs a start_year, end_year, and table_type as arguments. Valid table types can be found in the class' docstring.
A usage demonstration can be found at the bottom of the code. It scrapes 2017 Passing Data and 2018 Passing Data. You can also view the code on GitHub.
"""
This module contains a FootballRefScraper class used to scrape NFL data from www.pro-football-reference.com. It places
the data into a Pandas data frame, which can be saved as a CSV file. Built using Python 3.7.0.
"""
import requests
import bs4
import pandas as pd
class FootballRefScraper(object):
"""
Scrapes NFL data from www.pro-football-reference.com and places it into a Pandas data frame. Multiple years of data
can be scraped and placed into a single data frame for the same statistical category. Each category is referred to
as a 'table type'. Possible table types include:
'rushing': Rushing data.
'passing': Passing data.
'receiving': Receiving data.
'kicking': Field goal, point after touchdown, and punt data.
'returns': Punt and kick return data.
'scoring': All types of scoring data, such as touchdowns (defense/offense), two point conversions, kicking, etc.
'fantasy': Rushing, receiving, and passing stats, along with fantasy point totals from various leagues.
'defense': Defensive player stats.
Each player on Pro Football Reference has their own unique URL. This URL, combined with the year for the player's
specific season of data, is used as a unique identifier for each row in the data frame. It is used as the data
frame's index.
"""
def __init__(self):
self._tables = ['rushing', 'passing', 'receiving', 'kicking', 'returns', 'scoring', 'fantasy', 'defense']
self._kicking_cols_to_rename = {
'fga1': 'att_0-19',
'fgm1': 'made_0-19',
'fga2': 'att_20-29',
'fgm2': 'made_20-29',
'fga3': 'att_30-39',
'fgm3': 'made_30-39',
'fga4': 'att_40-49',
'fgm4': 'made_40-49',
'fga5': 'att_50_plus',
'fgm5': 'made_50_plus'
}
@property
def tables(self):
"""getter: Returns a list of the possible table types to scrape from."""
return self._tables
def get_data(self, start_year, end_year, table_type, remove_pro_bowl=True, remove_all_pro=True):
"""
Gets a data frame of NFL player stats from Pro Football Reference for one for more seasons.
:param start_year: First season to scrape data from (string or int)
:param end_year: Final season (inclusive) to scrape data from (string or int)
:param table_type: Stat category to scrape
:param remove_pro_bowl: Boolean - If true, removes Pro Bowl accolade ('*') from player's name
:param remove_all_pro: Boolean - If true, removes All-Pro accolade ('+') from player's name
:return: Data frame of one or more seasons of data for a given stat category.
"""
self._check_table_type(table_type)
start_year, end_year = self._check_start_and_end_years(start_year, end_year)
if start_year == end_year:
df = self._get_single_season(start_year, table_type)
else:
df = self._get_multiple_seasons(start_year, end_year, table_type)
# Unique identifier for each player's season of data.
df.set_index('player_url', inplace=True)
# Change data from string to numeric, where applicable.
df = df.apply(pd.to_numeric, errors='ignore')
if remove_pro_bowl or remove_all_pro:
self._remove_player_accolades(df, remove_pro_bowl, remove_all_pro)
if table_type.lower() == 'kicking':
# For kicking data, rename some columns so field goal distance is obvious.
df = df.rename(index=str, columns=self._kicking_cols_to_rename)
return df
def _get_multiple_seasons(self, start_year, end_year, table_type):
"""
Scrapes multiple seasons of data from Pro Football Reference and puts it into a Pandas data frame.
:param start_year: First season to scrape data from (string or int)
:param end_year: Final season (inclusive) to scrape data from (string or int)
:param table_type: Stat category to scrape
:return: Data frame with multiple seasons of data for a given stat category.
"""
# Get seasons to iterate through.
year_range = self._get_year_range(start_year, end_year)
# Get a data frame of each season.
seasons = [self._get_single_season(year, table_type) for year in year_range]
# Combine all seasons into one large df.
# sort = False prevents FutureWarning when concatenating data frames with different number of columns (1/18/19)
big_df = pd.concat(seasons, sort=False)
return big_df
def _get_year_range(self, start_year, end_year):
"""
Uses start_year and end_year to build an iterable sequence.
:param start_year: Year to begin iterable at.
:param end_year: Final year in iterable.
:return: An iterable sequence.
"""
# Build range iterator depending on how start_year and end_year are related.
if start_year > end_year:
year_range = range(start_year, end_year - 1, -1)
else:
year_range = range(start_year, end_year + 1)
return year_range
def _check_start_and_end_years(self, start_year, end_year):
"""
Tries to convert start_year and end_year to int, if necessary. Raises ValueError for unsuccessful conversions.
:param start_year: Data to convert to int
:param end_year: Data to convert to int
:return: Tuple - (start_year, end_year)
"""
# Convert years to int, if needed.
if not isinstance(start_year, int):
try:
start_year = int(start_year)
except ValueError:
raise ValueError('Cannot convert start_year to type int.')
if not isinstance(end_year, int):
try:
end_year = int(end_year)
except ValueError:
raise ValueError('Cannot convert end_year to type int.')
return start_year, end_year
def _get_single_season(self, year, table_type):
"""
Scrapes a single table from Pro Football Reference and puts it into a Pandas data frame.
:param year: Season's year.
:param table_type: String representing the type of table to be scraped.
:return: A data frame of the scraped table for a single season.
"""
table = self._get_table(year, table_type)
header_row = self._get_table_headers(table)
df_cols = self._get_df_columns(header_row)
player_elements = self._get_player_rows(table)
if not player_elements:
# Table found, but it doesn't have data.
raise RuntimeError(table_type.capitalize() + " stats table found for year " + str(year)
+ ", but it does not contain data.")
season_data = self._get_player_stats(player_elements)
# Final data frame for single season
return self._make_df(year, season_data, df_cols)
def _get_table(self, year, table_type):
"""
Sends a GET request to Pro-Football Reference and uses BeautifulSoup to find the HTML table.
:param year: Season's year.
:param table_type: String representing the type of table to be scraped.
:return: BeautifulSoup table element.
"""
# Send a GET request to Pro-Football Reference
url = 'https://www.pro-football-reference.com/years/' + str(year) + '/' + table_type + '.htm'
response = requests.get(url)
response.raise_for_status()
# Create a BeautifulSoup object.
soup = bs4.BeautifulSoup(response.text, 'lxml')
table = soup.find('table', id=table_type)
if table is None:
# No table found
raise RuntimeError(table_type.capitalize() + " stats table not found for year " + str(year) + ".")
# Return the table containing the data.
return table
def _get_table_headers(self, table_element):
"""
Extracts the top row of a BeautifulSoup table element.
:param table_element: BeautifulSoup table element.
:return: List of header cells from a table.
"""
# 'thead' contains the table's header row
head = table_element.find('thead')
# 'tr' refers to a table row
col_names = head.find_all('tr')[-1]
# 'th' is a table header cell
return col_names.find_all('th')
def _get_df_columns(self, header_elements):
"""
Extracts stat names from column header cells.
:param header_elements: List of header cells
:return: List of stat names.
"""
cols_for_single_season = [header_cell['data-stat'] for header_cell in header_elements[1:]]
cols_for_single_season.insert(1, 'player_url')
return cols_for_single_season
def _get_player_rows(self, table_element):
"""
Gets a list of rows from an HTML table.
:param table_element: HTML table.
:return: A list of table row elements.
"""
# 'tbody' is the table's body
body = table_element.find('tbody')
# 'tr' refers to a table row
return body.find_all('tr')
def _get_player_stats(self, player_row_elements):
"""
Gets stats for each player in a table for a season.
:param player_row_elements: List of table rows where each row is a player's season stat line.
:return: List where each element is a list containing a player's data for the season.
"""
season_stats =
for player in player_row_elements:
# 'td' is an HTML table cell
player_stats = player.find_all('td')
# Some rows do not contain player data.
if player_stats:
clean_stats = self._get_clean_stats(player_stats)
season_stats.append(clean_stats)
return season_stats
def _get_clean_stats(self, stat_row):
"""
Gets clean text stats for a player's season.
:param stat_row: List of table cells representing a player's stat line for a season.
:return: List of strings representing a player's season stat line.
"""
clean_player_stats =
for stat_cell in stat_row:
clean_player_stats.append(stat_cell.text)
# Also grab the player's URL so they have a unique identifier when combined with the season's year.
if stat_cell['data-stat'] == 'player':
url = self._get_player_url(stat_cell)
clean_player_stats.append(url)
return clean_player_stats
def _get_player_url(self, player_cell):
"""
Get's a player's unique URL.
:param player_cell: HTML table cell.
:return: String - player's unique URL.
"""
# 'href' is the URL of the page the link goes to.
href = player_cell.find_all('a', href=True)
# Return URL string
return href[0]['href']
def _make_df(self, year, league_stats, column_names):
"""
:param year: Season's year.
:param league_stats: List where each element is a list of stats for a single player.
:param column_names: List used for data frame's column names.
:return: A data frame.
"""
df = pd.DataFrame(data=league_stats, columns=column_names)
df.insert(loc=3, column='year', value=year) # Column for current year.
# Combined player_url + year acts as a unique identifier for a player's season of data.
df['player_url'] = df['player_url'].apply(lambda x: x + str(year))
return df
def _remove_player_accolades(self, df, remove_pro_bowl, remove_all_pro):
"""
Removes Pro Bowl ('*') and All-Pro ('+') accolades from a player's name.
:param remove_pro_bowl: Boolean; remove if True
:param remove_all_pro: Boolean; remove if True
:return: No return value
"""
if remove_pro_bowl and not remove_all_pro:
# Remove '*' in player's name.
df['player'] = df['player'].apply(lambda x: ''.join(x.split('*')) if '*' in x else x)
elif not remove_pro_bowl and remove_all_pro:
# Remove '+' in player's name.
df['player'] = df['player'].apply(lambda x: ''.join(x.split('+')) if '+' in x else x)
elif remove_pro_bowl and remove_all_pro:
# Remove '*', '+', or '*+'.
df['player'] = df['player'].apply(self._remove_chars)
def _remove_chars(self, string):
"""
Removes any combination of a single '*' and '+' from the end of a string.
:param string: String
:return: String
"""
if string.endswith('*+'):
string = string[:-2]
elif string.endswith('*') or string.endswith('+'):
string = string[:-1]
return string
def _check_table_type(self, table_type):
"""
Checks for valid table types. Raises value error for invalid table.
:param table_type: String
:return: No return value
"""
# Only scrapes from tables in self._tables.
if table_type.lower() not in self._tables:
raise ValueError("Error, make sure to specify table_type. "
+ "Can only currently handle the following table names: "
+ str(self._tables))
if __name__ == '__main__':
football_ref = FootballRefScraper()
df = football_ref.get_data(start_year=2017, end_year=2018, table_type='passing')
df.to_csv('sample_data.csv')
python python-3.x web-scraping pandas beautifulsoup
edited 10 mins ago
Jamal♦
30.3k11117227
asked yesterday
shmibleshmible
162
New contributor
shmible is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
3
$begingroup$
I made a Python 3 class that scrapes data from Pro Football Reference. It uses requests and beautifulsoup4 to gather the data and places it into a pandas data frame. All you need to do is create an object and use the get_data() method to get the data frame. This method needs a start_year, end_year, and table_type as arguments. Valid table types can be found in the class' docstring.
A usage demonstration can be found at the bottom of the code. It scrapes 2017 Passing Data and 2018 Passing Data. You can also view the code on GitHub.
"""
This module contains a FootballRefScraper class used to scrape NFL data from www.pro-football-reference.com. It places
the data into a Pandas data frame, which can be saved as a CSV file. Built using Python 3.7.0.
"""
import requests
import bs4
import pandas as pd
class FootballRefScraper(object):
"""
Scrapes NFL data from www.pro-football-reference.com and places it into a Pandas data frame. Multiple years of data
can be scraped and placed into a single data frame for the same statistical category. Each category is referred to
as a 'table type'. Possible table types include:
'rushing': Rushing data.
'passing': Passing data.
'receiving': Receiving data.
'kicking': Field goal, point after touchdown, and punt data.
'returns': Punt and kick return data.
'scoring': All types of scoring data, such as touchdowns (defense/offense), two point conversions, kicking, etc.
'fantasy': Rushing, receiving, and passing stats, along with fantasy point totals from various leagues.
'defense': Defensive player stats.
Each player on Pro Football Reference has their own unique URL. This URL, combined with the year for the player's
specific season of data, is used as a unique identifier for each row in the data frame. It is used as the data
frame's index.
"""
def __init__(self):
self._tables = ['rushing', 'passing', 'receiving', 'kicking', 'returns', 'scoring', 'fantasy', 'defense']
self._kicking_cols_to_rename = {
'fga1': 'att_0-19',
'fgm1': 'made_0-19',
'fga2': 'att_20-29',
'fgm2': 'made_20-29',
'fga3': 'att_30-39',
'fgm3': 'made_30-39',
'fga4': 'att_40-49',
'fgm4': 'made_40-49',
'fga5': 'att_50_plus',
'fgm5': 'made_50_plus'
}
@property
def tables(self):
"""getter: Returns a list of the possible table types to scrape from."""
return self._tables
def get_data(self, start_year, end_year, table_type, remove_pro_bowl=True, remove_all_pro=True):
"""
Gets a data frame of NFL player stats from Pro Football Reference for one for more seasons.
:param start_year: First season to scrape data from (string or int)
:param end_year: Final season (inclusive) to scrape data from (string or int)
:param table_type: Stat category to scrape
:param remove_pro_bowl: Boolean - If true, removes Pro Bowl accolade ('*') from player's name
:param remove_all_pro: Boolean - If true, removes All-Pro accolade ('+') from player's name
:return: Data frame of one or more seasons of data for a given stat category.
"""
self._check_table_type(table_type)
start_year, end_year = self._check_start_and_end_years(start_year, end_year)
if start_year == end_year:
df = self._get_single_season(start_year, table_type)
else:
df = self._get_multiple_seasons(start_year, end_year, table_type)
# Unique identifier for each player's season of data.
df.set_index('player_url', inplace=True)
# Change data from string to numeric, where applicable.
df = df.apply(pd.to_numeric, errors='ignore')
if remove_pro_bowl or remove_all_pro:
self._remove_player_accolades(df, remove_pro_bowl, remove_all_pro)
if table_type.lower() == 'kicking':
# For kicking data, rename some columns so field goal distance is obvious.
df = df.rename(index=str, columns=self._kicking_cols_to_rename)
return df
def _get_multiple_seasons(self, start_year, end_year, table_type):
"""
Scrapes multiple seasons of data from Pro Football Reference and puts it into a Pandas data frame.
:param start_year: First season to scrape data from (string or int)
:param end_year: Final season (inclusive) to scrape data from (string or int)
:param table_type: Stat category to scrape
:return: Data frame with multiple seasons of data for a given stat category.
"""
# Get seasons to iterate through.
year_range = self._get_year_range(start_year, end_year)
# Get a data frame of each season.
seasons = [self._get_single_season(year, table_type) for year in year_range]
# Combine all seasons into one large df.
# sort = False prevents FutureWarning when concatenating data frames with different number of columns (1/18/19)
big_df = pd.concat(seasons, sort=False)
return big_df
def _get_year_range(self, start_year, end_year):
"""
Uses start_year and end_year to build an iterable sequence.
:param start_year: Year to begin iterable at.
:param end_year: Final year in iterable.
:return: An iterable sequence.
"""
# Build range iterator depending on how start_year and end_year are related.
if start_year > end_year:
year_range = range(start_year, end_year - 1, -1)
else:
year_range = range(start_year, end_year + 1)
return year_range
def _check_start_and_end_years(self, start_year, end_year):
"""
Tries to convert start_year and end_year to int, if necessary. Raises ValueError for unsuccessful conversions.
:param start_year: Data to convert to int
:param end_year: Data to convert to int
:return: Tuple - (start_year, end_year)
"""
# Convert years to int, if needed.
if not isinstance(start_year, int):
try:
start_year = int(start_year)
except ValueError:
raise ValueError('Cannot convert start_year to type int.')
if not isinstance(end_year, int):
try:
end_year = int(end_year)
except ValueError:
raise ValueError('Cannot convert end_year to type int.')
return start_year, end_year
def _get_single_season(self, year, table_type):
"""
Scrapes a single table from Pro Football Reference and puts it into a Pandas data frame.
:param year: Season's year.
:param table_type: String representing the type of table to be scraped.
:return: A data frame of the scraped table for a single season.
"""
table = self._get_table(year, table_type)
header_row = self._get_table_headers(table)
df_cols = self._get_df_columns(header_row)
player_elements = self._get_player_rows(table)
if not player_elements:
# Table found, but it doesn't have data.
raise RuntimeError(table_type.capitalize() + " stats table found for year " + str(year)
+ ", but it does not contain data.")
season_data = self._get_player_stats(player_elements)
# Final data frame for single season
return self._make_df(year, season_data, df_cols)
def _get_table(self, year, table_type):
"""
Sends a GET request to Pro-Football Reference and uses BeautifulSoup to find the HTML table.
:param year: Season's year.
:param table_type: String representing the type of table to be scraped.
:return: BeautifulSoup table element.
"""
# Send a GET request to Pro-Football Reference
url = 'https://www.pro-football-reference.com/years/' + str(year) + '/' + table_type + '.htm'
response = requests.get(url)
response.raise_for_status()
# Create a BeautifulSoup object.
soup = bs4.BeautifulSoup(response.text, 'lxml')
table = soup.find('table', id=table_type)
if table is None:
# No table found
raise RuntimeError(table_type.capitalize() + " stats table not found for year " + str(year) + ".")
# Return the table containing the data.
return table
def _get_table_headers(self, table_element):
"""
Extracts the top row of a BeautifulSoup table element.
:param table_element: BeautifulSoup table element.
:return: List of header cells from a table.
"""
# 'thead' contains the table's header row
head = table_element.find('thead')
# 'tr' refers to a table row
col_names = head.find_all('tr')[-1]
# 'th' is a table header cell
return col_names.find_all('th')
def _get_df_columns(self, header_elements):
"""
Extracts stat names from column header cells.
:param header_elements: List of header cells
:return: List of stat names.
"""
cols_for_single_season = [header_cell['data-stat'] for header_cell in header_elements[1:]]
cols_for_single_season.insert(1, 'player_url')
return cols_for_single_season
def _get_player_rows(self, table_element):
"""
Gets a list of rows from an HTML table.
:param table_element: HTML table.
:return: A list of table row elements.
"""
# 'tbody' is the table's body
body = table_element.find('tbody')
# 'tr' refers to a table row
return body.find_all('tr')
def _get_player_stats(self, player_row_elements):
"""
Gets stats for each player in a table for a season.
:param player_row_elements: List of table rows where each row is a player's season stat line.
:return: List where each element is a list containing a player's data for the season.
"""
season_stats =
for player in player_row_elements:
# 'td' is an HTML table cell
player_stats = player.find_all('td')
# Some rows do not contain player data.
if player_stats:
clean_stats = self._get_clean_stats(player_stats)
season_stats.append(clean_stats)
return season_stats
def _get_clean_stats(self, stat_row):
"""
Gets clean text stats for a player's season.
:param stat_row: List of table cells representing a player's stat line for a season.
:return: List of strings representing a player's season stat line.
"""
clean_player_stats =
for stat_cell in stat_row:
clean_player_stats.append(stat_cell.text)
# Also grab the player's URL so they have a unique identifier when combined with the season's year.
if stat_cell['data-stat'] == 'player':
url = self._get_player_url(stat_cell)
clean_player_stats.append(url)
return clean_player_stats
def _get_player_url(self, player_cell):
"""
Get's a player's unique URL.
:param player_cell: HTML table cell.
:return: String - player's unique URL.
"""
# 'href' is the URL of the page the link goes to.
href = player_cell.find_all('a', href=True)
# Return URL string
return href[0]['href']
def _make_df(self, year, league_stats, column_names):
"""
:param year: Season's year.
:param league_stats: List where each element is a list of stats for a single player.
:param column_names: List used for data frame's column names.
:return: A data frame.
"""
df = pd.DataFrame(data=league_stats, columns=column_names)
df.insert(loc=3, column='year', value=year) # Column for current year.
# Combined player_url + year acts as a unique identifier for a player's season of data.
df['player_url'] = df['player_url'].apply(lambda x: x + str(year))
return df
def _remove_player_accolades(self, df, remove_pro_bowl, remove_all_pro):
"""
Removes Pro Bowl ('*') and All-Pro ('+') accolades from a player's name.
:param remove_pro_bowl: Boolean; remove if True
:param remove_all_pro: Boolean; remove if True
:return: No return value
"""
if remove_pro_bowl and not remove_all_pro:
# Remove '*' in player's name.
df['player'] = df['player'].apply(lambda x: ''.join(x.split('*')) if '*' in x else x)
elif not remove_pro_bowl and remove_all_pro:
# Remove '+' in player's name.
df['player'] = df['player'].apply(lambda x: ''.join(x.split('+')) if '+' in x else x)
elif remove_pro_bowl and remove_all_pro:
# Remove '*', '+', or '*+'.
df['player'] = df['player'].apply(self._remove_chars)
def _remove_chars(self, string):
"""
Removes any combination of a single '*' and '+' from the end of a string.
:param string: String
:return: String
"""
if string.endswith('*+'):
string = string[:-2]
elif string.endswith('*') or string.endswith('+'):
string = string[:-1]
return string
def _check_table_type(self, table_type):
"""
Checks for valid table types. Raises value error for invalid table.
:param table_type: String
:return: No return value
"""
# Only scrapes from tables in self._tables.
if table_type.lower() not in self._tables:
raise ValueError("Error, make sure to specify table_type. "
+ "Can only currently handle the following table names: "
+ str(self._tables))
if __name__ == '__main__':
football_ref = FootballRefScraper()
df = football_ref.get_data(start_year=2017, end_year=2018, table_type='passing')
df.to_csv('sample_data.csv')
python python-3.x web-scraping pandas beautifulsoup
edited 10 mins ago
Jamal♦
30.3k11117227
asked yesterday
shmibleshmible
162
New contributor
shmible is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
3
3
3
$begingroup$
I made a Python 3 class that scrapes data from Pro Football Reference. It uses requests and beautifulsoup4 to gather the data and places it into a pandas data frame. All you need to do is create an object and use the get_data() method to get the data frame. This method needs a start_year, end_year, and table_type as arguments. Valid table types can be found in the class' docstring.
A usage demonstration can be found at the bottom of the code. It scrapes 2017 Passing Data and 2018 Passing Data. You can also view the code on GitHub.
"""
This module contains a FootballRefScraper class used to scrape NFL data from www.pro-football-reference.com. It places
the data into a Pandas data frame, which can be saved as a CSV file. Built using Python 3.7.0.
"""
import requests
import bs4
import pandas as pd
class FootballRefScraper(object):
"""
Scrapes NFL data from www.pro-football-reference.com and places it into a Pandas data frame. Multiple years of data
can be scraped and placed into a single data frame for the same statistical category. Each category is referred to
as a 'table type'. Possible table types include:
'rushing': Rushing data.
'passing': Passing data.
'receiving': Receiving data.
'kicking': Field goal, point after touchdown, and punt data.
'returns': Punt and kick return data.
'scoring': All types of scoring data, such as touchdowns (defense/offense), two point conversions, kicking, etc.
'fantasy': Rushing, receiving, and passing stats, along with fantasy point totals from various leagues.
'defense': Defensive player stats.
Each player on Pro Football Reference has their own unique URL. This URL, combined with the year for the player's
specific season of data, is used as a unique identifier for each row in the data frame. It is used as the data
frame's index.
"""
def __init__(self):
self._tables = ['rushing', 'passing', 'receiving', 'kicking', 'returns', 'scoring', 'fantasy', 'defense']
self._kicking_cols_to_rename = {
'fga1': 'att_0-19',
'fgm1': 'made_0-19',
'fga2': 'att_20-29',
'fgm2': 'made_20-29',
'fga3': 'att_30-39',
'fgm3': 'made_30-39',
'fga4': 'att_40-49',
'fgm4': 'made_40-49',
'fga5': 'att_50_plus',
'fgm5': 'made_50_plus'
}
@property
def tables(self):
"""getter: Returns a list of the possible table types to scrape from."""
return self._tables
def get_data(self, start_year, end_year, table_type, remove_pro_bowl=True, remove_all_pro=True):
"""
Gets a data frame of NFL player stats from Pro Football Reference for one for more seasons.
:param start_year: First season to scrape data from (string or int)
:param end_year: Final season (inclusive) to scrape data from (string or int)
:param table_type: Stat category to scrape
:param remove_pro_bowl: Boolean - If true, removes Pro Bowl accolade ('*') from player's name
:param remove_all_pro: Boolean - If true, removes All-Pro accolade ('+') from player's name
:return: Data frame of one or more seasons of data for a given stat category.
"""
self._check_table_type(table_type)
start_year, end_year = self._check_start_and_end_years(start_year, end_year)
if start_year == end_year:
df = self._get_single_season(start_year, table_type)
else:
df = self._get_multiple_seasons(start_year, end_year, table_type)
# Unique identifier for each player's season of data.
df.set_index('player_url', inplace=True)
# Change data from string to numeric, where applicable.
df = df.apply(pd.to_numeric, errors='ignore')
if remove_pro_bowl or remove_all_pro:
self._remove_player_accolades(df, remove_pro_bowl, remove_all_pro)
if table_type.lower() == 'kicking':
# For kicking data, rename some columns so field goal distance is obvious.
df = df.rename(index=str, columns=self._kicking_cols_to_rename)
return df
def _get_multiple_seasons(self, start_year, end_year, table_type):
"""
Scrapes multiple seasons of data from Pro Football Reference and puts it into a Pandas data frame.
:param start_year: First season to scrape data from (string or int)
:param end_year: Final season (inclusive) to scrape data from (string or int)
:param table_type: Stat category to scrape
:return: Data frame with multiple seasons of data for a given stat category.
"""
# Get seasons to iterate through.
year_range = self._get_year_range(start_year, end_year)
# Get a data frame of each season.
seasons = [self._get_single_season(year, table_type) for year in year_range]
# Combine all seasons into one large df.
# sort = False prevents FutureWarning when concatenating data frames with different number of columns (1/18/19)
big_df = pd.concat(seasons, sort=False)
return big_df
def _get_year_range(self, start_year, end_year):
"""
Uses start_year and end_year to build an iterable sequence.
:param start_year: Year to begin iterable at.
:param end_year: Final year in iterable.
:return: An iterable sequence.
"""
# Build range iterator depending on how start_year and end_year are related.
if start_year > end_year:
year_range = range(start_year, end_year - 1, -1)
else:
year_range = range(start_year, end_year + 1)
return year_range
def _check_start_and_end_years(self, start_year, end_year):
"""
Tries to convert start_year and end_year to int, if necessary. Raises ValueError for unsuccessful conversions.
:param start_year: Data to convert to int
:param end_year: Data to convert to int
:return: Tuple - (start_year, end_year)
"""
# Convert years to int, if needed.
if not isinstance(start_year, int):
try:
start_year = int(start_year)
except ValueError:
raise ValueError('Cannot convert start_year to type int.')
if not isinstance(end_year, int):
try:
end_year = int(end_year)
except ValueError:
raise ValueError('Cannot convert end_year to type int.')
return start_year, end_year
def _get_single_season(self, year, table_type):
"""
Scrapes a single table from Pro Football Reference and puts it into a Pandas data frame.
:param year: Season's year.
:param table_type: String representing the type of table to be scraped.
:return: A data frame of the scraped table for a single season.
"""
table = self._get_table(year, table_type)
header_row = self._get_table_headers(table)
df_cols = self._get_df_columns(header_row)
player_elements = self._get_player_rows(table)
if not player_elements:
# Table found, but it doesn't have data.
raise RuntimeError(table_type.capitalize() + " stats table found for year " + str(year)
+ ", but it does not contain data.")
season_data = self._get_player_stats(player_elements)
# Final data frame for single season
return self._make_df(year, season_data, df_cols)
def _get_table(self, year, table_type):
"""
Sends a GET request to Pro-Football Reference and uses BeautifulSoup to find the HTML table.
:param year: Season's year.
:param table_type: String representing the type of table to be scraped.
:return: BeautifulSoup table element.
"""
# Send a GET request to Pro-Football Reference
url = 'https://www.pro-football-reference.com/years/' + str(year) + '/' + table_type + '.htm'
response = requests.get(url)
response.raise_for_status()
# Create a BeautifulSoup object.
soup = bs4.BeautifulSoup(response.text, 'lxml')
table = soup.find('table', id=table_type)
if table is None:
# No table found
raise RuntimeError(table_type.capitalize() + " stats table not found for year " + str(year) + ".")
# Return the table containing the data.
return table
def _get_table_headers(self, table_element):
"""
Extracts the top row of a BeautifulSoup table element.
:param table_element: BeautifulSoup table element.
:return: List of header cells from a table.
"""
# 'thead' contains the table's header row
head = table_element.find('thead')
# 'tr' refers to a table row
col_names = head.find_all('tr')[-1]
# 'th' is a table header cell
return col_names.find_all('th')
def _get_df_columns(self, header_elements):
"""
Extracts stat names from column header cells.
:param header_elements: List of header cells
:return: List of stat names.
"""
cols_for_single_season = [header_cell['data-stat'] for header_cell in header_elements[1:]]
cols_for_single_season.insert(1, 'player_url')
return cols_for_single_season
def _get_player_rows(self, table_element):
"""
Gets a list of rows from an HTML table.
:param table_element: HTML table.
:return: A list of table row elements.
"""
# 'tbody' is the table's body
body = table_element.find('tbody')
# 'tr' refers to a table row
return body.find_all('tr')
def _get_player_stats(self, player_row_elements):
"""
Gets stats for each player in a table for a season.
:param player_row_elements: List of table rows where each row is a player's season stat line.
:return: List where each element is a list containing a player's data for the season.
"""
season_stats =
for player in player_row_elements:
# 'td' is an HTML table cell
player_stats = player.find_all('td')
# Some rows do not contain player data.
if player_stats:
clean_stats = self._get_clean_stats(player_stats)
season_stats.append(clean_stats)
return season_stats
def _get_clean_stats(self, stat_row):
"""
Gets clean text stats for a player's season.
:param stat_row: List of table cells representing a player's stat line for a season.
:return: List of strings representing a player's season stat line.
"""
clean_player_stats =
for stat_cell in stat_row:
clean_player_stats.append(stat_cell.text)
# Also grab the player's URL so they have a unique identifier when combined with the season's year.
if stat_cell['data-stat'] == 'player':
url = self._get_player_url(stat_cell)
clean_player_stats.append(url)
return clean_player_stats
def _get_player_url(self, player_cell):
"""
Get's a player's unique URL.
:param player_cell: HTML table cell.
:return: String - player's unique URL.
"""
# 'href' is the URL of the page the link goes to.
href = player_cell.find_all('a', href=True)
# Return URL string
return href[0]['href']
def _make_df(self, year, league_stats, column_names):
"""
:param year: Season's year.
:param league_stats: List where each element is a list of stats for a single player.
:param column_names: List used for data frame's column names.
:return: A data frame.
"""
df = pd.DataFrame(data=league_stats, columns=column_names)
df.insert(loc=3, column='year', value=year) # Column for current year.
# Combined player_url + year acts as a unique identifier for a player's season of data.
df['player_url'] = df['player_url'].apply(lambda x: x + str(year))
return df
def _remove_player_accolades(self, df, remove_pro_bowl, remove_all_pro):
"""
Removes Pro Bowl ('*') and All-Pro ('+') accolades from a player's name.
:param remove_pro_bowl: Boolean; remove if True
:param remove_all_pro: Boolean; remove if True
:return: No return value
"""
if remove_pro_bowl and not remove_all_pro:
# Remove '*' in player's name.
df['player'] = df['player'].apply(lambda x: ''.join(x.split('*')) if '*' in x else x)
elif not remove_pro_bowl and remove_all_pro:
# Remove '+' in player's name.
df['player'] = df['player'].apply(lambda x: ''.join(x.split('+')) if '+' in x else x)
elif remove_pro_bowl and remove_all_pro:
# Remove '*', '+', or '*+'.
df['player'] = df['player'].apply(self._remove_chars)
def _remove_chars(self, string):
"""
Removes any combination of a single '*' and '+' from the end of a string.
:param string: String
:return: String
"""
if string.endswith('*+'):
string = string[:-2]
elif string.endswith('*') or string.endswith('+'):
string = string[:-1]
return string
def _check_table_type(self, table_type):
"""
Checks for valid table types. Raises value error for invalid table.
:param table_type: String
:return: No return value
"""
# Only scrapes from tables in self._tables.
if table_type.lower() not in self._tables:
raise ValueError("Error, make sure to specify table_type. "
+ "Can only currently handle the following table names: "
+ str(self._tables))
if __name__ == '__main__':
football_ref = FootballRefScraper()
df = football_ref.get_data(start_year=2017, end_year=2018, table_type='passing')
df.to_csv('sample_data.csv')
python python-3.x web-scraping pandas beautifulsoup
edited 10 mins ago
Jamal♦
30.3k11117227
asked yesterday
shmibleshmible
162
New contributor
shmible is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I made a Python 3 class that scrapes data from Pro Football Reference. It uses requests and beautifulsoup4 to gather the data and places it into a pandas data frame. All you need to do is create an object and use the get_data() method to get the data frame. This method needs a start_year, end_year, and table_type as arguments. Valid table types can be found in the class' docstring.
A usage demonstration can be found at the bottom of the code. It scrapes 2017 Passing Data and 2018 Passing Data. You can also view the code on GitHub.
"""
This module contains a FootballRefScraper class used to scrape NFL data from www.pro-football-reference.com. It places
the data into a Pandas data frame, which can be saved as a CSV file. Built using Python 3.7.0.
"""
import requests
import bs4
import pandas as pd
class FootballRefScraper(object):
"""
Scrapes NFL data from www.pro-football-reference.com and places it into a Pandas data frame. Multiple years of data
can be scraped and placed into a single data frame for the same statistical category. Each category is referred to
as a 'table type'. Possible table types include:
'rushing': Rushing data.
'passing': Passing data.
'receiving': Receiving data.
'kicking': Field goal, point after touchdown, and punt data.
'returns': Punt and kick return data.
'scoring': All types of scoring data, such as touchdowns (defense/offense), two point conversions, kicking, etc.
'fantasy': Rushing, receiving, and passing stats, along with fantasy point totals from various leagues.
'defense': Defensive player stats.
Each player on Pro Football Reference has their own unique URL. This URL, combined with the year for the player's
specific season of data, is used as a unique identifier for each row in the data frame. It is used as the data
frame's index.
"""
def __init__(self):
self._tables = ['rushing', 'passing', 'receiving', 'kicking', 'returns', 'scoring', 'fantasy', 'defense']
self._kicking_cols_to_rename = {
'fga1': 'att_0-19',
'fgm1': 'made_0-19',
'fga2': 'att_20-29',
'fgm2': 'made_20-29',
'fga3': 'att_30-39',
'fgm3': 'made_30-39',
'fga4': 'att_40-49',
'fgm4': 'made_40-49',
'fga5': 'att_50_plus',
'fgm5': 'made_50_plus'
}
@property
def tables(self):
"""getter: Returns a list of the possible table types to scrape from."""
return self._tables
def get_data(self, start_year, end_year, table_type, remove_pro_bowl=True, remove_all_pro=True):
"""
Gets a data frame of NFL player stats from Pro Football Reference for one for more seasons.
:param start_year: First season to scrape data from (string or int)
:param end_year: Final season (inclusive) to scrape data from (string or int)
:param table_type: Stat category to scrape
:param remove_pro_bowl: Boolean - If true, removes Pro Bowl accolade ('*') from player's name
:param remove_all_pro: Boolean - If true, removes All-Pro accolade ('+') from player's name
:return: Data frame of one or more seasons of data for a given stat category.
"""
self._check_table_type(table_type)
start_year, end_year = self._check_start_and_end_years(start_year, end_year)
if start_year == end_year:
df = self._get_single_season(start_year, table_type)
else:
df = self._get_multiple_seasons(start_year, end_year, table_type)
# Unique identifier for each player's season of data.
df.set_index('player_url', inplace=True)
# Change data from string to numeric, where applicable.
df = df.apply(pd.to_numeric, errors='ignore')
if remove_pro_bowl or remove_all_pro:
self._remove_player_accolades(df, remove_pro_bowl, remove_all_pro)
if table_type.lower() == 'kicking':
# For kicking data, rename some columns so field goal distance is obvious.
df = df.rename(index=str, columns=self._kicking_cols_to_rename)
return df
def _get_multiple_seasons(self, start_year, end_year, table_type):
"""
Scrapes multiple seasons of data from Pro Football Reference and puts it into a Pandas data frame.
:param start_year: First season to scrape data from (string or int)
:param end_year: Final season (inclusive) to scrape data from (string or int)
:param table_type: Stat category to scrape
:return: Data frame with multiple seasons of data for a given stat category.
"""
# Get seasons to iterate through.
year_range = self._get_year_range(start_year, end_year)
# Get a data frame of each season.
seasons = [self._get_single_season(year, table_type) for year in year_range]
# Combine all seasons into one large df.
# sort = False prevents FutureWarning when concatenating data frames with different number of columns (1/18/19)
big_df = pd.concat(seasons, sort=False)
return big_df
def _get_year_range(self, start_year, end_year):
"""
Uses start_year and end_year to build an iterable sequence.
:param start_year: Year to begin iterable at.
:param end_year: Final year in iterable.
:return: An iterable sequence.
"""
# Build range iterator depending on how start_year and end_year are related.
if start_year > end_year:
year_range = range(start_year, end_year - 1, -1)
else:
year_range = range(start_year, end_year + 1)
return year_range
def _check_start_and_end_years(self, start_year, end_year):
"""
Tries to convert start_year and end_year to int, if necessary. Raises ValueError for unsuccessful conversions.
:param start_year: Data to convert to int
:param end_year: Data to convert to int
:return: Tuple - (start_year, end_year)
"""
# Convert years to int, if needed.
if not isinstance(start_year, int):
try:
start_year = int(start_year)
except ValueError:
raise ValueError('Cannot convert start_year to type int.')
if not isinstance(end_year, int):
try:
end_year = int(end_year)
except ValueError:
raise ValueError('Cannot convert end_year to type int.')
return start_year, end_year
def _get_single_season(self, year, table_type):
"""
Scrapes a single table from Pro Football Reference and puts it into a Pandas data frame.
:param year: Season's year.
:param table_type: String representing the type of table to be scraped.
:return: A data frame of the scraped table for a single season.
"""
table = self._get_table(year, table_type)
header_row = self._get_table_headers(table)
df_cols = self._get_df_columns(header_row)
player_elements = self._get_player_rows(table)
if not player_elements:
# Table found, but it doesn't have data.
raise RuntimeError(table_type.capitalize() + " stats table found for year " + str(year)
+ ", but it does not contain data.")
season_data = self._get_player_stats(player_elements)
# Final data frame for single season
return self._make_df(year, season_data, df_cols)
def _get_table(self, year, table_type):
"""
Sends a GET request to Pro-Football Reference and uses BeautifulSoup to find the HTML table.
:param year: Season's year.
:param table_type: String representing the type of table to be scraped.
:return: BeautifulSoup table element.
"""
# Send a GET request to Pro-Football Reference
url = 'https://www.pro-football-reference.com/years/' + str(year) + '/' + table_type + '.htm'
response = requests.get(url)
response.raise_for_status()
# Create a BeautifulSoup object.
soup = bs4.BeautifulSoup(response.text, 'lxml')
table = soup.find('table', id=table_type)
if table is None:
# No table found
raise RuntimeError(table_type.capitalize() + " stats table not found for year " + str(year) + ".")
# Return the table containing the data.
return table
def _get_table_headers(self, table_element):
"""
Extracts the top row of a BeautifulSoup table element.
:param table_element: BeautifulSoup table element.
:return: List of header cells from a table.
"""
# 'thead' contains the table's header row
head = table_element.find('thead')
# 'tr' refers to a table row
col_names = head.find_all('tr')[-1]
# 'th' is a table header cell
return col_names.find_all('th')
def _get_df_columns(self, header_elements):
"""
Extracts stat names from column header cells.
:param header_elements: List of header cells
:return: List of stat names.
"""
cols_for_single_season = [header_cell['data-stat'] for header_cell in header_elements[1:]]
cols_for_single_season.insert(1, 'player_url')
return cols_for_single_season
def _get_player_rows(self, table_element):
"""
Gets a list of rows from an HTML table.
:param table_element: HTML table.
:return: A list of table row elements.
"""
# 'tbody' is the table's body
body = table_element.find('tbody')
# 'tr' refers to a table row
return body.find_all('tr')
def _get_player_stats(self, player_row_elements):
"""
Gets stats for each player in a table for a season.
:param player_row_elements: List of table rows where each row is a player's season stat line.
:return: List where each element is a list containing a player's data for the season.
"""
season_stats =
for player in player_row_elements:
# 'td' is an HTML table cell
player_stats = player.find_all('td')
# Some rows do not contain player data.
if player_stats:
clean_stats = self._get_clean_stats(player_stats)
season_stats.append(clean_stats)
return season_stats
def _get_clean_stats(self, stat_row):
"""
Gets clean text stats for a player's season.
:param stat_row: List of table cells representing a player's stat line for a season.
:return: List of strings representing a player's season stat line.
"""
clean_player_stats =
for stat_cell in stat_row:
clean_player_stats.append(stat_cell.text)
# Also grab the player's URL so they have a unique identifier when combined with the season's year.
if stat_cell['data-stat'] == 'player':
url = self._get_player_url(stat_cell)
clean_player_stats.append(url)
return clean_player_stats
def _get_player_url(self, player_cell):
"""
Get's a player's unique URL.
:param player_cell: HTML table cell.
:return: String - player's unique URL.
"""
# 'href' is the URL of the page the link goes to.
href = player_cell.find_all('a', href=True)
# Return URL string
return href[0]['href']
def _make_df(self, year, league_stats, column_names):
"""
:param year: Season's year.
:param league_stats: List where each element is a list of stats for a single player.
:param column_names: List used for data frame's column names.
:return: A data frame.
"""
df = pd.DataFrame(data=league_stats, columns=column_names)
df.insert(loc=3, column='year', value=year) # Column for current year.
# Combined player_url + year acts as a unique identifier for a player's season of data.
df['player_url'] = df['player_url'].apply(lambda x: x + str(year))
return df
def _remove_player_accolades(self, df, remove_pro_bowl, remove_all_pro):
"""
Removes Pro Bowl ('*') and All-Pro ('+') accolades from a player's name.
:param remove_pro_bowl: Boolean; remove if True
:param remove_all_pro: Boolean; remove if True
:return: No return value
"""
if remove_pro_bowl and not remove_all_pro:
# Remove '*' in player's name.
df['player'] = df['player'].apply(lambda x: ''.join(x.split('*')) if '*' in x else x)
elif not remove_pro_bowl and remove_all_pro:
# Remove '+' in player's name.
df['player'] = df['player'].apply(lambda x: ''.join(x.split('+')) if '+' in x else x)
elif remove_pro_bowl and remove_all_pro:
# Remove '*', '+', or '*+'.
df['player'] = df['player'].apply(self._remove_chars)
def _remove_chars(self, string):
"""
Removes any combination of a single '*' and '+' from the end of a string.
:param string: String
:return: String
"""
if string.endswith('*+'):
string = string[:-2]
elif string.endswith('*') or string.endswith('+'):
string = string[:-1]
return string
def _check_table_type(self, table_type):
"""
Checks for valid table types. Raises value error for invalid table.
:param table_type: String
:return: No return value
"""
# Only scrapes from tables in self._tables.
if table_type.lower() not in self._tables:
raise ValueError("Error, make sure to specify table_type. "
+ "Can only currently handle the following table names: "
+ str(self._tables))
if __name__ == '__main__':
football_ref = FootballRefScraper()
df = football_ref.get_data(start_year=2017, end_year=2018, table_type='passing')
df.to_csv('sample_data.csv')
python python-3.x web-scraping pandas beautifulsoup
python python-3.x web-scraping pandas beautifulsoup
edited 10 mins ago
Jamal♦
30.3k11117227
asked yesterday
shmibleshmible
162
New contributor
shmible is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 10 mins ago
Jamal♦
30.3k11117227
asked yesterday
shmibleshmible
162
New contributor
shmible is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 10 mins ago
Jamal♦
30.3k11117227
edited 10 mins ago
Jamal♦
30.3k11117227
edited 10 mins ago
Jamal♦
30.3k11117227
30.3k11117227
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shmibleshmible
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asked yesterday
shmibleshmible
162
asked yesterday
shmibleshmible
162
162
New contributor
shmible is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
shmible is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
shmible is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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$begingroup$
You need to work on your names. Looking at your github repo, this file is pro_ref2.py? The class name is FootballRefScraper. The site chosen is pro-football-reference.com? What is going on here?
I don't know why this class exists. The class name should make it obvious. Does this thing exist to be a scraper? Or does it exist to be a data source? IMO, it does not exist to be a scraper, because that would imply it was one of many scrapers and so it would probably be a subclass of some
AbstractScraperbase class. So I think it's a data source, but then why is it called...Scraper? Also, what kind of data source is it? ApparentlyFootballRef, but again what's that?
Assuming this is really intended to be a data source, give the class a better name.
FootballStatisticsorNflStatisticsmake more sense. Alternatively, you might name it after the website, in which case calling itProFootballReferencewould seem logical.
If this file contains only the one class, then I suggest you rename the file to
pfr.pyorprofootballreference.pyor some such, and then maybe name the class something likeGatewayorAPI. Finally, don't call itget_tables. Call itget_statssince thats what you provide. (You're returning a DataFrame, but you wouldn't call itget_dfunless there was some alternate flavor to get...)
import pfr | import pfr
gateway = pfr.Gateway() | api = pfr.API()
df = gateway.get_stats(...) | df = api.get_stats(...)
As a corollary to #1, consider adding explicit methods for each of the table access calls. Instead of calling
df = gateway.get_stats(..., table_type='passing')it would be easier and clearer to simply saydf = gateway.get_passing_stats(...). Not to mention that a missing method name is easier to debug than a call with a misspelled text string (and your editor might auto-suggest/auto-correct the method!).
Don't store the table types and kicking table rename data in the instances. That is class data:
class No:
def __init__(self):
self.foo = 'foo' # No
class Yes:
foo = 'foo' # Yes
def __init__(self):
pass
The
.tablesproperty should be.table_typesor.stats_types
The
get_data(a.k.a.get_stats) function has an awkward interface. Instead of trying to jam everything intostart_yearandend_year, try allowing multiple named parameters and requiring them to be exclusive. Also, be willing to accept multiple parameter types:
# Cannot use year=, years= in same call
df = api.get_passing_stats(year=2017)
df = api.get_passing_stats(years=(2013, 2017)) # Two explicit years
df = api.get_passing_stats(years=range(2011, 2019, 2)) # Only odd year data?
# Cannot use tables= in get_XXX_stats call.
# Cannot use table= and tables= in same call
df = api.get_stats(year=2018, table='kicking')
df = api.get_stats(years=(2017, 2018), tables=('passing', 'running', 'scoring'))
- Don't be afraid of a DataFrame with >2 dimensions. If someone requests passing, running, and kicking data for 2014, 2018, and 2018, then return a single dataframe with 4 dimensions (year, table, player, stats).
get_datashould automatically strip off the pro-bowl and all-pro data, and add separate columns for that.Your _check_start_and_end_years method doesn't actually check. What happens if I request data for 1861?
answered 13 hours ago
Austin HastingsAustin Hastings
6,3891232
$endgroup$
$begingroup$
Thanks, I appreciate your input. I have also been working on something that scrapes data from The Football Database. A lot of the code is similar to the Pro Football Reference scraper, so would it make sense to have aProFootballReferenceandFootballDatabaseclass that inherit from something likeAbstractNflScraper? I'm more familiar with abstract classes in Java, where abstract methods don't contain implementation. I'm unsure what to do about potential repeated code because of this.
$endgroup$
– shmible
2 hours ago
$begingroup$
I think that's going to depend on how similar the two sites are. Does The Football Database produce similar stats? Does it make sense for both scrapers to haveget_passing_stats()methods, for example? If so, then maybe there's a common base class. If not, if TFD provides different data completely, then you will end up "knowing" which one you are using, so sharing a common interface doesn't make sense.
$endgroup$
– Austin Hastings
1 hour ago
add a comment |
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$begingroup$
You need to work on your names. Looking at your github repo, this file is pro_ref2.py? The class name is FootballRefScraper. The site chosen is pro-football-reference.com? What is going on here?
I don't know why this class exists. The class name should make it obvious. Does this thing exist to be a scraper? Or does it exist to be a data source? IMO, it does not exist to be a scraper, because that would imply it was one of many scrapers and so it would probably be a subclass of some
AbstractScraperbase class. So I think it's a data source, but then why is it called...Scraper? Also, what kind of data source is it? ApparentlyFootballRef, but again what's that?
Assuming this is really intended to be a data source, give the class a better name.
FootballStatisticsorNflStatisticsmake more sense. Alternatively, you might name it after the website, in which case calling itProFootballReferencewould seem logical.
If this file contains only the one class, then I suggest you rename the file to
pfr.pyorprofootballreference.pyor some such, and then maybe name the class something likeGatewayorAPI. Finally, don't call itget_tables. Call itget_statssince thats what you provide. (You're returning a DataFrame, but you wouldn't call itget_dfunless there was some alternate flavor to get...)
import pfr | import pfr
gateway = pfr.Gateway() | api = pfr.API()
df = gateway.get_stats(...) | df = api.get_stats(...)
As a corollary to #1, consider adding explicit methods for each of the table access calls. Instead of calling
df = gateway.get_stats(..., table_type='passing')it would be easier and clearer to simply saydf = gateway.get_passing_stats(...). Not to mention that a missing method name is easier to debug than a call with a misspelled text string (and your editor might auto-suggest/auto-correct the method!).
Don't store the table types and kicking table rename data in the instances. That is class data:
class No:
def __init__(self):
self.foo = 'foo' # No
class Yes:
foo = 'foo' # Yes
def __init__(self):
pass
The
.tablesproperty should be.table_typesor.stats_types
The
get_data(a.k.a.get_stats) function has an awkward interface. Instead of trying to jam everything intostart_yearandend_year, try allowing multiple named parameters and requiring them to be exclusive. Also, be willing to accept multiple parameter types:
# Cannot use year=, years= in same call
df = api.get_passing_stats(year=2017)
df = api.get_passing_stats(years=(2013, 2017)) # Two explicit years
df = api.get_passing_stats(years=range(2011, 2019, 2)) # Only odd year data?
# Cannot use tables= in get_XXX_stats call.
# Cannot use table= and tables= in same call
df = api.get_stats(year=2018, table='kicking')
df = api.get_stats(years=(2017, 2018), tables=('passing', 'running', 'scoring'))
- Don't be afraid of a DataFrame with >2 dimensions. If someone requests passing, running, and kicking data for 2014, 2018, and 2018, then return a single dataframe with 4 dimensions (year, table, player, stats).
get_datashould automatically strip off the pro-bowl and all-pro data, and add separate columns for that.Your _check_start_and_end_years method doesn't actually check. What happens if I request data for 1861?
answered 13 hours ago
Austin HastingsAustin Hastings
6,3891232
$endgroup$
$begingroup$
Thanks, I appreciate your input. I have also been working on something that scrapes data from The Football Database. A lot of the code is similar to the Pro Football Reference scraper, so would it make sense to have aProFootballReferenceandFootballDatabaseclass that inherit from something likeAbstractNflScraper? I'm more familiar with abstract classes in Java, where abstract methods don't contain implementation. I'm unsure what to do about potential repeated code because of this.
$endgroup$
– shmible
2 hours ago
$begingroup$
I think that's going to depend on how similar the two sites are. Does The Football Database produce similar stats? Does it make sense for both scrapers to haveget_passing_stats()methods, for example? If so, then maybe there's a common base class. If not, if TFD provides different data completely, then you will end up "knowing" which one you are using, so sharing a common interface doesn't make sense.
$endgroup$
– Austin Hastings
1 hour ago
add a comment |
1
$begingroup$
You need to work on your names. Looking at your github repo, this file is pro_ref2.py? The class name is FootballRefScraper. The site chosen is pro-football-reference.com? What is going on here?
I don't know why this class exists. The class name should make it obvious. Does this thing exist to be a scraper? Or does it exist to be a data source? IMO, it does not exist to be a scraper, because that would imply it was one of many scrapers and so it would probably be a subclass of some
AbstractScraperbase class. So I think it's a data source, but then why is it called...Scraper? Also, what kind of data source is it? ApparentlyFootballRef, but again what's that?
Assuming this is really intended to be a data source, give the class a better name.
FootballStatisticsorNflStatisticsmake more sense. Alternatively, you might name it after the website, in which case calling itProFootballReferencewould seem logical.
If this file contains only the one class, then I suggest you rename the file to
pfr.pyorprofootballreference.pyor some such, and then maybe name the class something likeGatewayorAPI. Finally, don't call itget_tables. Call itget_statssince thats what you provide. (You're returning a DataFrame, but you wouldn't call itget_dfunless there was some alternate flavor to get...)
import pfr | import pfr
gateway = pfr.Gateway() | api = pfr.API()
df = gateway.get_stats(...) | df = api.get_stats(...)
As a corollary to #1, consider adding explicit methods for each of the table access calls. Instead of calling
df = gateway.get_stats(..., table_type='passing')it would be easier and clearer to simply saydf = gateway.get_passing_stats(...). Not to mention that a missing method name is easier to debug than a call with a misspelled text string (and your editor might auto-suggest/auto-correct the method!).
Don't store the table types and kicking table rename data in the instances. That is class data:
class No:
def __init__(self):
self.foo = 'foo' # No
class Yes:
foo = 'foo' # Yes
def __init__(self):
pass
The
.tablesproperty should be.table_typesor.stats_types
The
get_data(a.k.a.get_stats) function has an awkward interface. Instead of trying to jam everything intostart_yearandend_year, try allowing multiple named parameters and requiring them to be exclusive. Also, be willing to accept multiple parameter types:
# Cannot use year=, years= in same call
df = api.get_passing_stats(year=2017)
df = api.get_passing_stats(years=(2013, 2017)) # Two explicit years
df = api.get_passing_stats(years=range(2011, 2019, 2)) # Only odd year data?
# Cannot use tables= in get_XXX_stats call.
# Cannot use table= and tables= in same call
df = api.get_stats(year=2018, table='kicking')
df = api.get_stats(years=(2017, 2018), tables=('passing', 'running', 'scoring'))
- Don't be afraid of a DataFrame with >2 dimensions. If someone requests passing, running, and kicking data for 2014, 2018, and 2018, then return a single dataframe with 4 dimensions (year, table, player, stats).
get_datashould automatically strip off the pro-bowl and all-pro data, and add separate columns for that.Your _check_start_and_end_years method doesn't actually check. What happens if I request data for 1861?
answered 13 hours ago
Austin HastingsAustin Hastings
6,3891232
$endgroup$
$begingroup$
Thanks, I appreciate your input. I have also been working on something that scrapes data from The Football Database. A lot of the code is similar to the Pro Football Reference scraper, so would it make sense to have aProFootballReferenceandFootballDatabaseclass that inherit from something likeAbstractNflScraper? I'm more familiar with abstract classes in Java, where abstract methods don't contain implementation. I'm unsure what to do about potential repeated code because of this.
$endgroup$
– shmible
2 hours ago
$begingroup$
I think that's going to depend on how similar the two sites are. Does The Football Database produce similar stats? Does it make sense for both scrapers to haveget_passing_stats()methods, for example? If so, then maybe there's a common base class. If not, if TFD provides different data completely, then you will end up "knowing" which one you are using, so sharing a common interface doesn't make sense.
$endgroup$
– Austin Hastings
1 hour ago
add a comment |
1
1
1
$begingroup$
You need to work on your names. Looking at your github repo, this file is pro_ref2.py? The class name is FootballRefScraper. The site chosen is pro-football-reference.com? What is going on here?
I don't know why this class exists. The class name should make it obvious. Does this thing exist to be a scraper? Or does it exist to be a data source? IMO, it does not exist to be a scraper, because that would imply it was one of many scrapers and so it would probably be a subclass of some
AbstractScraperbase class. So I think it's a data source, but then why is it called...Scraper? Also, what kind of data source is it? ApparentlyFootballRef, but again what's that?
Assuming this is really intended to be a data source, give the class a better name.
FootballStatisticsorNflStatisticsmake more sense. Alternatively, you might name it after the website, in which case calling itProFootballReferencewould seem logical.
If this file contains only the one class, then I suggest you rename the file to
pfr.pyorprofootballreference.pyor some such, and then maybe name the class something likeGatewayorAPI. Finally, don't call itget_tables. Call itget_statssince thats what you provide. (You're returning a DataFrame, but you wouldn't call itget_dfunless there was some alternate flavor to get...)
import pfr | import pfr
gateway = pfr.Gateway() | api = pfr.API()
df = gateway.get_stats(...) | df = api.get_stats(...)
As a corollary to #1, consider adding explicit methods for each of the table access calls. Instead of calling
df = gateway.get_stats(..., table_type='passing')it would be easier and clearer to simply saydf = gateway.get_passing_stats(...). Not to mention that a missing method name is easier to debug than a call with a misspelled text string (and your editor might auto-suggest/auto-correct the method!).
Don't store the table types and kicking table rename data in the instances. That is class data:
class No:
def __init__(self):
self.foo = 'foo' # No
class Yes:
foo = 'foo' # Yes
def __init__(self):
pass
The
.tablesproperty should be.table_typesor.stats_types
The
get_data(a.k.a.get_stats) function has an awkward interface. Instead of trying to jam everything intostart_yearandend_year, try allowing multiple named parameters and requiring them to be exclusive. Also, be willing to accept multiple parameter types:
# Cannot use year=, years= in same call
df = api.get_passing_stats(year=2017)
df = api.get_passing_stats(years=(2013, 2017)) # Two explicit years
df = api.get_passing_stats(years=range(2011, 2019, 2)) # Only odd year data?
# Cannot use tables= in get_XXX_stats call.
# Cannot use table= and tables= in same call
df = api.get_stats(year=2018, table='kicking')
df = api.get_stats(years=(2017, 2018), tables=('passing', 'running', 'scoring'))
- Don't be afraid of a DataFrame with >2 dimensions. If someone requests passing, running, and kicking data for 2014, 2018, and 2018, then return a single dataframe with 4 dimensions (year, table, player, stats).
get_datashould automatically strip off the pro-bowl and all-pro data, and add separate columns for that.Your _check_start_and_end_years method doesn't actually check. What happens if I request data for 1861?
answered 13 hours ago
Austin HastingsAustin Hastings
6,3891232
$endgroup$
You need to work on your names. Looking at your github repo, this file is pro_ref2.py? The class name is FootballRefScraper. The site chosen is pro-football-reference.com? What is going on here?
I don't know why this class exists. The class name should make it obvious. Does this thing exist to be a scraper? Or does it exist to be a data source? IMO, it does not exist to be a scraper, because that would imply it was one of many scrapers and so it would probably be a subclass of some
AbstractScraperbase class. So I think it's a data source, but then why is it called...Scraper? Also, what kind of data source is it? ApparentlyFootballRef, but again what's that?
Assuming this is really intended to be a data source, give the class a better name.
FootballStatisticsorNflStatisticsmake more sense. Alternatively, you might name it after the website, in which case calling itProFootballReferencewould seem logical.
If this file contains only the one class, then I suggest you rename the file to
pfr.pyorprofootballreference.pyor some such, and then maybe name the class something likeGatewayorAPI. Finally, don't call itget_tables. Call itget_statssince thats what you provide. (You're returning a DataFrame, but you wouldn't call itget_dfunless there was some alternate flavor to get...)
import pfr | import pfr
gateway = pfr.Gateway() | api = pfr.API()
df = gateway.get_stats(...) | df = api.get_stats(...)
As a corollary to #1, consider adding explicit methods for each of the table access calls. Instead of calling
df = gateway.get_stats(..., table_type='passing')it would be easier and clearer to simply saydf = gateway.get_passing_stats(...). Not to mention that a missing method name is easier to debug than a call with a misspelled text string (and your editor might auto-suggest/auto-correct the method!).
Don't store the table types and kicking table rename data in the instances. That is class data:
class No:
def __init__(self):
self.foo = 'foo' # No
class Yes:
foo = 'foo' # Yes
def __init__(self):
pass
The
.tablesproperty should be.table_typesor.stats_types
The
get_data(a.k.a.get_stats) function has an awkward interface. Instead of trying to jam everything intostart_yearandend_year, try allowing multiple named parameters and requiring them to be exclusive. Also, be willing to accept multiple parameter types:
# Cannot use year=, years= in same call
df = api.get_passing_stats(year=2017)
df = api.get_passing_stats(years=(2013, 2017)) # Two explicit years
df = api.get_passing_stats(years=range(2011, 2019, 2)) # Only odd year data?
# Cannot use tables= in get_XXX_stats call.
# Cannot use table= and tables= in same call
df = api.get_stats(year=2018, table='kicking')
df = api.get_stats(years=(2017, 2018), tables=('passing', 'running', 'scoring'))
- Don't be afraid of a DataFrame with >2 dimensions. If someone requests passing, running, and kicking data for 2014, 2018, and 2018, then return a single dataframe with 4 dimensions (year, table, player, stats).
get_datashould automatically strip off the pro-bowl and all-pro data, and add separate columns for that.Your _check_start_and_end_years method doesn't actually check. What happens if I request data for 1861?
answered 13 hours ago
Austin HastingsAustin Hastings
6,3891232
answered 13 hours ago
Austin HastingsAustin Hastings
6,3891232
answered 13 hours ago
Austin HastingsAustin Hastings
6,3891232
answered 13 hours ago
Austin HastingsAustin Hastings
6,3891232
6,3891232
$begingroup$
Thanks, I appreciate your input. I have also been working on something that scrapes data from The Football Database. A lot of the code is similar to the Pro Football Reference scraper, so would it make sense to have aProFootballReferenceandFootballDatabaseclass that inherit from something likeAbstractNflScraper? I'm more familiar with abstract classes in Java, where abstract methods don't contain implementation. I'm unsure what to do about potential repeated code because of this.
$endgroup$
– shmible
2 hours ago
$begingroup$
I think that's going to depend on how similar the two sites are. Does The Football Database produce similar stats? Does it make sense for both scrapers to haveget_passing_stats()methods, for example? If so, then maybe there's a common base class. If not, if TFD provides different data completely, then you will end up "knowing" which one you are using, so sharing a common interface doesn't make sense.
$endgroup$
– Austin Hastings
1 hour ago
add a comment |
$begingroup$
Thanks, I appreciate your input. I have also been working on something that scrapes data from The Football Database. A lot of the code is similar to the Pro Football Reference scraper, so would it make sense to have aProFootballReferenceandFootballDatabaseclass that inherit from something likeAbstractNflScraper? I'm more familiar with abstract classes in Java, where abstract methods don't contain implementation. I'm unsure what to do about potential repeated code because of this.
$endgroup$
– shmible
2 hours ago
$begingroup$
I think that's going to depend on how similar the two sites are. Does The Football Database produce similar stats? Does it make sense for both scrapers to haveget_passing_stats()methods, for example? If so, then maybe there's a common base class. If not, if TFD provides different data completely, then you will end up "knowing" which one you are using, so sharing a common interface doesn't make sense.
$endgroup$
– Austin Hastings
1 hour ago
$begingroup$
Thanks, I appreciate your input. I have also been working on something that scrapes data from The Football Database. A lot of the code is similar to the Pro Football Reference scraper, so would it make sense to have a
ProFootballReference and FootballDatabase class that inherit from something like AbstractNflScraper? I'm more familiar with abstract classes in Java, where abstract methods don't contain implementation. I'm unsure what to do about potential repeated code because of this.$endgroup$
– shmible
2 hours ago
$begingroup$
Thanks, I appreciate your input. I have also been working on something that scrapes data from The Football Database. A lot of the code is similar to the Pro Football Reference scraper, so would it make sense to have a
ProFootballReference and FootballDatabase class that inherit from something like AbstractNflScraper? I'm more familiar with abstract classes in Java, where abstract methods don't contain implementation. I'm unsure what to do about potential repeated code because of this.$endgroup$
– shmible
2 hours ago
$begingroup$
I think that's going to depend on how similar the two sites are. Does The Football Database produce similar stats? Does it make sense for both scrapers to have
get_passing_stats() methods, for example? If so, then maybe there's a common base class. If not, if TFD provides different data completely, then you will end up "knowing" which one you are using, so sharing a common interface doesn't make sense.$endgroup$
– Austin Hastings
1 hour ago
$begingroup$
I think that's going to depend on how similar the two sites are. Does The Football Database produce similar stats? Does it make sense for both scrapers to have
get_passing_stats() methods, for example? If so, then maybe there's a common base class. If not, if TFD provides different data completely, then you will end up "knowing" which one you are using, so sharing a common interface doesn't make sense.$endgroup$
– Austin Hastings
1 hour ago
add a comment |
shmible is a new contributor. Be nice, and check out our Code of Conduct.
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shmible is a new contributor. Be nice, and check out our Code of Conduct.
shmible is a new contributor. Be nice, and check out our Code of Conduct.
shmible is a new contributor. Be nice, and check out our Code of Conduct.
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