Nsryjdtyk

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' doc string.

A usage demonstration can be found at the bottom of the code. It scrapes 2017 Passing Data and 2018 Passing Data. This is my first time here asking for a code review, so I hope I provided enough necessary information. Any input is greatly appreciated. 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')