# Pro-Football-Talk Web Scraper
Developed by Devon Connors (c) 2022
## Before Continuing:
<strong>Out of respect for Pro Football Reference</strong> each instance of scraping will have a 5-10 second delay as to not spam their server. So in the instances where you obtain a list of URLs of players to scrape you will need to understand it could take some time to process that list.
##### Example: 400 players could take up to 1 hour to scrape.
I am working through updating this tool to use asyncronous request while also being respectful so for the time being please be patient.
## Examples of How To Use (Alpha Version)
### Scraping Team Data
#### Creating Team Data Scraper and Method Usage
```python
from PFRWebScraper import ScrapeTeamData
# Creates an instance of Team Data Scraper
team_data_scraper = ScrapeTeamData()
# Obtains the abbreviation for the team you wish to scrape
team_abbreviation = team_data_scraper.get_team_abbreviation("Las Vegas Raiders")
# Scrapes defensive data for the team for a number of years back.
# Uses 4 years by default.
default_defensive_data = team_data_scraper.scrape_defense(team_abbreviation)
# Scrapes defensive data for the team's last 2 years
last_two_years_defense_data = team_data_scraper.scrape_defense(team_abbreviation, 2)
# # Scrapes offensive data for the team for a number of years back.
# Uses 4 years by default.
default_offensive_data = team_data_scraper.scrape_offense(team_abbreviation)
# Scrapes offensive data for the team's last 2 years
last_two_years_offensive_data = team_data_scraper.scrape_offense(team_abbreviation, 2)
```
#### Obtaining Specific Data From The Team Data Object
```python
from PFRWebScraper import ScrapeTeamData
# Creates an instance of Team Data Scraper
team_data_scraper = ScrapeTeamData()
# Obtains the abbreviation for the team you wish to scrape
team_abbreviation = team_data_scraper.get_team_abbreviation("Las Vegas Raiders")
# Scrapes offensive data for the team's last 2 years
offensive_data = team_data_scraper.scrape_offense(team_abbreviation, 2)
# Obtains the years that returned data if you are unsure
# In this instance you will receive a list:
# [2021, 2022]
valid_years_with_data = offensive_data.get_list_of_years()
# You will then need to set the year from which you will like data
offensive_data.set_reference_year(2022)
# After you have set the reference year you can begin pulling stats
team_points = offensive_data.get_points()
team_total_yards = offensive_data.get_total_yards()
```
#### Obtaining Whole Data Sets
```python
from PFRWebScraper import ScrapeTeamData
# Creates an instance of Team Data Scraper
team_data_scraper = ScrapeTeamData()
# Obtains the abbreviation for the team you wish to scrape
team_abbreviation = team_data_scraper.get_team_abbreviation("Las Vegas Raiders")
# Scrapes offensive data for the team's last 2 years
offensive_data = team_data_scraper.scrape_offense(team_abbreviation, 2)
# Obtains the raw data as a Pandas Dataframe
offensive_dataframe = team_data_scraper.get_dataframe_of_stats()
# Obtains the raw data as a dictionary
offensive_dictionary = team_data_scraper.get_dictionary_of_stats()
```
### Scraping Player Data
#### Create Player Scraper and Method Usage
##### Scraping Passing Data
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all passing data on a player's page
passing_data = player_scraper.scrape_passing("https://www.pro-football-reference.com/players/C/CarrDe02.htm")
# You can also specify the sections of data you would like. You pass them in as a list.
# This is set to all data by default.
# Different Input:
# 1. 'passing' - Scrapes Regular Season and Playoff data on a passer.
# 2. 'advanced' - Scrapes Air Yards, Accuracy, Pressure, and Play Type data on a passer.
# 3. 'adjusted' - Scrapes Adjusted data on a passer.
# Example of only passsing and advanced data
passing_advanced_data = player_scraper.scrape_passing("https://www.pro-football-reference.com/players/C/CarrDe02.htm", ['passing', 'advanced'])
# Example of only adjusted data
adjusted_data = player_scraper.scrape_passing("https://www.pro-football-reference.com/players/C/CarrDe02.htm", ['adjusted'])
```
##### Scrape Rushing and Receiving Data
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all rushing and receiving data on a player's page
rushing_receiving_data = player_scraper.scrape_rushing_receiving("https://www.pro-football-reference.com/players/J/JacoJo01.htm")
```
##### Scrape Scoring Data
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all scoring data on a player's page
scoring_data = player_scraper.scrape_scoring("https://www.pro-football-reference.com/players/R/RenfHu00.htm")
```
##### Scrape Snap Counts Data
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all snap counts data on a player's page
snap_counts_data = player_scraper.scrape_snap_counts("https://www.pro-football-reference.com/players/W/WallDa01.htm")
```
##### Scrape Defense and Fumbles Data
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all defense and fumbles data on a player's page
defense_and_fumbles_data = player_scraper.scrape_defense_and_fumbles("https://www.pro-football-reference.com/players/A/AdamDa01.htm")
```
##### Scrape Kick and Punt Returns Data
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all kick and punt returns data on a player's page
returns_data = player_scraper.scrape_kick_and_punt_returns("https://www.pro-football-reference.com/players/A/AbduAm00.htm")
```
##### Scrape Kicking Data
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all kicking data on a player's page
kicking_data = player_scraper.scrape_kicking("https://www.pro-football-reference.com/players/C/CarlDa00.htm")
```
#### Utilizing Player Data Objects
##### Passing Data Object Usage
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all passing data on a player's page
passing_data = player_scraper.scrape_passing("https://www.pro-football-reference.com/players/C/CarrDe02.htm")
# passing_data is now a passing object that has all the information stored within sub-objects
# Methods will need to be called to obtain the relevant data to work with it
# Passing Data Regular Season
regular_season_passing_data = passing_data.get_passing_data_regular_season()
# Passing Data Playoffs
playoffs_passing_data = passing_data.get_passing_data_playoffs()
# Passing Data Advanced (Air Yards)
air_yards_passing_data = passing_data.get_passing_data_advanced_air_yards()
# Passing Data Advanced (Accuracy)
accuracy_passing_data = passing_data.get_passing_data_advanced_accuracy()
# Passing Data Advanced (Pressure)
pressure_passing_data = passing_data.get_passing_data_advanced_pressure()
# Passing Data Advanced (Play Type)
play_type_passing_data = passing_data.get_passing_data_advanced_play_type()
# Passing Data Adjusted
adjusted_passing_data = passing_data.get_passing_data_adjusted()
# Once you have decided which data object you would like you can then utilize them the same way as Team Data.
# Example: Regular Season Passing Data
# Obtain the whole data set
regular_season_passing_dataframe = regular_season_passing_data.get_dataframe_of_stats()
regular_season_passing_dictionary = regular_season_passing_data.get_dictionary_of_stats()
# Obtain specific data points
# Set the reference year
regular_season_passing_data.set_reference_year(2022)
# Call the methods for specific data points
players_age = regular_season_passing_data.get_age()
games_played = regular_season_passing_data.get_games_played()
games_started = regular_season_passing_data.get_games_started()
```
##### Rushing and Receiving Data Object Usage
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all rushing and receiving data on a player's page
rushing_receiving_data = player_scraper.scrape_rushing_receiving("https://www.pro-football-reference.com/players/J/JacoJo01.htm")
# rushing_receiving_data is now a rushing and receiving object that has all the information stored within sub-objects
# Methods will need to be called to obtain the relevant data to work with it
# Rushing and Receiving Data Regular Season
regular_season_rushing_receiving = rushing_receiving_data.get_rushing_receiving_data_regular_season()
# Rushing and Receiving Data Playoffs
playoffs_rushing_receiving = rushing_receiving_data.get_rushing_receiving_data_playoffs()
# Rushing and Receiving Data Advanced
advanced_rushing_receiving = rushing_receiving_data.get_rushing_receiving_data_advanced()
# Once you have decided which data object you would like you can then utilize them the same way as Team Data.
# Example: Regular Season Rushing and Receiving Data
# Obtain the whole data set
regular_season_rushing_receiving_dataframe = regular_season_rushing_receiving.get_dataframe_of_stats()
regular_season_rushing_receiving_dictionary = regular_season_rushing_receiving.get_dictionary_of_stats()
# Obtain specific data points
# Set the reference year
regular_season_rushing_receiving.set_reference_year(2022)
# Call the methods for specific data points
players_age = regular_season_rushing_receiving.get_age()
games_played = regular_season_rushing_receiving.get_games_played()
games_started = regular_season_rushing_receiving.get_games_started()
```
##### Scoring Data Object Usage
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all scoring data on a player's page
scoring_data = player_scraper.scrape_scoring("https://www.pro-football-reference.com/players/R/RenfHu00.htm")
# scoring_data is now a scoring object that has all the information stored within sub-objects
# Methods will need to be called to obtain the relevant data to work with it
# Scoring Data Regular Season
regular_season_scoring = scoring_data.get_scoring_data_regular_season()
# Scoring Data Playoffs
playoffs_scoring = scoring_data.get_scoring_data_playoffs()
# Once you have decided which data object you would like you can then utilize them the same way as Team Data.
# Example: Regular Season Scoring Data
# Obtain the whole data set
regular_season_scoring_dataframe = regular_season_scoring.get_dataframe_of_stats()
regular_season_scoring_dictionary = regular_season_scoring.get_dictionary_of_stats()
# Obtain specific data points
# Set the reference year
regular_season_scoring.set_reference_year(2022)
# Call the methods for specific data points
players_age = regular_season_scoring.get_age()
games_played = regular_season_scoring.get_games_played()
games_started = regular_season_scoring.get_games_started()
```
##### Snap Counts Data Object Usage
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all snap counts data on a player's page
snap_counts_data = player_scraper.scrape_snap_counts("https://www.pro-football-reference.com/players/W/WallDa01.htm")
# snap_counts_data is now a snap counts object that has all the information stored within sub-objects
# Methods will need to be called to obtain the relevant data to work with it
# Snap Counts Data Regular Season
regular_season_snap_counts = snap_counts_data.get_snap_counts_data_regular_season()
# Once you have decided which data object you would like you can then utilize them the same way as Team Data.
# Example: Regular Season Snap Counts Data
# Obtain the whole data set
regular_season_snap_counts_dataframe = regular_season_snap_counts.get_dataframe_of_stats()
regular_season_snap_counts_dictionary = regular_season_snap_counts.get_dictionary_of_stats()
# Obtain specific data points
# Set the reference year
regular_season_snap_counts.set_reference_year(2022)
# Call the methods for specific data points
players_age = regular_season_snap_counts.get_age()
games_played = regular_season_snap_counts.get_games_played()
games_started = regular_season_snap_counts.get_games_started()
```
##### Defense and Fumbles Data Object Usage
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all defense and fumbles data on a player's page
defense_and_fumbles_data = player_scraper.scrape_defense_and_fumbles("https://www.pro-football-reference.com/players/A/AdamDa01.htm")
# defense_and_fumbles_data is now a defense and fumbles object that has all the information stored within sub-objects
# Methods will need to be called to obtain the relevant data to work with it
# Defense and Fumbles Data Regular Season
regular_season_defense_and_fumbles = defense_and_fumbles_data.get_defense_and_fumbles_data_regular_season()
# Defense and Fumbles Data Playoffs
playoffs_defense_and_fumbles = defense_and_fumbles_data.get_defense_and_fumbles_data_playoffs()
# Once you have decided which data object you would like you can then utilize them the same way as Team Data.
# Example: Regular Season Defense and Fumbles Data
# Obtain the whole data set
regular_season_defense_and_fumbles_dataframe = regular_season_defense_and_fumbles.get_dataframe_of_stats()
regular_season_defense_and_fumbles_dictionary = regular_season_defense_and_fumbles.get_dictionary_of_stats()
# Obtain specific data points
# Set the reference year
regular_season_defense_and_fumbles.set_reference_year(2022)
# Call the methods for specific data points
players_age = regular_season_defense_and_fumbles.get_age()
games_played = regular_season_defense_and_fumbles.get_games_played()
games_started = regular_season_defense_and_fumbles.get_games_started()
```
##### Kick and Punt Returns Data Object Usage
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all kick and punt returns data on a player's page
returns_data = player_scraper.scrape_kick_and_punt_returns("https://www.pro-football-reference.com/players/A/AbduAm00.htm")
# returns_data is now a kick and punt returns object that has all the information stored within sub-objects
# Methods will need to be called to obtain the relevant data to work with it
# Kick and Punt Returns Data Regular Season
regular_season_returns = returns_data.get_returns_data_regular_season()
# Kick and Punt Returns Data Playoffs
playoffs_returns = returns_data.get_returns_data_playoffs()
# Once you have decided which data object you would like you can then utilize them the same way as Team Data.
# Example: Regular Season Kick and Punt Returns Data
# Obtain the whole data set
regular_season_returns_dataframe = regular_season_returns.get_dataframe_of_stats()
regular_season_returns_dictionary = regular_season_returns.get_dictionary_of_stats()
# Obtain specific data points
# Set the reference year
regular_season_returns.set_reference_year(2022)
# Call the methods for specific data points
players_age = regular_season_returns.get_age()
games_played = regular_season_returns.get_games_played()
games_started = regular_season_returns.get_games_started()
```
##### Kicking Data Object Usage
```python
from PFRWebScraper import ScrapePlayerData
# Creates an instance of the Player Scraper Object
player_scraper = ScrapePlayerData()
# Scrapes for all kicking data on a player's page
kicking_data = player_scraper.scrape_kicking("https://www.pro-football-reference.com/players/C/CarlDa00.htm")
# kicking_data is now a kicking object that has all the information stored within sub-objects
# Methods will need to be called to obtain the relevant data to work with it
# Kicking Data Regular Season
regular_season_kicking = kicking_data.get_kicking_data_regular_season()
# Kicking Data Playoffs
playoffs_kicking = kicking_data.get_kicking_data_playoffs()
# Once you have decided which data object you would like you can then utilize them the same way as Team Data.
# Example: Regular Season Kicking Data
# Obtain the whole data set
regular_season_returns_dataframe = regular_season_kicking.get_dataframe_of_stats()
regular_season_returns_dictionary = regular_season_kicking.get_dictionary_of_stats()
# Obtain specific data points
# Set the reference year
regular_season_kicking.set_reference_year(2022)
# Call the methods for specific data points
players_age = regular_season_kicking.get_age()
games_played = regular_season_kicking.get_games_played()
games_started = regular_season_kicking.get_games_started()
```
### Scraping URL Data
#### Create URL Scraper and Method Usage
##### Scraping Team for Player URLs
```python
from PFRWebScraper import ScrapeURLs
# Creates an instance of the URL Scraper Object
url_scraper = ScrapeURLs()
# Scrapes for Player's URLs that are on the specified team
# You can set the specific year BUT if you dont want to it is always set
# to the current year
player_url_data = url_scraper.scrape_team_for_player_urls("Las Vegas Raiders")
# Example on how to scrape for specific year
player_url_data = url_scraper.scrape_team_for_player_urls("Las Vegas Raiders", 2021)
# player_url_data will now be an object containing the player's URLs
# Methods can be called on that object to access the information
# Examples:
# Obtain a dictionary of all the players with the position as the KEY
# and the VALUE will be a list of dictionaries containing the player's
# name and URL
team_players_urls_dict = player_url_data.get_dictionaries_of_urls()
# Example Data from get_dictionaries_of_urls():
# {
# "QB":
# [
# {
# "name": "Derek Carr",
# "url": "https://www.pro-football-reference.com/players/C/CarrDe02.htm"
# },
# {
# "name": "Jarrett Stidham",
# "url": "https://www.pro-football-reference.com/players/S/StidJa00.htm"
# }
# ],
# "RB":
# [
# {
# "name": "Josh Jacobs",
# "url": "https://www.pro-football-reference.com/players/J/JacoJo01.htm"
# }
# ]
# }
# Obtaining the URLs of players listed as a Quarterback in the form of a list of dictionaries
quarterback_urls = player_url_data.get_quarterbacks()
# Obtaining the URLs of players listed as a Running Back in the form of a list of dictionaries
running_back_urls = player_url_data.get_running_backs()
# Obtaining the URLs of players listed as a Fullback in the form of a list of dictionaries
fullback_urls = player_url_data.get_fullbacks()
# Obtaining the URLs of players listed as a Wide Receiver in the form of a list of dictionaries
wide_receiver_urls = player_url_data.get_wide_receivers()
# Obtaining the URLs of players listed as a Tight End in the form of a list of dictionaries
tight_end_urls = player_url_data.get_tight_ends()
# Obtaining the URLs of players listed as a Kicker in the form of a list of dictionaries
kicker_urls = player_url_data.get_kickers()
# Example Data from get_quarterbacks():
# [
# {
# "name": "Derek Carr",
# "url": "https://www.pro-football-reference.com/players/C/CarrDe02.htm"
# },
# {
# "name": "Jarrett Stidham",
# "url": "https://www.pro-football-reference.com/players/S/StidJa00.htm"
# }
# ]
```
##### Scraping Stat Type for Player URLs
```python
from PFRWebScraper import ScrapeURLs
# Creates an instance of the URL Scraper Object
url_scraper = ScrapeURLs()
# Scrapes for Player's URLs that are listed within that specific stat type list
# You can set the specific year BUT if you dont want to it is always set
# to the current year
# Example on how to scrape for current year and passing list
passing_player_url_data = url_scraper.scrape_stat_type_for_player_urls("passing")
# Example on how to scrape for 2021 and rushing list
rushing_player_url_data = url_scraper.scrape_stat_type_for_player_urls("rushing", 2021)
# Example on how to scrape for 2020 and receiving list
receiving_player_url_data = url_scraper.scrape_stat_type_for_player_urls("receiving", 2020)
# Example on how to scrape for current year and kicking list
kicking_player_url_data = url_scraper.scrape_stat_type_for_player_urls("kicking")
# Example on how to scrape for current year and returns list
returns_player_url_data = url_scraper.scrape_stat_type_for_player_urls("returns")
# Example on how to scrape for current year and scoring list
scoring_player_url_data = url_scraper.scrape_stat_type_for_player_urls("scoring")
# Obtain a list of dictionaries containing the player's name and url
passing_players_urls_list = passing_player_url_data.get_list_of_urls()
# Example Data from get_list_of_urls():
# [
# {
# "name": "Derek Carr",
# "url": "https://www.pro-football-reference.com/players/C/CarrDe02.htm"
# },
# {
# "name": "Patrick Mahomes",
# "url": "https://www.pro-football-reference.com/players/M/MahoPa00.htm"
# },
# {
# "name": "Joe Burrow",
# "url": "https://www.pro-football-reference.com/players/B/BurrJo01.htm"
# },
# {
# "name": "Justin Herbert",
# "url": "https://www.pro-football-reference.com/players/H/HerbJu00.htm"
# },
# {
# "name": "Tom Brady",
# "url": "https://www.pro-football-reference.com/players/B/BradTo00.htm"
# }
# ]
# Obtain the number of dictionaries within the list
count_of_passing_players_urls_list = passing_player_url_data.get_count_of_urls()
# Using the sample data the method get_count_of_urls() would return 5
# Obtain a list of dictionaries, to the specified range, containing the player's name and url
range_of_passing_players_urls_list = passing_player_url_data.get_range_of_urls(1, 3)
# Example Data from get_range_of_urls(1, 3):
# [
# {
# "name": "Derek Carr",
# "url": "https://www.pro-football-reference.com/players/C/CarrDe02.htm"
# },
# {
# "name": "Patrick Mahomes",
# "url": "https://www.pro-football-reference.com/players/M/MahoPa00.htm"
# },
# {
# "name": "Joe Burrow",
# "url": "https://www.pro-football-reference.com/players/B/BurrJo01.htm"
# }
# ]
```
Raw data
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"name": "PFRWebScraper",
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"keywords": "python,pro-football-reference,football,fantasy football,american football,pro football reference,web scraper,scraper",
"author": "Devon Connors",
"author_email": "<dconns1@outlook.com>",
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"platform": null,
"description": "# Pro-Football-Talk Web Scraper\n\nDeveloped by Devon Connors (c) 2022\n\n## Before Continuing:\n<strong>Out of respect for Pro Football Reference</strong> each instance of scraping will have a 5-10 second delay as to not spam their server. So in the instances where you obtain a list of URLs of players to scrape you will need to understand it could take some time to process that list.\n\n##### Example: 400 players could take up to 1 hour to scrape.\n\nI am working through updating this tool to use asyncronous request while also being respectful so for the time being please be patient.\n\n## Examples of How To Use (Alpha Version)\n\n### Scraping Team Data\n#### Creating Team Data Scraper and Method Usage\n```python\nfrom PFRWebScraper import ScrapeTeamData\n\n# Creates an instance of Team Data Scraper\nteam_data_scraper = ScrapeTeamData()\n\n# Obtains the abbreviation for the team you wish to scrape\nteam_abbreviation = team_data_scraper.get_team_abbreviation(\"Las Vegas Raiders\")\n\n# Scrapes defensive data for the team for a number of years back. \n# Uses 4 years by default.\ndefault_defensive_data = team_data_scraper.scrape_defense(team_abbreviation)\n\n# Scrapes defensive data for the team's last 2 years\nlast_two_years_defense_data = team_data_scraper.scrape_defense(team_abbreviation, 2)\n\n# # Scrapes offensive data for the team for a number of years back. \n# Uses 4 years by default.\ndefault_offensive_data = team_data_scraper.scrape_offense(team_abbreviation)\n\n# Scrapes offensive data for the team's last 2 years\nlast_two_years_offensive_data = team_data_scraper.scrape_offense(team_abbreviation, 2)\n```\n\n#### Obtaining Specific Data From The Team Data Object\n```python\nfrom PFRWebScraper import ScrapeTeamData\n\n# Creates an instance of Team Data Scraper\nteam_data_scraper = ScrapeTeamData()\n\n# Obtains the abbreviation for the team you wish to scrape\nteam_abbreviation = team_data_scraper.get_team_abbreviation(\"Las Vegas Raiders\")\n\n# Scrapes offensive data for the team's last 2 years\noffensive_data = team_data_scraper.scrape_offense(team_abbreviation, 2)\n\n# Obtains the years that returned data if you are unsure\n# In this instance you will receive a list: \n# [2021, 2022]\nvalid_years_with_data = offensive_data.get_list_of_years()\n\n# You will then need to set the year from which you will like data\noffensive_data.set_reference_year(2022)\n\n# After you have set the reference year you can begin pulling stats\nteam_points = offensive_data.get_points()\nteam_total_yards = offensive_data.get_total_yards()\n```\n\n#### Obtaining Whole Data Sets\n```python\nfrom PFRWebScraper import ScrapeTeamData\n\n# Creates an instance of Team Data Scraper\nteam_data_scraper = ScrapeTeamData()\n\n# Obtains the abbreviation for the team you wish to scrape\nteam_abbreviation = team_data_scraper.get_team_abbreviation(\"Las Vegas Raiders\")\n\n# Scrapes offensive data for the team's last 2 years\noffensive_data = team_data_scraper.scrape_offense(team_abbreviation, 2)\n\n# Obtains the raw data as a Pandas Dataframe\noffensive_dataframe = team_data_scraper.get_dataframe_of_stats()\n\n# Obtains the raw data as a dictionary\noffensive_dictionary = team_data_scraper.get_dictionary_of_stats()\n```\n\n### Scraping Player Data\n#### Create Player Scraper and Method Usage\n##### Scraping Passing Data\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all passing data on a player's page\npassing_data = player_scraper.scrape_passing(\"https://www.pro-football-reference.com/players/C/CarrDe02.htm\")\n\n# You can also specify the sections of data you would like. You pass them in as a list.\n# This is set to all data by default. \n# Different Input: \n# 1. 'passing' - Scrapes Regular Season and Playoff data on a passer.\n# 2. 'advanced' - Scrapes Air Yards, Accuracy, Pressure, and Play Type data on a passer.\n# 3. 'adjusted' - Scrapes Adjusted data on a passer.\n\n# Example of only passsing and advanced data\npassing_advanced_data = player_scraper.scrape_passing(\"https://www.pro-football-reference.com/players/C/CarrDe02.htm\", ['passing', 'advanced'])\n\n# Example of only adjusted data\nadjusted_data = player_scraper.scrape_passing(\"https://www.pro-football-reference.com/players/C/CarrDe02.htm\", ['adjusted'])\n```\n\n##### Scrape Rushing and Receiving Data\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all rushing and receiving data on a player's page\nrushing_receiving_data = player_scraper.scrape_rushing_receiving(\"https://www.pro-football-reference.com/players/J/JacoJo01.htm\")\n```\n\n##### Scrape Scoring Data\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all scoring data on a player's page\nscoring_data = player_scraper.scrape_scoring(\"https://www.pro-football-reference.com/players/R/RenfHu00.htm\")\n```\n\n##### Scrape Snap Counts Data\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all snap counts data on a player's page\nsnap_counts_data = player_scraper.scrape_snap_counts(\"https://www.pro-football-reference.com/players/W/WallDa01.htm\")\n```\n\n##### Scrape Defense and Fumbles Data\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all defense and fumbles data on a player's page\ndefense_and_fumbles_data = player_scraper.scrape_defense_and_fumbles(\"https://www.pro-football-reference.com/players/A/AdamDa01.htm\")\n```\n\n##### Scrape Kick and Punt Returns Data\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all kick and punt returns data on a player's page\nreturns_data = player_scraper.scrape_kick_and_punt_returns(\"https://www.pro-football-reference.com/players/A/AbduAm00.htm\")\n```\n\n##### Scrape Kicking Data\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all kicking data on a player's page\nkicking_data = player_scraper.scrape_kicking(\"https://www.pro-football-reference.com/players/C/CarlDa00.htm\")\n```\n\n#### Utilizing Player Data Objects\n##### Passing Data Object Usage\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all passing data on a player's page\npassing_data = player_scraper.scrape_passing(\"https://www.pro-football-reference.com/players/C/CarrDe02.htm\")\n\n# passing_data is now a passing object that has all the information stored within sub-objects\n# Methods will need to be called to obtain the relevant data to work with it\n\n# Passing Data Regular Season\nregular_season_passing_data = passing_data.get_passing_data_regular_season()\n\n# Passing Data Playoffs\nplayoffs_passing_data = passing_data.get_passing_data_playoffs()\n\n# Passing Data Advanced (Air Yards)\nair_yards_passing_data = passing_data.get_passing_data_advanced_air_yards()\n\n# Passing Data Advanced (Accuracy)\naccuracy_passing_data = passing_data.get_passing_data_advanced_accuracy()\n\n# Passing Data Advanced (Pressure)\npressure_passing_data = passing_data.get_passing_data_advanced_pressure()\n\n# Passing Data Advanced (Play Type)\nplay_type_passing_data = passing_data.get_passing_data_advanced_play_type()\n\n# Passing Data Adjusted\nadjusted_passing_data = passing_data.get_passing_data_adjusted()\n\n# Once you have decided which data object you would like you can then utilize them the same way as Team Data.\n\n# Example: Regular Season Passing Data\n\n# Obtain the whole data set\nregular_season_passing_dataframe = regular_season_passing_data.get_dataframe_of_stats()\nregular_season_passing_dictionary = regular_season_passing_data.get_dictionary_of_stats()\n\n# Obtain specific data points\n# Set the reference year\nregular_season_passing_data.set_reference_year(2022)\n\n# Call the methods for specific data points\nplayers_age = regular_season_passing_data.get_age()\ngames_played = regular_season_passing_data.get_games_played()\ngames_started = regular_season_passing_data.get_games_started()\n```\n\n##### Rushing and Receiving Data Object Usage\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all rushing and receiving data on a player's page\nrushing_receiving_data = player_scraper.scrape_rushing_receiving(\"https://www.pro-football-reference.com/players/J/JacoJo01.htm\")\n\n# rushing_receiving_data is now a rushing and receiving object that has all the information stored within sub-objects\n# Methods will need to be called to obtain the relevant data to work with it\n\n# Rushing and Receiving Data Regular Season\nregular_season_rushing_receiving = rushing_receiving_data.get_rushing_receiving_data_regular_season()\n\n# Rushing and Receiving Data Playoffs\nplayoffs_rushing_receiving = rushing_receiving_data.get_rushing_receiving_data_playoffs()\n\n# Rushing and Receiving Data Advanced\nadvanced_rushing_receiving = rushing_receiving_data.get_rushing_receiving_data_advanced()\n\n# Once you have decided which data object you would like you can then utilize them the same way as Team Data.\n\n# Example: Regular Season Rushing and Receiving Data\n\n# Obtain the whole data set\nregular_season_rushing_receiving_dataframe = regular_season_rushing_receiving.get_dataframe_of_stats()\nregular_season_rushing_receiving_dictionary = regular_season_rushing_receiving.get_dictionary_of_stats()\n\n# Obtain specific data points\n# Set the reference year\nregular_season_rushing_receiving.set_reference_year(2022)\n\n# Call the methods for specific data points\nplayers_age = regular_season_rushing_receiving.get_age()\ngames_played = regular_season_rushing_receiving.get_games_played()\ngames_started = regular_season_rushing_receiving.get_games_started()\n```\n\n##### Scoring Data Object Usage\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all scoring data on a player's page\nscoring_data = player_scraper.scrape_scoring(\"https://www.pro-football-reference.com/players/R/RenfHu00.htm\")\n\n# scoring_data is now a scoring object that has all the information stored within sub-objects\n# Methods will need to be called to obtain the relevant data to work with it\n\n# Scoring Data Regular Season\nregular_season_scoring = scoring_data.get_scoring_data_regular_season()\n\n# Scoring Data Playoffs\nplayoffs_scoring = scoring_data.get_scoring_data_playoffs()\n\n# Once you have decided which data object you would like you can then utilize them the same way as Team Data.\n\n# Example: Regular Season Scoring Data\n\n# Obtain the whole data set\nregular_season_scoring_dataframe = regular_season_scoring.get_dataframe_of_stats()\nregular_season_scoring_dictionary = regular_season_scoring.get_dictionary_of_stats()\n\n# Obtain specific data points\n# Set the reference year\nregular_season_scoring.set_reference_year(2022)\n\n# Call the methods for specific data points\nplayers_age = regular_season_scoring.get_age()\ngames_played = regular_season_scoring.get_games_played()\ngames_started = regular_season_scoring.get_games_started()\n```\n\n##### Snap Counts Data Object Usage\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all snap counts data on a player's page\nsnap_counts_data = player_scraper.scrape_snap_counts(\"https://www.pro-football-reference.com/players/W/WallDa01.htm\")\n\n# snap_counts_data is now a snap counts object that has all the information stored within sub-objects\n# Methods will need to be called to obtain the relevant data to work with it\n\n# Snap Counts Data Regular Season\nregular_season_snap_counts = snap_counts_data.get_snap_counts_data_regular_season()\n\n# Once you have decided which data object you would like you can then utilize them the same way as Team Data.\n\n# Example: Regular Season Snap Counts Data\n\n# Obtain the whole data set\nregular_season_snap_counts_dataframe = regular_season_snap_counts.get_dataframe_of_stats()\nregular_season_snap_counts_dictionary = regular_season_snap_counts.get_dictionary_of_stats()\n\n# Obtain specific data points\n# Set the reference year\nregular_season_snap_counts.set_reference_year(2022)\n\n# Call the methods for specific data points\nplayers_age = regular_season_snap_counts.get_age()\ngames_played = regular_season_snap_counts.get_games_played()\ngames_started = regular_season_snap_counts.get_games_started()\n```\n\n##### Defense and Fumbles Data Object Usage\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all defense and fumbles data on a player's page\ndefense_and_fumbles_data = player_scraper.scrape_defense_and_fumbles(\"https://www.pro-football-reference.com/players/A/AdamDa01.htm\")\n\n# defense_and_fumbles_data is now a defense and fumbles object that has all the information stored within sub-objects\n# Methods will need to be called to obtain the relevant data to work with it\n\n# Defense and Fumbles Data Regular Season\nregular_season_defense_and_fumbles = defense_and_fumbles_data.get_defense_and_fumbles_data_regular_season()\n\n# Defense and Fumbles Data Playoffs\nplayoffs_defense_and_fumbles = defense_and_fumbles_data.get_defense_and_fumbles_data_playoffs()\n\n# Once you have decided which data object you would like you can then utilize them the same way as Team Data.\n\n# Example: Regular Season Defense and Fumbles Data\n\n# Obtain the whole data set\nregular_season_defense_and_fumbles_dataframe = regular_season_defense_and_fumbles.get_dataframe_of_stats()\nregular_season_defense_and_fumbles_dictionary = regular_season_defense_and_fumbles.get_dictionary_of_stats()\n\n# Obtain specific data points\n# Set the reference year\nregular_season_defense_and_fumbles.set_reference_year(2022)\n\n# Call the methods for specific data points\nplayers_age = regular_season_defense_and_fumbles.get_age()\ngames_played = regular_season_defense_and_fumbles.get_games_played()\ngames_started = regular_season_defense_and_fumbles.get_games_started()\n```\n\n##### Kick and Punt Returns Data Object Usage\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all kick and punt returns data on a player's page\nreturns_data = player_scraper.scrape_kick_and_punt_returns(\"https://www.pro-football-reference.com/players/A/AbduAm00.htm\")\n\n# returns_data is now a kick and punt returns object that has all the information stored within sub-objects\n# Methods will need to be called to obtain the relevant data to work with it\n\n# Kick and Punt Returns Data Regular Season\nregular_season_returns = returns_data.get_returns_data_regular_season()\n\n# Kick and Punt Returns Data Playoffs\nplayoffs_returns = returns_data.get_returns_data_playoffs()\n\n# Once you have decided which data object you would like you can then utilize them the same way as Team Data.\n\n# Example: Regular Season Kick and Punt Returns Data\n\n# Obtain the whole data set\nregular_season_returns_dataframe = regular_season_returns.get_dataframe_of_stats()\nregular_season_returns_dictionary = regular_season_returns.get_dictionary_of_stats()\n\n# Obtain specific data points\n# Set the reference year\nregular_season_returns.set_reference_year(2022)\n\n# Call the methods for specific data points\nplayers_age = regular_season_returns.get_age()\ngames_played = regular_season_returns.get_games_played()\ngames_started = regular_season_returns.get_games_started()\n```\n\n##### Kicking Data Object Usage\n```python\nfrom PFRWebScraper import ScrapePlayerData\n\n# Creates an instance of the Player Scraper Object\nplayer_scraper = ScrapePlayerData()\n\n# Scrapes for all kicking data on a player's page\nkicking_data = player_scraper.scrape_kicking(\"https://www.pro-football-reference.com/players/C/CarlDa00.htm\")\n\n# kicking_data is now a kicking object that has all the information stored within sub-objects\n# Methods will need to be called to obtain the relevant data to work with it\n\n# Kicking Data Regular Season\nregular_season_kicking = kicking_data.get_kicking_data_regular_season()\n\n# Kicking Data Playoffs\nplayoffs_kicking = kicking_data.get_kicking_data_playoffs()\n\n# Once you have decided which data object you would like you can then utilize them the same way as Team Data.\n\n# Example: Regular Season Kicking Data\n\n# Obtain the whole data set\nregular_season_returns_dataframe = regular_season_kicking.get_dataframe_of_stats()\nregular_season_returns_dictionary = regular_season_kicking.get_dictionary_of_stats()\n\n# Obtain specific data points\n# Set the reference year\nregular_season_kicking.set_reference_year(2022)\n\n# Call the methods for specific data points\nplayers_age = regular_season_kicking.get_age()\ngames_played = regular_season_kicking.get_games_played()\ngames_started = regular_season_kicking.get_games_started()\n```\n\n### Scraping URL Data\n#### Create URL Scraper and Method Usage\n##### Scraping Team for Player URLs\n```python\nfrom PFRWebScraper import ScrapeURLs\n\n# Creates an instance of the URL Scraper Object\nurl_scraper = ScrapeURLs()\n\n# Scrapes for Player's URLs that are on the specified team\n# You can set the specific year BUT if you dont want to it is always set \n# to the current year\nplayer_url_data = url_scraper.scrape_team_for_player_urls(\"Las Vegas Raiders\")\n\n# Example on how to scrape for specific year\nplayer_url_data = url_scraper.scrape_team_for_player_urls(\"Las Vegas Raiders\", 2021)\n\n# player_url_data will now be an object containing the player's URLs\n# Methods can be called on that object to access the information\n\n# Examples:\n\n# Obtain a dictionary of all the players with the position as the KEY \n# and the VALUE will be a list of dictionaries containing the player's \n# name and URL\nteam_players_urls_dict = player_url_data.get_dictionaries_of_urls()\n\n# Example Data from get_dictionaries_of_urls(): \n# {\n# \"QB\": \n# [\n# {\n# \"name\": \"Derek Carr\", \n# \"url\": \"https://www.pro-football-reference.com/players/C/CarrDe02.htm\"\n# }, \n# { \n# \"name\": \"Jarrett Stidham\", \n# \"url\": \"https://www.pro-football-reference.com/players/S/StidJa00.htm\"\n# }\n# ], \n# \"RB\": \n# [\n# { \n# \"name\": \"Josh Jacobs\", \n# \"url\": \"https://www.pro-football-reference.com/players/J/JacoJo01.htm\"\n# }\n# ]\n# }\n\n# Obtaining the URLs of players listed as a Quarterback in the form of a list of dictionaries\nquarterback_urls = player_url_data.get_quarterbacks()\n\n# Obtaining the URLs of players listed as a Running Back in the form of a list of dictionaries\nrunning_back_urls = player_url_data.get_running_backs()\n\n# Obtaining the URLs of players listed as a Fullback in the form of a list of dictionaries\nfullback_urls = player_url_data.get_fullbacks()\n\n# Obtaining the URLs of players listed as a Wide Receiver in the form of a list of dictionaries\nwide_receiver_urls = player_url_data.get_wide_receivers()\n\n# Obtaining the URLs of players listed as a Tight End in the form of a list of dictionaries\ntight_end_urls = player_url_data.get_tight_ends()\n\n# Obtaining the URLs of players listed as a Kicker in the form of a list of dictionaries\nkicker_urls = player_url_data.get_kickers()\n\n# Example Data from get_quarterbacks():\n# [\n# {\n# \"name\": \"Derek Carr\", \n# \"url\": \"https://www.pro-football-reference.com/players/C/CarrDe02.htm\"\n# }, \n# { \n# \"name\": \"Jarrett Stidham\", \n# \"url\": \"https://www.pro-football-reference.com/players/S/StidJa00.htm\"\n# }\n# ]\n```\n\n##### Scraping Stat Type for Player URLs\n```python\nfrom PFRWebScraper import ScrapeURLs\n\n# Creates an instance of the URL Scraper Object\nurl_scraper = ScrapeURLs()\n\n# Scrapes for Player's URLs that are listed within that specific stat type list\n# You can set the specific year BUT if you dont want to it is always set \n# to the current year\n\n# Example on how to scrape for current year and passing list\npassing_player_url_data = url_scraper.scrape_stat_type_for_player_urls(\"passing\")\n\n# Example on how to scrape for 2021 and rushing list\nrushing_player_url_data = url_scraper.scrape_stat_type_for_player_urls(\"rushing\", 2021)\n\n# Example on how to scrape for 2020 and receiving list\nreceiving_player_url_data = url_scraper.scrape_stat_type_for_player_urls(\"receiving\", 2020)\n\n# Example on how to scrape for current year and kicking list\nkicking_player_url_data = url_scraper.scrape_stat_type_for_player_urls(\"kicking\")\n\n# Example on how to scrape for current year and returns list\nreturns_player_url_data = url_scraper.scrape_stat_type_for_player_urls(\"returns\")\n\n# Example on how to scrape for current year and scoring list\nscoring_player_url_data = url_scraper.scrape_stat_type_for_player_urls(\"scoring\")\n\n# Obtain a list of dictionaries containing the player's name and url\npassing_players_urls_list = passing_player_url_data.get_list_of_urls()\n\n# Example Data from get_list_of_urls():\n# [\n# {\n# \"name\": \"Derek Carr\", \n# \"url\": \"https://www.pro-football-reference.com/players/C/CarrDe02.htm\"\n# }, \n# { \n# \"name\": \"Patrick Mahomes\", \n# \"url\": \"https://www.pro-football-reference.com/players/M/MahoPa00.htm\"\n# }, \n# { \n# \"name\": \"Joe Burrow\", \n# \"url\": \"https://www.pro-football-reference.com/players/B/BurrJo01.htm\"\n# }, \n# { \n# \"name\": \"Justin Herbert\", \n# \"url\": \"https://www.pro-football-reference.com/players/H/HerbJu00.htm\"\n# }, \n# { \n# \"name\": \"Tom Brady\", \n# \"url\": \"https://www.pro-football-reference.com/players/B/BradTo00.htm\"\n# }\n# ]\n\n# Obtain the number of dictionaries within the list\ncount_of_passing_players_urls_list = passing_player_url_data.get_count_of_urls()\n\n# Using the sample data the method get_count_of_urls() would return 5\n\n# Obtain a list of dictionaries, to the specified range, containing the player's name and url\nrange_of_passing_players_urls_list = passing_player_url_data.get_range_of_urls(1, 3)\n\n# Example Data from get_range_of_urls(1, 3):\n# [\n# {\n# \"name\": \"Derek Carr\", \n# \"url\": \"https://www.pro-football-reference.com/players/C/CarrDe02.htm\"\n# }, \n# { \n# \"name\": \"Patrick Mahomes\", \n# \"url\": \"https://www.pro-football-reference.com/players/M/MahoPa00.htm\"\n# }, \n# { \n# \"name\": \"Joe Burrow\", \n# \"url\": \"https://www.pro-football-reference.com/players/B/BurrJo01.htm\"\n# }\n# ]\n```",
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