import pandas as pd
import argparse
# Define Player attributes
goalkeeper = {
"role_name": "goalkeeper",
"primary_multiplier": 5,
"primary_attributes": ["Agi", "Ref"],
"secondary_multiplier": 3,
"secondary_attributes": ["1v1", "Ant", "Cmd", "Cnt", "Kic", "Pos"],
"tertiary_multiplier": 1,
"tertiary_attributes": ["Acc", "Aer", "Cmp", "Dec", "Fir", "Han", "Pas", "Thr", "Vis"]
}
def load_html_data_to_dataframe(filepath: str) -> pd.DataFrame:
"""Read HTML file exported by FM into a Dataframe
Keyword arguments:
filepath -- path to fm player html file
"""
player_df = pd.read_html(filepath, header=0, encoding="utf-8", keep_default_na=False)[0]
# Clean Dataframe to get rid of unknown values and ability ranges (takes the lowest value)
# This casts to a string to be able to split, so we have to cast back to an int later.
player_df = player_df.replace("-", 0)
player_df = player_df.map(lambda x: str(x).split("-")[0])
return player_df
def export_html_from_dataframe(player_df: pd.DataFrame, filepath: str) -> str:
"""Export Dataframe as html with jQuery Data Tables
Taken from: https://www.thepythoncode.com/article/convert-pandas-dataframe-to-html-table-python.
Keyword arguments:
filepath -- path to fm player html file
"""
table_html = player_df.to_html(table_id="table", index=False)
html = f"""
{table_html}
"""
open(filepath, "w", encoding="utf-8").write(html)
# TODO: Do I even want this?
def calc_composite_scores(player_df: pd.DataFrame) -> pd.DataFrame:
"""Calculate Speed, Workrate and Set Piece scores
Keyword arguments:
player_df: Dataframe of Players and Attributes
"""
player_df['Spd'] = ( player_df['Pac'] + player_df['Acc'] ) / 2
player_df['Work'] = ( player_df['Wor'] + player_df['Sta'] ) / 2
player_df['SetP'] = ( player_df['Jum'] + player_df['Bra'] ) / 2
return player_df
def sum_attributes(player_df: pd.DataFrame, role: str, attribute_type: str, attributes: [str]) -> pd.DataFrame:
"""Create a new Column containing the sum of provided attribute columns
Keyword arguments:
player_df: Dataframe of Players and Attributes
role: Name of role to be used as additional column in dataframe
attribute_type: Type of Attribute [Primary, Secondary, Tertiary]
attributes: List of Attributes to Sum
"""
player_df[f'{role}_{attribute_type}'] = 0
for attribute in attributes:
player_df[f'{role}_{attribute_type}'] += pd.to_numeric(player_df[attribute])
player_df[f'{role}_{attribute_type}'] = round(player_df[f'{role}_{attribute_type}'] / len(attributes), 2)
return player_df
def calc_role_scores(player_df: pd.DataFrame, role: dict) -> pd.DataFrame:
"""Calculate Player Role scores based on selected attributes.
Keyword arguments:
player_df: Dataframe of Players and Attributes
role: Dictionary containing role name, role attributes and role attribute weightings
"""
player_df = sum_attributes(player_df, role["role_name"], "primary", role["primary_attributes"])
player_df = sum_attributes(player_df, role["role_name"], "secondary", role["secondary_attributes"])
player_df = sum_attributes(player_df, role["role_name"], "tertiary", role["tertiary_attributes"])
divisor = role["primary_multiplier"] + role["secondary_multiplier"] + role["tertiary_multiplier"]
player_df[f'{role["role_name"]}'] = round((((player_df[f'{role["role_name"]}_primary'] * 5) + (player_df[f'{role["role_name"]}_secondary'] * 3) + (player_df[f'{role["role_name"]}_tertiary'] * 1)) / divisor ), 2)
return player_df
def calc_player_scores(player_df: pd.DataFrame):
# TODO: Create objects for each role that can be used here.
player_df = calc_role_scores(player_df, goalkeeper)
# TODO: Add roles.
return player_df
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input-filepath", type=str)
parser.add_argument("-o", "--output-filepath", type=str)
args = parser.parse_args()
input_filepath = args.input_filepath
output_filepath = args.output_filepath
player_df = load_html_data_to_dataframe(input_filepath)
player_df = calc_player_scores(player_df)
export_html_from_dataframe(player_df, output_filepath)