initial script, with goalkeeper calculation

README.md

@@ -1,2 +1,4 @@
 # football-manager-squad-and-recruitment
-Python code to evaluate Squad and Scouted Players in Football Manager.
+Python code to evaluate Squad and Scouted Players in Football Manager. Work in progress.
+
+Inspired by squirrel_plays_FOF's video [FM24 player recruitment using python](https://www.youtube.com/watch?v=hnAuOakqR90)

position_score_calculator.py

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+import pandas as pd
+import argparse
+
+def load_data(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
+
+# 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])
+    return player_df
+
+def calc_role_scores(player_df: pd.DataFrame, role: str, primary_attributes: [str], secondary_attributes: [str], tertiary_attributes: [str]) -> pd.DataFrame:
+    """Calculate Player Role scores based on selected attributes.
+
+    Keyword arguments:
+    player_df: Dataframe of Players and Attributes
+    role: Name of role to be used as additional column in dataframe
+    primary_attributes: List of Most important attributes for a role
+    secondary_attributes: List of Most secondary attributes for a role
+    tertiary_attributes: List of Most tertiary attributes for a role
+    """
+    player_df = sum_attributes(player_df, role, "primary", primary_attributes)
+    player_df = sum_attributes(player_df, role, "secondary", secondary_attributes)
+    player_df = sum_attributes(player_df, role, "tertiary", tertiary_attributes)
+    divisor = (len(primary_attributes) * 5) + (len(secondary_attributes) * 3) + (len(tertiary_attributes) * 1)
+    player_df[f'{role}'] = (((player_df[f'{role}_primary'] * 5) + (player_df[f'{role}_secondary'] * 3) + (player_df[f'{role}_tertiary'] * 1)) / divisor )
+    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", primary_attributes=["Agi", "Ref"], 
+        secondary_attributes=["1v1", "Ant", "Cmd", "Cnt", "Kic", "Pos"], 
+        tertiary_attributes=["Acc", "Aer", "Cmp", "Dec", "Fir", "Han", "Pas", "Thr", "Vis"])
+    # TODO: Add roles.
+    print(player_df)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-f", "--filepath", type=str)
+    args = parser.parse_args()
+    filepath = args.filepath
+
+    player_df = load_data(filepath)
+    calc_player_scores(player_df)

requirements.txt

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+pandas
+lxml