Python - reduce number of if statements

Question

I am filtering a pandas dataframe based on one or more conditions, like so:

def filter_dataframe(dataframe, position=None, team_id=None, home=None, window=None, min_games=0):
        
        df = dataframe.copy()

        if position:
            df = df[df['position_id'] == position] 
        
        if clube_id:
            df = df[df['team_id'] == team_id]
        
        if home:
            if home == 'home':
                df = df[df['home_dummy'] == 1.0]
            elif home == 'away':
                df = df[df['home_dummy'] == 0.0]
        
        if window:
            df = df[df['round_id'].between(1, window)]
        
        if min_games:
            df = df[df['games_num'] >= min_games]

        return df

---

But I don't think this is elegant.

Is there a simpler way of achieving the same result?

I though of creating rules for conditions like in this SO answer and then use the method any(rules) in order to apply the filtering, if any, but I don't know how to approach this. Any ideas?

Answer 1

You could try something like this:

def filter_dataframe(dataframe, position=None, clube_id=None, team_id=None, home=None, window=None, min_games=0):
    df = dataframe.copy()
    masks = {
        "mask1": [position is not None, df[df["position_id"] == position]],
        "mask2": [clube_id is not None, df[df["team_id"] == team_id]],
        "mask3": [home == "home", df[df["home_dummy"] == 1.0]],
        "mask4": [home == "away", df[df["home_dummy"] == 0.0]],
        "mask5": [window is not None, df[df["round_id"].between(1, window)]],
        "mask6": [min_games is not None, df[df["games_num"] >= min_games]],
    }
    for value in masks.values():
        if value[0]:
            df = value[1]
    return df