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import pandas as pd
from datetime import datetime
from typing import Optional, Any
from .config import DEFAULT_ROUNDS_DURATION
def clean_numeric_column(series: pd.Series) -> pd.Series:
"""A helper to clean string columns into numbers, handling errors."""
series_str = series.astype(str)
return pd.to_numeric(series_str.str.replace(r'[^0-9.]', '', regex=True), errors='coerce')
def calculate_age(dob_str: str, fight_date_str: str) -> Optional[float]:
"""Calculates age in years from a date of birth string and fight date string."""
if pd.isna(dob_str) or not dob_str:
return None
try:
dob = datetime.strptime(dob_str, '%b %d, %Y')
fight_date = datetime.strptime(fight_date_str, '%B %d, %Y')
return (fight_date - dob).days / 365.25
except (ValueError, TypeError):
return None
def parse_round_time_to_seconds(round_str: str, time_str: str) -> int:
"""Converts fight duration from round and time to total seconds."""
try:
rounds = int(round_str)
minutes, seconds = map(int, time_str.split(':'))
# Assuming 5-minute rounds for calculation simplicity
return ((rounds - 1) * DEFAULT_ROUNDS_DURATION) + (minutes * 60) + seconds
except (ValueError, TypeError, AttributeError):
return 0
def parse_striking_stats(stat_str: str) -> tuple[int, int]:
"""Parses striking stats string like '10 of 20' into (landed, attempted)."""
try:
landed, attempted = map(int, stat_str.split(' of '))
return landed, attempted
except (ValueError, TypeError, AttributeError):
return 0, 0
def to_int_safe(val: Any) -> int:
"""Safely converts a value to an integer, returning 0 if it's invalid or empty."""
if pd.isna(val):
return 0
try:
# handle strings with whitespace or empty strings
return int(str(val).strip() or 0)
except (ValueError, TypeError):
return 0
def prepare_fighters_data(fighters_df: pd.DataFrame) -> pd.DataFrame:
"""Prepares fighter data for analysis by cleaning and standardizing."""
fighters_prepared = fighters_df.copy()
fighters_prepared['full_name'] = fighters_prepared['first_name'] + ' ' + fighters_prepared['last_name']
# Handle duplicate fighter names by keeping the first entry
fighters_prepared = fighters_prepared.drop_duplicates(subset=['full_name'], keep='first')
fighters_prepared = fighters_prepared.set_index('full_name')
for col in ['height_cm', 'reach_in', 'elo']:
if col in fighters_prepared.columns:
fighters_prepared[col] = clean_numeric_column(fighters_prepared[col])
return fighters_prepared |