function_app.py

#Exploratory analysis
import pandas as pd

# ------------------------------------------------------------------ #
# 0.  Load raw data                                                  #
# ------------------------------------------------------------------ #
FILE_PATH = "/content/drive/My Drive/sportsanalytics/nhldraft.csv"
hockey_df = pd.read_csv(FILE_PATH)

# ------------------------------------------------------------------ #
# 1.  Clean function (must exist BEFORE using it)                    #
# ------------------------------------------------------------------ #
def clean_hockey_df(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()
    df.columns = df.columns.str.strip()         # Clean column names
    df.dropna(how='all', inplace=True)          # Drop empty rows
    df = df[df['points']>0]

    # Make sure 'points' exists and is numeric, then fill NaNs
    if 'points' in df.columns:
        df['points'] = pd.to_numeric(df['points'], errors='coerce').fillna(0).astype(int)
    else:
        raise KeyError("'points' column not found in DataFrame")
    return df

# ------------------------------------------------------------------ #
# 2.  Clean, but DO NOT overwrite the function!                      #
# ------------------------------------------------------------------ #
hockey_clean = clean_hockey_df(hockey_df)   # ← keep object & function distinct

# ------------------------------------------------------------------ #
# 3.  One-liner footprint helper                                     #
# ------------------------------------------------------------------ #
def footprint(df: pd.DataFrame) -> dict:
    """Return shape & memory stats for any DataFrame."""
    mem_bytes = df.memory_usage(deep=True).sum()
    return {
        "rows"          : len(df),
        "cols"          : df.shape[1],
        "memory_MB"     : round(mem_bytes / 1024 ** 2, 3),
        "bytes_per_row" : round(mem_bytes / len(df), 1) if len(df) else 0,
        "min_year": df.year.min(),
        "max_year": df.year.max(), 
    }

print("After cleaning:")
for k, v in footprint(hockey_clean).items():
    print(f"  {k:>13}: {v}")

# ------------------------------------------------------------------ #
# 4.  Optional peek                                                  #
# ------------------------------------------------------------------ #
print(hockey_clean.head())
hockey_clean.columns