如何有效地处理熊猫数据框中缺失的数据并计算条件统计?

import pandas as pd

data = {'ID': [1, 2, 3, 4, 5],
    'Age': [25, 30, 22, 27, 21],
    'Gender': ['Male', 'Female', None, 'Male', 'Female'],
    'Score': [85.0, 90.0, 78.0, None, 80.0]
}

df = pd.DataFrame(data)
age_bins = [0, 20, 25, 30, 40, float('inf')]
age_labels = ['0-20', '21-25', '26-30', '31-40', '41+']

df['AgeRange'] = pd.cut(df['Age'], bins=age_bins, labels=age_labels)

mean_score_by_age_range = df.groupby('AgeRange')['Score'].mean()

print(mean_score_by_age_range)

输出:

AgeRange
0-20      NaN
21-25    81.0
26-30    90.0
31-40     NaN
41+       NaN
Name: Score, dtype: float64