我有一个游戏发行和评级的数据框
name,platform,year_of_release,genre,na_sales,eu_sales,jp_sales,other_sales,critic_score,user_score,rating
Wii Sports,Wii,2006.0,Sports,41.36,28.96,3.77,8.45,76.0,8.0,E
Super Mario Bros.,NES,1985.0,Platform,29.08,3.58,6.81,0.77,,,
Mario Kart Wii,Wii,2008.0,Racing,15.68,12.76,3.79,3.29,82.0,8.3,E
Wii Sports Resort,Wii,2009.0,Sports,15.61,10.93,3.28,2.95,80.0,8.0,E
Pokemon Red/Pokemon Blue,GB,1996.0,Role-Playing,11.27,8.89,10.22,1.0,,,
我想用相同流派的平均值填充 user_score 列中的 NaN 值。如果游戏有体育类型，并且该行中的 user_score 为 NaN，我想用体育的平均用户评分替换空值。
最佳答案
该数据已删除第二场体育比赛的 user_score，以便我们演示代码。
name,platform,year_of_release,genre,na_sales,eu_sales,jp_sales,other_sales,critic_score,user_score,rating
Wii Sports,Wii,2006.0,Sports,41.36,28.96,3.77,8.45,76.0,8.0,E
Super Mario Bros.,NES,1985.0,Platform,29.08,3.58,6.81,0.77,,,
Mario Kart Wii,Wii,2008.0,Racing,15.68,12.76,3.79,3.29,82.0,8.3,E
Wii Sports Resort,Wii,2009.0,Sports,15.61,10.93,3.28,2.95,80.0,,E
Pokemon Red/Pokemon Blue,GB,1996.0,Role-Playing,11.27,8.89,10.22,1.0,,,
查看 Wii Sports Resort 的用户评分
df.iloc[3]['user_score']
nan
将 NaN 替换为按流派划分的 user_score 平均值
df['user_score'] = df.groupby('genre')['user_score'].transform(lambda x: x.fillna(x.mean()))
检查更新后同一游戏的输出
df.iloc[3]['user_score']
8.0
关于python - 如何根据另一列中的值将 NaN 值替换为另一列的平均值？ Pandas ，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/62775549/