python - 在 Pandas 中使用 fillna() 和 lambda 函数替换 NaN 值

Question

我正在尝试在 Pandas 中编写 fillna() 或 lambda 函数来检查“user_score”列是否为 NaN，如果是，则使用来自另一个 DataFrame 的列数据。我尝试了两种选择:

games_data['user_score'].fillna(
    genre_score[games_data['genre']]['user_score']
    if np.isnan(games_data['user_score'])
    else games_data['user_score'],
    inplace = True
)

# but here is 'ValueError: The truth value of a Series is ambiguous'

和

games_data['user_score'] = games_data.apply(
    lambda row: 
    genre_score[row['genre']]['user_score'] 
    if np.isnan(row['user_score'])
    else row['user_score'],
    axis=1
)

# but here is 'KeyError' with another column from games_data

我的数据框:

游戏数据

流派评分

我很乐意提供任何帮助!

Answer 1

您也可以fillna()直接使用 user_score_by_genre 映射:

user_score_by_genre = games_data.genre.map(genre_score.user_score)
games_data.user_score = games_data.user_score.fillna(user_score_by_genre)

顺便说一句，如果 games_data.user_score 永远不会偏离 genre_score 值，您可以跳过 fillna() 并直接分配给 games_data.user_score:

games_data.user_score = games_data.genre.map(genre_score.user_score)

---

Pandas 的内置 Series.where也可以，而且更简洁:

df1.user_score.where(df1.user_score.isna(), df2.user_score, inplace=True)