在 Pandas 中使用 fillna() 和 lambda function 替換 NaN 值

Question

I'm trying to write fillna() or a lambda function in Pandas that checks if 'user_score' column is a NaN and if so, uses column's data from another DataFrame. 我嘗試了兩種選擇：

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

我的數據框：

游戲數據

流派分數

我會很高興得到任何幫助！

Answer 1

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

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)

順便說一句，如果games_data.user_score永遠不會偏離genre_score值，您可以跳過fillna()並直接分配給games_data.user_score ：

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

---

Pandas 內置的Series.where也可以使用，而且更加簡潔：

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

Answer 2

使用numpy.where ：

import numpy as np

df1['user_score'] = np.where(df1['user_score'].isna(), df2['user_score'], df1['user_score'])

Answer 3

我在這里找到了解決方案的一部分

我使用 series.map：

user_score_by_genre = games_data['genre'].map(genre_score['user_score'])

之后我使用@MayankPorwal 回答：

games_data['user_score'] = np.where(games_data['user_score'].isna(), user_score_by_genre, games_data['user_score'])

我不確定這是最好的方法，但它對我有用。