如何在熊猫数据框中用均值替换NaN值？
[英]How can I replace NaN value with mean in a Pandas dataframe?
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问题描述
我有一个数据框来填充NaN值，如下所示：
Category Rating
ART_AND_DESIGN 4.4
AUTO_AND_VEHICLES 4.2
BEAUTY 4.3
BOOKS_AND_REFERENCE 4.3
BUSINESS 4.1
COMICS 4.2
COMMUNICATION 4.2
DATING 4.0
EDUCATION 4.4
我打算根据其类别将以下NaN值替换为上述Rating。
App Category Rating
A DATING NaN (4.0)
B BEAUTY NaN (4.3)
C BUSINESS NaN (4.1)
例如，由于约会类别的评分为4.0，因此应用A应填写4.0。
1 条回复
1楼 Sandeep Kadapa 1 已采纳 2018-09-28 04:11:04
在set_index之后使用df1来Category ，并将df2['Category']与df1['Rating'] map为：
df1.set_index('Category',inplace=True)
df2['Rating'] = df2['Category'].map(df1['Rating'])
或使用replace ：
df2['Rating'] = df2['Category'].replace(df1['Rating'])
print(df2)
App Category Rating
0 A DATING 4.0
1 B BEAUTY 4.3
2 C BUSINESS 4.1
映射数据帧之前：
print(df1)
Rating
Category
ART_AND_DESIGN 4.4
AUTO_AND_VEHICLES 4.2
BEAUTY 4.3
BOOKS_AND_REFERENCE 4.3
BUSINESS 4.1
COMICS 4.2
COMMUNICATION 4.2
DATING 4.0
EDUCATION 4.4
print(df2)
App Category Rating
0 A DATING NaN
1 B BEAUTY NaN
2 C BUSINESS NaN
问题未解决？试试搜索： 如何在熊猫数据框中用均值替换NaN值？。
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