pandas 熊猫替换列子集的空值
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/37191893/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
pandas replace null values for a subset of columns
提问by breezymri
I have a data frame with many columns, say:
我有一个包含许多列的数据框，例如：
df:
name salary age title
John 100 35 eng
Bill 200 NaN adm
Lena NaN 28 NaN
Jane 120 45 eng
I want to replace the null values in salary and age, but no in the other columns. I know I can do something like this:
我想替换薪水和年龄中的空值，但不替换其他列中的空值。我知道我可以做这样的事情：
u = df[['salary', 'age']]
df[['salary', 'age']] = u.fillna(-1)
But this seems terse as it involves copying. Is there a more efficient way to do this?
但这似乎很简洁，因为它涉及复制。有没有更有效的方法来做到这一点？
回答by piRSquared
Try this:
尝试这个：
df.loc[:, ['salary', 'age']].fillna(-1, inplace=True)
回答by Muhammad Raihan Muhaimin
According to Pandas documentation in 23.3
values = {'salary': -1, 'age': -1}
df = df.fillna(value=values)
回答by xirururu
It is not so beautiful, but it works:
它不是那么漂亮，但它有效：
df.salary.fillna(-1, inplace=True)
df.age.fillna(-1, inplace=True)
df
>>> name salary age title
0 John 101.0 35.0 eng
1 Bill 200.0 -1.0 adm
2 Lena -1.0 28.0 NaN
3 Jane 120.0 45.0 eng
