指定属性上的 Pandas .dropna() [英] Pandas .dropna() on specify attribute

问题描述

我有这段代码可以从 Type 列中删除空值，特别是查看 Dog.

I have this code to drop null values from column Type, specifically looking at Dog.

cd.loc[cd['Type'] == 'Dog'].dropna(subset = ['Killed'], inplace = True)

当与 Type = Dog 关联的 ['Killed'] 列具有 NaN 值时，我想删除.

I would like to dropna when the ['Killed'] column associating with Type = Dog has NaN value.

上面的代码生成了这个熊猫错误:

The code above generate this pandas error:

 A value is trying to be set on a copy of a slice from a DataFrame

当 ['Type'] == 'Dog' 时，有没有其他方法可以在 ['Killed'] 上放置?

Is there another way where can I dropna on ['Killed'] when ['Type'] == 'Dog'?

(这是我的第一篇文章)，如果我不能正确解释，请见谅干杯

(This is my first post), sorry if I can't explain properly Cheers

推荐答案

与@BrenBarn 的回答非常相似，但使用了 drop 和 inplace

Very similar to @BrenBarn's answer but using drop and inplace

cd.drop(cd[(cd.Type == 'Dog') & (cd.Killed.isnull())].index, inplace=True)

设置

cd = pd.DataFrame([
        ['Dog', 'Yorkie'],
        ['Cat', 'Rag Doll'],
        ['Cat', None],
        ['Bird', 'Caique'],
        ['Dog', None],
    ], columns=['Type', 'Killed'])

解决方案

cd.drop(cd[(cd.Type == 'Dog') & (cd.Killed.isnull())].index, inplace=True)

cd

等同于德摩根定律

cond1 = cd.Type == 'Dog'
cond2 = cd.Killed.isnull()
cd[~cond1 | ~cond2]

<小时>

一个愚蠢的，因为我喜欢它！

---

A silly one, because I felt like it!

cd.groupby('Type', group_keys=False) \
    .apply(lambda df: df.dropna(subset=['Killed']) if df.name == 'Dog' else df)

这篇关于指定属性上的 Pandas .dropna()的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！