df.isnull().sum() is still showing values as null,even though I dropped na values using dropna function.Why?

Question

I am using Python 3.7.6 .I am working on Bank Marketing project. The CSV file have "age" column, that has some null values in it. So I want to drop those age values with NaN. I was able to succesfully drop those values using dropna function. But when I checked the null values again using df.isnull().sum(),it shows me the same null values

Can anyone help?

My code

There are other columns associated with which are not null where age appears to be null. Hence the rows are not completely dropped. If you want to drop the rows that where age is null, use inp0.dropna(inplace=True) and check. This will delete rows if any value is null. Check https://stackoverflow.com/questions/13413590/how-to-drop-rows-of-pandas-dataframe-whose-value-in-a-certain-column-is-nan as well. — Abhay, Jul 19 '20 at 12:55

score 0 · Answer 1 · answered Jul 19 '20 at 12:57

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When you select a column, you cannot dropna and expect it to affect the original data frame.

Use this:

inp0.dropna(subset=['age'], inplace=True)

answered Jul 19 '20 at 12:57

Pramote Kuacharoen

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But Why Out[156] is 0 instead of 1? – Jeeva S Jul 19 '20 at 13:00
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Try to select inp0['age'], you will get a series without nan. – Pramote Kuacharoen Jul 19 '20 at 13:01
It is a view. You can drop nan there. But it does not affect the original data frame. – Pramote Kuacharoen Jul 19 '20 at 13:09

df.isnull().sum() is still showing values as null,even though I dropped na values using dropna function.Why?

1 Answers1