如何使用 python 过滤缺失的数据行

Question

I have a dataframe df and one of the features called mort_acc have missing data. I want to filter out those rows that contains missing data for mort_acc and I used the following way

df[df['mort_acc'].apply(lambda x:x == " ")]

It didn't work. I got output 0 . So I used the following lambda way

df[df['mort_acc'].apply(lambda x:len(x)<0)]

It didn't work too and this time got error object of type 'float' has no len()

So I tried this way

df[df['mort_acc'].apply(lambda x:x == NaN)]

Error happened again name 'NaN' is not defined

Does anyone know how to do it?

Answer 1

bad_values_row_mask = df['mort_acc'].isna()
df[bad_values_row_mask]

sounds like what you want I guess

Answer 2

there is no datatype as NaN in python use pd.isna() to check if it's nan.

df[df['mort_acc'].apply(lambda x:pd.isna(x))]

Answer 3

This will give you rows where the column value is having NaN values.

df[df.mort_acc.isnull()]