How to drop all rows those have a "non - null value" in a particular column?

Question

I am using Python 3.7. I am working on Titanic survival prediction ML project. The CSV file have "age" column, that has some null values in it. So I want to train my model in two sets - 1. train set having age column with some valid values; 2. train set for the rows which have null value in age column.

I have the 1st train set. How can I acquire all those rows with null values in "age" column?

possible duplicate of : https://stackoverflow.com/questions/43831539/how-to-select-rows-with-nan-in-particular-column — Razmik Melikbekyan, Jan 11 '20 at 16:39

score 0 · Answer 1 · answered Jan 11 '20 at 16:37

You can filter by using the methods isnull or notnull from pandas.Series.

>>> df = pd.DataFrame({"age": [1, 2, None, 3, 4], "name": ["a", "b", "c", "d", "e"]})
>>> df[ df.age.isnull() ]
   age name
2  NaN    c
>>> df[ df.age.notnull() ]
   age name
0  1.0    a
1  2.0    b
3  3.0    d
4  4.0    e

How to drop all rows those have a "non - null value" in a particular column?

1 Answers1