How to replace dataframe column values with NaN based on a condition?

Question

For example, the dataframe looks like:

df = pd.DataFrame(
    [[1, 1], [2, 120], [3, 25], [4, np.NaN], [5, 45]],
    columns=["ID", "Age"])

In the Age column, the values below 5 and greater than 100 have to convert to NaN.

I am trying out the same with replace function, DF["Age"].replace(to_replace=DF["Age"]<5, value=np.NaN) Is that not possible to do with replace ? — Raja Sahe S
– Raja Sahe S, Commented Dec 31, 2018 at 4:42

wjandrea · Accepted Answer · 2023-08-22 16:43:59Z

16

df['Age'] = df.Age.where(df.Age.between(5, 100))
df
   ID   Age
0   1   NaN
1   2   NaN
2   3  25.0
3   4   NaN
4   5  45.0

Another option using .loc:

df.loc[df.Age.between(5, 100), 'Age'] = np.nan

wjandrea

33.8k10 gold badges69 silver badges105 bronze badges

answered Dec 31, 2018 at 4:30

BENY

324k22 gold badges176 silver badges250 bronze badges

Sign up to request clarification or add additional context in comments.

1 Answer 1