medium10 pts · first answer countspythonpandasdata-cleaning

Converting a column with NaN to integers

df["age"] has a few missing values. df["age"].astype(int) raises an error. What keeps the missing values and gives whole numbers?

A.df["age"].astype(int, errors="ignore")B.df["age"].astype("Int64")C.df["age"].fillna(0).astype(int)D.df["age"].round().astype(int)

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