How Can I Convert a Pandas Column with NaN Values to an Integer Data Type?

Converting Pandas Column with Missing Values to Integer Dtype

In Pandas, casting a column containing missing values (NaNs) to integer often results in errors. This is because integer types cannot hold missing information by default. However, Pandas now offers a solution through nullable integer data types.

Nullable Integer Dtype

In versions 0.24. of Pandas, you can use nullable integer data types to represent integer values with possible missing values. This datatype is implemented as arrays.IntegerArray and requires explicit specification when creating an array or Series:

arr = pd.array([1, 2, np.nan], dtype=pd.Int64Dtype())
pd.Series(arr)

0      1
1      2
2    NaN
dtype: Int64

Converting Column to Nullable Integer

To convert a column to a nullable integer datatype, use the following syntax:

df['myCol'] = df['myCol'].astype('Int64')

By specifying the Int64 dtype, you are explicitly informing Pandas that the column should have an integer datatype capable of accommodating missing values (NaN). This approach allows you to represent integer values with missing information without encountering type conversion errors.

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