How to Convert a Pandas Column with NaN Values to Integer Type?

Converting Pandas Column Containing NaN to Dtype Int

To convert a Pandas column containing missing values (NaNs) to integer type, pandas version 0.24. introduces the nullable Integer Data Type, represented by IntegerArray.

Nullable Integer Data Type

Arrays.IntegerArray allows the representation of integer data with missing values. It differs from the default integer dtype and must be explicitly specified when creating an array or Series.

Example:

import pandas as pd

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

# Output:
0      1
1      2
2    NaN
dtype: Int64

Converting a Column to Nullable Integers

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

This will convert the column 'myCol' to nullable integers, allowing missing values to be represented as NaN.

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