Integer NA issue in Pandas

DataFrame and Series objects with integer data need special care due to integer NA issues in Pandas. By default, Pandas dynamically converts integer columns to floating point when missing values (NAs) are needed (which can result in loss of precision). This is because Pandas uses the NaN floating point value as NA, and Numpy does not support NaN values for integers. Bodo does not perform this conversion unless enough information is available at compilation time.

Pandas introduced a new nullable integer data type that can solve this issue, which is also supported by Bodo. For example, this code reads column A into a nullable integer array (the capital "I" denotes nullable integer type):

@bodo.jit
def example(fname):
  dtype = {'A': 'Int64', 'B': 'float64'}
  df = pd.read_csv(fname,
      names=dtype.keys(),
      dtype=dtype,
  )
  ...