cudf.to_numeric#

cudf.to_numeric(arg, errors='raise', downcast=None)#

Convert argument into numerical types.

Parameters:

argcolumn-convertible

The object to convert to numeric types

errors{‘raise’, ‘ignore’, ‘coerce’}, defaults ‘raise’

Policy to handle errors during parsing.

‘raise’ will notify user all errors encountered.
‘ignore’ will skip error and returns arg.
‘coerce’ will leave invalid values as nulls.

downcast{‘integer’, ‘signed’, ‘unsigned’, ‘float’}, defaults None

If set, will try to down-convert the datatype of the parsed results to smallest possible type. For each downcast type, this method will determine the smallest possible dtype from the following sets:

{‘integer’, ‘signed’}: all integer types greater or equal to np.int8
{‘unsigned’}: all unsigned types greater or equal to np.uint8
{‘float’}: all floating types greater or equal to np.float32

Note that downcast behavior is decoupled from parsing. Errors encountered during downcast is raised regardless of errors parameter.

Returns:

Series or ndarray: Depending on the input, if series is passed in, series is returned, otherwise ndarray

Notes

An important difference from pandas is that this function does not accept mixed numeric/non-numeric type sequences. For example [1, 'a']. A TypeError will be raised when such input is received, regardless of errors parameter.

Examples

>>> s = cudf.Series(['1', '2.0', '3e3'])
>>> cudf.to_numeric(s)
0       1.0
1       2.0
2    3000.0
dtype: float64
>>> cudf.to_numeric(s, downcast='float')
0       1.0
1       2.0
2    3000.0
dtype: float32
>>> cudf.to_numeric(s, downcast='signed')
0       1
1       2
2    3000
dtype: int16
>>> s = cudf.Series(['apple', '1.0', '3e3'])
>>> cudf.to_numeric(s, errors='ignore')
0    apple
1      1.0
2      3e3
dtype: object
>>> cudf.to_numeric(s, errors='coerce')
0      <NA>
1       1.0
2    3000.0
dtype: float64