pandas.
to_numeric
Convert argument to a numeric type.
The default return dtype is float64 or int64 depending on the data supplied. Use the downcast parameter to obtain other dtypes.
Please note that precision loss may occur if really large numbers are passed in. Due to the internal limitations of ndarray, if numbers smaller than -9223372036854775808 (np.iinfo(np.int64).min) or larger than 18446744073709551615 (np.iinfo(np.uint64).max) are passed in, it is very likely they will be converted to float so that they can stored in an ndarray. These warnings apply similarly to Series since it internally leverages ndarray.
If ‘raise’, then invalid parsing will raise an exception.
If ‘coerce’, then invalid parsing will be set as NaN.
If ‘ignore’, then invalid parsing will return the input.
If not None, and if the data has been successfully cast to a numerical dtype (or if the data was numeric to begin with), downcast that resulting data to the smallest numerical dtype possible according to the following rules:
‘integer’ or ‘signed’: smallest signed int dtype (min.: np.int8)
‘unsigned’: smallest unsigned int dtype (min.: np.uint8)
‘float’: smallest float dtype (min.: np.float32)
As this behaviour is separate from the core conversion to numeric values, any errors raised during the downcasting will be surfaced regardless of the value of the ‘errors’ input.
In addition, downcasting will only occur if the size of the resulting data’s dtype is strictly larger than the dtype it is to be cast to, so if none of the dtypes checked satisfy that specification, no downcasting will be performed on the data.
Return type depends on input. Series if Series, otherwise ndarray.
See also
DataFrame.astype
Cast argument to a specified dtype.
to_datetime
Convert argument to datetime.
to_timedelta
Convert argument to timedelta.
numpy.ndarray.astype
Cast a numpy array to a specified type.
convert_dtypes
Convert dtypes.
Examples
Take separate series and convert to numeric, coercing when told to
>>> s = pd.Series(['1.0', '2', -3]) >>> pd.to_numeric(s) 0 1.0 1 2.0 2 -3.0 dtype: float64 >>> pd.to_numeric(s, downcast='float') 0 1.0 1 2.0 2 -3.0 dtype: float32 >>> pd.to_numeric(s, downcast='signed') 0 1 1 2 2 -3 dtype: int8 >>> s = pd.Series(['apple', '1.0', '2', -3]) >>> pd.to_numeric(s, errors='ignore') 0 apple 1 1.0 2 2 3 -3 dtype: object >>> pd.to_numeric(s, errors='coerce') 0 NaN 1 1.0 2 2.0 3 -3.0 dtype: float64