to_numeric(arg, errors='raise', downcast=None)
Convert argument to a numeric type.
arg : list, tuple, 1-d array, or Series
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- 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
downcast : {'integer', 'signed', 'unsigned', 'float'} , default None
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
ret : numeric if parsing succeeded.
Return type depends on input. Series if Series, otherwise ndarray
Take separate series and convert to numeric, coercing when told to
>>> s = pd.Series(['1.0', '2', -3])
>>> pd.to_numeric(s, downcast='float')
>>> pd.to_numeric(s, downcast='signed')
>>> s = pd.Series(['apple', '1.0', '2', -3])
>>> pd.to_numeric(s, errors='ignore')
>>> pd.to_numeric(s, errors='coerce')
pandas.DataFrame.astype : Cast argument to a specified dtype.
pandas.to_datetime : Convert argument to datetime.
pandas.to_timedelta : Convert argument to timedelta.
numpy.ndarray.astype : Cast a numpy array to a specified type.