pandas.to_numeric(arg, errors='raise', downcast=None)
[source]
Convert argument to a numeric type.
Parameters: |
arg : list, tuple, 1-d array, or Series errors : {‘ignore’, ‘raise’, ‘coerce’}, default ‘raise’
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:
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. New in version 0.19.0. |
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Returns: |
ret : numeric if parsing succeeded. Return type depends on input. Series if Series, otherwise ndarray |
See also
pandas.DataFrame.astype
pandas.to_datetime
pandas.to_timedelta
numpy.ndarray.astype
Take separate series and convert to numeric, coercing when told to
>>> import pandas as pd >>> 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
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Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.to_numeric.html