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Convert argument to a numeric type.
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The default return dtype is `float64` or `int64`
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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 be stored in an `ndarray`. These warnings apply similarly to
|
`Series` since it internally leverages `ndarray`.
|
|
Parameters
|
----------
|
arg : scalar, list, tuple, 1-d array, or Series
|
Argument to be converted.
|
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 : str, default None
|
Can be 'integer', 'signed', 'unsigned', or 'float'.
|
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.
|
dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames
|
Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
|
arrays, nullable dtypes are used for all dtypes that have a nullable
|
implementation when "numpy_nullable" is set, pyarrow is used for all
|
dtypes if "pyarrow" is set.
|
|
The dtype_backends are still experimential.
|
|
.. versionadded:: 2.0
|
|
Returns
|
-------
|
ret
|
Numeric if parsing succeeded.
|
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.
|
DataFrame.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
|
|
Downcasting of nullable integer and floating dtypes is supported:
|
|
>>> s = pd.Series([1, 2, 3], dtype="Int64")
|
>>> pd.to_numeric(s, downcast="integer")
|
0 1
|
1 2
|
2 3
|
dtype: Int8
|
>>> s = pd.Series([1.0, 2.1, 3.0], dtype="Float64")
|
>>> pd.to_numeric(s, downcast="float")
|
0 1.0
|
1 2.1
|
2 3.0
|
dtype: Float32
|
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