pandas 3.0.1 raise numeric issue

[michaelabdelmalak]

FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing errors and catch exceptions explicitly instead
return data.apply(lambda x: pd.to_numeric(x, errors=errors))

In pandas 3.0.1, the to_numeric method has been modified such that it does not accept 'ignore' errors anymore

def to_numeric(
arg,
errors: DateTimeErrorChoices = "raise",
downcast: Literal["integer", "signed", "unsigned", "float"] | None = None,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
):
"""
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 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 : { 'raise', 'coerce'}, default 'raise'
    - If 'raise', then invalid parsing will raise an exception.
    - If 'coerce', then invalid parsing will be set as NaN.

This results in raising ValueError

return data.apply(lambda x: pd.to_numeric(x, errors=errors)) ~~~~~^^^^^^^^^^^^^^^^^^ File "...\Lib\site-packages\pandas\core\tools\numeric.py", line 183, in to_numeric raise ValueError("invalid error value specified")
ValueError: invalid error value specified

Solution:
we need to remove 'ignore' errors