Source code for dask.dataframe.numeric

from __future__ import annotations

import pandas as pd
from pandas.api.types import is_scalar as pd_is_scalar

from dask.array import Array
from dask.dataframe.core import Series
from dask.delayed import delayed
from dask.utils import derived_from

__all__ = ("to_numeric",)

[docs]@derived_from(pd, ua_args=["downcast"]) def to_numeric(arg, errors="raise", meta=None): """ Return type depends on input. Delayed if scalar, otherwise same as input. For errors, only "raise" and "coerce" are allowed. """ if errors not in ("raise", "coerce"): raise ValueError("invalid error value specified") is_series = isinstance(arg, Series) is_array = isinstance(arg, Array) is_scalar = pd_is_scalar(arg) if not any([is_series, is_array, is_scalar]): raise TypeError( "arg must be a list, tuple, dask.array.Array, or dask.dataframe.Series" ) if meta is not None: if is_scalar: raise KeyError("``meta`` is not allowed when input is a scalar.") else: if is_series or is_array: meta = pd.to_numeric(arg._meta) if is_series: return arg.map_partitions( pd.to_numeric, token=arg._name + "-to_numeric", meta=meta, enforce_metadata=False, errors=errors, ) if is_array: return arg.map_blocks( pd.to_numeric, name=arg._name + "-to_numeric", meta=meta, errors=errors, ) if is_scalar: return delayed(pd.to_numeric, pure=True)(arg, errors=errors)