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REF: use lighter-weight casting function #38322

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Merged
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Dec 8, 2020
Merged

REF: use lighter-weight casting function #38322

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e3f3ba1
REF: use lighter-weight casting function
jbrockmendel Dec 5, 2020
6e14000
doctest fixup
jbrockmendel Dec 5, 2020
7561b4f
Doctest troubleshoot
jbrockmendel Dec 6, 2020
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4 changes: 0 additions & 4 deletions pandas/core/dtypes/cast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,10 +241,6 @@ def maybe_downcast_numeric(result, dtype: DtypeObj, do_round: bool = False):
# e.g. SparseDtype has no itemsize attr
return result

if isinstance(result, list):
# reached via groupby.agg._ohlc; really this should be handled earlier
result = np.array(result)

def trans(x):
if do_round:
return x.round()
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6 changes: 3 additions & 3 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -51,7 +51,7 @@ class providing the base-class of operations.
from pandas.errors import AbstractMethodError
from pandas.util._decorators import Appender, Substitution, cache_readonly, doc

from pandas.core.dtypes.cast import maybe_downcast_to_dtype
from pandas.core.dtypes.cast import maybe_downcast_numeric
from pandas.core.dtypes.common import (
ensure_float,
is_bool_dtype,
Expand Down Expand Up @@ -1178,7 +1178,7 @@ def _python_agg_general(self, func, *args, **kwargs):
key = base.OutputKey(label=name, position=idx)

if is_numeric_dtype(obj.dtype):
result = maybe_downcast_to_dtype(result, obj.dtype)
result = maybe_downcast_numeric(result, obj.dtype)

if self.grouper._filter_empty_groups:
mask = counts.ravel() > 0
Expand All @@ -1188,7 +1188,7 @@ def _python_agg_general(self, func, *args, **kwargs):
if is_numeric_dtype(values.dtype):
values = ensure_float(values)

result = maybe_downcast_to_dtype(values[mask], result.dtype)
result = maybe_downcast_numeric(values[mask], result.dtype)

output[key] = result

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4 changes: 2 additions & 2 deletions pandas/core/indexes/interval.py
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Expand Up @@ -20,7 +20,7 @@
find_common_type,
infer_dtype_from_scalar,
maybe_box_datetimelike,
maybe_downcast_to_dtype,
maybe_downcast_numeric,
)
from pandas.core.dtypes.common import (
ensure_platform_int,
Expand Down Expand Up @@ -1274,7 +1274,7 @@ def interval_range(
breaks = np.linspace(start, end, periods)
if all(is_integer(x) for x in com.not_none(start, end, freq)):
# np.linspace always produces float output
breaks = maybe_downcast_to_dtype(breaks, "int64")
breaks = maybe_downcast_numeric(breaks, np.dtype("int64"))
else:
# delegate to the appropriate range function
if isinstance(endpoint, Timestamp):
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7 changes: 4 additions & 3 deletions pandas/core/tools/numeric.py
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Expand Up @@ -2,7 +2,7 @@

from pandas._libs import lib

from pandas.core.dtypes.cast import maybe_downcast_to_dtype
from pandas.core.dtypes.cast import maybe_downcast_numeric
from pandas.core.dtypes.common import (
ensure_object,
is_datetime_or_timedelta_dtype,
Expand Down Expand Up @@ -180,8 +180,9 @@ def to_numeric(arg, errors="raise", downcast=None):
if typecodes is not None:
# from smallest to largest
for dtype in typecodes:
if np.dtype(dtype).itemsize <= values.dtype.itemsize:
values = maybe_downcast_to_dtype(values, dtype)
dtype = np.dtype(dtype)
if dtype.itemsize <= values.dtype.itemsize:
values = maybe_downcast_numeric(values, dtype)

# successful conversion
if values.dtype == dtype:
Expand Down