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TYPING: --check-untyped-defs for Index.__new__ #28141

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Aug 26, 2019
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TYPING: --check-untyped-defs for Index.__new__ #28141

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56 changes: 14 additions & 42 deletions pandas/core/indexes/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
import pandas._libs.join as libjoin
from pandas._libs.lib import is_datetime_array
from pandas._libs.tslibs import OutOfBoundsDatetime, Timestamp
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas._libs.tslibs.timezones import tz_compare
from pandas.compat import set_function_name
from pandas.compat.numpy import function as nv
Expand Down Expand Up @@ -262,7 +263,13 @@ def __new__(
fastpath=None,
tupleize_cols=True,
**kwargs
):
) -> "Index":

from .range import RangeIndex
from pandas import PeriodIndex, DatetimeIndex, TimedeltaIndex
from .numeric import Float64Index, Int64Index, UInt64Index
from .interval import IntervalIndex
from .category import CategoricalIndex

if name is None and hasattr(data, "name"):
name = data.name
Expand All @@ -277,8 +284,6 @@ def __new__(
if fastpath:
return cls._simple_new(data, name)

from .range import RangeIndex

if isinstance(data, ABCPandasArray):
# ensure users don't accidentally put a PandasArray in an index.
data = data.to_numpy()
Expand All @@ -291,16 +296,12 @@ def __new__(

# categorical
elif is_categorical_dtype(data) or is_categorical_dtype(dtype):
from .category import CategoricalIndex

return CategoricalIndex(data, dtype=dtype, copy=copy, name=name, **kwargs)

# interval
elif (
is_interval_dtype(data) or is_interval_dtype(dtype)
) and not is_object_dtype(dtype):
from .interval import IntervalIndex

closed = kwargs.get("closed", None)
return IntervalIndex(data, dtype=dtype, name=name, copy=copy, closed=closed)

Expand All @@ -309,42 +310,31 @@ def __new__(
or is_datetime64_any_dtype(dtype)
or "tz" in kwargs
):
from pandas import DatetimeIndex

if is_dtype_equal(_o_dtype, dtype):
# GH#23524 passing `dtype=object` to DatetimeIndex is invalid,
# will raise in the where `data` is already tz-aware. So
# we leave it out of this step and cast to object-dtype after
# the DatetimeIndex construction.
# Note we can pass copy=False because the .astype below
# will always make a copy
result = DatetimeIndex(data, copy=False, name=name, **kwargs)
result = DatetimeIndex(
data, copy=False, name=name, **kwargs
) # type: "Index"
return result.astype(object)
else:
result = DatetimeIndex(
data, copy=copy, name=name, dtype=dtype, **kwargs
)
return result
return DatetimeIndex(data, copy=copy, name=name, dtype=dtype, **kwargs)

elif is_timedelta64_dtype(data) or is_timedelta64_dtype(dtype):
from pandas import TimedeltaIndex

if is_dtype_equal(_o_dtype, dtype):
# Note we can pass copy=False because the .astype below
# will always make a copy
result = TimedeltaIndex(data, copy=False, name=name, **kwargs)
return result.astype(object)
else:
result = TimedeltaIndex(
data, copy=copy, name=name, dtype=dtype, **kwargs
)
return result
return TimedeltaIndex(data, copy=copy, name=name, dtype=dtype, **kwargs)

elif is_period_dtype(data) and not is_object_dtype(dtype):
from pandas import PeriodIndex

result = PeriodIndex(data, copy=copy, name=name, **kwargs)
return result
return PeriodIndex(data, copy=copy, name=name, **kwargs)

# extension dtype
elif is_extension_array_dtype(data) or is_extension_array_dtype(dtype):
Expand Down Expand Up @@ -387,8 +377,6 @@ def __new__(
pass

# Return an actual float index.
from .numeric import Float64Index

return Float64Index(data, copy=copy, dtype=dtype, name=name)

elif inferred == "string":
Expand All @@ -405,19 +393,11 @@ def __new__(
data = np.array(data, dtype=dtype, copy=copy)

# maybe coerce to a sub-class
from pandas.core.indexes.period import PeriodIndex, IncompatibleFrequency

if is_signed_integer_dtype(data.dtype):
from .numeric import Int64Index

return Int64Index(data, copy=copy, dtype=dtype, name=name)
elif is_unsigned_integer_dtype(data.dtype):
from .numeric import UInt64Index

return UInt64Index(data, copy=copy, dtype=dtype, name=name)
elif is_float_dtype(data.dtype):
from .numeric import Float64Index

return Float64Index(data, copy=copy, dtype=dtype, name=name)
elif issubclass(data.dtype.type, np.bool) or is_bool_dtype(data):
subarr = data.astype("object")
Expand All @@ -440,12 +420,8 @@ def __new__(
return Index(subarr, copy=copy, dtype=object, name=name)
elif inferred in ["floating", "mixed-integer-float", "integer-na"]:
# TODO: Returns IntegerArray for integer-na case in the future
from .numeric import Float64Index

return Float64Index(subarr, copy=copy, name=name)
elif inferred == "interval":
from .interval import IntervalIndex

try:
return IntervalIndex(subarr, name=name, copy=copy)
except ValueError:
Expand All @@ -456,8 +432,6 @@ def __new__(
pass
elif inferred != "string":
if inferred.startswith("datetime"):
from pandas import DatetimeIndex

try:
return DatetimeIndex(subarr, copy=copy, name=name, **kwargs)
except (ValueError, OutOfBoundsDatetime):
Expand All @@ -467,8 +441,6 @@ def __new__(
pass

elif inferred.startswith("timedelta"):
from pandas import TimedeltaIndex

return TimedeltaIndex(subarr, copy=copy, name=name, **kwargs)
elif inferred == "period":
try:
Expand Down