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TYP: some types for pandas/core/arrays/sparse/array.py #29898

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Nov 29, 2019
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TYP: some types for pandas/core/arrays/sparse/array.py #29898

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TYP: some types for pandas/core/arrays/sparse/array.py
simonjayhawkins Nov 27, 2019
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update per comments
simonjayhawkins Nov 27, 2019
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21 changes: 15 additions & 6 deletions pandas/core/arrays/sparse/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
from collections import abc
import numbers
import operator
from typing import Any, Callable
from typing import Any, Callable, List, Type, TypeVar
import warnings

import numpy as np
Expand Down Expand Up @@ -50,6 +50,8 @@

from .dtype import SparseDtype

_SparseArrayT = TypeVar("_SparseArrayT", bound="SparseArray")

# ----------------------------------------------------------------------------
# Array

Expand Down Expand Up @@ -260,6 +262,7 @@ class SparseArray(PandasObject, ExtensionArray, ExtensionOpsMixin):
_pandas_ftype = "sparse"
_subtyp = "sparse_array" # register ABCSparseArray
_deprecations = PandasObject._deprecations | frozenset(["get_values"])
_sparse_index: SparseIndex

def __init__(
self,
Expand Down Expand Up @@ -372,8 +375,11 @@ def __init__(

@classmethod
def _simple_new(
cls, sparse_array: np.ndarray, sparse_index: SparseIndex, dtype: SparseDtype
) -> ABCSparseArray:
cls: Type[_SparseArrayT],
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needing to annotate cls seems weird. is that just me?

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annotated for generic self. see https://mypy.readthedocs.io/en/latest/generics.html#generic-methods-and-generic-self

again could see how far we get without this.

sparse_array: np.ndarray,
sparse_index: SparseIndex,
dtype: SparseDtype,
) -> _SparseArrayT:
new = cls([])
new._sparse_index = sparse_index
new._sparse_values = sparse_array
Expand Down Expand Up @@ -805,13 +811,14 @@ def _get_val_at(self, loc):
else:
return libindex.get_value_at(self.sp_values, sp_loc)

def take(self, indices, allow_fill=False, fill_value=None):
def take(self, indices, allow_fill: bool = False, fill_value=None):
if is_scalar(indices):
raise ValueError(
"'indices' must be an array, not a scalar '{}'.".format(indices)
)
indices = np.asarray(indices, dtype=np.int32)

result: List
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not saying anything about contents?

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not until _take_with_fill and _take_without_fill are annotated. removed till then.

if indices.size == 0:
result = []
kwargs = {"dtype": self.dtype}
Expand Down Expand Up @@ -1392,8 +1399,10 @@ def __abs__(self):
# ------------------------------------------------------------------------

@classmethod
def _create_unary_method(cls, op):
def sparse_unary_method(self):
def _create_unary_method(
cls: Type[_SparseArrayT], op
) -> Callable[[_SparseArrayT], _SparseArrayT]:
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does mypy specifically need this? my kneejerk reaction is that it adds verbosity but not clarity

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we could see how far we get without it. but remember your frustration with subclass of ChainMap not returning the same type?

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In case I haven't mentioned it recently, I'm totally happy to defer to you when it comes to annotations

def sparse_unary_method(self: _SparseArrayT) -> _SparseArrayT:
fill_value = op(np.array(self.fill_value)).item()
values = op(self.sp_values)
dtype = SparseDtype(values.dtype, fill_value)
Expand Down