diff --git a/pandas/_typing.py b/pandas/_typing.py
index e1b6a5e2e6876..977812e5286af 100644
--- a/pandas/_typing.py
+++ b/pandas/_typing.py
@@ -75,3 +75,5 @@
# to maintain type information across generic functions and parametrization
T = TypeVar("T")
+FuncType = Callable[..., Any]
+F = TypeVar("F", bound=FuncType)
diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py
index 3547a33ea357b..6570e0782a69a 100644
--- a/pandas/compat/__init__.py
+++ b/pandas/compat/__init__.py
@@ -12,6 +12,8 @@
import sys
import warnings
+from pandas._typing import F
+
PY37 = sys.version_info >= (3, 7)
PY38 = sys.version_info >= (3, 8)
PYPY = platform.python_implementation() == "PyPy"
@@ -25,7 +27,7 @@
# found at https://bitbucket.org/gutworth/six
-def set_function_name(f, name, cls):
+def set_function_name(f: F, name: str, cls) -> F:
"""
Bind the name/qualname attributes of the function.
"""
diff --git a/pandas/compat/chainmap.py b/pandas/compat/chainmap.py
index a84dbb4a661e4..7bca4d278aed6 100644
--- a/pandas/compat/chainmap.py
+++ b/pandas/compat/chainmap.py
@@ -1,4 +1,6 @@
-from typing import ChainMap, MutableMapping, TypeVar, cast
+from typing import ChainMap, List, MutableMapping, TypeVar
+
+from pandas._typing import T
_KT = TypeVar("_KT")
_VT = TypeVar("_VT")
@@ -11,13 +13,17 @@ class DeepChainMap(ChainMap[_KT, _VT]):
Only works when all passed mapping are mutable.
"""
+ # error: Incompatible types in assignment (expression has type
+ # "List[MutableMapping[_KT, _VT]]", base class "ChainMap" defined the type
+ # as "List[Mapping[_KT, _VT]]") [assignment]
+ maps: List[MutableMapping[_KT, _VT]] # type: ignore
+
def __setitem__(self, key: _KT, value: _VT) -> None:
for mapping in self.maps:
- mutable_mapping = cast(MutableMapping[_KT, _VT], mapping)
- if key in mutable_mapping:
- mutable_mapping[key] = value
+ if key in mapping:
+ mapping[key] = value
return
- cast(MutableMapping[_KT, _VT], self.maps[0])[key] = value
+ self.maps[0][key] = value
def __delitem__(self, key: _KT) -> None:
"""
@@ -27,8 +33,11 @@ def __delitem__(self, key: _KT) -> None:
If `key` doesn't exist.
"""
for mapping in self.maps:
- mutable_mapping = cast(MutableMapping[_KT, _VT], mapping)
if key in mapping:
- del mutable_mapping[key]
+ del mapping[key]
return
raise KeyError(key)
+
+ # FIXME: return type of new_child incorrect in typeshed
+ def new_child(self: T, m) -> T: # type: ignore
+ return super().new_child(m) # type: ignore
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 62a3808d36ba2..7388cd7cac150 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -4,7 +4,7 @@
"""
import operator
from textwrap import dedent
-from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Dict, Optional, Tuple, Union
from warnings import catch_warnings, simplefilter, warn
import numpy as np
@@ -1074,6 +1074,9 @@ def __init__(self, obj, n: int, keep: str):
if self.keep not in ("first", "last", "all"):
raise ValueError('keep must be either "first", "last" or "all"')
+ def compute(self, method):
+ raise NotImplementedError
+
def nlargest(self):
return self.compute("nlargest")
@@ -1430,7 +1433,7 @@ def _take_nd_object(arr, indexer, out, axis: int, fill_value, mask_info):
def _get_take_nd_function(
ndim: int, arr_dtype, out_dtype, axis: int = 0, mask_info=None
-):
+) -> Callable:
if ndim <= 2:
tup = (arr_dtype.name, out_dtype.name)
if ndim == 1:
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index 22ce5a6f87a43..553cff8d1ad34 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -1,5 +1,6 @@
from operator import le, lt
import textwrap
+from typing import Any, Sequence, Tuple
import numpy as np
@@ -432,30 +433,33 @@ def from_arrays(cls, left, right, closed="right", copy=False, dtype=None):
),
)
)
- def from_tuples(cls, data, closed="right", copy=False, dtype=None):
+ def from_tuples(
+ cls, data: Sequence[Tuple[Any, Any]], closed="right", copy=False, dtype=None
+ ):
+ left: Sequence
+ right: Sequence
if len(data):
left, right = [], []
+ for d in data:
+ if isna(d):
+ lhs = rhs = np.nan
+ else:
+ name = cls.__name__
+ try:
+ # need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...]
+ lhs, rhs = d
+ except ValueError:
+ msg = f"{name}.from_tuples requires tuples of length 2, got {d}"
+ raise ValueError(msg)
+ except TypeError:
+ msg = f"{name}.from_tuples received an invalid item, {d}"
+ raise TypeError(msg)
+ left.append(lhs)
+ right.append(rhs)
else:
# ensure that empty data keeps input dtype
left = right = data
- for d in data:
- if isna(d):
- lhs = rhs = np.nan
- else:
- name = cls.__name__
- try:
- # need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...]
- lhs, rhs = d
- except ValueError as err:
- msg = f"{name}.from_tuples requires tuples of length 2, got {d}"
- raise ValueError(msg) from err
- except TypeError as err:
- msg = f"{name}.from_tuples received an invalid item, {d}"
- raise TypeError(msg) from err
- left.append(lhs)
- right.append(rhs)
-
return cls.from_arrays(left, right, closed, copy=False, dtype=dtype)
def _validate(self):
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index a98875ace09aa..89499a4f3ab67 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -4,7 +4,7 @@
from collections import abc
import numbers
import operator
-from typing import Any, Callable, Union
+from typing import Any, Callable, Type, TypeVar, Union
import warnings
import numpy as np
@@ -51,6 +51,8 @@
import pandas.io.formats.printing as printing
+_SparseArrayT = TypeVar("_SparseArrayT", bound="SparseArray")
+
# ----------------------------------------------------------------------------
# Array
@@ -396,8 +398,11 @@ def __init__(
@classmethod
def _simple_new(
- cls, sparse_array: np.ndarray, sparse_index: SparseIndex, dtype: SparseDtype
- ) -> "SparseArray":
+ cls: Type[_SparseArrayT],
+ sparse_array: np.ndarray,
+ sparse_index: SparseIndex,
+ dtype: SparseDtype,
+ ) -> _SparseArrayT:
new = object.__new__(cls)
new._sparse_index = sparse_index
new._sparse_values = sparse_array
@@ -821,11 +826,12 @@ def _get_val_at(self, loc):
val = com.maybe_box_datetimelike(val, self.sp_values.dtype)
return val
- def take(self, indices, allow_fill=False, fill_value=None) -> "SparseArray":
+ def take(self, indices, allow_fill: bool = False, fill_value=None) -> "SparseArray":
if is_scalar(indices):
raise ValueError(f"'indices' must be an array, not a scalar '{indices}'.")
indices = np.asarray(indices, dtype=np.int32)
+ result: Union[np.ndarray, "SparseArray"]
if indices.size == 0:
result = np.array([], dtype="object")
kwargs = {"dtype": self.dtype}
@@ -1386,8 +1392,10 @@ def __abs__(self):
# ------------------------------------------------------------------------
@classmethod
- def _create_unary_method(cls, op) -> Callable[["SparseArray"], "SparseArray"]:
- def sparse_unary_method(self) -> "SparseArray":
+ def _create_unary_method(
+ cls: Type[_SparseArrayT], op
+ ) -> Callable[[_SparseArrayT], _SparseArrayT]:
+ 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)
@@ -1479,14 +1487,14 @@ def cmp_method(self, other):
@classmethod
def _add_unary_ops(cls):
- cls.__pos__ = cls._create_unary_method(operator.pos)
- cls.__neg__ = cls._create_unary_method(operator.neg)
- cls.__invert__ = cls._create_unary_method(operator.invert)
+ setattr(cls, "__pos__", cls._create_unary_method(operator.pos))
+ setattr(cls, "__neg__", cls._create_unary_method(operator.neg))
+ setattr(cls, "__invert__", cls._create_unary_method(operator.invert))
@classmethod
def _add_comparison_ops(cls):
- cls.__and__ = cls._create_comparison_method(operator.and_)
- cls.__or__ = cls._create_comparison_method(operator.or_)
+ setattr(cls, "__and__", cls._create_comparison_method(operator.and_))
+ setattr(cls, "__or__", cls._create_comparison_method(operator.or_))
cls.__xor__ = cls._create_arithmetic_method(operator.xor)
super()._add_comparison_ops()
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 5945d8a4b432d..734ea8eba65d6 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -4,7 +4,17 @@
import builtins
import textwrap
-from typing import Any, Dict, FrozenSet, List, Optional, Union
+from typing import (
+ TYPE_CHECKING,
+ Any,
+ Callable,
+ Dict,
+ FrozenSet,
+ List,
+ Optional,
+ Tuple,
+ Union,
+)
import numpy as np
@@ -34,6 +44,9 @@
from pandas.core.construction import create_series_with_explicit_dtype
import pandas.core.nanops as nanops
+if TYPE_CHECKING:
+ from pandas import DataFrame, Series # noqa: F401
+
_shared_docs: Dict[str, str] = dict()
_indexops_doc_kwargs = dict(
klass="IndexOpsMixin",
@@ -244,7 +257,7 @@ def _gotitem(self, key, ndim: int, subset=None):
"""
raise AbstractMethodError(self)
- def aggregate(self, func, *args, **kwargs):
+ def aggregate(self, func, **kwargs):
raise AbstractMethodError(self)
agg = aggregate
@@ -279,13 +292,15 @@ def _try_aggregate_string_function(self, arg: str, *args, **kwargs):
f"'{arg}' is not a valid function for '{type(self).__name__}' object"
)
- def _aggregate(self, arg, *args, **kwargs):
+ def _aggregate(
+ self, arg: Union[str, Dict], *args, **kwargs
+ ) -> Tuple[Optional[Union[Dict, "Series", "DataFrame"]], Optional[bool]]:
"""
provide an implementation for the aggregators
Parameters
----------
- arg : string, dict, function
+ arg : str, dict, function
*args : args to pass on to the function
**kwargs : kwargs to pass on to the function
@@ -318,7 +333,7 @@ def _aggregate(self, arg, *args, **kwargs):
# eg. {'A' : ['mean']}, normalize all to
# be list-likes
if any(is_aggregator(x) for x in arg.values()):
- new_arg = {}
+ new_arg: Dict[Any, Union[Tuple, List, Dict]] = {}
for k, v in arg.items():
if not isinstance(v, (tuple, list, dict)):
new_arg[k] = [v]
@@ -374,7 +389,7 @@ def _agg_2dim(how):
colg = self._gotitem(self._selection, ndim=2, subset=obj)
return colg.aggregate(how)
- def _agg(arg, func):
+ def _agg(arg: Dict, func: Callable) -> Dict:
"""
run the aggregations over the arg with func
return a dict
@@ -386,7 +401,7 @@ def _agg(arg, func):
# set the final keys
keys = list(arg.keys())
- result = {}
+ result: Any = {}
if self._selection is not None:
@@ -460,7 +475,7 @@ def is_any_frame() -> bool:
return result, True
# fall thru
- from pandas import DataFrame, Series
+ from pandas import DataFrame, Series # noqa: F811
try:
result = DataFrame(result)
@@ -546,7 +561,7 @@ def _aggregate_multiple_funcs(self, arg, _axis):
# we are concatting non-NDFrame objects,
# e.g. a list of scalars
- from pandas import Series
+ from pandas import Series # noqa: F811
result = Series(results, index=keys, name=self.name)
if is_nested_object(result):
diff --git a/pandas/core/computation/expressions.py b/pandas/core/computation/expressions.py
index 7f93472c766d7..5b50964e83faf 100644
--- a/pandas/core/computation/expressions.py
+++ b/pandas/core/computation/expressions.py
@@ -6,6 +6,7 @@
"""
+from typing import Callable, List, Set
import warnings
import numpy as np
@@ -20,10 +21,10 @@
import numexpr as ne
_TEST_MODE = None
-_TEST_RESULT = None
+_TEST_RESULT: List[bool]
_USE_NUMEXPR = _NUMEXPR_INSTALLED
-_evaluate = None
-_where = None
+_evaluate: Callable
+_where: Callable
# the set of dtypes that we will allow pass to numexpr
_ALLOWED_DTYPES = {
@@ -74,7 +75,7 @@ def _can_use_numexpr(op, op_str, a, b, dtype_check):
# required min elements (otherwise we are adding overhead)
if np.prod(a.shape) > _MIN_ELEMENTS:
# check for dtype compatibility
- dtypes = set()
+ dtypes: Set[str] = set()
for o in [a, b]:
# Series implements dtypes, check for dimension count as well
if hasattr(o, "dtypes") and o.ndim > 1:
@@ -213,7 +214,7 @@ def where(cond, a, b, use_numexpr=True):
return _where(cond, a, b) if use_numexpr else _where_standard(cond, a, b)
-def set_test_mode(v=True):
+def set_test_mode(v: bool = True) -> None:
"""
Keeps track of whether numexpr was used. Stores an additional ``True``
for every successful use of evaluate with numexpr since the last
diff --git a/pandas/core/computation/pytables.py b/pandas/core/computation/pytables.py
index 15d9987310f18..3050312d14320 100644
--- a/pandas/core/computation/pytables.py
+++ b/pandas/core/computation/pytables.py
@@ -2,7 +2,7 @@
import ast
from functools import partial
-from typing import Any, Dict, Optional, Tuple
+from typing import Any, Callable, Dict, Optional, Tuple
import numpy as np
@@ -62,7 +62,7 @@ def _resolve_name(self):
except UndefinedVariableError:
return self.name
- # read-only property overwriting read/write property
+ # error: Read-only property cannot override read-write property [misc]
@property # type: ignore
def value(self):
return self._value
@@ -183,6 +183,7 @@ def convert_value(self, v) -> "TermValue":
"""
def stringify(value):
+ encoder: Callable
if self.encoding is not None:
encoder = partial(pprint_thing_encoded, encoding=self.encoding)
else:
diff --git a/pandas/core/computation/scope.py b/pandas/core/computation/scope.py
index 83bf92ad737e4..210e3b2ebf5d0 100644
--- a/pandas/core/computation/scope.py
+++ b/pandas/core/computation/scope.py
@@ -9,7 +9,7 @@
import pprint
import struct
import sys
-from typing import List
+from typing import Dict, List, Optional, Union
import numpy as np
@@ -108,8 +108,14 @@ class Scope:
__slots__ = ["level", "scope", "target", "resolvers", "temps"]
def __init__(
- self, level, global_dict=None, local_dict=None, resolvers=(), target=None
+ self,
+ level,
+ global_dict=None,
+ local_dict: Optional[Union["Scope", Dict]] = None,
+ resolvers=(),
+ target=None,
):
+ self.resolvers: DeepChainMap
self.level = level + 1
# shallow copy because we don't want to keep filling this up with what
@@ -139,7 +145,7 @@ def __init__(
if isinstance(local_dict, Scope):
resolvers += tuple(local_dict.resolvers.maps)
self.resolvers = DeepChainMap(*resolvers)
- self.temps = {}
+ self.temps: Dict[str, object] = {}
def __repr__(self) -> str:
scope_keys = _get_pretty_string(list(self.scope.keys()))
@@ -161,7 +167,7 @@ def has_resolvers(self) -> bool:
"""
return bool(len(self.resolvers))
- def resolve(self, key: str, is_local: bool):
+ def resolve(self, key: str, is_local: bool) -> object:
"""
Resolve a variable name in a possibly local context.
@@ -271,7 +277,7 @@ def _update(self, level: int):
finally:
del stack[:], stack
- def add_tmp(self, value) -> str:
+ def add_tmp(self, value: object) -> str:
"""
Add a temporary variable to the scope.
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 7dda6850ba4f7..e352b9d08b559 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -75,7 +75,7 @@
if TYPE_CHECKING:
from pandas import Series
- from pandas.core.arrays import ExtensionArray # noqa: F401
+ from pandas.core.arrays.base import ExtensionArray # noqa: F401
_int8_max = np.iinfo(np.int8).max
_int16_max = np.iinfo(np.int16).max
diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index b2301ab0190c7..1a26ff98ca94f 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -2,7 +2,7 @@
Common type operations.
"""
-from typing import Any, Callable, Union
+from typing import Any, Callable, Optional, Union, cast
import warnings
import numpy as np
@@ -63,7 +63,7 @@
_INT64_DTYPE = np.dtype(np.int64)
# oh the troubles to reduce import time
-_is_scipy_sparse = None
+_is_scipy_sparse: Optional[Callable] = None
ensure_float64 = algos.ensure_float64
ensure_float32 = algos.ensure_float32
@@ -160,10 +160,12 @@ def ensure_int_or_float(arr: ArrayLike, copy: bool = False) -> np.array:
"""
# TODO: GH27506 potential bug with ExtensionArrays
try:
+ # error: Unexpected keyword argument "casting" for "astype"
return arr.astype("int64", copy=copy, casting="safe") # type: ignore
except TypeError:
pass
try:
+ # error: Unexpected keyword argument "casting" for "astype"
return arr.astype("uint64", copy=copy, casting="safe") # type: ignore
except TypeError:
if is_extension_array_dtype(arr.dtype):
@@ -324,6 +326,8 @@ def is_scipy_sparse(arr) -> bool:
if _is_scipy_sparse is None:
try:
from scipy.sparse import issparse as _is_scipy_sparse
+
+ _is_scipy_sparse = cast(Callable, _is_scipy_sparse)
except ImportError:
_is_scipy_sparse = lambda _: False
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index c3018861bce57..7501f2064cd61 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -29,6 +29,7 @@
Set,
Tuple,
Type,
+ TypeVar,
Union,
cast,
)
@@ -135,6 +136,8 @@
from pandas.io.formats.info import info
import pandas.plotting
+_DataFrameT = TypeVar("_DataFrameT", bound="DataFrame")
+
if TYPE_CHECKING:
from pandas.core.groupby.generic import DataFrameGroupBy
from pandas.io.formats.style import Styler
@@ -773,7 +776,7 @@ def to_string(
max_rows: Optional[int] = None,
min_rows: Optional[int] = None,
max_cols: Optional[int] = None,
- show_dimensions: bool = False,
+ show_dimensions: Union[bool, str] = False,
decimal: str = ".",
line_width: Optional[int] = None,
max_colwidth: Optional[int] = None,
@@ -963,7 +966,9 @@ def iterrows(self) -> Iterable[Tuple[Label, Series]]:
s = klass(v, index=columns, name=k)
yield k, s
- def itertuples(self, index=True, name="Pandas"):
+ def itertuples(
+ self, index: bool = True, name: Optional[str] = "Pandas"
+ ) -> Iterable:
"""
Iterate over DataFrame rows as namedtuples.
@@ -1039,7 +1044,11 @@ def itertuples(self, index=True, name="Pandas"):
# Python versions before 3.7 support at most 255 arguments to constructors
can_return_named_tuples = PY37 or len(self.columns) + index < 255
if name is not None and can_return_named_tuples:
- itertuple = collections.namedtuple(name, fields, rename=True)
+ # https://github.com/python/mypy/issues/848
+ # error: namedtuple() expects a string literal as the first argument [misc]
+ itertuple = collections.namedtuple( # type: ignore
+ name, fields, rename=True
+ )
return map(itertuple._make, zip(*arrays))
# fallback to regular tuples
@@ -1665,13 +1674,13 @@ def from_records(
arrays = [data[k] for k in columns]
else:
arrays = []
- arr_columns = []
+ arr_columns_ = []
for k, v in data.items():
if k in columns:
- arr_columns.append(k)
+ arr_columns_.append(k)
arrays.append(v)
- arrays, arr_columns = reorder_arrays(arrays, arr_columns, columns)
+ arrays, arr_columns = reorder_arrays(arrays, arr_columns_, columns)
elif isinstance(data, (np.ndarray, DataFrame)):
arrays, columns = to_arrays(data, columns)
@@ -3277,10 +3286,15 @@ def select_dtypes(self, include=None, exclude=None) -> "DataFrame":
def extract_unique_dtypes_from_dtypes_set(
dtypes_set: FrozenSet[Dtype], unique_dtypes: np.ndarray
) -> List[Dtype]:
+ # error: Argument 1 to "tuple" has incompatible type
+ # "FrozenSet[Union[str, Any, ExtensionDtype]]";
+ # expected "Iterable[Union[type, Tuple[Any, ...]]]"
extracted_dtypes = [
unique_dtype
for unique_dtype in unique_dtypes
- if issubclass(unique_dtype.type, tuple(dtypes_set)) # type: ignore
+ if issubclass(
+ unique_dtype.type, tuple(dtypes_set) # type: ignore
+ )
]
return extracted_dtypes
@@ -3320,7 +3334,7 @@ def insert(self, loc, column, value, allow_duplicates=False) -> None:
value = self._sanitize_column(column, value, broadcast=False)
self._mgr.insert(loc, column, value, allow_duplicates=allow_duplicates)
- def assign(self, **kwargs) -> "DataFrame":
+ def assign(self: _DataFrameT, **kwargs) -> _DataFrameT:
r"""
Assign new columns to a DataFrame.
@@ -4203,7 +4217,7 @@ def set_index(
frame = self.copy()
arrays = []
- names = []
+ names: List = []
if append:
names = list(self.index.names)
if isinstance(self.index, ABCMultiIndex):
@@ -4449,7 +4463,9 @@ def _maybe_casted_values(index, labels=None):
values, _ = maybe_upcast_putmask(values, mask, np.nan)
if issubclass(values_type, DatetimeLikeArray):
- values = values_type(values, dtype=values_dtype)
+ # TODO: DatetimeLikeArray is a mixin not a base class
+ # error: Too many arguments for "DatetimeLikeArrayMixin"
+ values = values_type(values, dtype=values_dtype) # type: ignore
return values
@@ -6866,26 +6882,26 @@ def _gotitem(
**_shared_doc_kwargs,
)
@Appender(_shared_docs["aggregate"])
- def aggregate(self, func, axis=0, *args, **kwargs):
+ def aggregate(self, func, axis=0, **kwargs):
axis = self._get_axis_number(axis)
result = None
try:
- result, how = self._aggregate(func, axis=axis, *args, **kwargs)
+ result, how = self._aggregate(func, axis=axis, **kwargs)
except TypeError:
pass
if result is None:
- return self.apply(func, axis=axis, args=args, **kwargs)
+ return self.apply(func, axis=axis, **kwargs)
return result
- def _aggregate(self, arg, axis=0, *args, **kwargs):
+ def _aggregate(self, arg, axis=0, **kwargs):
if axis == 1:
# NDFrame.aggregate returns a tuple, and we need to transpose
# only result
- result, how = self.T._aggregate(arg, *args, **kwargs)
+ result, how = self.T._aggregate(arg, **kwargs)
result = result.T if result is not None else result
return result, how
- return super()._aggregate(arg, *args, **kwargs)
+ return super()._aggregate(arg, **kwargs)
agg = aggregate
@@ -7253,8 +7269,14 @@ def append(
)
def join(
- self, other, on=None, how="left", lsuffix="", rsuffix="", sort=False
- ) -> "DataFrame":
+ self: _DataFrameT,
+ other,
+ on=None,
+ how: str = "left",
+ lsuffix: str = "",
+ rsuffix: str = "",
+ sort: bool = False,
+ ) -> _DataFrameT:
"""
Join columns of another DataFrame.
@@ -8277,6 +8299,7 @@ def idxmin(self, axis=0, skipna=True) -> Series:
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmin(self.values, axis=axis, skipna=skipna)
+ assert not isinstance(indices, int) # needed for mypy
index = self._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return Series(result, index=self._get_agg_axis(axis))
@@ -8344,6 +8367,7 @@ def idxmax(self, axis=0, skipna=True) -> Series:
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmax(self.values, axis=axis, skipna=skipna)
+ assert not isinstance(indices, int) # needed for mypy
index = self._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return Series(result, index=self._get_agg_axis(axis))
@@ -8730,7 +8754,7 @@ def isin(self, values) -> "DataFrame":
def _from_nested_dict(data):
# TODO: this should be seriously cythonized
- new_data = collections.defaultdict(dict)
+ new_data: Dict = collections.defaultdict(dict)
for index, s in data.items():
for col, v in s.items():
new_data[col][index] = v
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 47a2b22abe103..9638a96679b10 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -22,6 +22,7 @@
Tuple,
Type,
Union,
+ cast,
)
import warnings
import weakref
@@ -107,6 +108,7 @@
from pandas.tseries.frequencies import to_offset
if TYPE_CHECKING:
+ from pandas import Series # noqa: F401
from pandas.core.resample import Resampler
# goal is to be able to define the docs close to function, while still being
@@ -391,7 +393,7 @@ def _get_block_manager_axis(cls, axis):
return m - axis
return axis
- def _get_axis_resolvers(self, axis: str) -> Dict[str, ABCSeries]:
+ def _get_axis_resolvers(self, axis: str) -> Dict[str, Union["Series", MultiIndex]]:
# index or columns
axis_index = getattr(self, axis)
d = dict()
@@ -421,10 +423,10 @@ def _get_axis_resolvers(self, axis: str) -> Dict[str, ABCSeries]:
d[axis] = dindex
return d
- def _get_index_resolvers(self) -> Dict[str, ABCSeries]:
+ def _get_index_resolvers(self) -> Dict[str, Union["Series", MultiIndex]]:
from pandas.core.computation.parsing import clean_column_name
- d: Dict[str, ABCSeries] = {}
+ d: Dict[str, Union["Series", MultiIndex]] = {}
for axis_name in self._AXIS_ORDERS:
d.update(self._get_axis_resolvers(axis_name))
@@ -1861,7 +1863,7 @@ def __setstate__(self, state):
elif len(state) == 2:
raise NotImplementedError("Pre-0.12 pickles are no longer supported")
- self._item_cache = {}
+ self._item_cache: Dict = {}
# ----------------------------------------------------------------------
# Rendering Methods
@@ -4598,14 +4600,15 @@ def filter(
return self.reindex(**{name: [r for r in items if r in labels]})
elif like:
- def f(x):
+ def f(x) -> bool:
+ assert like is not None # needed for mypy
return like in ensure_str(x)
values = labels.map(f)
return self.loc(axis=axis)[values]
elif regex:
- def f(x):
+ def f(x) -> bool:
return matcher.search(ensure_str(x)) is not None
matcher = re.compile(regex)
@@ -5423,7 +5426,7 @@ def dtypes(self):
string object
dtype: object
"""
- from pandas import Series
+ from pandas import Series # noqa: F811
return Series(self._mgr.get_dtypes(), index=self._info_axis, dtype=np.object_)
@@ -6508,8 +6511,11 @@ def replace(
to_replace = regex
regex = True
- items = list(to_replace.items())
- keys, values = zip(*items) if items else ([], [])
+ items = list(cast(dict, to_replace).items())
+ if items:
+ keys, values = zip(*items)
+ else:
+ keys, values = ([], [])
are_mappings = [is_dict_like(v) for v in values]
@@ -7018,7 +7024,7 @@ def asof(self, where, subset=None):
if where < start:
if not is_series:
- from pandas import Series
+ from pandas import Series # noqa: F811
return Series(index=self.columns, name=where, dtype=np.float64)
return np.nan
@@ -11181,7 +11187,13 @@ def cum_func(self, axis=None, skipna=True, *args, **kwargs):
axis = self._get_axis_number(axis)
if axis == 1:
- return cum_func(self.T, axis=0, skipna=skipna, *args, **kwargs).T
+ # pandas\core\generic.py:11099:20: error: "cum_func" gets multiple values
+ # for keyword argument "axis" [misc]
+ # pandas\core\generic.py:11099:20: error: "cum_func" gets multiple values
+ # for keyword argument "skipna" [misc]
+ return cum_func( # type: ignore
+ self.T, axis=0, skipna=skipna, *args, **kwargs
+ ).T
def block_accum_func(blk_values):
values = blk_values.T if hasattr(blk_values, "T") else blk_values
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 208cbfc5b06d6..5375cdd2036bd 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -9,7 +9,6 @@
import copy
from functools import partial
from textwrap import dedent
-import typing
from typing import (
TYPE_CHECKING,
Any,
@@ -22,6 +21,7 @@
Sequence,
Tuple,
Type,
+ TypeVar,
Union,
cast,
)
@@ -88,7 +88,7 @@
# TODO: validate types on ScalarResult and move to _typing
# Blocked from using by https://github.com/python/mypy/issues/1484
# See note at _mangle_lambda_list
-ScalarResult = typing.TypeVar("ScalarResult")
+ScalarResult = TypeVar("ScalarResult")
def generate_property(name: str, klass: Type[FrameOrSeries]):
@@ -450,7 +450,7 @@ def _get_index() -> Index:
return self._reindex_output(result)
def _aggregate_named(self, func, *args, **kwargs):
- result = {}
+ result: Dict = {}
for name, group in self:
group.name = name
@@ -531,7 +531,7 @@ def _transform_fast(self, result, func_nm: str) -> Series:
out = maybe_cast_result(out, self.obj, how=func_nm)
return Series(out, index=self.obj.index, name=self.obj.name)
- def filter(self, func, dropna=True, *args, **kwargs):
+ def filter(self, func: Callable, dropna: bool = True, *args, **kwargs):
"""
Return a copy of a Series excluding elements from groups that
do not satisfy the boolean criterion specified by func.
@@ -566,8 +566,8 @@ def filter(self, func, dropna=True, *args, **kwargs):
wrapper = lambda x: func(x, *args, **kwargs)
# Interpret np.nan as False.
- def true_and_notna(x, *args, **kwargs) -> bool:
- b = wrapper(x, *args, **kwargs)
+ def true_and_notna(x) -> bool:
+ b = wrapper(x)
return b and notna(b)
try:
@@ -1240,9 +1240,10 @@ def first_not_none(values):
# this is to silence a DeprecationWarning
# TODO: Remove when default dtype of empty Series is object
kwargs = v._construct_axes_dict()
+ backup: NDFrame
if v._constructor is Series:
backup = create_series_with_explicit_dtype(
- **kwargs, dtype_if_empty=object
+ dtype_if_empty=object, **kwargs
)
else:
backup = v._constructor(**kwargs)
@@ -1870,7 +1871,10 @@ def groupby_series(obj, col=None):
boxplot = boxplot_frame_groupby
-def _recast_datetimelike_result(result: DataFrame) -> DataFrame:
+_T = TypeVar("_T", bound=DataFrame)
+
+
+def _recast_datetimelike_result(result: _T) -> _T:
"""
If we have date/time like in the original, then coerce dates
as we are stacking can easily have object dtypes here.
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 7a7ac58b9d11b..36e56e9d88bff 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -328,7 +328,7 @@ def f(self):
f.__name__ = "plot"
return self._groupby.apply(f)
- def __getattr__(self, name: str):
+ def __getattr__(self, name):
def attr(*args, **kwargs):
def f(self):
return getattr(self.plot, name)(*args, **kwargs)
@@ -573,7 +573,7 @@ def _set_result_index_ordered(self, result):
def _dir_additions(self):
return self.obj._dir_additions() | self._apply_whitelist
- def __getattr__(self, attr: str):
+ def __getattr__(self, attr):
if attr in self._internal_names_set:
return object.__getattribute__(self, attr)
if attr in self.obj:
@@ -2373,8 +2373,6 @@ def head(self, n=5):
from the original DataFrame with original index and order preserved
(``as_index`` flag is ignored).
- Does not work for negative values of `n`.
-
Returns
-------
Series or DataFrame
@@ -2388,10 +2386,6 @@ def head(self, n=5):
A B
0 1 2
2 5 6
- >>> df.groupby('A').head(-1)
- Empty DataFrame
- Columns: [A, B]
- Index: []
"""
self._reset_group_selection()
mask = self._cumcount_array() < n
@@ -2407,8 +2401,6 @@ def tail(self, n=5):
from the original DataFrame with original index and order preserved
(``as_index`` flag is ignored).
- Does not work for negative values of `n`.
-
Returns
-------
Series or DataFrame
@@ -2422,10 +2414,6 @@ def tail(self, n=5):
A B
1 a 2
3 b 2
- >>> df.groupby('A').tail(-1)
- Empty DataFrame
- Columns: [A, B]
- Index: []
"""
self._reset_group_selection()
mask = self._cumcount_array(ascending=False) < n
diff --git a/pandas/core/groupby/grouper.py b/pandas/core/groupby/grouper.py
index 2f50845fda4dc..358cc488bc1c4 100644
--- a/pandas/core/groupby/grouper.py
+++ b/pandas/core/groupby/grouper.py
@@ -3,7 +3,7 @@
split-apply-combine paradigm.
"""
-from typing import Dict, Hashable, List, Optional, Tuple
+from typing import Dict, Generic, Hashable, List, Optional, Tuple
import numpy as np
@@ -33,7 +33,7 @@
from pandas.io.formats.printing import pprint_thing
-class Grouper:
+class Grouper(Generic[FrameOrSeries]):
"""
A Grouper allows the user to specify a groupby instruction for an object.
@@ -108,7 +108,7 @@ def __init__(self, key=None, level=None, freq=None, axis=0, sort=False):
self.sort = sort
self.grouper = None
- self.obj = None
+ self.obj: Optional[FrameOrSeries] = None
self.indexer = None
self.binner = None
self._grouper = None
@@ -130,6 +130,7 @@ def _get_grouper(self, obj, validate: bool = True):
a tuple of binner, grouper, obj (possibly sorted)
"""
self._set_grouper(obj)
+ assert self.obj is not None
self.grouper, _, self.obj = get_grouper(
self.obj,
[self.key],
@@ -151,8 +152,6 @@ def _set_grouper(self, obj: FrameOrSeries, sort: bool = False):
sort : bool, default False
whether the resulting grouper should be sorted
"""
- assert obj is not None
-
if self.key is not None and self.level is not None:
raise ValueError("The Grouper cannot specify both a key and a level!")
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 8d535374a083f..e99b5413df23b 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -7,7 +7,7 @@
"""
import collections
-from typing import List, Optional, Sequence, Tuple, Type
+from typing import Any, Dict, Generic, List, Optional, Sequence, Tuple, Type
import numpy as np
@@ -42,7 +42,6 @@
from pandas.core.base import SelectionMixin
import pandas.core.common as com
from pandas.core.frame import DataFrame
-from pandas.core.generic import NDFrame
from pandas.core.groupby import base, grouper
from pandas.core.indexes.api import Index, MultiIndex, ensure_index
from pandas.core.series import Series
@@ -126,7 +125,9 @@ def get_iterator(self, data: FrameOrSeries, axis: int = 0):
for key, (i, group) in zip(keys, splitter):
yield key, group
- def _get_splitter(self, data: FrameOrSeries, axis: int = 0) -> "DataSplitter":
+ def _get_splitter(
+ self, data: FrameOrSeries, axis: int = 0
+ ) -> "DataSplitter[FrameOrSeries]":
comp_ids, _, ngroups = self.group_info
return get_splitter(data, comp_ids, ngroups, axis=axis)
@@ -154,7 +155,7 @@ def apply(self, f, data: FrameOrSeries, axis: int = 0):
group_keys = self._get_group_keys()
result_values = None
- sdata: FrameOrSeries = splitter._get_sorted_data()
+ sdata = splitter._get_sorted_data()
if sdata.ndim == 2 and np.any(sdata.dtypes.apply(is_extension_array_dtype)):
# calling splitter.fast_apply will raise TypeError via apply_frame_axis0
# if we pass EA instead of ndarray
@@ -250,8 +251,8 @@ def groups(self):
if len(self.groupings) == 1:
return self.groupings[0].groups
else:
- to_groupby = zip(*(ping.grouper for ping in self.groupings))
- to_groupby = Index(to_groupby)
+ to_groupby_ = zip(*(ping.grouper for ping in self.groupings))
+ to_groupby = Index(to_groupby_)
return self.axis.groupby(to_groupby)
@cache_readonly
@@ -560,7 +561,7 @@ def _cython_operation(
if vdim == 1 and arity == 1:
result = result[:, 0]
- names: Optional[List[str]] = self._name_functions.get(how, None)
+ names = self._name_functions.get(how, None)
if swapped:
result = result.swapaxes(0, axis)
@@ -787,7 +788,7 @@ def get_iterator(self, data: FrameOrSeries, axis: int = 0):
@cache_readonly
def indices(self):
- indices = collections.defaultdict(list)
+ indices: Dict[Any, List] = collections.defaultdict(list)
i = 0
for label, bin in zip(self.binlabels, self.bins):
@@ -871,7 +872,7 @@ def _is_indexed_like(obj, axes) -> bool:
# Splitting / application
-class DataSplitter:
+class DataSplitter(Generic[FrameOrSeries]):
def __init__(self, data: FrameOrSeries, labels, ngroups: int, axis: int = 0):
self.data = data
self.labels = ensure_int64(labels)
@@ -906,7 +907,7 @@ def __iter__(self):
def _get_sorted_data(self) -> FrameOrSeries:
return self.data.take(self.sort_idx, axis=self.axis)
- def _chop(self, sdata, slice_obj: slice) -> NDFrame:
+ def _chop(self, sdata: FrameOrSeries, slice_obj: slice) -> FrameOrSeries:
raise AbstractMethodError(self)
@@ -930,9 +931,10 @@ def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
def get_splitter(
data: FrameOrSeries, labels: np.ndarray, ngroups: int, axis: int = 0
-) -> DataSplitter:
+) -> "DataSplitter[FrameOrSeries]":
+ klass: Type[DataSplitter]
if isinstance(data, Series):
- klass: Type[DataSplitter] = SeriesSplitter
+ klass = SeriesSplitter
else:
# i.e. DataFrame
klass = FrameSplitter
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index df58593bc930c..863612db9fa2e 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -2,7 +2,17 @@
from datetime import datetime
import operator
from textwrap import dedent
-from typing import TYPE_CHECKING, Any, FrozenSet, Hashable, Union
+from typing import (
+ TYPE_CHECKING,
+ Any,
+ Callable,
+ FrozenSet,
+ Hashable,
+ Optional,
+ Sequence,
+ TypeVar,
+ Union,
+)
import warnings
import numpy as np
@@ -88,6 +98,7 @@
if TYPE_CHECKING:
from pandas import Series
+str_ = str
__all__ = ["Index"]
@@ -141,7 +152,7 @@ def index_arithmetic_method(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame, ABCTimedeltaIndex)):
return NotImplemented
- from pandas import Series
+ from pandas import Series # noqa: F811
result = op(Series(self), other)
if isinstance(result, tuple):
@@ -181,6 +192,9 @@ def _new_Index(cls, d):
return cls.__new__(cls, **d)
+_IndexT = TypeVar("_IndexT", bound="Index")
+
+
class Index(IndexOpsMixin, PandasObject):
"""
Immutable ndarray implementing an ordered, sliceable set. The basic object
@@ -281,6 +295,16 @@ def _outer_indexer(self, left, right):
str = CachedAccessor("str", StringMethods)
+ # --------------------------------------------------------------------
+ # Type declarations for Generated Arithmetic, Comparison, and Unary Methods
+
+ __eq__: Callable
+ __ne__: Callable
+ __lt__: Callable
+ __gt__: Callable
+ __le__: Callable
+ __ge__: Callable
+
# --------------------------------------------------------------------
# Constructors
@@ -801,7 +825,7 @@ def repeat(self, repeats, axis=None):
# --------------------------------------------------------------------
# Copying Methods
- def copy(self, name=None, deep=False, dtype=None, names=None):
+ def copy(self: _IndexT, name=None, deep=False, dtype=None, names=None) -> _IndexT:
"""
Make a copy of this object.
@@ -828,6 +852,7 @@ def copy(self, name=None, deep=False, dtype=None, names=None):
``deep``, but if ``deep`` is passed it will attempt to deepcopy.
"""
if deep:
+ assert self._data is not None
new_index = self._shallow_copy(self._data.copy())
else:
new_index = self._shallow_copy()
@@ -951,9 +976,9 @@ def _format_with_header(self, header, na_rep="NaN", **kwargs):
# could have nans
mask = isna(values)
if mask.any():
- result = np.array(result)
- result[mask] = na_rep
- result = result.tolist()
+ result_ = np.array(result)
+ result_[mask] = na_rep
+ result = result_.tolist()
else:
result = _trim_front(format_array(values, None, justify="left"))
@@ -1072,7 +1097,7 @@ def to_series(self, index=None, name=None):
Series
The dtype will be based on the type of the Index values.
"""
- from pandas import Series
+ from pandas import Series # noqa: F811
if index is None:
index = self._shallow_copy()
@@ -2356,12 +2381,12 @@ def _get_unique_index(self, dropna: bool = False):
def __add__(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame)):
return NotImplemented
- from pandas import Series
+ from pandas import Series # noqa: F811
return Index(Series(self) + other)
def __radd__(self, other):
- from pandas import Series
+ from pandas import Series # noqa: F811
return Index(other + Series(self))
@@ -2374,7 +2399,7 @@ def __sub__(self, other):
def __rsub__(self, other):
# wrap Series to ensure we pin name correctly
- from pandas import Series
+ from pandas import Series # noqa: F811
return Index(other - Series(self))
@@ -4879,7 +4904,9 @@ def _get_string_slice(self, key: str_t, use_lhs: bool = True, use_rhs: bool = Tr
# overridden in DatetimeIndex, TimedeltaIndex and PeriodIndex
raise NotImplementedError
- def slice_indexer(self, start=None, end=None, step=None, kind=None):
+ def slice_indexer(
+ self, start=None, end=None, step=None, kind: Optional[str_] = None
+ ):
"""
For an ordered or unique index, compute the slice indexer for input
labels and step.
@@ -5509,7 +5536,9 @@ def ensure_index_from_sequences(sequences, names=None):
return MultiIndex.from_arrays(sequences, names=names)
-def ensure_index(index_like, copy: bool = False):
+def ensure_index(
+ index_like: Union[Index, ExtensionArray, "Series", Sequence], copy: bool = False
+) -> Index:
"""
Ensure that we have an index from some index-like object.
@@ -5544,8 +5573,15 @@ def ensure_index(index_like, copy: bool = False):
if copy:
index_like = index_like.copy()
return index_like
+ # https://github.com/python/mypy/issues/1424
+ # error: Item "ExtensionArray" of "Union[ExtensionArray, Series, Sequence[Any]]"
+ # has no attribute "name" [union-attr]
+ # error: Item "Sequence[Any]" of "Union[ExtensionArray, Series, Sequence[Any]]"
+ # has no attribute "name" [union-attr]
if hasattr(index_like, "name"):
- return Index(index_like, name=index_like.name, copy=copy)
+ return Index(
+ index_like, name=index_like.name, copy=copy # type: ignore
+ )
if is_iterator(index_like):
index_like = list(index_like)
diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index 68d6229e798f5..8e3bf39a0e3b0 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -1,6 +1,6 @@
from datetime import date, datetime, time, timedelta, tzinfo
import operator
-from typing import Optional
+from typing import TYPE_CHECKING, Callable, Optional
import warnings
import numpy as np
@@ -31,6 +31,9 @@
from pandas.tseries.frequencies import Resolution, to_offset
from pandas.tseries.offsets import prefix_mapping
+if TYPE_CHECKING:
+ from pandas import Int64Index
+
def _new_DatetimeIndex(cls, d):
"""
@@ -204,6 +207,12 @@ class DatetimeIndex(DatetimeTimedeltaMixin):
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
"""
+ # Attributes
+ dayofweek: "Int64Index"
+ # Methods
+ tz_convert: Callable
+ tz_localize: Callable
+
_typ = "datetimeindex"
_engine_type = libindex.DatetimeEngine
@@ -604,7 +613,7 @@ def _maybe_cast_for_get_loc(self, key) -> Timestamp:
key = key.tz_convert(self.tz)
return key
- def _maybe_cast_slice_bound(self, label, side: str, kind):
+ def _maybe_cast_slice_bound(self, label, side: str, kind: Optional[str]):
"""
If label is a string, cast it to datetime according to resolution.
@@ -649,7 +658,9 @@ def _get_string_slice(self, key: str, use_lhs: bool = True, use_rhs: bool = True
loc = self._partial_date_slice(reso, parsed, use_lhs=use_lhs, use_rhs=use_rhs)
return loc
- def slice_indexer(self, start=None, end=None, step=None, kind=None):
+ def slice_indexer(
+ self, start=None, end=None, step=None, kind: Optional[str] = None
+ ):
"""
Return indexer for specified label slice.
Index.slice_indexer, customized to handle time slicing.
@@ -699,7 +710,8 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None):
end_casted = self._maybe_cast_slice_bound(end, "right", kind)
mask = (self <= end_casted) & mask
- indexer = mask.nonzero()[0][::step]
+ # error: "bool" has no attribute "nonzero" [attr-defined]
+ indexer = mask.nonzero()[0][::step] # type: ignore
if len(indexer) == len(self):
return slice(None)
else:
@@ -855,7 +867,7 @@ def date_range(
Returns
-------
- rng : DatetimeIndex
+ DatetimeIndex
See Also
--------
diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py
index 18e995ce4efd7..0c0902a436723 100644
--- a/pandas/core/indexes/interval.py
+++ b/pandas/core/indexes/interval.py
@@ -1,7 +1,7 @@
""" define the IntervalIndex """
from operator import le, lt
import textwrap
-from typing import Any, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Optional, Set, Tuple, Type, Union, cast
import numpy as np
@@ -58,6 +58,9 @@
from pandas.tseries.frequencies import to_offset
from pandas.tseries.offsets import DateOffset
+if TYPE_CHECKING:
+ from pandas import CategoricalIndex # noqa: F401
+
_VALID_CLOSED = {"left", "right", "both", "neither"}
_index_doc_kwargs = dict(ibase._index_doc_kwargs)
@@ -151,6 +154,23 @@ def func(intvidx_self, other, sort=False):
return func
+def _setop(op_name: str, sort=None):
+ @SetopCheck(op_name=op_name)
+ def func(self, other, sort=sort):
+ result = getattr(self._multiindex, op_name)(other._multiindex, sort=sort)
+ result_name = get_op_result_name(self, other)
+
+ # GH 19101: ensure empty results have correct dtype
+ if result.empty:
+ result = result._values.astype(self.dtype.subtype)
+ else:
+ result = result._values
+
+ return type(self).from_tuples(result, closed=self.closed, name=result_name)
+
+ return func
+
+
@Appender(
_interval_shared_docs["class"]
% dict(
@@ -436,7 +456,7 @@ def is_monotonic_decreasing(self) -> bool:
return self[::-1].is_monotonic_increasing
@cache_readonly
- def is_unique(self):
+ def is_unique(self) -> bool:
"""
Return True if the IntervalIndex contains unique elements, else False.
"""
@@ -449,7 +469,7 @@ def is_unique(self):
if left.is_unique or right.is_unique:
return True
- seen_pairs = set()
+ seen_pairs: Set[Tuple] = set()
check_idx = np.where(left.duplicated(keep=False))[0]
for idx in check_idx:
pair = (left[idx], right[idx])
@@ -605,7 +625,9 @@ def _maybe_convert_i8(self, key):
# convert left/right and reconstruct
left = self._maybe_convert_i8(key.left)
right = self._maybe_convert_i8(key.right)
- constructor = Interval if scalar else IntervalIndex.from_arrays
+ constructor: Union[
+ Type[Interval], Callable[..., IntervalIndex]
+ ] = Interval if scalar else IntervalIndex.from_arrays
return constructor(left, right, closed=self.closed)
if scalar:
@@ -788,6 +810,7 @@ def get_indexer(
right_indexer = self.right.get_indexer(target_as_index.right)
indexer = np.where(left_indexer == right_indexer, left_indexer, -1)
elif is_categorical_dtype(target_as_index.dtype):
+ target_as_index = cast("CategoricalIndex", target_as_index)
# get an indexer for unique categories then propagate to codes via take_1d
categories_indexer = self.get_indexer(target_as_index.categories)
indexer = take_1d(categories_indexer, target_as_index.codes, fill_value=-1)
@@ -1096,22 +1119,6 @@ def _intersection_non_unique(self, other: "IntervalIndex") -> "IntervalIndex":
return self[mask]
- def _setop(op_name: str, sort=None):
- @SetopCheck(op_name=op_name)
- def func(self, other, sort=sort):
- result = getattr(self._multiindex, op_name)(other._multiindex, sort=sort)
- result_name = get_op_result_name(self, other)
-
- # GH 19101: ensure empty results have correct dtype
- if result.empty:
- result = result._values.astype(self.dtype.subtype)
- else:
- result = result._values
-
- return type(self).from_tuples(result, closed=self.closed, name=result_name)
-
- return func
-
@property
def is_all_dates(self) -> bool:
"""
@@ -1325,6 +1332,7 @@ def interval_range(
breaks = maybe_downcast_to_dtype(breaks, "int64")
else:
# delegate to the appropriate range function
+ range_func: Callable
if isinstance(endpoint, Timestamp):
range_func = date_range
else:
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 7aa1456846612..07443c83093cd 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -7,6 +7,7 @@
List,
Optional,
Sequence,
+ Set,
Tuple,
Union,
)
@@ -240,7 +241,7 @@ class MultiIndex(Index):
# initialize to zero-length tuples to make everything work
_typ = "multiindex"
- _names = FrozenList()
+ _names: List[Optional[Hashable]] = FrozenList()
_levels = FrozenList()
_codes = FrozenList()
_comparables = ["names"]
@@ -497,7 +498,7 @@ def from_tuples(cls, tuples, sortorder=None, names=None):
if len(tuples) == 0:
if names is None:
raise TypeError("Cannot infer number of levels from empty list")
- arrays = [[]] * len(names)
+ arrays: List[List] = [[]] * len(names)
elif isinstance(tuples, (np.ndarray, Index)):
if isinstance(tuples, Index):
tuples = tuples._values
@@ -718,6 +719,7 @@ def _set_levels(
if level is not None and len(levels) != len(level):
raise ValueError("Length of levels must match length of level.")
+ new_levels: Sequence
if level is None:
new_levels = FrozenList(
ensure_index(lev, copy=copy)._shallow_copy() for lev in levels
@@ -885,11 +887,13 @@ def _set_codes(
)
else:
level_numbers = [self._get_level_number(lev) for lev in level]
- new_codes = list(self._codes)
+ _new_codes = list(self._codes)
for lev_num, level_codes in zip(level_numbers, codes):
lev = self.levels[lev_num]
- new_codes[lev_num] = _coerce_indexer_frozen(level_codes, lev, copy=copy)
- new_codes = FrozenList(new_codes)
+ _new_codes[lev_num] = _coerce_indexer_frozen(
+ level_codes, lev, copy=copy
+ )
+ new_codes = FrozenList(_new_codes)
if verify_integrity:
new_codes = self._verify_integrity(codes=new_codes)
@@ -2407,9 +2411,8 @@ def _get_partial_string_timestamp_match_key(self, key):
"""
# GH#10331
if isinstance(key, str) and self.levels[0]._supports_partial_string_indexing:
- # Convert key '2016-01-01' to
- # ('2016-01-01'[, slice(None, None, None)]+)
- key = tuple([key] + [slice(None)] * (len(self.levels) - 1))
+ # Convert key '2016-01-01' to ('2016-01-01'[, slice(None, None, None)]+)
+ key = tuple((key, *([slice(None)] * (len(self.levels) - 1))))
if isinstance(key, tuple):
# Convert (..., '2016-01-01', ...) in tuple to
@@ -3059,7 +3062,7 @@ def _update_indexer(idxr, indexer=indexer):
elif is_list_like(k):
# a collection of labels to include from this level (these
# are or'd)
- indexers = None
+ indexers: Optional[Int64Index] = None
for x in k:
try:
idxrs = _convert_to_indexer(
@@ -3402,7 +3405,7 @@ def intersection(self, other, sort=False):
lvals = self._values
rvals = other._values
- uniq_tuples = None # flag whether _inner_indexer was successful
+ uniq_tuples: Optional[List] = None # flag whether _inner_indexer was successful
if self.is_monotonic and other.is_monotonic:
try:
uniq_tuples = self._inner_indexer(lvals, rvals)[0]
@@ -3412,10 +3415,12 @@ def intersection(self, other, sort=False):
if uniq_tuples is None:
other_uniq = set(rvals)
- seen = set()
- uniq_tuples = [
- x for x in lvals if x in other_uniq and not (x in seen or seen.add(x))
- ]
+ seen: Set = set()
+ uniq_tuples = []
+ for x in lvals:
+ if x in other_uniq and x not in seen:
+ uniq_tuples.append(x)
+ seen.add(x)
if sort is None:
uniq_tuples = sorted(uniq_tuples)
diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py
index e2be58a56018d..d0f07d079c229 100644
--- a/pandas/core/indexes/numeric.py
+++ b/pandas/core/indexes/numeric.py
@@ -46,6 +46,7 @@ class NumericIndex(Index):
"""
_is_numeric_dtype = True
+ _default_dtype: np.number
def __new__(cls, data=None, dtype=None, copy=False, name=None):
cls._validate_dtype(dtype)
diff --git a/pandas/core/indexes/period.py b/pandas/core/indexes/period.py
index 1f565828ec7a5..ad29dd68c5877 100644
--- a/pandas/core/indexes/period.py
+++ b/pandas/core/indexes/period.py
@@ -143,6 +143,27 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index):
PeriodIndex(['2000Q1', '2002Q3'], dtype='period[Q-DEC]', freq='Q-DEC')
"""
+ # day
+ # dayofweek
+ # dayofyear
+ # days_in_month
+ # daysinmonth
+ # end_time
+ freq: DateOffset
+ # freqstr
+ hour: Any
+ # is_leap_year
+ minute: Any
+ # month
+ # quarter
+ # qyear
+ second: Any
+ # start_time
+ # week
+ # weekday
+ # weekofyear
+ # year
+
_typ = "periodindex"
_attributes = ["name", "freq"]
@@ -151,7 +172,6 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index):
_infer_as_myclass = True
_data: PeriodArray
- freq: DateOffset
_engine_type = libindex.PeriodEngine
_supports_partial_string_indexing = True
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 22a44d65a947a..36ea7ce42e87e 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -998,6 +998,38 @@ def _multi_take(self, tup: Tuple):
# -------------------------------------------------------------------
+ def _get_partial_string_timestamp_match_key(self, key, labels):
+ """
+ Translate any partial string timestamp matches in key, returning the
+ new key.
+
+ (GH 10331)
+ """
+ if isinstance(labels, ABCMultiIndex):
+ if (
+ isinstance(key, str)
+ and labels.levels[0]._supports_partial_string_indexing
+ ):
+ # Convert key '2016-01-01' to
+ # ('2016-01-01'[, slice(None, None, None)]+)
+ key = tuple(key, *([slice(None)] * (len(labels.levels) - 1)))
+
+ if isinstance(key, tuple):
+ # Convert (..., '2016-01-01', ...) in tuple to
+ # (..., slice('2016-01-01', '2016-01-01', None), ...)
+ new_key = []
+ for i, component in enumerate(key):
+ if (
+ isinstance(component, str)
+ and labels.levels[i]._supports_partial_string_indexing
+ ):
+ new_key.append(slice(component, component, None))
+ else:
+ new_key.append(component)
+ key = tuple(new_key)
+
+ return key
+
def _getitem_iterable(self, key, axis: int):
"""
Index current object with an an iterable collection of keys.
@@ -2019,6 +2051,8 @@ class _ScalarAccessIndexer(_NDFrameIndexerBase):
Access scalars quickly.
"""
+ _takeable: bool
+
def _convert_key(self, key, is_setter: bool = False):
raise AbstractMethodError(self)
diff --git a/pandas/core/internals/concat.py b/pandas/core/internals/concat.py
index 720e6799a3bf3..07af62a17e581 100644
--- a/pandas/core/internals/concat.py
+++ b/pandas/core/internals/concat.py
@@ -1,6 +1,7 @@
# TODO: Needs a better name; too many modules are already called "concat"
from collections import defaultdict
import copy
+from typing import Dict, List
import numpy as np
@@ -89,10 +90,10 @@ def _get_mgr_concatenation_plan(mgr, indexers):
"""
# Calculate post-reindex shape , save for item axis which will be separate
# for each block anyway.
- mgr_shape = list(mgr.shape)
+ mgr_shape_ = list(mgr.shape)
for ax, indexer in indexers.items():
- mgr_shape[ax] = len(indexer)
- mgr_shape = tuple(mgr_shape)
+ mgr_shape_[ax] = len(indexer)
+ mgr_shape = tuple(mgr_shape_)
if 0 in indexers:
ax0_indexer = indexers.pop(0)
@@ -115,9 +116,9 @@ def _get_mgr_concatenation_plan(mgr, indexers):
join_unit_indexers = indexers.copy()
- shape = list(mgr_shape)
- shape[0] = len(placements)
- shape = tuple(shape)
+ shape_ = list(mgr_shape)
+ shape_[0] = len(placements)
+ shape = tuple(shape_)
if blkno == -1:
unit = JoinUnit(None, shape)
@@ -342,8 +343,8 @@ def _get_empty_dtype_and_na(join_units):
else:
dtypes[i] = unit.dtype
- upcast_classes = defaultdict(list)
- null_upcast_classes = defaultdict(list)
+ upcast_classes: Dict[str, List] = defaultdict(list)
+ null_upcast_classes: Dict[str, List] = defaultdict(list)
for dtype, unit in zip(dtypes, join_units):
if dtype is None:
continue
diff --git a/pandas/core/missing.py b/pandas/core/missing.py
index c46aed999f45a..a256413019870 100644
--- a/pandas/core/missing.py
+++ b/pandas/core/missing.py
@@ -1,6 +1,7 @@
"""
Routines for filling missing data.
"""
+from typing import Set
import numpy as np
@@ -238,24 +239,24 @@ def interpolate_1d(
# set preserve_nans based on direction using _interp_limit
if limit_direction == "forward":
- preserve_nans = start_nans | set(_interp_limit(invalid, limit, 0))
+ preserve_nans_ = start_nans | set(_interp_limit(invalid, limit, 0))
elif limit_direction == "backward":
- preserve_nans = end_nans | set(_interp_limit(invalid, 0, limit))
+ preserve_nans_ = end_nans | set(_interp_limit(invalid, 0, limit))
else:
# both directions... just use _interp_limit
- preserve_nans = set(_interp_limit(invalid, limit, limit))
+ preserve_nans_ = set(_interp_limit(invalid, limit, limit))
# if limit_area is set, add either mid or outside indices
# to preserve_nans GH #16284
if limit_area == "inside":
# preserve NaNs on the outside
- preserve_nans |= start_nans | end_nans
+ preserve_nans_ |= start_nans | end_nans
elif limit_area == "outside":
# preserve NaNs on the inside
- preserve_nans |= mid_nans
+ preserve_nans_ |= mid_nans
# sort preserve_nans and covert to list
- preserve_nans = sorted(preserve_nans)
+ preserve_nans = sorted(preserve_nans_)
xvalues = getattr(xvalues, "values", xvalues)
yvalues = getattr(yvalues, "values", yvalues)
@@ -634,8 +635,8 @@ def _interp_limit(invalid, fw_limit, bw_limit):
# 1. operate on the reversed array
# 2. subtract the returned indices from N - 1
N = len(invalid)
- f_idx = set()
- b_idx = set()
+ f_idx: Set = set()
+ b_idx: Set = set()
def inner(invalid, limit):
limit = min(limit, N)
@@ -659,8 +660,8 @@ def inner(invalid, limit):
# just use forwards
return f_idx
else:
- b_idx = list(inner(invalid[::-1], bw_limit))
- b_idx = set(N - 1 - np.asarray(b_idx))
+ b_idx_ = list(inner(invalid[::-1], bw_limit))
+ b_idx = set(N - 1 - np.asarray(b_idx_))
if fw_limit == 0:
return b_idx
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 9494248a423a8..d7d7dd134b9c8 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -1,7 +1,7 @@
import functools
import itertools
import operator
-from typing import Any, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Optional, Tuple, TypeVar, Union, cast
import numpy as np
@@ -34,11 +34,18 @@
from pandas.core.construction import extract_array
+if TYPE_CHECKING:
+ from pandas.core.arrays import ExtensionArray # noqa: F401
+
bn = import_optional_dependency("bottleneck", raise_on_missing=False, on_version="warn")
_BOTTLENECK_INSTALLED = bn is not None
_USE_BOTTLENECK = False
+_FuncType = Callable[..., Any]
+_F = TypeVar("_F", bound=_FuncType)
+
+
def set_use_bottleneck(v: bool = True) -> None:
# set/unset to use bottleneck
global _USE_BOTTLENECK
@@ -57,7 +64,7 @@ def __init__(self, *dtypes):
def check(self, obj) -> bool:
return hasattr(obj, "dtype") and issubclass(obj.dtype.type, self.dtypes)
- def __call__(self, f):
+ def __call__(self, f: _F) -> _F:
@functools.wraps(f)
def _f(*args, **kwargs):
obj_iter = itertools.chain(args, kwargs.values())
@@ -78,7 +85,7 @@ def _f(*args, **kwargs):
raise TypeError(e) from e
raise
- return _f
+ return cast(_F, _f)
class bottleneck_switch:
@@ -86,7 +93,7 @@ def __init__(self, name=None, **kwargs):
self.name = name
self.kwargs = kwargs
- def __call__(self, alt):
+ def __call__(self, alt: _F) -> _F:
bn_name = self.name or alt.__name__
try:
@@ -130,7 +137,7 @@ def f(
return result
- return f
+ return cast(_F, f)
def _bn_ok_dtype(dtype: Dtype, name: str) -> bool:
@@ -520,13 +527,18 @@ def nansum(
@bottleneck_switch()
-def nanmean(values, axis=None, skipna=True, mask=None):
+def nanmean(
+ values: Union[np.ndarray, "ExtensionArray"],
+ axis: Optional[int] = None,
+ skipna: bool = True,
+ mask=None,
+) -> float:
"""
Compute the mean of the element along an axis ignoring NaNs
Parameters
----------
- values : ndarray
+ values : ndarray or ExtensionArray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
@@ -534,7 +546,7 @@ def nanmean(values, axis=None, skipna=True, mask=None):
Returns
-------
- result : float
+ float
Unless input is a float array, in which case use the same
precision as the input array.
@@ -558,7 +570,8 @@ def nanmean(values, axis=None, skipna=True, mask=None):
count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
- if axis is not None and getattr(the_sum, "ndim", False):
+ if not is_scalar(count):
+ count = cast(np.ndarray, count)
with np.errstate(all="ignore"):
# suppress division by zero warnings
the_mean = the_sum / count
@@ -883,7 +896,7 @@ def nanargmax(
axis: Optional[int] = None,
skipna: bool = True,
mask: Optional[np.ndarray] = None,
-) -> int:
+) -> Union[int, np.ndarray]:
"""
Parameters
----------
@@ -895,8 +908,10 @@ def nanargmax(
Returns
-------
- result : int
- The index of max value in specified axis or -1 in the NA case
+ int or ndarray of ints
+ The index of max value in specified axis or -1 in the NA case. By default,
+ the index is into the flattened array, otherwise along the specified axis.
+ This has the same shape as values.shape with the dimension along axis removed.
Examples
--------
@@ -1211,7 +1226,7 @@ def _get_counts(
Returns
-------
- count : scalar or array
+ scalar or array
"""
dtype = _get_dtype(dtype)
if axis is None:
diff --git a/pandas/core/ops/array_ops.py b/pandas/core/ops/array_ops.py
index 5dd7af454cbd1..4b0ae1705d7e9 100644
--- a/pandas/core/ops/array_ops.py
+++ b/pandas/core/ops/array_ops.py
@@ -415,6 +415,8 @@ def fill_bool(x, left=None):
filler = fill_int if is_self_int_dtype and is_other_int_dtype else fill_bool
res_values = na_logical_op(lvalues, rvalues, op)
+ # https://github.com/python/mypy/issues/5128
+ # error: Cannot call function of unknown type
res_values = filler(res_values) # type: ignore
return res_values
diff --git a/pandas/core/ops/docstrings.py b/pandas/core/ops/docstrings.py
index 7b03b4b449ea5..b9d608eec14f7 100644
--- a/pandas/core/ops/docstrings.py
+++ b/pandas/core/ops/docstrings.py
@@ -1,7 +1,20 @@
"""
Templating for ops docstrings
"""
-from typing import Dict, Optional
+from typing import TYPE_CHECKING, Dict, Optional
+
+if TYPE_CHECKING:
+ from mypy_extensions import TypedDict
+
+ class _OpDescriptionsBase(TypedDict):
+ op: str
+ desc: str
+
+ class _OpDescriptions(_OpDescriptionsBase, total=False):
+ reverse: Optional[str]
+ series_examples: Optional[str]
+ df_examples: Optional[str]
+ series_returns: Optional[str]
def _make_flex_doc(op_name, typ):
@@ -36,7 +49,7 @@ def _make_flex_doc(op_name, typ):
reverse=op_desc["reverse"],
series_returns=op_desc["series_returns"],
)
- if op_desc["series_examples"]:
+ if op_desc["series_examples"] is not None:
doc = doc_no_examples + op_desc["series_examples"]
else:
doc = doc_no_examples
@@ -267,7 +280,7 @@ def _make_flex_doc(op_name, typ):
_returns_tuple = """2-Tuple of Series\n The result of the operation."""
-_op_descriptions: Dict[str, Dict[str, Optional[str]]] = {
+_op_descriptions: Dict[str, "_OpDescriptions"] = {
# Arithmetic Operators
"add": {
"op": "+",
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 281586879a158..7096c1f2ff839 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1,7 +1,7 @@
import copy
from datetime import timedelta
from textwrap import dedent
-from typing import Dict, no_type_check
+from typing import TYPE_CHECKING, Any, Dict, Union, no_type_check
import numpy as np
@@ -31,6 +31,9 @@
from pandas.tseries.frequencies import to_offset
from pandas.tseries.offsets import DateOffset, Day, Nano, Tick
+if TYPE_CHECKING:
+ from pandas import Series # noqa: F401
+
_shared_docs_kwargs: Dict[str, str] = dict()
@@ -413,7 +416,7 @@ def _get_resampler_for_grouping(self, groupby, **kwargs):
"""
return self._resampler_for_grouping(self, groupby=groupby, **kwargs)
- def _wrap_result(self, result):
+ def _wrap_result(self, result: Union[Any, "Series"]) -> Union[Any, "Series"]:
"""
Potentially wrap any results.
"""
@@ -860,7 +863,7 @@ def var(self, ddof=1, *args, **kwargs):
def size(self):
result = self._downsample("size")
if not len(self.ax):
- from pandas import Series
+ from pandas import Series # noqa: F811
if self._selected_obj.ndim == 1:
name = self._selected_obj.name
@@ -1526,14 +1529,15 @@ def _get_time_period_bins(self, ax):
return binner, bins, labels
- def _get_period_bins(self, ax):
+ def _get_period_bins(self, ax: PeriodIndex):
if not isinstance(ax, PeriodIndex):
raise TypeError(
"axis must be a PeriodIndex, but got "
f"an instance of {type(ax).__name__}"
)
- memb = ax.asfreq(self.freq, how=self.convention)
+ # error: "PeriodIndex" has no attribute "asfreq"
+ memb = ax.asfreq(self.freq, how=self.convention) # type: ignore
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
nat_count = 0
@@ -1762,7 +1766,7 @@ def _adjust_dates_anchored(first, last, offset, closed="right", base=0):
return fresult, lresult
-def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None):
+def asfreq(obj, freq, method=None, how=None, normalize: bool = False, fill_value=None):
"""
Utility frequency conversion method for Series/DataFrame.
"""
@@ -1774,7 +1778,8 @@ def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None):
how = "E"
new_obj = obj.copy()
- new_obj.index = obj.index.asfreq(freq, how=how)
+ # error: "PeriodIndex" has no attribute "asfreq"
+ new_obj.index = obj.index.asfreq(freq, how=how) # type: ignore
elif len(obj.index) == 0:
new_obj = obj.copy()
diff --git a/pandas/core/reshape/melt.py b/pandas/core/reshape/melt.py
index c3e170b0e39c4..760fe565204f2 100644
--- a/pandas/core/reshape/melt.py
+++ b/pandas/core/reshape/melt.py
@@ -1,5 +1,5 @@
import re
-from typing import List
+from typing import List, TypeVar
import numpy as np
@@ -17,6 +17,8 @@
from pandas.core.reshape.concat import concat
from pandas.core.tools.numeric import to_numeric
+_DFT = TypeVar("_DFT", bound="DataFrame")
+
@Appender(
_shared_docs["melt"]
@@ -189,8 +191,8 @@ def lreshape(data: DataFrame, groups, dropna: bool = True, label=None) -> DataFr
def wide_to_long(
- df: DataFrame, stubnames, i, j, sep: str = "", suffix: str = r"\d+"
-) -> DataFrame:
+ df: _DFT, stubnames, i, j, sep: str = "", suffix: str = r"\d+"
+) -> _DFT:
r"""
Wide panel to long format. Less flexible but more user-friendly than melt.
@@ -419,7 +421,7 @@ def get_var_names(df, stub: str, sep: str, suffix: str) -> List[str]:
pattern = re.compile(regex)
return [col for col in df.columns if pattern.match(col)]
- def melt_stub(df, stub: str, i, j, value_vars, sep: str):
+ def melt_stub(df: _DFT, stub: str, i, j, value_vars, sep: str) -> _DFT:
newdf = melt(
df,
id_vars=i,
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index 607a1b75dcfcd..77b27eeeefd8d 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -6,7 +6,7 @@
import datetime
from functools import partial
import string
-from typing import TYPE_CHECKING, Optional, Tuple, Union
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import warnings
import numpy as np
@@ -966,7 +966,7 @@ def _get_merge_keys(self):
"""
left_keys = []
right_keys = []
- join_names = []
+ join_names: List = []
right_drop = []
left_drop = []
diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py
index 17473ac26dfd6..9ed42f7136cb7 100644
--- a/pandas/core/reshape/pivot.py
+++ b/pandas/core/reshape/pivot.py
@@ -1,7 +1,8 @@
-from typing import TYPE_CHECKING, Callable, Dict, List, Tuple, Union
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
+from pandas._typing import Axis
from pandas.util._decorators import Appender, Substitution
from pandas.core.dtypes.cast import maybe_downcast_to_dtype
@@ -467,13 +468,13 @@ def crosstab(
index,
columns,
values=None,
- rownames=None,
- colnames=None,
- aggfunc=None,
- margins=False,
+ rownames: Optional[Sequence] = None,
+ colnames: Optional[Sequence] = None,
+ aggfunc: Optional[Callable] = None,
+ margins: bool = False,
margins_name: str = "All",
dropna: bool = True,
- normalize=False,
+ normalize: Union[bool, Axis] = False,
) -> "DataFrame":
"""
Compute a simple cross tabulation of two (or more) factors. By default
@@ -624,9 +625,10 @@ def crosstab(
return table
-def _normalize(table, normalize, margins: bool, margins_name="All"):
+def _normalize(table, normalize: Union[bool, Axis], margins: bool, margins_name="All"):
if not isinstance(normalize, (bool, str)):
+ # TODO: can NDFrame._get_axis_name be used here instead?
axis_subs = {0: "index", 1: "columns"}
try:
normalize = axis_subs[normalize]
@@ -705,7 +707,7 @@ def _normalize(table, normalize, margins: bool, margins_name="All"):
return table
-def _get_names(arrs, names, prefix: str = "row"):
+def _get_names(arrs, names, prefix: str = "row") -> List:
if names is None:
names = []
for i, arr in enumerate(arrs):
diff --git a/pandas/core/reshape/tile.py b/pandas/core/reshape/tile.py
index 66c2f5c9b927f..75fb1c759a5be 100644
--- a/pandas/core/reshape/tile.py
+++ b/pandas/core/reshape/tile.py
@@ -1,6 +1,8 @@
"""
Quantilization functions and related stuff
"""
+from typing import Callable
+
import numpy as np
from pandas._libs import Timedelta, Timestamp
@@ -517,6 +519,7 @@ def _format_labels(
""" based on the dtype, return our labels """
closed = "right" if right else "left"
+ formatter: Callable
if is_datetime64tz_dtype(dtype):
formatter = lambda x: Timestamp(x, tz=dtype.tz)
adjust = lambda x: x - Timedelta("1ns")
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 66caa4623f9ad..6ba1c0d37b68f 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -14,6 +14,7 @@
Optional,
Tuple,
Type,
+ Union,
)
import warnings
@@ -1354,7 +1355,7 @@ def to_string(
float_format=None,
header=True,
index=True,
- length=False,
+ length: Union[bool, str] = False,
dtype=False,
name=False,
max_rows=None,
diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py
index 5496eca46b992..3f728e9263a05 100644
--- a/pandas/core/sorting.py
+++ b/pandas/core/sorting.py
@@ -242,7 +242,7 @@ def lexsort_indexer(keys, orders=None, na_position: str = "last"):
def nargsort(
items, kind: str = "quicksort", ascending: bool = True, na_position: str = "last"
-):
+) -> np.ndarray:
"""
Intended to be a drop-in replacement for np.argsort which handles NaNs.
diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index 52d9a81489db4..230e45650e61e 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -2,7 +2,19 @@
from functools import wraps
import re
import textwrap
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Pattern, Type, Union
+from typing import (
+ TYPE_CHECKING,
+ Any,
+ Callable,
+ Dict,
+ List,
+ Optional,
+ Pattern,
+ Set,
+ Tuple,
+ Type,
+ Union,
+)
import warnings
import numpy as np
@@ -41,6 +53,9 @@
if TYPE_CHECKING:
from pandas.arrays import StringArray
+if TYPE_CHECKING:
+ from pandas import Series # noqa: F401
+
_cpython_optimized_encoders = (
"utf-8",
"utf8",
@@ -1174,7 +1189,7 @@ def str_extractall(arr, pat, flags=0):
return result
-def str_get_dummies(arr, sep="|"):
+def str_get_dummies(arr, sep: str = "|") -> Tuple:
"""
Split each string in the Series by sep and return a DataFrame
of dummy/indicator variables.
@@ -1214,10 +1229,10 @@ def str_get_dummies(arr, sep="|"):
except TypeError:
arr = sep + arr.astype(str) + sep
- tags = set()
+ tags_: Set[str] = set()
for ts in arr.str.split(sep):
- tags.update(ts)
- tags = sorted(tags - {""})
+ tags_.update(ts)
+ tags = sorted(tags_ - {""})
dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
@@ -2180,7 +2195,7 @@ def _wrap_result(
returns_string=True,
):
- from pandas import Index, Series, MultiIndex
+ from pandas import Index, Series, MultiIndex # noqa: F811
# for category, we do the stuff on the categories, so blow it up
# to the full series again
@@ -2201,6 +2216,7 @@ def _wrap_result(
# case we'll want to return the same dtype as the input.
# Or we can be wrapping a numeric output, in which case we don't want
# to return a StringArray.
+ dtype: Optional[str]
if self._is_string and returns_string:
dtype = "string"
else:
@@ -2270,7 +2286,7 @@ def cons_row(x):
result = cons(result, name=name, index=index, dtype=dtype)
return result
- def _get_series_list(self, others):
+ def _get_series_list(self, others) -> List["Series"]:
"""
Auxiliary function for :meth:`str.cat`. Turn potentially mixed input
into a list of Series (elements without an index must match the length
@@ -2286,7 +2302,7 @@ def _get_series_list(self, others):
list of Series
Others transformed into list of Series.
"""
- from pandas import Series, DataFrame
+ from pandas import Series, DataFrame # noqa: F811
# self._orig is either Series or Index
idx = self._orig if isinstance(self._orig, ABCIndexClass) else self._orig.index
@@ -2313,7 +2329,7 @@ def _get_series_list(self, others):
or (isinstance(x, np.ndarray) and x.ndim == 1)
for x in others
):
- los = []
+ los: List[Series] = []
while others: # iterate through list and append each element
los = los + self._get_series_list(others.pop(0))
return los
@@ -2462,7 +2478,7 @@ def cat(self, others=None, sep=None, na_rep=None, join="left"):
For more examples, see :ref:`here <text.concatenate>`.
"""
- from pandas import Index, Series, concat
+ from pandas import Index, Series, concat # noqa: F811
if isinstance(others, str):
raise ValueError("Did you mean to supply a `sep` keyword?")
diff --git a/pandas/core/util/hashing.py b/pandas/core/util/hashing.py
index d9c8611c94cdb..77afde5b52ab3 100644
--- a/pandas/core/util/hashing.py
+++ b/pandas/core/util/hashing.py
@@ -2,7 +2,7 @@
data hash pandas / numpy objects
"""
import itertools
-from typing import Optional
+from typing import Iterator, Optional
import numpy as np
@@ -116,7 +116,7 @@ def hash_pandas_object(
h = Series(h, index=obj.index, dtype="uint64", copy=False)
elif isinstance(obj, ABCDataFrame):
- hashes = (hash_array(series.values) for _, series in obj.items())
+ hashes: Iterator = (hash_array(series.values) for _, series in obj.items())
num_items = len(obj.columns)
if index:
index_hash_generator = (
@@ -126,7 +126,7 @@ def hash_pandas_object(
encoding=encoding,
hash_key=hash_key,
categorize=categorize,
- ).values # noqa
+ ).values
for _ in [None]
)
num_items += 1
diff --git a/pandas/core/window/common.py b/pandas/core/window/common.py
index 05f19de19f9f7..f91bb655227e8 100644
--- a/pandas/core/window/common.py
+++ b/pandas/core/window/common.py
@@ -48,6 +48,8 @@ class WindowGroupByMixin(GroupByMixin):
Provide the groupby facilities.
"""
+ _shallow_copy: Callable
+
def __init__(self, obj, *args, **kwargs):
kwargs.pop("parent", None)
groupby = kwargs.pop("groupby", None)
@@ -56,7 +58,9 @@ def __init__(self, obj, *args, **kwargs):
self._groupby = groupby
self._groupby.mutated = True
self._groupby.grouper.mutated = True
- super().__init__(obj, *args, **kwargs)
+ # https://github.com/python/mypy/issues/5887
+ # error: Too many arguments for "__init__" of "object"
+ super().__init__(obj, *args, **kwargs) # type: ignore
count = _dispatch("count")
corr = _dispatch("corr", other=None, pairwise=None)
diff --git a/pandas/core/window/ewm.py b/pandas/core/window/ewm.py
index 2759280dc1d1c..94259d6bf05c6 100644
--- a/pandas/core/window/ewm.py
+++ b/pandas/core/window/ewm.py
@@ -1,4 +1,5 @@
from textwrap import dedent
+from typing import Any, List
import numpy as np
@@ -212,7 +213,7 @@ def _apply(self, func, **kwargs):
block_list = list(blocks)
results = []
- exclude = []
+ exclude: List[Any] = []
for i, b in enumerate(blocks):
try:
values = self._prep_values(b.values)
diff --git a/pandas/core/window/expanding.py b/pandas/core/window/expanding.py
index 146c139806bca..f0176925f06de 100644
--- a/pandas/core/window/expanding.py
+++ b/pandas/core/window/expanding.py
@@ -129,8 +129,8 @@ def aggregate(self, func, *args, **kwargs):
@Substitution(name="expanding")
@Appender(_shared_docs["count"])
- def count(self, **kwargs):
- return super().count(**kwargs)
+ def count(self):
+ return super().count()
@Substitution(name="expanding")
@Appender(_shared_docs["apply"])
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index 3fdf81c4bb570..c78bb5dbd1ac4 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -74,7 +74,7 @@ def __init__(
obj,
window=None,
min_periods: Optional[int] = None,
- center: Optional[bool] = False,
+ center: bool = False,
win_type: Optional[str] = None,
axis: Axis = 0,
on: Optional[Union[str, Index]] = None,
diff --git a/pandas/io/common.py b/pandas/io/common.py
index 0fce8f5382686..51be57879dcba 100644
--- a/pandas/io/common.py
+++ b/pandas/io/common.py
@@ -128,6 +128,7 @@ def stringify_path(
"""
if hasattr(filepath_or_buffer, "__fspath__"):
# https://github.com/python/mypy/issues/1424
+ # error: Item "str" of "Union[str, Path, IO[str]]" has no attribute "__fspath__"
return filepath_or_buffer.__fspath__() # type: ignore
elif isinstance(filepath_or_buffer, pathlib.Path):
return str(filepath_or_buffer)
@@ -474,6 +475,18 @@ def get_handle(
return f, handles
+# error: Definition of "__enter__" in base class "ZipFile" is incompatible with
+# definition in base class "BytesIO" [misc]
+# error: Definition of "__enter__" in base class "ZipFile" is incompatible with
+# definition in base class "BinaryIO" [misc]
+# error: Definition of "__enter__" in base class "ZipFile" is incompatible with
+# definition in base class "IO" [misc]
+# error: Definition of "read" in base class "ZipFile" is incompatible with
+# definition in base class "BytesIO" [misc]
+# error: Definition of "read" in base class "ZipFile" is incompatible with
+# definition in base class "IO" [misc]
+# error: Definition of "__exit__" in base class "ZipFile" is incompatible with
+# definition in base class "BytesIO" [misc]
class _BytesZipFile(zipfile.ZipFile, BytesIO): # type: ignore
"""
Wrapper for standard library class ZipFile and allow the returned file-like
@@ -497,7 +510,11 @@ def __init__(
super().__init__(file, mode, zipfile.ZIP_DEFLATED, **kwargs)
def write(self, data):
+ # TODO: fixme, self.filename could be None
+ # error: Argument 1 to "writestr" of "ZipFile" has incompatible type
+ # "Optional[str]"; expected "Union[str, ZipInfo]" [arg-type]
archive_name = self.filename
+ assert archive_name is not None
if self.archive_name is not None:
archive_name = self.archive_name
super().writestr(archive_name, data)
diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py
index d1139f640cef4..f7e15450e20a3 100644
--- a/pandas/io/excel/_base.py
+++ b/pandas/io/excel/_base.py
@@ -3,6 +3,7 @@
from io import BytesIO
import os
from textwrap import fill
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, Union
from pandas._config import config
@@ -30,6 +31,9 @@
)
from pandas.io.parsers import TextParser
+if TYPE_CHECKING:
+ from pandas import Series # noqa: F401
+
_read_excel_doc = (
"""
Read an Excel file into a pandas DataFrame.
@@ -428,7 +432,7 @@ def parse(
# handle same-type duplicates.
sheets = list(dict.fromkeys(sheets).keys())
- output = {}
+ output: Dict[Union[str, int], Union["Series", DataFrame]] = {}
for asheetname in sheets:
if verbose:
@@ -643,7 +647,7 @@ def __new__(cls, path, engine=None, **kwargs):
@property
@abc.abstractmethod
- def supported_extensions(self):
+ def supported_extensions(self) -> Tuple[str, ...]:
"""Extensions that writer engine supports."""
pass
@@ -655,7 +659,12 @@ def engine(self):
@abc.abstractmethod
def write_cells(
- self, cells, sheet_name=None, startrow=0, startcol=0, freeze_panes=None
+ self,
+ cells,
+ sheet_name: Optional[str] = None,
+ startrow: int = 0,
+ startcol: int = 0,
+ freeze_panes: Optional[Tuple[int, int]] = None,
):
"""
Write given formatted cells into Excel an excel sheet
@@ -698,7 +707,7 @@ def __init__(
self.check_extension(ext)
self.path = path
- self.sheets = {}
+ self.sheets: Dict[str, Any] = {}
self.cur_sheet = None
if date_format is None:
@@ -713,9 +722,10 @@ def __init__(
self.mode = mode
def __fspath__(self):
+ assert self.path is not None
return stringify_path(self.path)
- def _get_sheet_name(self, sheet_name):
+ def _get_sheet_name(self, sheet_name: Optional[str]):
if sheet_name is None:
sheet_name = self.cur_sheet
if sheet_name is None: # pragma: no cover
@@ -802,7 +812,9 @@ class ExcelFile:
from pandas.io.excel._xlrd import _XlrdReader
from pandas.io.excel._pyxlsb import _PyxlsbReader
- _engines = {
+ _engines: Dict[
+ str, Type[Union[_XlrdReader, _OpenpyxlReader, _ODFReader, _PyxlsbReader]]
+ ] = {
"xlrd": _XlrdReader,
"openpyxl": _OpenpyxlReader,
"odf": _ODFReader,
diff --git a/pandas/io/excel/_util.py b/pandas/io/excel/_util.py
index 7c8e1abb497bc..9c31bf408da45 100644
--- a/pandas/io/excel/_util.py
+++ b/pandas/io/excel/_util.py
@@ -1,3 +1,5 @@
+from typing import List, Optional, Tuple
+
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import is_integer, is_list_like
@@ -83,7 +85,7 @@ def _excel2num(x):
return index - 1
-def _range2cols(areas):
+def _range2cols(areas: str) -> List[int]:
"""
Convert comma separated list of column names and ranges to indices.
@@ -104,12 +106,12 @@ def _range2cols(areas):
>>> _range2cols('A,C,Z:AB')
[0, 2, 25, 26, 27]
"""
- cols = []
+ cols: List[int] = []
for rng in areas.split(","):
if ":" in rng:
- rng = rng.split(":")
- cols.extend(range(_excel2num(rng[0]), _excel2num(rng[1]) + 1))
+ rng_ = rng.split(":")
+ cols.extend(range(_excel2num(rng_[0]), _excel2num(rng_[1]) + 1))
else:
cols.append(_excel2num(rng))
@@ -145,7 +147,7 @@ def _maybe_convert_usecols(usecols):
return usecols
-def _validate_freeze_panes(freeze_panes):
+def _validate_freeze_panes(freeze_panes: Optional[Tuple[int, int]]) -> bool:
if freeze_panes is not None:
if len(freeze_panes) == 2 and all(
isinstance(item, int) for item in freeze_panes
diff --git a/pandas/io/excel/_xlsxwriter.py b/pandas/io/excel/_xlsxwriter.py
index 85a1bb031f457..6156c27e16e62 100644
--- a/pandas/io/excel/_xlsxwriter.py
+++ b/pandas/io/excel/_xlsxwriter.py
@@ -1,3 +1,5 @@
+from typing import Dict, List
+
import pandas._libs.json as json
from pandas.io.excel._base import ExcelWriter
@@ -8,7 +10,7 @@ class _XlsxStyler:
# Map from openpyxl-oriented styles to flatter xlsxwriter representation
# Ordering necessary for both determinism and because some are keyed by
# prefixes of others.
- STYLE_MAPPING = {
+ STYLE_MAPPING: Dict[str, List] = {
"font": [
(("name",), "font_name"),
(("sz",), "font_size"),
@@ -190,11 +192,13 @@ def save(self):
"""
Save workbook to disk.
"""
+ assert self.book is not None
return self.book.close()
def write_cells(
self, cells, sheet_name=None, startrow=0, startcol=0, freeze_panes=None
):
+ assert self.book is not None
# Write the frame cells using xlsxwriter.
sheet_name = self._get_sheet_name(sheet_name)
diff --git a/pandas/io/excel/_xlwt.py b/pandas/io/excel/_xlwt.py
index 78efe77e9fe2d..090a52c3026bc 100644
--- a/pandas/io/excel/_xlwt.py
+++ b/pandas/io/excel/_xlwt.py
@@ -1,3 +1,5 @@
+from typing import Dict, Optional, Tuple
+
import pandas._libs.json as json
from pandas.io.excel._base import ExcelWriter
@@ -32,7 +34,12 @@ def save(self):
return self.book.save(self.path)
def write_cells(
- self, cells, sheet_name=None, startrow=0, startcol=0, freeze_panes=None
+ self,
+ cells,
+ sheet_name: Optional[str] = None,
+ startrow: int = 0,
+ startcol: int = 0,
+ freeze_panes: Optional[Tuple[int, int]] = None,
):
# Write the frame cells using xlwt.
@@ -41,15 +48,18 @@ def write_cells(
if sheet_name in self.sheets:
wks = self.sheets[sheet_name]
else:
+ assert self.book is not None
wks = self.book.add_sheet(sheet_name)
self.sheets[sheet_name] = wks
if _validate_freeze_panes(freeze_panes):
+ assert freeze_panes is not None
wks.set_panes_frozen(True)
- wks.set_horz_split_pos(freeze_panes[0])
- wks.set_vert_split_pos(freeze_panes[1])
+ row, column = freeze_panes
+ wks.set_horz_split_pos(row)
+ wks.set_vert_split_pos(column)
- style_dict = {}
+ style_dict: Dict = {}
for cell in cells:
val, fmt = self._value_with_fmt(cell.val)
diff --git a/pandas/io/formats/console.py b/pandas/io/formats/console.py
index bed29e1fd4792..fe6f5d12e4e1c 100644
--- a/pandas/io/formats/console.py
+++ b/pandas/io/formats/console.py
@@ -69,7 +69,8 @@ def check_main():
return not hasattr(main, "__file__") or get_option("mode.sim_interactive")
try:
- return __IPYTHON__ or check_main() # noqa
+ # error: Name '__IPYTHON__' is not defined
+ return __IPYTHON__ or check_main() # type: ignore # noqa
except NameError:
return check_main()
@@ -83,7 +84,8 @@ def in_ipython_frontend():
bool
"""
try:
- ip = get_ipython() # noqa
+ # error: Name 'get_ipython' is not defined
+ ip = get_ipython() # type: ignore # noqa
return "zmq" in str(type(ip)).lower()
except NameError:
pass
diff --git a/pandas/io/formats/css.py b/pandas/io/formats/css.py
index b40d2a57b8106..27e4398cf5b73 100644
--- a/pandas/io/formats/css.py
+++ b/pandas/io/formats/css.py
@@ -3,6 +3,7 @@
"""
import re
+from typing import Optional
import warnings
@@ -73,6 +74,8 @@ def __call__(self, declarations_str, inherited=None):
('font-size', '24pt'),
('font-weight', 'bold')]
"""
+ font_size: Optional[float]
+
props = dict(self.atomize(self.parse(declarations_str)))
if inherited is None:
inherited = {}
@@ -179,7 +182,10 @@ def _error():
return self.size_to_pt("1!!default", conversions=conversions)
try:
- val, unit = re.match(r"^(\S*?)([a-zA-Z%!].*)", in_val).groups()
+ # error: Item "None" of "Optional[Match[Any]]" has no attribute "groups"
+ val, unit = re.match( # type: ignore
+ r"^(\S*?)([a-zA-Z%!].*)", in_val
+ ).groups()
except AttributeError:
return _error()
if val == "":
diff --git a/pandas/io/formats/excel.py b/pandas/io/formats/excel.py
index aac1df5dcd396..978852d2a7f66 100644
--- a/pandas/io/formats/excel.py
+++ b/pandas/io/formats/excel.py
@@ -5,7 +5,7 @@
from functools import reduce
import itertools
import re
-from typing import Callable, Dict, Optional, Sequence, Union
+from typing import Callable, Dict, Iterable, Optional, Sequence, Union
import warnings
import numpy as np
@@ -531,7 +531,7 @@ def _format_header(self):
else:
gen = self._format_header_regular()
- gen2 = ()
+ gen2: Iterable[ExcelCell] = ()
if self.df.index.names:
row = [x if x is not None else "" for x in self.df.index.names] + [
""
@@ -727,7 +727,11 @@ def write(
if isinstance(writer, ExcelWriter):
need_save = False
else:
- writer = ExcelWriter(stringify_path(writer), engine=engine)
+ # TODO: check ExcelWriter implementation for PEP 3119 compliance
+ # error: Cannot instantiate abstract class 'ExcelWriter' with abstract
+ # attributes 'engine', 'save', 'supported_extensions' and 'write_cells'
+ # [abstract]
+ writer = ExcelWriter(stringify_path(writer), engine=engine) # type: ignore
need_save = True
formatted_cells = self.get_formatted_cells()
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 59542a8da535e..acc580a1e48d4 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -1199,11 +1199,10 @@ def _format_strings(self) -> List[str]:
else:
float_format = self.float_format
- formatter = (
- self.formatter
- if self.formatter is not None
- else (lambda x: pprint_thing(x, escape_chars=("\t", "\r", "\n")))
- )
+ if self.formatter is None:
+ formatter = lambda x: pprint_thing(x, escape_chars=("\t", "\r", "\n"))
+ else:
+ formatter = self.formatter
def _format(x):
if self.na_rep is not None and is_scalar(x) and isna(x):
@@ -1287,7 +1286,15 @@ def _value_formatter(
if float_format:
def base_formatter(v):
- return float_format(value=v) if notna(v) else self.na_rep
+ # error: "str" not callable
+ # error: Unexpected keyword argument "value" for "__call__" of
+ # "EngFormatter"
+ # error: "None" not callable
+ return (
+ float_format(value=v) # type: ignore
+ if notna(v)
+ else self.na_rep
+ )
else:
@@ -1716,7 +1723,9 @@ def _make_fixed_width(
def just(x):
if conf_max is not None:
- if (conf_max > 3) & (adj.len(x) > max_len):
+ # https://github.com/python/mypy/issues/2608
+ # error: Item "None" of "Optional[TextAdjustment]" has no attribute "len"
+ if (conf_max > 3) & (adj.len(x) > max_len): # type: ignore
x = x[: max_len - 3] + "..."
return x
diff --git a/pandas/io/formats/html.py b/pandas/io/formats/html.py
index 1be0f977f9b20..efa899e0ef457 100644
--- a/pandas/io/formats/html.py
+++ b/pandas/io/formats/html.py
@@ -85,6 +85,7 @@ def _get_columns_formatted_values(self) -> Iterable:
return self.columns
# https://github.com/python/mypy/issues/1237
+ # error: Signature of "is_truncated" incompatible with supertype "TableFormatter"
@property
def is_truncated(self) -> bool: # type: ignore
return self.fmt.is_truncated
diff --git a/pandas/io/formats/printing.py b/pandas/io/formats/printing.py
index 36e774305b577..6b16a44ff4dea 100644
--- a/pandas/io/formats/printing.py
+++ b/pandas/io/formats/printing.py
@@ -4,6 +4,7 @@
import sys
from typing import (
+ TYPE_CHECKING,
Any,
Callable,
Dict,
@@ -21,6 +22,9 @@
from pandas.core.dtypes.inference import is_sequence
+if TYPE_CHECKING:
+ from pandas import MultiIndex # noqa: F401
+
EscapeChars = Union[Mapping[str, str], Iterable[str]]
_KT = TypeVar("_KT")
_VT = TypeVar("_VT")
@@ -495,11 +499,11 @@ def _justify(
# error: Incompatible return value type (got "Tuple[List[Sequence[str]],
# List[Sequence[str]]]", expected "Tuple[List[Tuple[str, ...]],
# List[Tuple[str, ...]]]")
- return head, tail # type: ignore
+ return head, tail # type: ignore # noqa: F723
def format_object_attrs(
- obj: Sequence, include_dtype: bool = True
+ obj: Union[Sequence, "MultiIndex"], include_dtype: bool = True
) -> List[Tuple[str, Union[str, int]]]:
"""
Return a list of tuples of the (attr, formatted_value)
@@ -525,7 +529,9 @@ def format_object_attrs(
# error: "Sequence[Any]" has no attribute "name"
attrs.append(("name", default_pprint(obj.name))) # type: ignore
# error: "Sequence[Any]" has no attribute "names"
- elif getattr(obj, "names", None) is not None and any(obj.names): # type: ignore
+ elif getattr(obj, "names", None) is not None and any(
+ obj.names # type: ignore
+ ):
# error: "Sequence[Any]" has no attribute "names"
attrs.append(("names", default_pprint(obj.names))) # type: ignore
max_seq_items = get_option("display.max_seq_items") or len(obj)
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index fecdf3b758f0f..8b0b79ffcb6cb 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -36,7 +36,7 @@
import pandas.core.common as com
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
-from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice
+from pandas.core.indexing import _IndexSlice, _maybe_numeric_slice, _non_reducing_slice
jinja2 = import_optional_dependency("jinja2", extra="DataFrame.style requires jinja2.")
@@ -59,6 +59,11 @@ def _mpl(func: Callable):
raise ImportError(no_mpl_message.format(func.__name__))
+_ApplyArgs = Tuple[
+ Callable[[FrameOrSeries], FrameOrSeries], Axis, Optional[_IndexSlice]
+]
+
+
class Styler:
"""
Helps style a DataFrame or Series according to the data with HTML and CSS.
@@ -146,7 +151,7 @@ def __init__(
na_rep: Optional[str] = None,
):
self.ctx: DefaultDict[Tuple[int, int], List[str]] = defaultdict(list)
- self._todo: List[Tuple[Callable, Tuple, Dict]] = []
+ self._todo: List[Tuple[Callable, _ApplyArgs, Dict]] = []
if not isinstance(data, (pd.Series, pd.DataFrame)):
raise TypeError("``data`` must be a Series or DataFrame")
@@ -167,7 +172,7 @@ def __init__(
self.precision = precision
self.table_attributes = table_attributes
self.hidden_index = False
- self.hidden_columns: Sequence[int] = []
+ self.hidden_columns: List[int] = []
self.cell_ids = cell_ids
self.na_rep = na_rep
@@ -262,7 +267,7 @@ def format_attr(pair):
idx_lengths = _get_level_lengths(self.index)
col_lengths = _get_level_lengths(self.columns, hidden_columns)
- cell_context = dict()
+ cell_context: Dict[str, Dict] = dict()
n_rlvls = self.data.index.nlevels
n_clvls = self.data.columns.nlevels
@@ -1410,7 +1415,7 @@ def _highlight_extrema(
)
@classmethod
- def from_custom_template(cls, searchpath, name):
+ def from_custom_template(cls, searchpath, name: str):
"""
Factory function for creating a subclass of ``Styler``.
@@ -1430,7 +1435,10 @@ def from_custom_template(cls, searchpath, name):
"""
loader = jinja2.ChoiceLoader([jinja2.FileSystemLoader(searchpath), cls.loader])
- class MyStyler(cls):
+ # https://github.com/python/mypy/issues/2477
+ # error: Variable "cls" is not valid as a type [valid-type]
+ # error: Invalid base class "cls"
+ class MyStyler(cls): # type: ignore
env = jinja2.Environment(loader=loader)
template = env.get_template(name)
diff --git a/pandas/io/html.py b/pandas/io/html.py
index c4ffe332e3020..c13e0b18c152b 100644
--- a/pandas/io/html.py
+++ b/pandas/io/html.py
@@ -8,6 +8,7 @@
import numbers
import os
import re
+from typing import Iterable, List, Set, Tuple, TypeVar
from pandas.compat._optional import import_optional_dependency
from pandas.errors import AbstractMethodError, EmptyDataError
@@ -26,6 +27,8 @@
_HAS_LXML = False
_HAS_HTML5LIB = False
+_T = TypeVar("_T")
+
def _importers():
# import things we need
@@ -436,7 +439,7 @@ def _expand_colspan_rowspan(self, rows):
to subsequent cells.
"""
all_texts = [] # list of rows, each a list of str
- remainder = [] # list of (index, text, nrows)
+ remainder: List[Tuple[int, str, int]] = [] # list of (index, text, nrows)
for tr in rows:
texts = [] # the output for this row
@@ -488,7 +491,9 @@ def _expand_colspan_rowspan(self, rows):
return all_texts
- def _handle_hidden_tables(self, tbl_list, attr_name):
+ def _handle_hidden_tables(
+ self, tbl_list: Iterable[_T], attr_name: str
+ ) -> Iterable[_T]:
"""
Return list of tables, potentially removing hidden elements
@@ -544,7 +549,7 @@ def _parse_tables(self, doc, match, attrs):
raise ValueError("No tables found")
result = []
- unique_tables = set()
+ unique_tables: Set[Iterable] = set()
tables = self._handle_hidden_tables(tables, "attrs")
for table in tables:
@@ -737,7 +742,7 @@ def _build_doc(self):
return r
def _parse_thead_tr(self, table):
- rows = []
+ rows: List = []
for thead in table.xpath(".//thead"):
rows.extend(thead.xpath("./tr"))
@@ -911,7 +916,9 @@ def _parse(flavor, io, match, attrs, encoding, displayed_only, **kwargs):
else:
break
else:
- raise retained
+ # https://github.com/pandas-dev/pandas/commit/a38a004629f8a2d4da9392133e3e1162261b1e3f#r35680919
+ # error: Exception must be derived from BaseException [misc]
+ raise retained # type: ignore
ret = []
for table in tables:
diff --git a/pandas/io/json/_json.py b/pandas/io/json/_json.py
index 20724a498b397..c01b017754ad3 100644
--- a/pandas/io/json/_json.py
+++ b/pandas/io/json/_json.py
@@ -115,6 +115,7 @@ def __init__(
self.obj = obj
if orient is None:
+ # error: "Writer" has no attribute "_default_orient"
orient = self._default_orient # type: ignore
self.orient = orient
diff --git a/pandas/io/json/_table_schema.py b/pandas/io/json/_table_schema.py
index 6061af72901a5..01aa60dc2c2cd 100644
--- a/pandas/io/json/_table_schema.py
+++ b/pandas/io/json/_table_schema.py
@@ -3,10 +3,11 @@
https://specs.frictionlessdata.io/json-table-schema/
"""
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast
import warnings
import pandas._libs.json as json
-from pandas._typing import DtypeObj
+from pandas._typing import DtypeObj, JSONSerializable
from pandas.core.dtypes.common import (
is_bool_dtype,
@@ -20,10 +21,15 @@
is_timedelta64_dtype,
)
from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.generic import ABCMultiIndex
from pandas import DataFrame
import pandas.core.common as com
+if TYPE_CHECKING:
+ from pandas import Series # noqa: F401
+ from pandas.core.indexes.multi import MultiIndex # noqa: F401
+
loads = json.loads
@@ -103,7 +109,10 @@ def convert_pandas_type_to_json_field(arr):
name = "values"
else:
name = arr.name
- field = {"name": name, "type": as_json_table_type(dtype)}
+ field: Dict[str, JSONSerializable] = {
+ "name": name,
+ "type": as_json_table_type(dtype),
+ }
if is_categorical_dtype(arr):
if hasattr(arr, "categories"):
@@ -188,7 +197,12 @@ def convert_json_field_to_pandas_type(field):
raise ValueError(f"Unsupported or invalid field type: {typ}")
-def build_table_schema(data, index=True, primary_key=None, version=True):
+def build_table_schema(
+ data: Union["Series", DataFrame],
+ index: bool = True,
+ primary_key: Optional[bool] = None,
+ version: bool = True,
+) -> Dict[str, Any]:
"""
Create a Table schema from ``data``.
@@ -237,12 +251,13 @@ def build_table_schema(data, index=True, primary_key=None, version=True):
if index is True:
data = set_default_names(data)
- schema = {}
+ schema: Dict[str, Any] = {}
fields = []
if index:
- if data.index.nlevels > 1:
- for level, name in zip(data.index.levels, data.index.names):
+ if isinstance(data.index, ABCMultiIndex):
+ _index = cast("MultiIndex", data.index)
+ for level, name in zip(_index.levels, _index.names):
new_field = convert_pandas_type_to_json_field(level)
new_field["name"] = name
fields.append(new_field)
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index a22251b29da54..3dd87ae6ed758 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -1916,9 +1916,7 @@ def is_indexed(self) -> bool:
if not hasattr(self.table, "cols"):
# e.g. if infer hasn't been called yet, self.table will be None.
return False
- # GH#29692 mypy doesn't recognize self.table as having a "cols" attribute
- # 'error: "None" has no attribute "cols"'
- return getattr(self.table.cols, self.cname).is_indexed # type: ignore
+ return getattr(self.table.cols, self.cname).is_indexed
def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
"""
diff --git a/pandas/io/sas/sas7bdat.py b/pandas/io/sas/sas7bdat.py
index d47dd2c71b86f..e3821856191e3 100644
--- a/pandas/io/sas/sas7bdat.py
+++ b/pandas/io/sas/sas7bdat.py
@@ -16,6 +16,7 @@
from collections import abc
from datetime import datetime
import struct
+from typing import Any, List
import numpy as np
@@ -29,11 +30,19 @@
class _subheader_pointer:
- pass
+ offset: Any
+ length: Any
+ compression: Any
+ ptype: Any
class _column:
- pass
+ col_id: Any
+ name: Any
+ label: Any
+ format: Any
+ ctype: Any
+ length: Any
# SAS7BDAT represents a SAS data file in SAS7BDAT format.
@@ -87,17 +96,17 @@ def __init__(
self.convert_header_text = convert_header_text
self.default_encoding = "latin-1"
- self.compression = ""
- self.column_names_strings = []
- self.column_names = []
- self.column_formats = []
- self.columns = []
+ self.compression = b""
+ self.column_names_strings: List = []
+ self.column_names: List = []
+ self.column_formats: List = []
+ self.columns: List = []
- self._current_page_data_subheader_pointers = []
+ self._current_page_data_subheader_pointers: List = []
self._cached_page = None
- self._column_data_lengths = []
- self._column_data_offsets = []
- self._column_types = []
+ self._column_data_lengths: List = []
+ self._column_data_offsets: List = []
+ self._column_types: List = []
self._current_row_in_file_index = 0
self._current_row_on_page_index = 0
@@ -362,7 +371,7 @@ def _get_subheader_index(self, signature, compression, ptype):
if index is None:
f1 = (compression == const.compressed_subheader_id) or (compression == 0)
f2 = ptype == const.compressed_subheader_type
- if (self.compression != "") and f1 and f2:
+ if (self.compression != b"") and f1 and f2:
index = const.SASIndex.data_subheader_index
else:
self.close()
@@ -481,7 +490,7 @@ def _process_columntext_subheader(self, offset, length):
self.column_names_strings.append(cname)
if len(self.column_names_strings) == 1:
- compression_literal = ""
+ compression_literal = b""
for cl in const.compression_literals:
if cl in cname_raw:
compression_literal = cl
@@ -494,7 +503,7 @@ def _process_columntext_subheader(self, offset, length):
buf = self._read_bytes(offset1, self._lcp)
compression_literal = buf.rstrip(b"\x00")
- if compression_literal == "":
+ if compression_literal == b"":
self._lcs = 0
offset1 = offset + 32
if self.U64:
diff --git a/pandas/io/sas/sasreader.py b/pandas/io/sas/sasreader.py
index 27d56d4ede403..4c8658ff224d1 100644
--- a/pandas/io/sas/sasreader.py
+++ b/pandas/io/sas/sasreader.py
@@ -1,16 +1,24 @@
"""
Read SAS sas7bdat or xport files.
"""
+from typing import TYPE_CHECKING, AnyStr, Optional, Union
+
+from pandas._typing import FilePathOrBuffer
+
from pandas.io.common import stringify_path
+if TYPE_CHECKING:
+ from pandas.io.sas.sas_xport import XportReader # noqa: F401
+ from pandas.io.sas.sas7bdat import SAS7BDATReader # noqa: F401
+
def read_sas(
- filepath_or_buffer,
- format=None,
+ filepath_or_buffer: FilePathOrBuffer[AnyStr],
+ format: Optional[str] = None,
index=None,
- encoding=None,
- chunksize=None,
- iterator=False,
+ encoding: Optional[str] = None,
+ chunksize: Optional[int] = None,
+ iterator: bool = False,
):
"""
Read SAS files stored as either XPORT or SAS7BDAT format files.
@@ -62,14 +70,15 @@ def read_sas(
else:
raise ValueError("unable to infer format of SAS file")
+ reader: Union["XportReader", "SAS7BDATReader"]
if format.lower() == "xport":
- from pandas.io.sas.sas_xport import XportReader
+ from pandas.io.sas.sas_xport import XportReader # noqa: F811
reader = XportReader(
filepath_or_buffer, index=index, encoding=encoding, chunksize=chunksize
)
elif format.lower() == "sas7bdat":
- from pandas.io.sas.sas7bdat import SAS7BDATReader
+ from pandas.io.sas.sas7bdat import SAS7BDATReader # noqa: F811
reader = SAS7BDATReader(
filepath_or_buffer, index=index, encoding=encoding, chunksize=chunksize
diff --git a/pandas/plotting/_matplotlib/converter.py b/pandas/plotting/_matplotlib/converter.py
index 8260684c02ea6..8f3c9d6fdb467 100644
--- a/pandas/plotting/_matplotlib/converter.py
+++ b/pandas/plotting/_matplotlib/converter.py
@@ -2,6 +2,7 @@
import datetime as pydt
from datetime import datetime, timedelta
import functools
+from typing import List
from dateutil.relativedelta import relativedelta
import matplotlib.dates as dates
@@ -529,7 +530,7 @@ def _get_default_annual_spacing(nyears):
return (min_spacing, maj_spacing)
-def period_break(dates, period):
+def period_break(dates: PeriodIndex, period):
"""
Returns the indices where the given period changes.
@@ -1052,7 +1053,7 @@ def __init__(self, freq, minor_locator=False, dynamic_mode=True, plot_obj=None):
freq = get_freq(freq)
self.format = None
self.freq = freq
- self.locs = []
+ self.locs: List[float] = []
self.formatdict = None
self.isminor = minor_locator
self.isdynamic = dynamic_mode
diff --git a/pandas/plotting/_matplotlib/misc.py b/pandas/plotting/_matplotlib/misc.py
index 7319e8de3ec6e..0cfb1a0f75d0b 100644
--- a/pandas/plotting/_matplotlib/misc.py
+++ b/pandas/plotting/_matplotlib/misc.py
@@ -1,4 +1,5 @@
import random
+from typing import Dict, Set
import matplotlib.lines as mlines
import matplotlib.patches as patches
@@ -130,7 +131,7 @@ def normalize(series):
if ax is None:
ax = plt.gca(xlim=[-1, 1], ylim=[-1, 1])
- to_plot = {}
+ to_plot: Dict = {}
colors = _get_standard_colors(
num_colors=len(classes), colormap=colormap, color_type="random", color=color
)
@@ -231,7 +232,7 @@ def f(t):
classes = frame[class_column].drop_duplicates()
df = frame.drop(class_column, axis=1)
t = np.linspace(-np.pi, np.pi, samples)
- used_legends = set()
+ used_legends: Set = set()
color_values = _get_standard_colors(
num_colors=len(classes), colormap=colormap, color_type="random", color=color
@@ -332,7 +333,7 @@ def parallel_coordinates(
else:
df = frame[cols]
- used_legends = set()
+ used_legends: Set = set()
ncols = len(df.columns)
diff --git a/pandas/tests/io/excel/test_writers.py b/pandas/tests/io/excel/test_writers.py
index 0811f2f822198..59b346de0c390 100644
--- a/pandas/tests/io/excel/test_writers.py
+++ b/pandas/tests/io/excel/test_writers.py
@@ -1287,7 +1287,7 @@ def test_register_writer(self):
class DummyClass(ExcelWriter):
called_save = False
called_write_cells = False
- supported_extensions = ["xlsx", "xls"]
+ supported_extensions = ("xlsx", "xls")
engine = "dummy"
def save(self):
diff --git a/pandas/tests/scalar/period/test_period.py b/pandas/tests/scalar/period/test_period.py
index 304033f82c7a2..4fc506554d0b3 100644
--- a/pandas/tests/scalar/period/test_period.py
+++ b/pandas/tests/scalar/period/test_period.py
@@ -1438,7 +1438,10 @@ def test_period_immutable():
@pytest.mark.xfail(
- StrictVersion(dateutil.__version__.split(".dev")[0]) < StrictVersion("2.7.0"),
+ # https://github.com/python/typeshed/tree/master/third_party/2and3/dateutil
+ # error: Module has no attribute "__version__" [attr-defined]
+ StrictVersion(dateutil.__version__.split(".dev")[0]) # type: ignore
+ < StrictVersion("2.7.0"),
reason="Bug in dateutil < 2.7.0 when parsing old dates: Period('0001-01-07', 'D')",
strict=False,
)
diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py
index d2049892705ea..4dca79b3b9257 100644
--- a/pandas/tests/tools/test_to_datetime.py
+++ b/pandas/tests/tools/test_to_datetime.py
@@ -290,19 +290,45 @@ def test_to_datetime_format_weeks(self, cache):
[
"%Y-%m-%d %H:%M:%S%z",
["2010-01-01 12:00:00+0100"] * 2,
- [pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(60))] * 2,
+ [
+ pd.Timestamp(
+ "2010-01-01 12:00:00",
+ # https://github.com/python/typeshed/pull/3393
+ # error: Module has no attribute "FixedOffset"
+ tzinfo=pytz.FixedOffset(60), # type:ignore
+ )
+ ]
+ * 2,
],
[
"%Y-%m-%d %H:%M:%S %z",
["2010-01-01 12:00:00 +0100"] * 2,
- [pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(60))] * 2,
+ [
+ pd.Timestamp(
+ "2010-01-01 12:00:00",
+ # https://github.com/python/typeshed/pull/3393
+ # error: Module has no attribute "FixedOffset"
+ tzinfo=pytz.FixedOffset(60), # type:ignore
+ )
+ ]
+ * 2,
],
[
"%Y-%m-%d %H:%M:%S %z",
["2010-01-01 12:00:00 +0100", "2010-01-01 12:00:00 -0100"],
[
- pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(60)),
- pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(-60)),
+ pd.Timestamp(
+ "2010-01-01 12:00:00",
+ # https://github.com/python/typeshed/pull/3393
+ # error: Module has no attribute "FixedOffset"
+ tzinfo=pytz.FixedOffset(60), # type:ignore
+ ),
+ pd.Timestamp(
+ "2010-01-01 12:00:00",
+ # https://github.com/python/typeshed/pull/3393
+ # error: Module has no attribute "FixedOffset"
+ tzinfo=pytz.FixedOffset(-60), # type:ignore
+ ),
],
],
[
@@ -310,9 +336,17 @@ def test_to_datetime_format_weeks(self, cache):
["2010-01-01 12:00:00 Z", "2010-01-01 12:00:00 Z"],
[
pd.Timestamp(
- "2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(0)
+ "2010-01-01 12:00:00",
+ # https://github.com/python/typeshed/pull/3393
+ # error: Module has no attribute "FixedOffset"
+ tzinfo=pytz.FixedOffset(0), # type:ignore
), # pytz coerces to UTC
- pd.Timestamp("2010-01-01 12:00:00", tzinfo=pytz.FixedOffset(0)),
+ pd.Timestamp(
+ "2010-01-01 12:00:00",
+ # https://github.com/python/typeshed/pull/3393
+ # error: Module has no attribute "FixedOffset"
+ tzinfo=pytz.FixedOffset(0), # type:ignore
+ ),
],
],
],
@@ -2164,12 +2198,16 @@ def test_parsers_timestring(self, cache):
[
(
"2013-01-01 05:45+0545",
- pytz.FixedOffset(345),
+ # https://github.com/python/typeshed/pull/3393
+ # error: Module has no attribute "FixedOffset"
+ pytz.FixedOffset(345), # type:ignore
"Timestamp('2013-01-01 05:45:00+0545', tz='pytz.FixedOffset(345)')",
),
(
"2013-01-01 05:30+0530",
- pytz.FixedOffset(330),
+ # https://github.com/python/typeshed/pull/3393
+ # error: Module has no attribute "FixedOffset"
+ pytz.FixedOffset(330), # type:ignore
"Timestamp('2013-01-01 05:30:00+0530', tz='pytz.FixedOffset(330)')",
),
],
diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py
index 8ab37f787bd10..c2c08be7fce47 100644
--- a/pandas/tseries/holiday.py
+++ b/pandas/tseries/holiday.py
@@ -1,5 +1,7 @@
+# mypy: ignore_errors
+
from datetime import datetime, timedelta
-from typing import List
+from typing import Iterable, List, Optional, Union, cast
import warnings
from dateutil.relativedelta import FR, MO, SA, SU, TH, TU, WE # noqa
@@ -11,6 +13,8 @@
from pandas.tseries.offsets import Day, Easter
+_DatetimeLike = Union[datetime, Timestamp, str, float]
+
def next_monday(dt):
"""
@@ -217,7 +221,12 @@ def __repr__(self) -> str:
repr = f"Holiday: {self.name} ({info})"
return repr
- def dates(self, start_date, end_date, return_name=False):
+ def dates(
+ self,
+ start_date: Optional[_DatetimeLike],
+ end_date: Optional[_DatetimeLike],
+ return_name: bool = False,
+ ) -> Union[DatetimeIndex, List[Timestamp], Series]:
"""
Calculate holidays observed between start date and end date
@@ -257,6 +266,8 @@ def dates(self, start_date, end_date, return_name=False):
filter_end_date = min(
self.end_date.tz_localize(filter_end_date.tz), filter_end_date
)
+ # TODO: comparison ops are created dynamically
+ # error: Unsupported left operand type for >= ("DatetimeIndex") [operator]
holiday_dates = holiday_dates[
(holiday_dates >= filter_start_date) & (holiday_dates <= filter_end_date)
]
@@ -264,7 +275,9 @@ def dates(self, start_date, end_date, return_name=False):
return Series(self.name, index=holiday_dates)
return holiday_dates
- def _reference_dates(self, start_date, end_date):
+ def _reference_dates(
+ self, start_date: Timestamp, end_date: Timestamp
+ ) -> DatetimeIndex:
"""
Get reference dates for the holiday.
@@ -297,7 +310,7 @@ def _reference_dates(self, start_date, end_date):
return dates
- def _apply_rule(self, dates):
+ def _apply_rule(self, dates: DatetimeIndex) -> DatetimeIndex:
"""
Apply the given offset/observance to a DatetimeIndex of dates.
@@ -363,12 +376,14 @@ class AbstractHolidayCalendar(metaclass=HolidayCalendarMetaClass):
Abstract interface to create holidays following certain rules.
"""
- rules: List[Holiday] = []
+ rules: Iterable[Holiday] = []
start_date = Timestamp(datetime(1970, 1, 1))
end_date = Timestamp(datetime(2200, 12, 31))
_cache = None
- def __init__(self, name=None, rules=None):
+ def __init__(
+ self, name: Optional[str] = None, rules: Optional[Iterable[Holiday]] = None
+ ):
"""
Initializes holiday object with a given set a rules. Normally
classes just have the rules defined within them.
@@ -395,7 +410,12 @@ def rule_from_name(self, name):
return None
- def holidays(self, start=None, end=None, return_name=False):
+ def holidays(
+ self,
+ start: Optional[_DatetimeLike] = None,
+ end: Optional[_DatetimeLike] = None,
+ return_name: bool = False,
+ ) -> Union[DatetimeIndex, Series]:
"""
Returns a curve with holidays between start_date and end_date
@@ -434,15 +454,18 @@ def holidays(self, start=None, end=None, return_name=False):
else:
holidays = Series(index=DatetimeIndex([]), dtype=object)
- self._cache = (start, end, holidays.sort_index())
+ # TODO: overload rule.dates(return_name=True) to return Series
+ self._cache = (start, end, cast(Series, holidays).sort_index())
- holidays = self._cache[2]
- holidays = holidays[start:end]
+ # TODO: concreate subclass must have rules
+ holidays = cast(Series, self._cache[2])
+ # TODO: slice indexing on a Series should return Series
+ holidays = cast(Series, holidays[start:end])
if return_name:
return holidays
else:
- return holidays.index
+ return cast(DatetimeIndex, holidays.index)
@staticmethod
def merge_class(base, other):
diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py
index bc20d784c8dee..f6c0ad6507547 100644
--- a/pandas/tseries/offsets.py
+++ b/pandas/tseries/offsets.py
@@ -1,7 +1,7 @@
from datetime import date, datetime, timedelta
import functools
import operator
-from typing import Any, Optional
+from typing import Any, Optional, Tuple
import warnings
from dateutil.easter import easter
@@ -248,6 +248,8 @@ def __add__(date):
Timestamp('2017-03-01 09:10:11')
"""
+ # https://github.com/python/mypy/issues/6185
+ # error: "Callable[[_BaseOffset], Tuple[Any, ...]]" has no attribute "fget"
_params = cache_readonly(BaseOffset._params.fget)
_use_relativedelta = False
_adjust_dst = False
@@ -499,6 +501,10 @@ class _CustomMixin:
and weekdays attributes.
"""
+ weekmask: str
+ holidays: Tuple[np.datetime64, ...]
+ calendar: np.busdaycalendar
+
def __init__(self, weekmask, holidays, calendar):
calendar, holidays = _get_calendar(
weekmask=weekmask, holidays=holidays, calendar=calendar
@@ -1123,6 +1129,9 @@ class MonthOffset(SingleConstructorOffset):
_adjust_dst = True
_attributes = frozenset(["n", "normalize"])
+ # error: Incompatible types in assignment (expression has type
+ # "Callable[[_BaseOffset, Any, Any], Any]", base class "object" defined the type
+ # as "Callable[[object], None]") [assignment]
__init__ = BaseOffset.__init__
@property
@@ -2468,6 +2477,9 @@ class Easter(DateOffset):
_adjust_dst = True
_attributes = frozenset(["n", "normalize"])
+ # error: Incompatible types in assignment (expression has type
+ # "Callable[[_BaseOffset, Any, Any], Any]", base class "object" defined the type
+ # as "Callable[[object], None]") [assignment]
__init__ = BaseOffset.__init__
@apply_wraps
diff --git a/pandas/util/_decorators.py b/pandas/util/_decorators.py
index 71d02db10c7ba..d603b983da552 100644
--- a/pandas/util/_decorators.py
+++ b/pandas/util/_decorators.py
@@ -100,7 +100,7 @@ def deprecate_kwarg(
new_arg_name: Optional[str],
mapping: Optional[Union[Mapping[Any, Any], Callable[[Any], Any]]] = None,
stacklevel: int = 2,
-) -> Callable[..., Any]:
+) -> Callable[[F], F]:
"""
Decorator to deprecate a keyword argument of a function.
diff --git a/setup.cfg b/setup.cfg
index fda4ba4065e2f..a5a4590ea37e4 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -22,7 +22,13 @@ ignore =
C406, # Unnecessary list literal - rewrite as a dict literal.
C408, # Unnecessary dict call - rewrite as a literal.
C409, # Unnecessary list passed to tuple() - rewrite as a tuple literal.
- S001 # found modulo formatter (incorrect picks up mod operations)
+ S001, # found modulo formatter (incorrect picks up mod operations)
+ # pydocstyle
+ D100, D101, D102, D103, D104, D105, D106, D107,
+ D200, D202, D203, D204, D205, D207, D209, D212, D213,
+ D301,
+ D400, D401, D402, D403, D413,
+
exclude =
doc/sphinxext/*.py,
doc/build/*.py,
@@ -122,10 +128,42 @@ skip_glob = env,
skip = pandas/__init__.py,pandas/core/api.py
[mypy]
-ignore_missing_imports=True
-no_implicit_optional=True
-check_untyped_defs=True
-strict_equality=True
+platform = linux-64
+python_version = 3.6
+ignore_missing_imports = True
+show_column_numbers = True
+strict_equality = True
+show_error_codes = True
+# --strict settings
+# Strict mode is not supported in configuration files: specify individual flags
+# instead (see 'mypy -h' for the list of flags enabled in strict mode)
+warn_unused_configs = True
+# disallow_subclassing_any = True
+# disallow_any_generics = True
+# disallow_untyped_calls = True
+# disallow_untyped_defs = True
+# disallow_incomplete_defs = True
+check_untyped_defs = True
+# disallow_untyped_decorators = True
+no_implicit_optional = True
+warn_redundant_casts = True
+# warn_unused_ignores = True
+# warn_return_any = True
+# no_implicit_reexport = True
+
+files =
+ pandas/_config,
+ pandas/_libs,
+ pandas/api,
+ pandas/arrays,
+ pandas/compat,
+ pandas/core,
+ pandas/errors,
+ pandas/io,
+ pandas/plotting,
+ pandas/tseries,
+ pandas/util,
+ pandas/*.py
[mypy-pandas.tests.*]
check_untyped_defs=False
@@ -133,13 +171,13 @@ check_untyped_defs=False
[mypy-pandas.conftest]
ignore_errors=True
-[mypy-pandas.tests.arithmetic.test_datetime64]
+[mypy-pandas.tests.indexes.datetimes.test_tools]
ignore_errors=True
-[mypy-pandas.tests.tools.test_to_datetime]
+[mypy-pandas.tests.scalar.period.test_period]
ignore_errors=True
-[mypy-pandas.tests.scalar.period.test_period]
+[mypy-pandas.io.formats.style]
ignore_errors=True
[mypy-pandas._testing]
@@ -148,135 +186,36 @@ check_untyped_defs=False
[mypy-pandas._version]
check_untyped_defs=False
-[mypy-pandas.core.arrays.interval]
-check_untyped_defs=False
-
-[mypy-pandas.core.base]
-check_untyped_defs=False
-
[mypy-pandas.core.computation.expr]
check_untyped_defs=False
-[mypy-pandas.core.computation.expressions]
-check_untyped_defs=False
-
-[mypy-pandas.core.computation.pytables]
-check_untyped_defs=False
-
-[mypy-pandas.core.computation.scope]
-check_untyped_defs=False
-
-[mypy-pandas.core.dtypes.cast]
-check_untyped_defs=False
-
-[mypy-pandas.core.frame]
-check_untyped_defs=False
-
-[mypy-pandas.core.generic]
-check_untyped_defs=False
-
-[mypy-pandas.core.groupby.generic]
-check_untyped_defs=False
-
[mypy-pandas.core.groupby.grouper]
check_untyped_defs=False
-[mypy-pandas.core.groupby.ops]
-check_untyped_defs=False
-
-[mypy-pandas.core.indexes.base]
-check_untyped_defs=False
-
-[mypy-pandas.core.indexes.datetimes]
-check_untyped_defs=False
-
-[mypy-pandas.core.indexes.interval]
-check_untyped_defs=False
-
-[mypy-pandas.core.indexes.multi]
-check_untyped_defs=False
-
[mypy-pandas.core.internals.blocks]
check_untyped_defs=False
-[mypy-pandas.core.internals.concat]
-check_untyped_defs=False
-
[mypy-pandas.core.internals.construction]
check_untyped_defs=False
[mypy-pandas.core.internals.managers]
check_untyped_defs=False
-[mypy-pandas.core.missing]
-check_untyped_defs=False
-
-[mypy-pandas.core.nanops]
-check_untyped_defs=False
-
-[mypy-pandas.core.ops.docstrings]
-check_untyped_defs=False
-
[mypy-pandas.core.resample]
check_untyped_defs=False
-[mypy-pandas.core.reshape.merge]
-check_untyped_defs=False
-
-[mypy-pandas.core.strings]
-check_untyped_defs=False
-
-[mypy-pandas.core.window.common]
-check_untyped_defs=False
-
-[mypy-pandas.core.window.ewm]
-check_untyped_defs=False
-
-[mypy-pandas.core.window.expanding]
-check_untyped_defs=False
-
[mypy-pandas.core.window.rolling]
check_untyped_defs=False
[mypy-pandas.io.clipboard]
check_untyped_defs=False
-[mypy-pandas.io.excel._base]
-check_untyped_defs=False
-
[mypy-pandas.io.excel._openpyxl]
check_untyped_defs=False
-[mypy-pandas.io.excel._util]
-check_untyped_defs=False
-
-[mypy-pandas.io.excel._xlwt]
-check_untyped_defs=False
-
-[mypy-pandas.io.formats.console]
-check_untyped_defs=False
-
-[mypy-pandas.io.formats.css]
-check_untyped_defs=False
-
-[mypy-pandas.io.formats.excel]
-check_untyped_defs=False
-
-[mypy-pandas.io.formats.format]
-check_untyped_defs=False
-
-[mypy-pandas.io.formats.style]
-check_untyped_defs=False
-
-[mypy-pandas.io.html]
-check_untyped_defs=False
-
[mypy-pandas.io.json._json]
check_untyped_defs=False
-[mypy-pandas.io.json._table_schema]
-check_untyped_defs=False
-
[mypy-pandas.io.parsers]
check_untyped_defs=False
@@ -286,26 +225,8 @@ check_untyped_defs=False
[mypy-pandas.io.sas.sas_xport]
check_untyped_defs=False
-[mypy-pandas.io.sas.sas7bdat]
-check_untyped_defs=False
-
-[mypy-pandas.io.sas.sasreader]
-check_untyped_defs=False
-
[mypy-pandas.io.stata]
check_untyped_defs=False
-[mypy-pandas.plotting._matplotlib.converter]
-check_untyped_defs=False
-
[mypy-pandas.plotting._matplotlib.core]
check_untyped_defs=False
-
-[mypy-pandas.plotting._matplotlib.misc]
-check_untyped_defs=False
-
-[mypy-pandas.tseries.holiday]
-check_untyped_defs=False
-
-[mypy-pandas.tseries.offsets]
-check_untyped_defs=False