⬆️ UPGRADE: Autoupdate pre-commit config

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
     -   id: absolufy-imports
         files: ^pandas/
 -   repo: https://github.com/python/black
-    rev: 20.8b1
+    rev: 21.5b1
     hooks:
     -   id: black
 -   repo: https://github.com/codespell-project/codespell
@@ -57,7 +57,7 @@ repos:
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v2.18.3
+    rev: v2.19.0
     hooks:
     -   id: pyupgrade
         args: [--py37-plus]

pandas/_config/config.py

@@ -189,7 +189,7 @@ def get_default_val(pat: str):
 
 
 class DictWrapper:
-    """ provide attribute-style access to a nested dict"""
+    """provide attribute-style access to a nested dict"""
 
     def __init__(self, d: dict[str, Any], prefix: str = ""):
         object.__setattr__(self, "d", d)
@@ -571,7 +571,7 @@ def _get_root(key: str) -> tuple[dict[str, Any], str]:
 
 
 def _is_deprecated(key: str) -> bool:
-    """ Returns True if the given option has been deprecated """
+    """Returns True if the given option has been deprecated"""
     key = key.lower()
     return key in _deprecated_options
 
@@ -643,7 +643,7 @@ def _warn_if_deprecated(key: str) -> bool:
 
 
 def _build_option_description(k: str) -> str:
-    """ Builds a formatted description of a registered option and prints it """
+    """Builds a formatted description of a registered option and prints it"""
     o = _get_registered_option(k)
     d = _get_deprecated_option(k)
 
@@ -667,7 +667,7 @@ def _build_option_description(k: str) -> str:
 
 
 def pp_options_list(keys: Iterable[str], width=80, _print: bool = False):
-    """ Builds a concise listing of available options, grouped by prefix """
+    """Builds a concise listing of available options, grouped by prefix"""
     from itertools import groupby
     from textwrap import wrap
 

pandas/_libs/arrays.pyi

@@ -10,36 +10,25 @@ from pandas._typing import (
 class NDArrayBacked:
     _dtype: DtypeObj
     _ndarray: np.ndarray
-
     def __init__(self, values: np.ndarray, dtype: DtypeObj): ...
-
     @classmethod
     def _simple_new(cls, values: np.ndarray, dtype: DtypeObj): ...
-
     def _from_backing_data(self, values: np.ndarray): ...
-
     def __setstate__(self, state): ...
-
     def __len__(self) -> int: ...
-
     @property
     def shape(self) -> Shape: ...
-
     @property
     def ndim(self) -> int: ...
-
     @property
     def size(self) -> int: ...
-
     @property
     def nbytes(self) -> int: ...
-
     def copy(self): ...
     def delete(self, loc, axis=0): ...
     def swapaxes(self, axis1, axis2): ...
     def repeat(self, repeats: int | Sequence[int], axis: int | None = ...): ...
     def reshape(self, *args, **kwargs): ...
     def ravel(self, order="C"): ...
-
     @property
     def T(self): ...

pandas/_libs/groupby.pyi

@@ -3,128 +3,111 @@ from typing import Literal
 import numpy as np
 
 def group_median_float64(
-    out: np.ndarray,       # ndarray[float64_t, ndim=2]
-    counts: np.ndarray,    # ndarray[int64_t]
-    values: np.ndarray,    # ndarray[float64_t, ndim=2]
-    labels: np.ndarray,    # ndarray[int64_t]
+    out: np.ndarray,  # ndarray[float64_t, ndim=2]
+    counts: np.ndarray,  # ndarray[int64_t]
+    values: np.ndarray,  # ndarray[float64_t, ndim=2]
+    labels: np.ndarray,  # ndarray[int64_t]
     min_count: int = ...,  # Py_ssize_t
 ) -> None: ...
-
 def group_cumprod_float64(
-    out: np.ndarray,     # float64_t[:, ::1]
+    out: np.ndarray,  # float64_t[:, ::1]
     values: np.ndarray,  # const float64_t[:, :]
     labels: np.ndarray,  # const int64_t[:]
     ngroups: int,
     is_datetimelike: bool,
     skipna: bool = ...,
 ) -> None: ...
-
 def group_cumsum(
-    out: np.ndarray,     # numeric[:, ::1]
+    out: np.ndarray,  # numeric[:, ::1]
     values: np.ndarray,  # ndarray[numeric, ndim=2]
     labels: np.ndarray,  # const int64_t[:]
     ngroups: int,
     is_datetimelike: bool,
     skipna: bool = ...,
 ) -> None: ...
-
-
 def group_shift_indexer(
-    out: np.ndarray,     # int64_t[::1]
+    out: np.ndarray,  # int64_t[::1]
     labels: np.ndarray,  # const int64_t[:]
     ngroups: int,
     periods: int,
 ) -> None: ...
-
-
 def group_fillna_indexer(
-    out: np.ndarray,     # ndarray[int64_t]
+    out: np.ndarray,  # ndarray[int64_t]
     labels: np.ndarray,  # ndarray[int64_t]
-    mask: np.ndarray,    # ndarray[uint8_t]
+    mask: np.ndarray,  # ndarray[uint8_t]
     direction: Literal["ffill", "bfill"],
-    limit: int,          # int64_t
+    limit: int,  # int64_t
     dropna: bool,
 ) -> None: ...
-
-
 def group_any_all(
-    out: np.ndarray,     # uint8_t[::1]
+    out: np.ndarray,  # uint8_t[::1]
     values: np.ndarray,  # const uint8_t[::1]
     labels: np.ndarray,  # const int64_t[:]
-    mask: np.ndarray,    # const uint8_t[::1]
+    mask: np.ndarray,  # const uint8_t[::1]
     val_test: Literal["any", "all"],
     skipna: bool,
 ) -> None: ...
-
 def group_add(
-    out: np.ndarray,     # complexfloating_t[:, ::1]
+    out: np.ndarray,  # complexfloating_t[:, ::1]
     counts: np.ndarray,  # int64_t[::1]
     values: np.ndarray,  # ndarray[complexfloating_t, ndim=2]
     labels: np.ndarray,  # const intp_t[:]
-    min_count: int = ...
+    min_count: int = ...,
 ) -> None: ...
-
 def group_prod(
-    out: np.ndarray,     # floating[:, ::1]
+    out: np.ndarray,  # floating[:, ::1]
     counts: np.ndarray,  # int64_t[::1]
     values: np.ndarray,  # ndarray[floating, ndim=2]
     labels: np.ndarray,  # const intp_t[:]
-    min_count: int = ...
+    min_count: int = ...,
 ) -> None: ...
-
 def group_var(
-    out: np.ndarray,       # floating[:, ::1]
-    counts: np.ndarray,    # int64_t[::1]
-    values: np.ndarray,    # ndarray[floating, ndim=2]
-    labels: np.ndarray,    # const intp_t[:]
+    out: np.ndarray,  # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
     min_count: int = ...,  # Py_ssize_t
-    ddof: int = ...,       # int64_t
+    ddof: int = ...,  # int64_t
 ) -> None: ...
-
 def group_mean(
-    out: np.ndarray,     # floating[:, ::1]
+    out: np.ndarray,  # floating[:, ::1]
     counts: np.ndarray,  # int64_t[::1]
     values: np.ndarray,  # ndarray[floating, ndim=2]
     labels: np.ndarray,  # const intp_t[:]
-    min_count: int = ...
+    min_count: int = ...,
 ) -> None: ...
-
 def group_ohlc(
-    out: np.ndarray,     # floating[:, ::1]
+    out: np.ndarray,  # floating[:, ::1]
     counts: np.ndarray,  # int64_t[::1]
     values: np.ndarray,  # ndarray[floating, ndim=2]
     labels: np.ndarray,  # const intp_t[:]
-    min_count: int = ...
+    min_count: int = ...,
 ) -> None: ...
-
 def group_quantile(
-    out: np.ndarray,     # ndarray[float64_t]
+    out: np.ndarray,  # ndarray[float64_t]
     values: np.ndarray,  # ndarray[numeric, ndim=1]
     labels: np.ndarray,  # ndarray[int64_t]
-    mask: np.ndarray,    # ndarray[uint8_t]
-    q: float,            # float64_t
+    mask: np.ndarray,  # ndarray[uint8_t]
+    q: float,  # float64_t
     interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
 ) -> None: ...
-
 def group_last(
-    out: np.ndarray,       # rank_t[:, ::1]
-    counts: np.ndarray,    # int64_t[::1]
-    values: np.ndarray,    # ndarray[rank_t, ndim=2]
-    labels: np.ndarray,    # const int64_t[:]
+    out: np.ndarray,  # rank_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
     min_count: int = ...,  # Py_ssize_t
 ) -> None: ...
-
 def group_nth(
-    out: np.ndarray,        # rank_t[:, ::1]
-    counts: np.ndarray,     # int64_t[::1]
-    values: np.ndarray,     # ndarray[rank_t, ndim=2]
-    labels: np.ndarray,     # const int64_t[:]
-    min_count: int = ...,   # int64_t
-    rank: int = ...,        # int64_t
+    out: np.ndarray,  # rank_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,  # int64_t
+    rank: int = ...,  # int64_t
 ) -> None: ...
-
 def group_rank(
-    out: np.ndarray,     # float64_t[:, ::1]
+    out: np.ndarray,  # float64_t[:, ::1]
     values: np.ndarray,  # ndarray[rank_t, ndim=2]
     labels: np.ndarray,  # const int64_t[:]
     ngroups: int,
@@ -134,35 +117,31 @@ def group_rank(
     pct: bool = ...,
     na_option: Literal["keep", "top", "bottom"] = ...,
 ) -> None: ...
-
 def group_max(
-    out: np.ndarray,     # groupby_t[:, ::1]
+    out: np.ndarray,  # groupby_t[:, ::1]
     counts: np.ndarray,  # int64_t[::1]
     values: np.ndarray,  # ndarray[groupby_t, ndim=2]
     labels: np.ndarray,  # const int64_t[:]
     min_count: int = ...,
 ) -> None: ...
-
 def group_min(
-    out: np.ndarray,     # groupby_t[:, ::1]
+    out: np.ndarray,  # groupby_t[:, ::1]
     counts: np.ndarray,  # int64_t[::1]
     values: np.ndarray,  # ndarray[groupby_t, ndim=2]
     labels: np.ndarray,  # const int64_t[:]
     min_count: int = ...,
 ) -> None: ...
-
 def group_cummin(
-    out: np.ndarray,      # groupby_t[:, ::1]
-    values: np.ndarray,   # ndarray[groupby_t, ndim=2]
-    labels: np.ndarray,   # const int64_t[:]
+    out: np.ndarray,  # groupby_t[:, ::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
     ngroups: int,
     is_datetimelike: bool,
 ) -> None: ...
-
 def group_cummax(
-    out: np.ndarray,      # groupby_t[:, ::1]
-    values: np.ndarray,   # ndarray[groupby_t, ndim=2]
-    labels: np.ndarray,   # const int64_t[:]
+    out: np.ndarray,  # groupby_t[:, ::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
     ngroups: int,
     is_datetimelike: bool,
 ) -> None: ...