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update black formatter (#46576)
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MarcoGorelli authored Mar 30, 2022
1 parent 89baca7 commit 4be9ffd
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Showing 138 changed files with 645 additions and 648 deletions.
2 changes: 1 addition & 1 deletion .pre-commit-config.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ repos:
- id: absolufy-imports
files: ^pandas/
- repo: https://github.com/python/black
rev: 21.12b0
rev: 22.3.0
hooks:
- id: black
- repo: https://github.com/codespell-project/codespell
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10 changes: 5 additions & 5 deletions asv_bench/benchmarks/algorithms.py
Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ class Factorize:
param_names = ["unique", "sort", "dtype"]

def setup(self, unique, sort, dtype):
N = 10 ** 5
N = 10**5
string_index = tm.makeStringIndex(N)
string_arrow = None
if dtype == "string[pyarrow]":
Expand Down Expand Up @@ -74,7 +74,7 @@ class Duplicated:
param_names = ["unique", "keep", "dtype"]

def setup(self, unique, keep, dtype):
N = 10 ** 5
N = 10**5
data = {
"int": pd.Index(np.arange(N), dtype="int64"),
"uint": pd.Index(np.arange(N), dtype="uint64"),
Expand All @@ -97,7 +97,7 @@ def time_duplicated(self, unique, keep, dtype):

class Hashing:
def setup_cache(self):
N = 10 ** 5
N = 10**5

df = pd.DataFrame(
{
Expand Down Expand Up @@ -145,7 +145,7 @@ class Quantile:
param_names = ["quantile", "interpolation", "dtype"]

def setup(self, quantile, interpolation, dtype):
N = 10 ** 5
N = 10**5
data = {
"int": np.arange(N),
"uint": np.arange(N).astype(np.uint64),
Expand All @@ -158,7 +158,7 @@ def time_quantile(self, quantile, interpolation, dtype):


class SortIntegerArray:
params = [10 ** 3, 10 ** 5]
params = [10**3, 10**5]

def setup(self, N):
data = np.arange(N, dtype=float)
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20 changes: 10 additions & 10 deletions asv_bench/benchmarks/algos/isin.py
Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ def setup(self, dtype):

elif dtype in ["category[object]", "category[int]"]:
# Note: sizes are different in this case than others
n = 5 * 10 ** 5
n = 5 * 10**5
sample_size = 100

arr = list(np.random.randint(0, n // 10, size=n))
Expand Down Expand Up @@ -174,7 +174,7 @@ class IsinWithArange:

def setup(self, dtype, M, offset_factor):
offset = int(M * offset_factor)
tmp = Series(np.random.randint(offset, M + offset, 10 ** 6))
tmp = Series(np.random.randint(offset, M + offset, 10**6))
self.series = tmp.astype(dtype)
self.values = np.arange(M).astype(dtype)

Expand All @@ -191,8 +191,8 @@ class IsInFloat64:
param_names = ["dtype", "title"]

def setup(self, dtype, title):
N_many = 10 ** 5
N_few = 10 ** 6
N_many = 10**5
N_few = 10**6
self.series = Series([1, 2], dtype=dtype)

if title == "many_different_values":
Expand Down Expand Up @@ -240,10 +240,10 @@ class IsInForObjects:
param_names = ["series_type", "vals_type"]

def setup(self, series_type, vals_type):
N_many = 10 ** 5
N_many = 10**5

if series_type == "nans":
ser_vals = np.full(10 ** 4, np.nan)
ser_vals = np.full(10**4, np.nan)
elif series_type == "short":
ser_vals = np.arange(2)
elif series_type == "long":
Expand All @@ -254,7 +254,7 @@ def setup(self, series_type, vals_type):
self.series = Series(ser_vals).astype(object)

if vals_type == "nans":
values = np.full(10 ** 4, np.nan)
values = np.full(10**4, np.nan)
elif vals_type == "short":
values = np.arange(2)
elif vals_type == "long":
Expand All @@ -277,7 +277,7 @@ class IsInLongSeriesLookUpDominates:
param_names = ["dtype", "MaxNumber", "series_type"]

def setup(self, dtype, MaxNumber, series_type):
N = 10 ** 7
N = 10**7

if series_type == "random_hits":
array = np.random.randint(0, MaxNumber, N)
Expand All @@ -304,15 +304,15 @@ class IsInLongSeriesValuesDominate:
param_names = ["dtype", "series_type"]

def setup(self, dtype, series_type):
N = 10 ** 7
N = 10**7

if series_type == "random":
vals = np.random.randint(0, 10 * N, N)
if series_type == "monotone":
vals = np.arange(N)

self.values = vals.astype(dtype.lower())
M = 10 ** 6 + 1
M = 10**6 + 1
self.series = Series(np.arange(M)).astype(dtype)

def time_isin(self, dtypes, series_type):
Expand Down
22 changes: 11 additions & 11 deletions asv_bench/benchmarks/arithmetic.py
Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,7 @@ def time_frame_op_with_scalar(self, dtype, scalar, op):
class OpWithFillValue:
def setup(self):
# GH#31300
arr = np.arange(10 ** 6)
arr = np.arange(10**6)
df = DataFrame({"A": arr})
ser = df["A"]

Expand Down Expand Up @@ -93,7 +93,7 @@ class MixedFrameWithSeriesAxis:
param_names = ["opname"]

def setup(self, opname):
arr = np.arange(10 ** 6).reshape(1000, -1)
arr = np.arange(10**6).reshape(1000, -1)
df = DataFrame(arr)
df["C"] = 1.0
self.df = df
Expand Down Expand Up @@ -201,7 +201,7 @@ def teardown(self, use_numexpr, threads):

class Ops2:
def setup(self):
N = 10 ** 3
N = 10**3
self.df = DataFrame(np.random.randn(N, N))
self.df2 = DataFrame(np.random.randn(N, N))

Expand Down Expand Up @@ -258,7 +258,7 @@ class Timeseries:
param_names = ["tz"]

def setup(self, tz):
N = 10 ** 6
N = 10**6
halfway = (N // 2) - 1
self.s = Series(date_range("20010101", periods=N, freq="T", tz=tz))
self.ts = self.s[halfway]
Expand All @@ -280,7 +280,7 @@ def time_timestamp_ops_diff_with_shift(self, tz):

class IrregularOps:
def setup(self):
N = 10 ** 5
N = 10**5
idx = date_range(start="1/1/2000", periods=N, freq="s")
s = Series(np.random.randn(N), index=idx)
self.left = s.sample(frac=1)
Expand All @@ -304,7 +304,7 @@ class CategoricalComparisons:
param_names = ["op"]

def setup(self, op):
N = 10 ** 5
N = 10**5
self.cat = pd.Categorical(list("aabbcd") * N, ordered=True)

def time_categorical_op(self, op):
Expand All @@ -317,7 +317,7 @@ class IndexArithmetic:
param_names = ["dtype"]

def setup(self, dtype):
N = 10 ** 6
N = 10**6
indexes = {"int": "makeIntIndex", "float": "makeFloatIndex"}
self.index = getattr(tm, indexes[dtype])(N)

Expand All @@ -343,7 +343,7 @@ class NumericInferOps:
param_names = ["dtype"]

def setup(self, dtype):
N = 5 * 10 ** 5
N = 5 * 10**5
self.df = DataFrame(
{"A": np.arange(N).astype(dtype), "B": np.arange(N).astype(dtype)}
)
Expand All @@ -367,7 +367,7 @@ def time_modulo(self, dtype):
class DateInferOps:
# from GH 7332
def setup_cache(self):
N = 5 * 10 ** 5
N = 5 * 10**5
df = DataFrame({"datetime64": np.arange(N).astype("datetime64[ms]")})
df["timedelta"] = df["datetime64"] - df["datetime64"]
return df
Expand All @@ -388,7 +388,7 @@ class AddOverflowScalar:
param_names = ["scalar"]

def setup(self, scalar):
N = 10 ** 6
N = 10**6
self.arr = np.arange(N)

def time_add_overflow_scalar(self, scalar):
Expand All @@ -397,7 +397,7 @@ def time_add_overflow_scalar(self, scalar):

class AddOverflowArray:
def setup(self):
N = 10 ** 6
N = 10**6
self.arr = np.arange(N)
self.arr_rev = np.arange(-N, 0)
self.arr_mixed = np.array([1, -1]).repeat(N / 2)
Expand Down
26 changes: 13 additions & 13 deletions asv_bench/benchmarks/categoricals.py
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@

class Constructor:
def setup(self):
N = 10 ** 5
N = 10**5
self.categories = list("abcde")
self.cat_idx = pd.Index(self.categories)
self.values = np.tile(self.categories, N)
Expand Down Expand Up @@ -71,16 +71,16 @@ def time_existing_series(self):

class AsType:
def setup(self):
N = 10 ** 5
N = 10**5

random_pick = np.random.default_rng().choice

categories = {
"str": list(string.ascii_letters),
"int": np.random.randint(2 ** 16, size=154),
"int": np.random.randint(2**16, size=154),
"float": sys.maxsize * np.random.random((38,)),
"timestamp": [
pd.Timestamp(x, unit="s") for x in np.random.randint(2 ** 18, size=578)
pd.Timestamp(x, unit="s") for x in np.random.randint(2**18, size=578)
],
}

Expand Down Expand Up @@ -112,7 +112,7 @@ def astype_datetime(self):

class Concat:
def setup(self):
N = 10 ** 5
N = 10**5
self.s = pd.Series(list("aabbcd") * N).astype("category")

self.a = pd.Categorical(list("aabbcd") * N)
Expand Down Expand Up @@ -148,7 +148,7 @@ class ValueCounts:
param_names = ["dropna"]

def setup(self, dropna):
n = 5 * 10 ** 5
n = 5 * 10**5
arr = [f"s{i:04d}" for i in np.random.randint(0, n // 10, size=n)]
self.ts = pd.Series(arr).astype("category")

Expand All @@ -166,7 +166,7 @@ def time_rendering(self):

class SetCategories:
def setup(self):
n = 5 * 10 ** 5
n = 5 * 10**5
arr = [f"s{i:04d}" for i in np.random.randint(0, n // 10, size=n)]
self.ts = pd.Series(arr).astype("category")

Expand All @@ -176,7 +176,7 @@ def time_set_categories(self):

class RemoveCategories:
def setup(self):
n = 5 * 10 ** 5
n = 5 * 10**5
arr = [f"s{i:04d}" for i in np.random.randint(0, n // 10, size=n)]
self.ts = pd.Series(arr).astype("category")

Expand All @@ -186,7 +186,7 @@ def time_remove_categories(self):

class Rank:
def setup(self):
N = 10 ** 5
N = 10**5
ncats = 100

self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
Expand Down Expand Up @@ -241,7 +241,7 @@ def time_categorical_series_is_monotonic_decreasing(self):

class Contains:
def setup(self):
N = 10 ** 5
N = 10**5
self.ci = tm.makeCategoricalIndex(N)
self.c = self.ci.values
self.key = self.ci.categories[0]
Expand All @@ -259,7 +259,7 @@ class CategoricalSlicing:
param_names = ["index"]

def setup(self, index):
N = 10 ** 6
N = 10**6
categories = ["a", "b", "c"]
values = [0] * N + [1] * N + [2] * N
if index == "monotonic_incr":
Expand Down Expand Up @@ -295,7 +295,7 @@ def time_getitem_bool_array(self, index):

class Indexing:
def setup(self):
N = 10 ** 5
N = 10**5
self.index = pd.CategoricalIndex(range(N), range(N))
self.series = pd.Series(range(N), index=self.index).sort_index()
self.category = self.index[500]
Expand Down Expand Up @@ -327,7 +327,7 @@ def time_sort_values(self):

class SearchSorted:
def setup(self):
N = 10 ** 5
N = 10**5
self.ci = tm.makeCategoricalIndex(N).sort_values()
self.c = self.ci.values
self.key = self.ci.categories[1]
Expand Down
6 changes: 3 additions & 3 deletions asv_bench/benchmarks/ctors.py
Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,7 @@ def setup(self, data_fmt, with_index, dtype):
raise NotImplementedError(
"Series constructors do not support using generators with indexes"
)
N = 10 ** 4
N = 10**4
if dtype == "float":
arr = np.random.randn(N)
else:
Expand All @@ -90,7 +90,7 @@ def time_series_constructor(self, data_fmt, with_index, dtype):

class SeriesDtypesConstructors:
def setup(self):
N = 10 ** 4
N = 10**4
self.arr = np.random.randn(N)
self.arr_str = np.array(["foo", "bar", "baz"], dtype=object)
self.s = Series(
Expand All @@ -114,7 +114,7 @@ def time_dtindex_from_index_with_series(self):

class MultiIndexConstructor:
def setup(self):
N = 10 ** 4
N = 10**4
self.iterables = [tm.makeStringIndex(N), range(20)]

def time_multiindex_from_iterables(self):
Expand Down
2 changes: 1 addition & 1 deletion asv_bench/benchmarks/eval.py
Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@ def teardown(self, engine, threads):

class Query:
def setup(self):
N = 10 ** 6
N = 10**6
halfway = (N // 2) - 1
index = pd.date_range("20010101", periods=N, freq="T")
s = pd.Series(index)
Expand Down
2 changes: 1 addition & 1 deletion asv_bench/benchmarks/frame_ctor.py
Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,7 @@ class FromDictwithTimestamp:
param_names = ["offset"]

def setup(self, offset):
N = 10 ** 3
N = 10**3
idx = date_range(Timestamp("1/1/1900"), freq=offset, periods=N)
df = DataFrame(np.random.randn(N, 10), index=idx)
self.d = df.to_dict()
Expand Down
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