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from importlib import import_module
import numpy as np
import pandas as pd
for imp in ["pandas.util", "pandas.tools.hashing"]:
try:
hashing = import_module(imp)
break
except (ImportError, TypeError, ValueError):
pass
class Factorize:
params = [
[True, False],
[True, False],
[
"int64",
"uint64",
"float64",
"object",
"object_str",
"datetime64[ns]",
"datetime64[ns, tz]",
"Int64",
"boolean",
"string[pyarrow]",
],
]
param_names = ["unique", "sort", "dtype"]
def setup(self, unique, sort, dtype):
N = 10**5
if dtype in ["int64", "uint64", "Int64", "object"]:
data = pd.Index(np.arange(N), dtype=dtype)
elif dtype == "float64":
data = pd.Index(np.random.randn(N), dtype=dtype)
elif dtype == "boolean":
data = pd.array(np.random.randint(0, 2, N), dtype=dtype)
elif dtype == "datetime64[ns]":
data = pd.date_range("2011-01-01", freq="h", periods=N)
elif dtype == "datetime64[ns, tz]":
data = pd.date_range("2011-01-01", freq="h", periods=N, tz="Asia/Tokyo")
elif dtype == "object_str":
data = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
elif dtype == "string[pyarrow]":
data = pd.array(
pd.Index([f"i-{i}" for i in range(N)], dtype=object),
dtype="string[pyarrow]",
)
else:
raise NotImplementedError
if not unique:
data = data.repeat(5)
self.data = data
def time_factorize(self, unique, sort, dtype):
pd.factorize(self.data, sort=sort)
def peakmem_factorize(self, unique, sort, dtype):
pd.factorize(self.data, sort=sort)
class Duplicated:
params = [
[True, False],
["first", "last", False],
[
"int64",
"uint64",
"float64",
"string",
"datetime64[ns]",
"datetime64[ns, tz]",
"timestamp[ms][pyarrow]",
"duration[s][pyarrow]",
],
]
param_names = ["unique", "keep", "dtype"]
def setup(self, unique, keep, dtype):
N = 10**5
if dtype in ["int64", "uint64"]:
data = pd.Index(np.arange(N), dtype=dtype)
elif dtype == "float64":
data = pd.Index(np.random.randn(N), dtype="float64")
elif dtype == "string":
data = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
elif dtype == "datetime64[ns]":
data = pd.date_range("2011-01-01", freq="h", periods=N)
elif dtype == "datetime64[ns, tz]":
data = pd.date_range("2011-01-01", freq="h", periods=N, tz="Asia/Tokyo")
elif dtype in ["timestamp[ms][pyarrow]", "duration[s][pyarrow]"]:
data = pd.Index(np.arange(N), dtype=dtype)
else:
raise NotImplementedError
if not unique:
data = data.repeat(5)
self.idx = data
# cache is_unique
self.idx.is_unique
def time_duplicated(self, unique, keep, dtype):
self.idx.duplicated(keep=keep)
class DuplicatedMaskedArray:
params = [
[True, False],
["first", "last", False],
["Int64", "Float64"],
]
param_names = ["unique", "keep", "dtype"]
def setup(self, unique, keep, dtype):
N = 10**5
data = pd.Series(np.arange(N), dtype=dtype)
data[list(range(1, N, 100))] = pd.NA
if not unique:
data = data.repeat(5)
self.ser = data
# cache is_unique
self.ser.is_unique
def time_duplicated(self, unique, keep, dtype):
self.ser.duplicated(keep=keep)
class Hashing:
def setup_cache(self):
N = 10**5
df = pd.DataFrame(
{
"strings": pd.Series(
pd.Index([f"i-{i}" for i in range(10000)], dtype=object).take(
np.random.randint(0, 10000, size=N)
)
),
"floats": np.random.randn(N),
"ints": np.arange(N),
"dates": pd.date_range("20110101", freq="s", periods=N),
"timedeltas": pd.timedelta_range("1 day", freq="s", periods=N),
}
)
df["categories"] = df["strings"].astype("category")
df.iloc[10:20] = np.nan
return df
def time_frame(self, df):
hashing.hash_pandas_object(df)
def time_series_int(self, df):
hashing.hash_pandas_object(df["ints"])
def time_series_string(self, df):
hashing.hash_pandas_object(df["strings"])
def time_series_float(self, df):
hashing.hash_pandas_object(df["floats"])
def time_series_categorical(self, df):
hashing.hash_pandas_object(df["categories"])
def time_series_timedeltas(self, df):
hashing.hash_pandas_object(df["timedeltas"])
def time_series_dates(self, df):
hashing.hash_pandas_object(df["dates"])
class Quantile:
params = [
[0, 0.5, 1],
["linear", "nearest", "lower", "higher", "midpoint"],
["float64", "int64", "uint64"],
]
param_names = ["quantile", "interpolation", "dtype"]
def setup(self, quantile, interpolation, dtype):
N = 10**5
if dtype in ["int64", "uint64"]:
data = np.arange(N, dtype=dtype)
elif dtype == "float64":
data = np.random.randn(N)
else:
raise NotImplementedError
self.ser = pd.Series(data.repeat(5))
def time_quantile(self, quantile, interpolation, dtype):
self.ser.quantile(quantile, interpolation=interpolation)
class SortIntegerArray:
params = [10**3, 10**5]
def setup(self, N):
data = np.arange(N, dtype=float)
data[40] = np.nan
self.array = pd.array(data, dtype="Int64")
def time_argsort(self, N):
self.array.argsort()
from .pandas_vb_common import setup # noqa: F401 isort:skip
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