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import warnings
import numpy as np
from pandas import (
NA,
Categorical,
DataFrame,
Index,
Series,
)
from pandas.arrays import StringArray
class Dtypes:
params = ["str", "string[python]", "string[pyarrow]"]
param_names = ["dtype"]
def setup(self, dtype):
try:
self.s = Series(
Index([f"i-{i}" for i in range(10000)], dtype=object), dtype=dtype
)
except ImportError:
raise NotImplementedError
class Construction:
params = (
["series", "frame", "categorical_series"],
["str", "string[python]", "string[pyarrow]"],
)
param_names = ["pd_type", "dtype"]
pd_mapping = {"series": Series, "frame": DataFrame, "categorical_series": Series}
dtype_mapping = {"str": "str", "string[python]": object, "string[pyarrow]": object}
def setup(self, pd_type, dtype):
series_arr = np.array(
[str(i) * 10 for i in range(100_000)], dtype=self.dtype_mapping[dtype]
)
if pd_type == "series":
self.arr = series_arr
elif pd_type == "frame":
self.arr = series_arr.reshape((50_000, 2)).copy()
elif pd_type == "categorical_series":
# GH37371. Testing construction of string series/frames from ExtensionArrays
self.arr = Categorical(series_arr)
def time_construction(self, pd_type, dtype):
self.pd_mapping[pd_type](self.arr, dtype=dtype)
def peakmem_construction(self, pd_type, dtype):
self.pd_mapping[pd_type](self.arr, dtype=dtype)
class Methods(Dtypes):
def time_center(self, dtype):
self.s.str.center(100)
def time_count(self, dtype):
self.s.str.count("A")
def time_endswith(self, dtype):
self.s.str.endswith("A")
def time_extract(self, dtype):
with warnings.catch_warnings(record=True):
self.s.str.extract("(\\w*)A(\\w*)")
def time_findall(self, dtype):
self.s.str.findall("[A-Z]+")
def time_find(self, dtype):
self.s.str.find("[A-Z]+")
def time_rfind(self, dtype):
self.s.str.rfind("[A-Z]+")
def time_fullmatch(self, dtype):
self.s.str.fullmatch("A")
def time_get(self, dtype):
self.s.str.get(0)
def time_len(self, dtype):
self.s.str.len()
def time_join(self, dtype):
self.s.str.join(" ")
def time_match(self, dtype):
self.s.str.match("A")
def time_normalize(self, dtype):
self.s.str.normalize("NFC")
def time_pad(self, dtype):
self.s.str.pad(100, side="both")
def time_partition(self, dtype):
self.s.str.partition("A")
def time_rpartition(self, dtype):
self.s.str.rpartition("A")
def time_replace(self, dtype):
self.s.str.replace("A", "\x01\x01")
def time_translate(self, dtype):
self.s.str.translate({"A": "\x01\x01"})
def time_slice(self, dtype):
self.s.str.slice(5, 15, 2)
def time_startswith(self, dtype):
self.s.str.startswith("A")
def time_strip(self, dtype):
self.s.str.strip("A")
def time_rstrip(self, dtype):
self.s.str.rstrip("A")
def time_lstrip(self, dtype):
self.s.str.lstrip("A")
def time_title(self, dtype):
self.s.str.title()
def time_upper(self, dtype):
self.s.str.upper()
def time_lower(self, dtype):
self.s.str.lower()
def time_wrap(self, dtype):
self.s.str.wrap(10)
def time_zfill(self, dtype):
self.s.str.zfill(10)
def time_isalnum(self, dtype):
self.s.str.isalnum()
def time_isalpha(self, dtype):
self.s.str.isalpha()
def time_isdecimal(self, dtype):
self.s.str.isdecimal()
def time_isdigit(self, dtype):
self.s.str.isdigit()
def time_islower(self, dtype):
self.s.str.islower()
def time_isnumeric(self, dtype):
self.s.str.isnumeric()
def time_isspace(self, dtype):
self.s.str.isspace()
def time_istitle(self, dtype):
self.s.str.istitle()
def time_isupper(self, dtype):
self.s.str.isupper()
class Repeat:
params = ["int", "array"]
param_names = ["repeats"]
def setup(self, repeats):
N = 10**5
self.s = Series(Index([f"i-{i}" for i in range(N)], dtype=object))
repeat = {"int": 1, "array": np.random.randint(1, 3, N)}
self.values = repeat[repeats]
def time_repeat(self, repeats):
self.s.str.repeat(self.values)
class Cat:
params = ([0, 3], [None, ","], [None, "-"], [0.0, 0.001, 0.15])
param_names = ["other_cols", "sep", "na_rep", "na_frac"]
def setup(self, other_cols, sep, na_rep, na_frac):
N = 10**5
mask_gen = lambda: np.random.choice([True, False], N, p=[1 - na_frac, na_frac])
self.s = Series(Index([f"i-{i}" for i in range(N)], dtype=object)).where(
mask_gen()
)
if other_cols == 0:
# str.cat self-concatenates only for others=None
self.others = None
else:
self.others = DataFrame(
{
i: Index([f"i-{i}" for i in range(N)], dtype=object).where(
mask_gen()
)
for i in range(other_cols)
}
)
def time_cat(self, other_cols, sep, na_rep, na_frac):
# before the concatenation (one caller + other_cols columns), the total
# expected fraction of rows containing any NaN is:
# reduce(lambda t, _: t + (1 - t) * na_frac, range(other_cols + 1), 0)
# for other_cols=3 and na_frac=0.15, this works out to ~48%
self.s.str.cat(others=self.others, sep=sep, na_rep=na_rep)
class Contains(Dtypes):
params = (Dtypes.params, [True, False])
param_names = ["dtype", "regex"]
def setup(self, dtype, regex):
super().setup(dtype)
def time_contains(self, dtype, regex):
self.s.str.contains("A", regex=regex)
class Split(Dtypes):
params = (Dtypes.params, [True, False])
param_names = ["dtype", "expand"]
def setup(self, dtype, expand):
super().setup(dtype)
self.s = self.s.str.join("--")
def time_split(self, dtype, expand):
self.s.str.split("--", expand=expand)
def time_rsplit(self, dtype, expand):
self.s.str.rsplit("--", expand=expand)
class Extract(Dtypes):
params = (Dtypes.params, [True, False])
param_names = ["dtype", "expand"]
def setup(self, dtype, expand):
super().setup(dtype)
def time_extract_single_group(self, dtype, expand):
with warnings.catch_warnings(record=True):
self.s.str.extract("(\\w*)A", expand=expand)
class Dummies(Dtypes):
def setup(self, dtype):
super().setup(dtype)
N = len(self.s) // 5
self.s = self.s[:N].str.join("|")
def time_get_dummies(self, dtype):
self.s.str.get_dummies("|")
class Encode:
def setup(self):
self.ser = Series(Index([f"i-{i}" for i in range(10_000)], dtype=object))
def time_encode_decode(self):
self.ser.str.encode("utf-8").str.decode("utf-8")
class Slice:
def setup(self):
self.s = Series(["abcdefg", np.nan] * 500000)
def time_vector_slice(self):
# GH 2602
self.s.str[:5]
class Iter(Dtypes):
def time_iter(self, dtype):
for i in self.s:
pass
class StringArrayConstruction:
def setup(self):
self.series_arr = np.array([str(i) * 10 for i in range(10**5)], dtype=object)
self.series_arr_nan = np.concatenate([self.series_arr, np.array([NA] * 1000)])
def time_string_array_construction(self):
StringArray(self.series_arr)
def time_string_array_with_nan_construction(self):
StringArray(self.series_arr_nan)
def peakmem_stringarray_construction(self):
StringArray(self.series_arr)
|
