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import numpy as np
import pandas as pd
class BooleanArray:
def setup(self):
self.values_bool = np.array([True, False, True, False])
self.values_float = np.array([1.0, 0.0, 1.0, 0.0])
self.values_integer = np.array([1, 0, 1, 0])
self.values_integer_like = [1, 0, 1, 0]
self.data = np.array([True, False, True, False])
self.mask = np.array([False, False, True, False])
def time_constructor(self):
pd.arrays.BooleanArray(self.data, self.mask)
def time_from_bool_array(self):
pd.array(self.values_bool, dtype="boolean")
def time_from_integer_array(self):
pd.array(self.values_integer, dtype="boolean")
def time_from_integer_like(self):
pd.array(self.values_integer_like, dtype="boolean")
def time_from_float_array(self):
pd.array(self.values_float, dtype="boolean")
class IntegerArray:
def setup(self):
N = 250_000
self.values_integer = np.tile(np.array([1, 0, 1, 0]), N)
self.data = np.tile(np.array([1, 2, 3, 4], dtype="int64"), N)
self.mask = np.tile(np.array([False, False, True, False]), N)
def time_constructor(self):
pd.arrays.IntegerArray(self.data, self.mask)
def time_from_integer_array(self):
pd.array(self.values_integer, dtype="Int64")
class IntervalArray:
def setup(self):
N = 10_000
self.tuples = [(i, i + 1) for i in range(N)]
def time_from_tuples(self):
pd.arrays.IntervalArray.from_tuples(self.tuples)
class StringArray:
def setup(self):
N = 100_000
values = np.array([str(i) for i in range(N)], dtype=object)
self.values_obj = np.array(values, dtype="object")
self.values_str = np.array(values, dtype="U")
self.values_list = values.tolist()
def time_from_np_object_array(self):
pd.array(self.values_obj, dtype="string")
def time_from_np_str_array(self):
pd.array(self.values_str, dtype="string")
def time_from_list(self):
pd.array(self.values_list, dtype="string")
class ArrowStringArray:
params = [False, True]
param_names = ["multiple_chunks"]
def setup(self, multiple_chunks):
try:
import pyarrow as pa
except ImportError:
raise NotImplementedError
strings = np.array([str(i) for i in range(10_000)], dtype=object)
if multiple_chunks:
chunks = [strings[i : i + 100] for i in range(0, len(strings), 100)]
self.array = pd.arrays.ArrowStringArray(pa.chunked_array(chunks))
else:
self.array = pd.arrays.ArrowStringArray(pa.array(strings))
def time_setitem(self, multiple_chunks):
for i in range(200):
self.array[i] = "foo"
def time_setitem_list(self, multiple_chunks):
indexer = list(range(50)) + list(range(-1000, 0, 50))
self.array[indexer] = ["foo"] * len(indexer)
def time_setitem_slice(self, multiple_chunks):
self.array[::10] = "foo"
def time_setitem_null_slice(self, multiple_chunks):
self.array[:] = "foo"
def time_tolist(self, multiple_chunks):
self.array.tolist()
class ArrowExtensionArray:
params = [
[
"boolean[pyarrow]",
"float64[pyarrow]",
"int64[pyarrow]",
"string[pyarrow]",
"timestamp[ns][pyarrow]",
],
[False, True],
]
param_names = ["dtype", "hasna"]
def setup(self, dtype, hasna):
N = 100_000
if dtype == "boolean[pyarrow]":
data = np.random.choice([True, False], N, replace=True)
elif dtype == "float64[pyarrow]":
data = np.random.randn(N)
elif dtype == "int64[pyarrow]":
data = np.arange(N)
elif dtype == "string[pyarrow]":
data = np.array([str(i) for i in range(N)], dtype=object)
elif dtype == "timestamp[ns][pyarrow]":
data = pd.date_range("2000-01-01", freq="s", periods=N)
else:
raise NotImplementedError
arr = pd.array(data, dtype=dtype)
if hasna:
arr[::2] = pd.NA
self.arr = arr
def time_to_numpy(self, dtype, hasna):
self.arr.to_numpy()
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