asv_bench/benchmarks/array.py - main - githubmirror/pandas

asv_bench/benchmarks/array.py
140 строк4 KB
Trinh Quoc Anh
STY: Enable B904 (#56941)
22 янв 2024, 16:02
22 янв 2024, 16:025740667
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 as err:
            raise NotImplementedError from err
        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()