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| # Licensed to the Apache Software Foundation (ASF) under one | |
| # or more contributor license agreements. See the NOTICE file | |
| # distributed with this work for additional information | |
| # regarding copyright ownership. The ASF licenses this file | |
| # to you under the Apache License, Version 2.0 (the | |
| # "License"); you may not use this file except in compliance | |
| # with the License. You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, | |
| # software distributed under the License is distributed on an | |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | |
| # KIND, either express or implied. See the License for the | |
| # specific language governing permissions and limitations | |
| # under the License. | |
| import numpy as np | |
| import pandas as pd | |
| import pyarrow as pa | |
| from pyarrow.tests.util import rands | |
| class PandasConversionsBase(object): | |
| def setup(self, n, dtype): | |
| if dtype == 'float64_nans': | |
| arr = np.arange(n).astype('float64') | |
| arr[arr % 10 == 0] = np.nan | |
| else: | |
| arr = np.arange(n).astype(dtype) | |
| self.data = pd.DataFrame({'column': arr}) | |
| class PandasConversionsToArrow(PandasConversionsBase): | |
| param_names = ('size', 'dtype') | |
| params = ((10, 10 ** 6), ('int64', 'float64', 'float64_nans', 'str')) | |
| def time_from_series(self, n, dtype): | |
| pa.Table.from_pandas(self.data) | |
| class PandasConversionsFromArrow(PandasConversionsBase): | |
| param_names = ('size', 'dtype') | |
| params = ((10, 10 ** 6), ('int64', 'float64', 'float64_nans', 'str')) | |
| def setup(self, n, dtype): | |
| super(PandasConversionsFromArrow, self).setup(n, dtype) | |
| self.arrow_data = pa.Table.from_pandas(self.data) | |
| def time_to_series(self, n, dtype): | |
| self.arrow_data.to_pandas() | |
| class ToPandasStrings(object): | |
| param_names = ('uniqueness', 'total') | |
| params = ((0.001, 0.01, 0.1, 0.5), (1000000,)) | |
| string_length = 25 | |
| def setup(self, uniqueness, total): | |
| nunique = int(total * uniqueness) | |
| unique_values = [rands(self.string_length) for i in range(nunique)] | |
| values = unique_values * (total // nunique) | |
| self.arr = pa.array(values, type=pa.string()) | |
| self.table = pa.Table.from_arrays([self.arr], ['f0']) | |
| def time_to_pandas_dedup(self, *args): | |
| self.arr.to_pandas() | |
| def time_to_pandas_no_dedup(self, *args): | |
| self.arr.to_pandas(deduplicate_objects=False) | |
| class SerializeDeserializePandas(object): | |
| def setup(self): | |
| # 10 million length | |
| n = 10000000 | |
| self.df = pd.DataFrame({'data': np.random.randn(n)}) | |
| self.serialized = pa.serialize_pandas(self.df) | |
| def time_serialize_pandas(self): | |
| pa.serialize_pandas(self.df) | |
| def time_deserialize_pandas(self): | |
| pa.deserialize_pandas(self.serialized) | |
| class TableFromPandasMicroperformance(object): | |
| # ARROW-4629 | |
| def setup(self): | |
| ser = pd.Series(range(10000)) | |
| df = pd.DataFrame({col: ser.copy(deep=True) for col in range(100)}) | |
| # Simulate a real dataset by converting some columns to strings | |
| self.df = df.astype({col: str for col in range(50)}) | |
| def time_Table_from_pandas(self): | |
| for _ in range(50): | |
| pa.Table.from_pandas(self.df, nthreads=1) | |