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# 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 A
class PyListConversions(object):
param_names = ('size',)
params = (1, 10 ** 5, 10 ** 6, 10 ** 7)
def setup(self, n):
self.data = list(range(n))
def time_from_pylist(self, n):
A.from_pylist(self.data)
def peakmem_from_pylist(self, n):
A.from_pylist(self.data)
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 = ((1, 10 ** 5, 10 ** 6, 10 ** 7), ('int64', 'float64', 'float64_nans', 'str'))
def time_from_series(self, n, dtype):
A.Table.from_pandas(self.data)
def peakmem_from_series(self, n, dtype):
A.Table.from_pandas(self.data)
class PandasConversionsFromArrow(PandasConversionsBase):
param_names = ('size', 'dtype')
params = ((1, 10 ** 5, 10 ** 6, 10 ** 7), ('int64', 'float64', 'float64_nans', 'str'))
def setup(self, n, dtype):
super(PandasConversionsFromArrow, self).setup(n, dtype)
self.arrow_data = A.Table.from_pandas(self.data)
def time_to_series(self, n, dtype):
self.arrow_data.to_pandas()
def peakmem_to_series(self, n, dtype):
self.arrow_data.to_pandas()
class ScalarAccess(object):
param_names = ('size',)
params = (1, 10 ** 5, 10 ** 6, 10 ** 7)
def setUp(self, n):
self._array = A.from_pylist(list(range(n)))
def time_as_py(self, n):
for i in range(n):
self._array[i].as_py()