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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)
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