text: # 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 pandas.util.testing as tm
  
  import pyarrow as pa
  
  
  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 = [tm.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 ZeroCopyPandasRead(object):
  
      def setup(self):
          # Transpose to make column-major
          values = np.random.randn(10, 100000)
  
          df = pd.DataFrame(values.T)
          ctx = pa.default_serialization_context()
  
          self.serialized = ctx.serialize(df)
          self.as_buffer = self.serialized.to_buffer()
          self.as_components = self.serialized.to_components()
  
      def time_deserialize_from_buffer(self):
          pa.deserialize(self.as_buffer)
  
      def time_deserialize_from_components(self):
          pa.deserialize_components(self.as_components)
  
  
  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)
  