code: # -*- coding: utf-8 -*-
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  # 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 decimal
  import json
  from collections import OrderedDict
  from datetime import date, datetime, time, timedelta
  
  import numpy as np
  import numpy.testing as npt
  import pandas as pd
  import pandas.util.testing as tm
  import pytest
  
  import pyarrow as pa
  import pyarrow.types as patypes
  from pyarrow.compat import PY2
  
  from .pandas_examples import dataframe_with_arrays, dataframe_with_lists
  
  
  def _alltypes_example(size=100):
      return pd.DataFrame({
          'uint8': np.arange(size, dtype=np.uint8),
          'uint16': np.arange(size, dtype=np.uint16),
          'uint32': np.arange(size, dtype=np.uint32),
          'uint64': np.arange(size, dtype=np.uint64),
          'int8': np.arange(size, dtype=np.int16),
          'int16': np.arange(size, dtype=np.int16),
          'int32': np.arange(size, dtype=np.int32),
          'int64': np.arange(size, dtype=np.int64),
          'float32': np.arange(size, dtype=np.float32),
          'float64': np.arange(size, dtype=np.float64),
          'bool': np.random.randn(size) > 0,
          # TODO(wesm): Pandas only support ns resolution, Arrow supports s, ms,
          # us, ns
          'datetime': np.arange("2016-01-01T00:00:00.001", size,
                                dtype='datetime64[ms]'),
          'str': [str(x) for x in range(size)],
          'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None],
          'empty_str': [''] * size
      })
  
  
  def _check_pandas_roundtrip(df, expected=None, use_threads=False,
                              expected_schema=None,
                              check_dtype=True, schema=None,
                              preserve_index=False,
                              as_batch=False):
      klass = pa.RecordBatch if as_batch else pa.Table
      table = klass.from_pandas(df, schema=schema,
                                preserve_index=preserve_index,
                                nthreads=2 if use_threads else 1)
      result = table.to_pandas(use_threads=use_threads)
  
      if expected_schema:
          # all occurences of _check_pandas_roundtrip passes expected_schema
          # without the pandas generated key-value metadata, so we need to
          # add it before checking schema equality
          expected_schema = expected_schema.add_metadata(table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
      if expected is None:
          expected = df
  
      tm.assert_frame_equal(result, expected, check_dtype=check_dtype,
                            check_index_type=('equiv' if preserve_index
                                              else False))
  
  
  def _check_series_roundtrip(s, type_=None, expected_pa_type=None):
      arr = pa.array(s, from_pandas=True, type=type_)
  
      if type_ is not None and expected_pa_type is None:
          expected_pa_type = type_
  
      if expected_pa_type is not None:
          assert arr.type == expected_pa_type
  
      result = pd.Series(arr.to_pandas(), name=s.name)
      if patypes.is_timestamp(arr.type) and arr.type.tz is not None:
          result = (result.dt.tz_localize('utc')
                    .dt.tz_convert(arr.type.tz))
  
      tm.assert_series_equal(s, result)
  
  
  def _check_array_roundtrip(values, expected=None, mask=None,
                             type=None):
      arr = pa.array(values, from_pandas=True, mask=mask, type=type)
      result = arr.to_pandas()
  
      values_nulls = pd.isnull(values)
      if mask is None:
          assert arr.null_count == values_nulls.sum()
      else:
          assert arr.null_count == (mask | values_nulls).sum()
  
      if mask is None:
          tm.assert_series_equal(pd.Series(result), pd.Series(values),
                                 check_names=False)
      else:
          expected = pd.Series(np.ma.masked_array(values, mask=mask))
          tm.assert_series_equal(pd.Series(result), expected,
                                 check_names=False)
  
  
  def _check_array_from_pandas_roundtrip(np_array):
      arr = pa.array(np_array, from_pandas=True)
      result = arr.to_pandas()
      npt.assert_array_equal(result, np_array)
  
  
  class TestConvertMetadata(object):
      """
      Conversion tests for Pandas metadata & indices.
      """
  
      def test_non_string_columns(self):
          df = pd.DataFrame({0: [1, 2, 3]})
          table = pa.Table.from_pandas(df)
          assert table.column(0).name == '0'
  
      def test_from_pandas_with_columns(self):
          df = pd.DataFrame({0: [1, 2, 3], 1: [1, 3, 3], 2: [2, 4, 5]})
  
          table = pa.Table.from_pandas(df, columns=[0, 1])
          expected = pa.Table.from_pandas(df[[0, 1]])
          assert expected.equals(table)
  
          record_batch_table = pa.RecordBatch.from_pandas(df, columns=[0, 1])
          record_batch_expected = pa.RecordBatch.from_pandas(df[[0, 1]])
          assert record_batch_expected.equals(record_batch_table)
  
      def test_column_index_names_are_preserved(self):
          df = pd.DataFrame({'data': [1, 2, 3]})
          df.columns.names = ['a']
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_multiindex_columns(self):
          columns = pd.MultiIndex.from_arrays([
              ['one', 'two'], ['X', 'Y']
          ])
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns)
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_multiindex_columns_with_dtypes(self):
          columns = pd.MultiIndex.from_arrays(
              [
                  ['one', 'two'],
                  pd.DatetimeIndex(['2017-08-01', '2017-08-02']),
              ],
              names=['level_1', 'level_2'],
          )
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns)
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_multiindex_columns_unicode(self):
          columns = pd.MultiIndex.from_arrays([[u'あ', u'い'], ['X', 'Y']])
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')], columns=columns)
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_integer_index_column(self):
          df = pd.DataFrame([(1, 'a'), (2, 'b'), (3, 'c')])
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_index_metadata_field_name(self):
          # test None case, and strangely named non-index columns
          df = pd.DataFrame(
              [(1, 'a', 3.1), (2, 'b', 2.2), (3, 'c', 1.3)],
              index=pd.MultiIndex.from_arrays(
                  [['c', 'b', 'a'], [3, 2, 1]],
                  names=[None, 'foo']
              ),
              columns=['a', None, '__index_level_0__'],
          )
          t = pa.Table.from_pandas(df, preserve_index=True)
          raw_metadata = t.schema.metadata
  
          js = json.loads(raw_metadata[b'pandas'].decode('utf8'))
  
          col1, col2, col3, idx0, foo = js['columns']
  
          assert col1['name'] == 'a'
          assert col1['name'] == col1['field_name']
  
          assert col2['name'] is None
          assert col2['field_name'] == 'None'
  
          assert col3['name'] == '__index_level_0__'
          assert col3['name'] == col3['field_name']
  
          idx0_name, foo_name = js['index_columns']
          assert idx0_name == '__index_level_0__'
          assert idx0['field_name'] == idx0_name
          assert idx0['name'] is None
  
          assert foo_name == 'foo'
          assert foo['field_name'] == foo_name
          assert foo['name'] == foo_name
  
      def test_categorical_column_index(self):
          df = pd.DataFrame(
              [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)],
              columns=pd.Index(list('def'), dtype='category')
          )
          t = pa.Table.from_pandas(df, preserve_index=True)
          raw_metadata = t.schema.metadata
          js = json.loads(raw_metadata[b'pandas'].decode('utf8'))
  
          column_indexes, = js['column_indexes']
          assert column_indexes['name'] is None
          assert column_indexes['pandas_type'] == 'categorical'
          assert column_indexes['numpy_type'] == 'int8'
  
          md = column_indexes['metadata']
          assert md['num_categories'] == 3
          assert md['ordered'] is False
  
      def test_string_column_index(self):
          df = pd.DataFrame(
              [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)],
              columns=pd.Index(list('def'), name='stringz')
          )
          t = pa.Table.from_pandas(df, preserve_index=True)
          raw_metadata = t.schema.metadata
          js = json.loads(raw_metadata[b'pandas'].decode('utf8'))
  
          column_indexes, = js['column_indexes']
          assert column_indexes['name'] == 'stringz'
          assert column_indexes['name'] == column_indexes['field_name']
          assert column_indexes['pandas_type'] == ('bytes' if PY2 else 'unicode')
          assert column_indexes['numpy_type'] == 'object'
  
          md = column_indexes['metadata']
  
          if not PY2:
              assert len(md) == 1
              assert md['encoding'] == 'UTF-8'
          else:
              assert md is None or 'encoding' not in md
  
      def test_datetimetz_column_index(self):
          df = pd.DataFrame(
              [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)],
              columns=pd.date_range(
                  start='2017-01-01', periods=3, tz='America/New_York'
              )
          )
          t = pa.Table.from_pandas(df, preserve_index=True)
          raw_metadata = t.schema.metadata
          js = json.loads(raw_metadata[b'pandas'].decode('utf8'))
  
          column_indexes, = js['column_indexes']
          assert column_indexes['name'] is None
          assert column_indexes['pandas_type'] == 'datetimetz'
          assert column_indexes['numpy_type'] == 'datetime64[ns]'
  
          md = column_indexes['metadata']
          assert md['timezone'] == 'America/New_York'
  
      def test_datetimetz_row_index(self):
          df = pd.DataFrame({
              'a': pd.date_range(
                  start='2017-01-01', periods=3, tz='America/New_York'
              )
          })
          df = df.set_index('a')
  
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_categorical_row_index(self):
          df = pd.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]})
          df['a'] = df.a.astype('category')
          df = df.set_index('a')
  
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_duplicate_column_names_does_not_crash(self):
          df = pd.DataFrame([(1, 'a'), (2, 'b')], columns=list('aa'))
          with pytest.raises(ValueError):
              pa.Table.from_pandas(df)
  
      def test_dictionary_indices_boundscheck(self):
          # ARROW-1658. No validation of indices leads to segfaults in pandas
          indices = [[0, 1], [0, -1]]
  
          for inds in indices:
              arr = pa.DictionaryArray.from_arrays(inds, ['a'], safe=False)
              batch = pa.RecordBatch.from_arrays([arr], ['foo'])
              table = pa.Table.from_batches([batch, batch, batch])
  
              with pytest.raises(pa.ArrowInvalid):
                  arr.to_pandas()
  
              with pytest.raises(pa.ArrowInvalid):
                  table.to_pandas()
  
      def test_unicode_with_unicode_column_and_index(self):
          df = pd.DataFrame({u'あ': [u'い']}, index=[u'う'])
  
          _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_mixed_unicode_column_names(self):
          df = pd.DataFrame({u'あ': [u'い'], b'a': 1}, index=[u'う'])
  
          # TODO(phillipc): Should this raise?
          with pytest.raises(AssertionError):
              _check_pandas_roundtrip(df, preserve_index=True)
  
      def test_binary_column_name(self):
          column_data = [u'い']
          key = u'あ'.encode('utf8')
          data = {key: column_data}
          df = pd.DataFrame(data)
  
          # we can't use _check_pandas_roundtrip here because our metdata
          # is always decoded as utf8: even if binary goes in, utf8 comes out
          t = pa.Table.from_pandas(df, preserve_index=True)
          df2 = t.to_pandas()
          assert df.values[0] == df2.values[0]
          assert df.index.values[0] == df2.index.values[0]
          assert df.columns[0] == key
  
      def test_multiindex_duplicate_values(self):
          num_rows = 3
          numbers = list(range(num_rows))
          index = pd.MultiIndex.from_arrays(
              [['foo', 'foo', 'bar'], numbers],
              names=['foobar', 'some_numbers'],
          )
  
          df = pd.DataFrame({'numbers': numbers}, index=index)
  
          table = pa.Table.from_pandas(df)
          result_df = table.to_pandas()
          tm.assert_frame_equal(result_df, df)
  
      def test_metadata_with_mixed_types(self):
          df = pd.DataFrame({'data': [b'some_bytes', u'some_unicode']})
          table = pa.Table.from_pandas(df)
          metadata = table.schema.metadata
          assert b'mixed' not in metadata[b'pandas']
  
          js = json.loads(metadata[b'pandas'].decode('utf8'))
          data_column = js['columns'][0]
          assert data_column['pandas_type'] == 'bytes'
          assert data_column['numpy_type'] == 'object'
  
      def test_list_metadata(self):
          df = pd.DataFrame({'data': [[1], [2, 3, 4], [5] * 7]})
          schema = pa.schema([pa.field('data', type=pa.list_(pa.int64()))])
          table = pa.Table.from_pandas(df, schema=schema)
          metadata = table.schema.metadata
          assert b'mixed' not in metadata[b'pandas']
  
          js = json.loads(metadata[b'pandas'].decode('utf8'))
          data_column = js['columns'][0]
          assert data_column['pandas_type'] == 'list[int64]'
          assert data_column['numpy_type'] == 'object'
  
      def test_decimal_metadata(self):
          expected = pd.DataFrame({
              'decimals': [
                  decimal.Decimal('394092382910493.12341234678'),
                  -decimal.Decimal('314292388910493.12343437128'),
              ]
          })
          table = pa.Table.from_pandas(expected)
          metadata = table.schema.metadata
          assert b'mixed' not in metadata[b'pandas']
  
          js = json.loads(metadata[b'pandas'].decode('utf8'))
          data_column = js['columns'][0]
          assert data_column['pandas_type'] == 'decimal'
          assert data_column['numpy_type'] == 'object'
          assert data_column['metadata'] == {'precision': 26, 'scale': 11}
  
      def test_table_column_subset_metadata(self):
          # ARROW-1883
          df = pd.DataFrame({
              'a': [1, 2, 3],
              'b': pd.date_range("2017-01-01", periods=3, tz='Europe/Brussels')})
          table = pa.Table.from_pandas(df)
  
          table_subset = table.remove_column(1)
          result = table_subset.to_pandas()
          tm.assert_frame_equal(result, df[['a']])
  
          table_subset2 = table_subset.remove_column(1)
          result = table_subset2.to_pandas()
          tm.assert_frame_equal(result, df[['a']])
  
          # non-default index
          for index in [
                  pd.Index(['a', 'b', 'c'], name='index'),
                  pd.date_range("2017-01-01", periods=3, tz='Europe/Brussels')]:
              df = pd.DataFrame({'a': [1, 2, 3],
                                 'b': [.1, .2, .3]}, index=index)
              table = pa.Table.from_pandas(df)
  
              table_subset = table.remove_column(1)
              result = table_subset.to_pandas()
              tm.assert_frame_equal(result, df[['a']])
  
              table_subset2 = table_subset.remove_column(1)
              result = table_subset2.to_pandas()
              tm.assert_frame_equal(result, df[['a']].reset_index(drop=True))
  
      def test_empty_list_metadata(self):
          # Create table with array of empty lists, forced to have type
          # list(string) in pyarrow
          c1 = [["test"], ["a", "b"], None]
          c2 = [[], [], []]
          arrays = OrderedDict([
              ('c1', pa.array(c1, type=pa.list_(pa.string()))),
              ('c2', pa.array(c2, type=pa.list_(pa.string()))),
          ])
          rb = pa.RecordBatch.from_arrays(
              list(arrays.values()),
              list(arrays.keys())
          )
          tbl = pa.Table.from_batches([rb])
  
          # First roundtrip changes schema, because pandas cannot preserve the
          # type of empty lists
          df = tbl.to_pandas()
          tbl2 = pa.Table.from_pandas(df, preserve_index=True)
          md2 = json.loads(tbl2.schema.metadata[b'pandas'].decode('utf8'))
  
          # Second roundtrip
          df2 = tbl2.to_pandas()
          expected = pd.DataFrame(OrderedDict([('c1', c1), ('c2', c2)]))
  
          tm.assert_frame_equal(df2, expected)
  
          assert md2['columns'] == [
              {
                  'name': 'c1',
                  'field_name': 'c1',
                  'metadata': None,
                  'numpy_type': 'object',
                  'pandas_type': 'list[unicode]',
              },
              {
                  'name': 'c2',
                  'field_name': 'c2',
                  'metadata': None,
                  'numpy_type': 'object',
                  'pandas_type': 'list[empty]',
              },
              {
                  'name': None,
                  'field_name': '__index_level_0__',
                  'metadata': None,
                  'numpy_type': 'int64',
                  'pandas_type': 'int64',
              }
          ]
  
  
  class TestConvertPrimitiveTypes(object):
      """
      Conversion tests for primitive (e.g. numeric) types.
      """
  
      def test_float_no_nulls(self):
          data = {}
          fields = []
          dtypes = [('f2', pa.float16()),
                    ('f4', pa.float32()),
                    ('f8', pa.float64())]
          num_values = 100
  
          for numpy_dtype, arrow_dtype in dtypes:
              values = np.random.randn(num_values)
              data[numpy_dtype] = values.astype(numpy_dtype)
              fields.append(pa.field(numpy_dtype, arrow_dtype))
  
          df = pd.DataFrame(data)
          schema = pa.schema(fields)
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_float_nulls(self):
          num_values = 100
  
          null_mask = np.random.randint(0, 10, size=num_values) < 3
          dtypes = [('f2', pa.float16()),
                    ('f4', pa.float32()),
                    ('f8', pa.float64())]
          names = ['f2', 'f4', 'f8']
          expected_cols = []
  
          arrays = []
          fields = []
          for name, arrow_dtype in dtypes:
              values = np.random.randn(num_values).astype(name)
  
              arr = pa.array(values, from_pandas=True, mask=null_mask)
              arrays.append(arr)
              fields.append(pa.field(name, arrow_dtype))
              values[null_mask] = np.nan
  
              expected_cols.append(values)
  
          ex_frame = pd.DataFrame(dict(zip(names, expected_cols)),
                                  columns=names)
  
          table = pa.Table.from_arrays(arrays, names)
          assert table.schema.equals(pa.schema(fields))
          result = table.to_pandas()
          tm.assert_frame_equal(result, ex_frame)
  
      def test_float_nulls_to_ints(self):
          # ARROW-2135
          df = pd.DataFrame({"a": [1.0, 2.0, pd.np.NaN]})
          schema = pa.schema([pa.field("a", pa.int16(), nullable=True)])
          table = pa.Table.from_pandas(df, schema=schema)
          assert table[0].to_pylist() == [1, 2, None]
          tm.assert_frame_equal(df, table.to_pandas())
  
      def test_integer_no_nulls(self):
          data = OrderedDict()
          fields = []
  
          numpy_dtypes = [
              ('i1', pa.int8()), ('i2', pa.int16()),
              ('i4', pa.int32()), ('i8', pa.int64()),
              ('u1', pa.uint8()), ('u2', pa.uint16()),
              ('u4', pa.uint32()), ('u8', pa.uint64()),
              ('longlong', pa.int64()), ('ulonglong', pa.uint64())
          ]
          num_values = 100
  
          for dtype, arrow_dtype in numpy_dtypes:
              info = np.iinfo(dtype)
              values = np.random.randint(max(info.min, np.iinfo(np.int_).min),
                                         min(info.max, np.iinfo(np.int_).max),
                                         size=num_values)
              data[dtype] = values.astype(dtype)
              fields.append(pa.field(dtype, arrow_dtype))
  
          df = pd.DataFrame(data)
          schema = pa.schema(fields)
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_all_integer_types(self):
          # Test all Numpy integer aliases
          data = OrderedDict()
          numpy_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8',
                          'byte', 'ubyte', 'short', 'ushort', 'intc', 'uintc',
                          'int_', 'uint', 'longlong', 'ulonglong']
          for dtype in numpy_dtypes:
              data[dtype] = np.arange(12, dtype=dtype)
          df = pd.DataFrame(data)
          _check_pandas_roundtrip(df)
  
      def test_integer_with_nulls(self):
          # pandas requires upcast to float dtype
  
          int_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8']
          num_values = 100
  
          null_mask = np.random.randint(0, 10, size=num_values) < 3
  
          expected_cols = []
          arrays = []
          for name in int_dtypes:
              values = np.random.randint(0, 100, size=num_values)
  
              arr = pa.array(values, mask=null_mask)
              arrays.append(arr)
  
              expected = values.astype('f8')
              expected[null_mask] = np.nan
  
              expected_cols.append(expected)
  
          ex_frame = pd.DataFrame(dict(zip(int_dtypes, expected_cols)),
                                  columns=int_dtypes)
  
          table = pa.Table.from_arrays(arrays, int_dtypes)
          result = table.to_pandas()
  
          tm.assert_frame_equal(result, ex_frame)
  
      def test_array_from_pandas_type_cast(self):
          arr = np.arange(10, dtype='int64')
  
          target_type = pa.int8()
  
          result = pa.array(arr, type=target_type)
          expected = pa.array(arr.astype('int8'))
          assert result.equals(expected)
  
      def test_boolean_no_nulls(self):
          num_values = 100
  
          np.random.seed(0)
  
          df = pd.DataFrame({'bools': np.random.randn(num_values) > 0})
          field = pa.field('bools', pa.bool_())
          schema = pa.schema([field])
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_boolean_nulls(self):
          # pandas requires upcast to object dtype
          num_values = 100
          np.random.seed(0)
  
          mask = np.random.randint(0, 10, size=num_values) < 3
          values = np.random.randint(0, 10, size=num_values) < 5
  
          arr = pa.array(values, mask=mask)
  
          expected = values.astype(object)
          expected[mask] = None
  
          field = pa.field('bools', pa.bool_())
          schema = pa.schema([field])
          ex_frame = pd.DataFrame({'bools': expected})
  
          table = pa.Table.from_arrays([arr], ['bools'])
          assert table.schema.equals(schema)
          result = table.to_pandas()
  
          tm.assert_frame_equal(result, ex_frame)
  
      def test_float_object_nulls(self):
          arr = np.array([None, 1.5, np.float64(3.5)] * 5, dtype=object)
          df = pd.DataFrame({'floats': arr})
          expected = pd.DataFrame({'floats': pd.to_numeric(arr)})
          field = pa.field('floats', pa.float64())
          schema = pa.schema([field])
          _check_pandas_roundtrip(df, expected=expected,
                                  expected_schema=schema)
  
      def test_int_object_nulls(self):
          arr = np.array([None, 1, np.int64(3)] * 5, dtype=object)
          df = pd.DataFrame({'ints': arr})
          expected = pd.DataFrame({'ints': pd.to_numeric(arr)})
          field = pa.field('ints', pa.int64())
          schema = pa.schema([field])
          _check_pandas_roundtrip(df, expected=expected,
                                  expected_schema=schema)
  
      def test_boolean_object_nulls(self):
          arr = np.array([False, None, True] * 100, dtype=object)
          df = pd.DataFrame({'bools': arr})
          field = pa.field('bools', pa.bool_())
          schema = pa.schema([field])
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_all_nulls_cast_numeric(self):
          arr = np.array([None], dtype=object)
  
          def _check_type(t):
              a2 = pa.array(arr, type=t)
              assert a2.type == t
              assert a2[0].as_py() is None
  
          _check_type(pa.int32())
          _check_type(pa.float64())
  
      def test_half_floats_from_numpy(self):
          arr = np.array([1.5, np.nan], dtype=np.float16)
          a = pa.array(arr, type=pa.float16())
          x, y = a.to_pylist()
          assert isinstance(x, np.float16)
          assert x == 1.5
          assert isinstance(y, np.float16)
          assert np.isnan(y)
  
          a = pa.array(arr, type=pa.float16(), from_pandas=True)
          x, y = a.to_pylist()
          assert isinstance(x, np.float16)
          assert x == 1.5
          assert y is None
  
  
  @pytest.mark.parametrize('dtype',
                           ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8'])
  def test_array_integer_object_nulls_option(dtype):
      num_values = 100
  
      null_mask = np.random.randint(0, 10, size=num_values) < 3
      values = np.random.randint(0, 100, size=num_values, dtype=dtype)
  
      array = pa.array(values, mask=null_mask)
  
      if null_mask.any():
          expected = values.astype('O')
          expected[null_mask] = None
      else:
          expected = values
  
      result = array.to_pandas(integer_object_nulls=True)
  
      np.testing.assert_equal(result, expected)
  
  
  @pytest.mark.parametrize('dtype',
                           ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8'])
  def test_table_integer_object_nulls_option(dtype):
      num_values = 100
  
      null_mask = np.random.randint(0, 10, size=num_values) < 3
      values = np.random.randint(0, 100, size=num_values, dtype=dtype)
  
      array = pa.array(values, mask=null_mask)
  
      if null_mask.any():
          expected = values.astype('O')
          expected[null_mask] = None
      else:
          expected = values
  
      expected = pd.DataFrame({dtype: expected})
  
      table = pa.Table.from_arrays([array], [dtype])
      result = table.to_pandas(integer_object_nulls=True)
  
      tm.assert_frame_equal(result, expected)
  
  
  class TestConvertDateTimeLikeTypes(object):
      """
      Conversion tests for datetime- and timestamp-like types (date64, etc.).
      """
  
      def test_timestamps_notimezone_no_nulls(self):
          df = pd.DataFrame({
              'datetime64': np.array([
                  '2007-07-13T01:23:34.123456789',
                  '2006-01-13T12:34:56.432539784',
                  '2010-08-13T05:46:57.437699912'],
                  dtype='datetime64[ns]')
          })
          field = pa.field('datetime64', pa.timestamp('ns'))
          schema = pa.schema([field])
          _check_pandas_roundtrip(
              df,
              expected_schema=schema,
          )
  
      def test_timestamps_notimezone_nulls(self):
          df = pd.DataFrame({
              'datetime64': np.array([
                  '2007-07-13T01:23:34.123456789',
                  None,
                  '2010-08-13T05:46:57.437699912'],
                  dtype='datetime64[ns]')
          })
          field = pa.field('datetime64', pa.timestamp('ns'))
          schema = pa.schema([field])
          _check_pandas_roundtrip(
              df,
              expected_schema=schema,
          )
  
      def test_timestamps_with_timezone(self):
          df = pd.DataFrame({
              'datetime64': np.array([
                  '2007-07-13T01:23:34.123',
                  '2006-01-13T12:34:56.432',
                  '2010-08-13T05:46:57.437'],
                  dtype='datetime64[ms]')
          })
          df['datetime64'] = (df['datetime64'].dt.tz_localize('US/Eastern')
                              .to_frame())
          _check_pandas_roundtrip(df)
  
          _check_series_roundtrip(df['datetime64'])
  
          # drop-in a null and ns instead of ms
          df = pd.DataFrame({
              'datetime64': np.array([
                  '2007-07-13T01:23:34.123456789',
                  None,
                  '2006-01-13T12:34:56.432539784',
                  '2010-08-13T05:46:57.437699912'],
                  dtype='datetime64[ns]')
          })
          df['datetime64'] = (df['datetime64'].dt.tz_localize('US/Eastern')
                              .to_frame())
  
          _check_pandas_roundtrip(df)
  
      def test_python_datetime(self):
          # ARROW-2106
          date_array = [datetime.today() + timedelta(days=x) for x in range(10)]
          df = pd.DataFrame({
              'datetime': pd.Series(date_array, dtype=object)
          })
  
          table = pa.Table.from_pandas(df)
          assert isinstance(table[0].data.chunk(0), pa.TimestampArray)
  
          result = table.to_pandas()
          expected_df = pd.DataFrame({
              'datetime': date_array
          })
          tm.assert_frame_equal(expected_df, result)
  
      def test_python_datetime_subclass(self):
  
          class MyDatetime(datetime):
              # see https://github.com/pandas-dev/pandas/issues/21142
              nanosecond = 0.0
  
          date_array = [MyDatetime(2000, 1, 1, 1, 1, 1)]
          df = pd.DataFrame({"datetime": pd.Series(date_array, dtype=object)})
  
          table = pa.Table.from_pandas(df)
          assert isinstance(table[0].data.chunk(0), pa.TimestampArray)
  
          result = table.to_pandas()
          expected_df = pd.DataFrame({"datetime": date_array})
  
          # https://github.com/pandas-dev/pandas/issues/21142
          expected_df["datetime"] = pd.to_datetime(expected_df["datetime"])
  
          tm.assert_frame_equal(expected_df, result)
  
      def test_python_date_subclass(self):
  
          class MyDate(date):
              pass
  
          date_array = [MyDate(2000, 1, 1)]
          df = pd.DataFrame({"date": pd.Series(date_array, dtype=object)})
  
          table = pa.Table.from_pandas(df)
          assert isinstance(table[0].data.chunk(0), pa.Date32Array)
  
          result = table.to_pandas()
          expected_df = pd.DataFrame(
              {"date": np.array(["2000-01-01"], dtype="datetime64[ns]")}
          )
          tm.assert_frame_equal(expected_df, result)
  
      def test_datetime64_to_date32(self):
          # ARROW-1718
          arr = pa.array([date(2017, 10, 23), None])
          c = pa.Column.from_array("d", arr)
          s = c.to_pandas()
  
          arr2 = pa.Array.from_pandas(s, type=pa.date32())
  
          assert arr2.equals(arr.cast('date32'))
  
      @pytest.mark.parametrize('mask', [
          None,
          np.ones(3),
          np.array([True, False, False]),
      ])
      def test_pandas_datetime_to_date64(self, mask):
          s = pd.to_datetime([
              '2018-05-10T00:00:00',
              '2018-05-11T00:00:00',
              '2018-05-12T00:00:00',
          ])
          arr = pa.Array.from_pandas(s, type=pa.date64(), mask=mask)
  
          data = np.array([
              date(2018, 5, 10),
              date(2018, 5, 11),
              date(2018, 5, 12)
          ])
          expected = pa.array(data, mask=mask, type=pa.date64())
  
          assert arr.equals(expected)
  
      @pytest.mark.parametrize('mask', [
          None,
          np.ones(3),
          np.array([True, False, False])
      ])
      def test_pandas_datetime_to_date64_failures(self, mask):
          s = pd.to_datetime([
              '2018-05-10T10:24:01',
              '2018-05-11T10:24:01',
              '2018-05-12T10:24:01',
          ])
  
          expected_msg = 'Timestamp value had non-zero intraday milliseconds'
          with pytest.raises(pa.ArrowInvalid, match=expected_msg):
              pa.Array.from_pandas(s, type=pa.date64(), mask=mask)
  
      def test_date_infer(self):
          df = pd.DataFrame({
              'date': [date(2000, 1, 1),
                       None,
                       date(1970, 1, 1),
                       date(2040, 2, 26)]})
          table = pa.Table.from_pandas(df, preserve_index=False)
          field = pa.field('date', pa.date32())
  
          # schema's metadata is generated by from_pandas conversion
          expected_schema = pa.schema([field], metadata=table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
          result = table.to_pandas()
          expected = df.copy()
          expected['date'] = pd.to_datetime(df['date'])
          tm.assert_frame_equal(result, expected)
  
      def test_date_mask(self):
          arr = np.array([date(2017, 4, 3), date(2017, 4, 4)],
                         dtype='datetime64[D]')
          mask = [True, False]
          result = pa.array(arr, mask=np.array(mask))
          expected = np.array([None, date(2017, 4, 4)], dtype='datetime64[D]')
          expected = pa.array(expected, from_pandas=True)
          assert expected.equals(result)
  
      def test_date_objects_typed(self):
          arr = np.array([
              date(2017, 4, 3),
              None,
              date(2017, 4, 4),
              date(2017, 4, 5)], dtype=object)
  
          arr_i4 = np.array([17259, -1, 17260, 17261], dtype='int32')
          arr_i8 = arr_i4.astype('int64') * 86400000
          mask = np.array([False, True, False, False])
  
          t32 = pa.date32()
          t64 = pa.date64()
  
          a32 = pa.array(arr, type=t32)
          a64 = pa.array(arr, type=t64)
  
          a32_expected = pa.array(arr_i4, mask=mask, type=t32)
          a64_expected = pa.array(arr_i8, mask=mask, type=t64)
  
          assert a32.equals(a32_expected)
          assert a64.equals(a64_expected)
  
          # Test converting back to pandas
          colnames = ['date32', 'date64']
          table = pa.Table.from_arrays([a32, a64], colnames)
          table_pandas = table.to_pandas()
  
          ex_values = (np.array(['2017-04-03', '2017-04-04', '2017-04-04',
                                 '2017-04-05'],
                                dtype='datetime64[D]')
                       .astype('datetime64[ns]'))
          ex_values[1] = pd.NaT.value
          expected_pandas = pd.DataFrame({'date32': ex_values,
                                          'date64': ex_values},
                                         columns=colnames)
          tm.assert_frame_equal(table_pandas, expected_pandas)
  
      def test_dates_from_integers(self):
          t1 = pa.date32()
          t2 = pa.date64()
  
          arr = np.array([17259, 17260, 17261], dtype='int32')
          arr2 = arr.astype('int64') * 86400000
  
          a1 = pa.array(arr, type=t1)
          a2 = pa.array(arr2, type=t2)
  
          expected = date(2017, 4, 3)
          assert a1[0].as_py() == expected
          assert a2[0].as_py() == expected
  
      @pytest.mark.xfail(reason="not supported ATM",
                         raises=NotImplementedError)
      def test_timedelta(self):
          # TODO(jreback): Pandas only support ns resolution
          # Arrow supports ??? for resolution
          df = pd.DataFrame({
              'timedelta': np.arange(start=0, stop=3 * 86400000,
                                     step=86400000,
                                     dtype='timedelta64[ms]')
          })
          pa.Table.from_pandas(df)
  
      def test_pytime_from_pandas(self):
          pytimes = [time(1, 2, 3, 1356),
                     time(4, 5, 6, 1356)]
  
          # microseconds
          t1 = pa.time64('us')
  
          aobjs = np.array(pytimes + [None], dtype=object)
          parr = pa.array(aobjs)
          assert parr.type == t1
          assert parr[0].as_py() == pytimes[0]
          assert parr[1].as_py() == pytimes[1]
          assert parr[2] is pa.NA
  
          # DataFrame
          df = pd.DataFrame({'times': aobjs})
          batch = pa.RecordBatch.from_pandas(df)
          assert batch[0].equals(parr)
  
          # Test ndarray of int64 values
          arr = np.array([_pytime_to_micros(v) for v in pytimes],
                         dtype='int64')
  
          a1 = pa.array(arr, type=pa.time64('us'))
          assert a1[0].as_py() == pytimes[0]
  
          a2 = pa.array(arr * 1000, type=pa.time64('ns'))
          assert a2[0].as_py() == pytimes[0]
  
          a3 = pa.array((arr / 1000).astype('i4'),
                        type=pa.time32('ms'))
          assert a3[0].as_py() == pytimes[0].replace(microsecond=1000)
  
          a4 = pa.array((arr / 1000000).astype('i4'),
                        type=pa.time32('s'))
          assert a4[0].as_py() == pytimes[0].replace(microsecond=0)
  
      def test_arrow_time_to_pandas(self):
          pytimes = [time(1, 2, 3, 1356),
                     time(4, 5, 6, 1356),
                     time(0, 0, 0)]
  
          expected = np.array(pytimes[:2] + [None])
          expected_ms = np.array([x.replace(microsecond=1000)
                                  for x in pytimes[:2]] +
                                 [None])
          expected_s = np.array([x.replace(microsecond=0)
                                 for x in pytimes[:2]] +
                                [None])
  
          arr = np.array([_pytime_to_micros(v) for v in pytimes],
                         dtype='int64')
          arr = np.array([_pytime_to_micros(v) for v in pytimes],
                         dtype='int64')
  
          null_mask = np.array([False, False, True], dtype=bool)
  
          a1 = pa.array(arr, mask=null_mask, type=pa.time64('us'))
          a2 = pa.array(arr * 1000, mask=null_mask,
                        type=pa.time64('ns'))
  
          a3 = pa.array((arr / 1000).astype('i4'), mask=null_mask,
                        type=pa.time32('ms'))
          a4 = pa.array((arr / 1000000).astype('i4'), mask=null_mask,
                        type=pa.time32('s'))
  
          names = ['time64[us]', 'time64[ns]', 'time32[ms]', 'time32[s]']
          batch = pa.RecordBatch.from_arrays([a1, a2, a3, a4], names)
          arr = a1.to_pandas()
          assert (arr == expected).all()
  
          arr = a2.to_pandas()
          assert (arr == expected).all()
  
          arr = a3.to_pandas()
          assert (arr == expected_ms).all()
  
          arr = a4.to_pandas()
          assert (arr == expected_s).all()
  
          df = batch.to_pandas()
          expected_df = pd.DataFrame({'time64[us]': expected,
                                      'time64[ns]': expected,
                                      'time32[ms]': expected_ms,
                                      'time32[s]': expected_s},
                                     columns=names)
  
          tm.assert_frame_equal(df, expected_df)
  
      def test_numpy_datetime64_columns(self):
          datetime64_ns = np.array([
                  '2007-07-13T01:23:34.123456789',
                  None,
                  '2006-01-13T12:34:56.432539784',
                  '2010-08-13T05:46:57.437699912'],
                  dtype='datetime64[ns]')
          _check_array_from_pandas_roundtrip(datetime64_ns)
  
          datetime64_us = np.array([
                  '2007-07-13T01:23:34.123456',
                  None,
                  '2006-01-13T12:34:56.432539',
                  '2010-08-13T05:46:57.437699'],
                  dtype='datetime64[us]')
          _check_array_from_pandas_roundtrip(datetime64_us)
  
          datetime64_ms = np.array([
                  '2007-07-13T01:23:34.123',
                  None,
                  '2006-01-13T12:34:56.432',
                  '2010-08-13T05:46:57.437'],
                  dtype='datetime64[ms]')
          _check_array_from_pandas_roundtrip(datetime64_ms)
  
          datetime64_s = np.array([
                  '2007-07-13T01:23:34',
                  None,
                  '2006-01-13T12:34:56',
                  '2010-08-13T05:46:57'],
                  dtype='datetime64[s]')
          _check_array_from_pandas_roundtrip(datetime64_s)
  
      def test_numpy_datetime64_day_unit(self):
          datetime64_d = np.array([
                  '2007-07-13',
                  None,
                  '2006-01-15',
                  '2010-08-19'],
                  dtype='datetime64[D]')
          _check_array_from_pandas_roundtrip(datetime64_d)
  
      def test_array_from_pandas_date_with_mask(self):
          m = np.array([True, False, True])
          data = pd.Series([
              date(1990, 1, 1),
              date(1991, 1, 1),
              date(1992, 1, 1)
          ])
  
          result = pa.Array.from_pandas(data, mask=m)
  
          expected = pd.Series([None, date(1991, 1, 1), None])
          assert pa.Array.from_pandas(expected).equals(result)
  
      def test_fixed_offset_timezone(self):
          df = pd.DataFrame({
              'a': [
                  pd.Timestamp('2012-11-11 00:00:00+01:00'),
                  pd.NaT
                  ]
               })
          _check_pandas_roundtrip(df)
          _check_serialize_components_roundtrip(df)
  
  
  class TestConvertStringLikeTypes(object):
      """
      Conversion tests for string and binary types.
      """
  
      def test_unicode(self):
          repeats = 1000
          values = [u'foo', None, u'bar', u'mañana', np.nan]
          df = pd.DataFrame({'strings': values * repeats})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
  
          _check_pandas_roundtrip(df, expected_schema=schema)
  
      def test_bytes_to_binary(self):
          values = [u'qux', b'foo', None, bytearray(b'barz'), 'qux', np.nan]
          df = pd.DataFrame({'strings': values})
  
          table = pa.Table.from_pandas(df)
          assert table[0].type == pa.binary()
  
          values2 = [b'qux', b'foo', None, b'barz', b'qux', np.nan]
          expected = pd.DataFrame({'strings': values2})
          _check_pandas_roundtrip(df, expected)
  
      @pytest.mark.large_memory
      def test_bytes_exceed_2gb(self):
          v1 = b'x' * 100000000
          v2 = b'x' * 147483646
  
          # ARROW-2227, hit exactly 2GB on the nose
          df = pd.DataFrame({
              'strings': [v1] * 20 + [v2] + ['x'] * 20
          })
          arr = pa.array(df['strings'])
          assert isinstance(arr, pa.ChunkedArray)
          assert arr.num_chunks == 2
          arr = None
  
          table = pa.Table.from_pandas(df)
          assert table[0].data.num_chunks == 2
  
      def test_fixed_size_bytes(self):
          values = [b'foo', None, bytearray(b'bar'), None, None, b'hey']
          df = pd.DataFrame({'strings': values})
          schema = pa.schema([pa.field('strings', pa.binary(3))])
          table = pa.Table.from_pandas(df, schema=schema)
          assert table.schema[0].type == schema[0].type
          assert table.schema[0].name == schema[0].name
          result = table.to_pandas()
          tm.assert_frame_equal(result, df)
  
      def test_fixed_size_bytes_does_not_accept_varying_lengths(self):
          values = [b'foo', None, b'ba', None, None, b'hey']
          df = pd.DataFrame({'strings': values})
          schema = pa.schema([pa.field('strings', pa.binary(3))])
          with pytest.raises(pa.ArrowInvalid):
              pa.Table.from_pandas(df, schema=schema)
  
      def test_variable_size_bytes(self):
          s = pd.Series([b'123', b'', b'a', None])
          _check_series_roundtrip(s, type_=pa.binary())
  
      def test_binary_from_bytearray(self):
          s = pd.Series([bytearray(b'123'), bytearray(b''), bytearray(b'a'),
                         None])
          # Explicitly set type
          _check_series_roundtrip(s, type_=pa.binary())
          # Infer type from bytearrays
          _check_series_roundtrip(s, expected_pa_type=pa.binary())
  
      def test_table_empty_str(self):
          values = ['', '', '', '', '']
          df = pd.DataFrame({'strings': values})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
          table = pa.Table.from_pandas(df, schema=schema)
  
          result1 = table.to_pandas(strings_to_categorical=False)
          expected1 = pd.DataFrame({'strings': values})
          tm.assert_frame_equal(result1, expected1, check_dtype=True)
  
          result2 = table.to_pandas(strings_to_categorical=True)
          expected2 = pd.DataFrame({'strings': pd.Categorical(values)})
          tm.assert_frame_equal(result2, expected2, check_dtype=True)
  
      def test_selective_categoricals(self):
          values = ['', '', '', '', '']
          df = pd.DataFrame({'strings': values})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
          table = pa.Table.from_pandas(df, schema=schema)
          expected_str = pd.DataFrame({'strings': values})
          expected_cat = pd.DataFrame({'strings': pd.Categorical(values)})
  
          result1 = table.to_pandas(categories=['strings'])
          tm.assert_frame_equal(result1, expected_cat, check_dtype=True)
          result2 = table.to_pandas(categories=[])
          tm.assert_frame_equal(result2, expected_str, check_dtype=True)
          result3 = table.to_pandas(categories=('strings',))
          tm.assert_frame_equal(result3, expected_cat, check_dtype=True)
          result4 = table.to_pandas(categories=tuple())
          tm.assert_frame_equal(result4, expected_str, check_dtype=True)
  
      def test_table_str_to_categorical_without_na(self):
          values = ['a', 'a', 'b', 'b', 'c']
          df = pd.DataFrame({'strings': values})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
          table = pa.Table.from_pandas(df, schema=schema)
  
          result = table.to_pandas(strings_to_categorical=True)
          expected = pd.DataFrame({'strings': pd.Categorical(values)})
          tm.assert_frame_equal(result, expected, check_dtype=True)
  
          with pytest.raises(pa.ArrowInvalid):
              table.to_pandas(strings_to_categorical=True,
                              zero_copy_only=True)
  
      def test_table_str_to_categorical_with_na(self):
          values = [None, 'a', 'b', np.nan]
          df = pd.DataFrame({'strings': values})
          field = pa.field('strings', pa.string())
          schema = pa.schema([field])
          table = pa.Table.from_pandas(df, schema=schema)
  
          result = table.to_pandas(strings_to_categorical=True)
          expected = pd.DataFrame({'strings': pd.Categorical(values)})
          tm.assert_frame_equal(result, expected, check_dtype=True)
  
          with pytest.raises(pa.ArrowInvalid):
              table.to_pandas(strings_to_categorical=True,
                              zero_copy_only=True)
  
      # Regression test for ARROW-2101
      def test_array_of_bytes_to_strings(self):
          converted = pa.array(np.array([b'x'], dtype=object), pa.string())
          assert converted.type == pa.string()
  
      # Make sure that if an ndarray of bytes is passed to the array
      # constructor and the type is string, it will fail if those bytes
      # cannot be converted to utf-8
      def test_array_of_bytes_to_strings_bad_data(self):
          with pytest.raises(
                  pa.lib.ArrowInvalid,
                  match=("'(utf8|utf-8)' codec can't decode byte 0x80 "
                         "in position 0: invalid start byte")):
              pa.array(np.array([b'\x80\x81'], dtype=object), pa.string())
  
      def test_numpy_string_array_to_fixed_size_binary(self):
          arr = np.array([b'foo', b'bar', b'baz'], dtype='|S3')
  
          converted = pa.array(arr, type=pa.binary(3))
          expected = pa.array(list(arr), type=pa.binary(3))
          assert converted.equals(expected)
  
          mask = np.array([True, False, True])
          converted = pa.array(arr, type=pa.binary(3), mask=mask)
          expected = pa.array([b'foo', None, b'baz'], type=pa.binary(3))
          assert converted.equals(expected)
  
          with pytest.raises(pa.lib.ArrowInvalid,
                             match='Got bytestring of length 3 \(expected 4\)'):
              arr = np.array([b'foo', b'bar', b'baz'], dtype='|S3')
              pa.array(arr, type=pa.binary(4))
  
          with pytest.raises(pa.lib.ArrowInvalid,
                             match='Got bytestring of length 12 \(expected 3\)'):
              arr = np.array([b'foo', b'bar', b'baz'], dtype='|U3')
              pa.array(arr, type=pa.binary(3))
  
  
  class TestConvertDecimalTypes(object):
      """
      Conversion test for decimal types.
      """
      decimal32 = [
          decimal.Decimal('-1234.123'),
          decimal.Decimal('1234.439')
      ]
      decimal64 = [
          decimal.Decimal('-129934.123331'),
          decimal.Decimal('129534.123731')
      ]
      decimal128 = [
          decimal.Decimal('394092382910493.12341234678'),
          decimal.Decimal('-314292388910493.12343437128')
      ]
  
      @pytest.mark.parametrize(('values', 'expected_type'), [
          pytest.param(decimal32, pa.decimal128(7, 3), id='decimal32'),
          pytest.param(decimal64, pa.decimal128(12, 6), id='decimal64'),
          pytest.param(decimal128, pa.decimal128(26, 11), id='decimal128')
      ])
      def test_decimal_from_pandas(self, values, expected_type):
          expected = pd.DataFrame({'decimals': values})
          table = pa.Table.from_pandas(expected, preserve_index=False)
          field = pa.field('decimals', expected_type)
  
          # schema's metadata is generated by from_pandas conversion
          expected_schema = pa.schema([field], metadata=table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
      @pytest.mark.parametrize('values', [
          pytest.param(decimal32, id='decimal32'),
          pytest.param(decimal64, id='decimal64'),
          pytest.param(decimal128, id='decimal128')
      ])
      def test_decimal_to_pandas(self, values):
          expected = pd.DataFrame({'decimals': values})
          converted = pa.Table.from_pandas(expected)
          df = converted.to_pandas()
          tm.assert_frame_equal(df, expected)
  
      def test_decimal_fails_with_truncation(self):
          data1 = [decimal.Decimal('1.234')]
          type1 = pa.decimal128(10, 2)
          with pytest.raises(pa.ArrowInvalid):
              pa.array(data1, type=type1)
  
          data2 = [decimal.Decimal('1.2345')]
          type2 = pa.decimal128(10, 3)
          with pytest.raises(pa.ArrowInvalid):
              pa.array(data2, type=type2)
  
      def test_decimal_with_different_precisions(self):
          data = [
              decimal.Decimal('0.01'),
              decimal.Decimal('0.001'),
          ]
          series = pd.Series(data)
          array = pa.array(series)
          assert array.to_pylist() == data
          assert array.type == pa.decimal128(3, 3)
  
          array = pa.array(data, type=pa.decimal128(12, 5))
          expected = [decimal.Decimal('0.01000'), decimal.Decimal('0.00100')]
          assert array.to_pylist() == expected
  
      def test_decimal_with_None_explicit_type(self):
          series = pd.Series([decimal.Decimal('3.14'), None])
          _check_series_roundtrip(series, type_=pa.decimal128(12, 5))
  
          # Test that having all None values still produces decimal array
          series = pd.Series([None] * 2)
          _check_series_roundtrip(series, type_=pa.decimal128(12, 5))
  
      def test_decimal_with_None_infer_type(self):
          series = pd.Series([decimal.Decimal('3.14'), None])
          _check_series_roundtrip(series, expected_pa_type=pa.decimal128(3, 2))
  
  
  class TestListTypes(object):
      """
      Conversion tests for list<> types.
      """
  
      def test_column_of_arrays(self):
          df, schema = dataframe_with_arrays()
          _check_pandas_roundtrip(df, schema=schema, expected_schema=schema)
          table = pa.Table.from_pandas(df, schema=schema, preserve_index=False)
  
          # schema's metadata is generated by from_pandas conversion
          expected_schema = schema.add_metadata(table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
          for column in df.columns:
              field = schema.field_by_name(column)
              _check_array_roundtrip(df[column], type=field.type)
  
      def test_column_of_arrays_to_py(self):
          # Test regression in ARROW-1199 not caught in above test
          dtype = 'i1'
          arr = np.array([
              np.arange(10, dtype=dtype),
              np.arange(5, dtype=dtype),
              None,
              np.arange(1, dtype=dtype)
          ])
          type_ = pa.list_(pa.int8())
          parr = pa.array(arr, type=type_)
  
          assert parr[0].as_py() == list(range(10))
          assert parr[1].as_py() == list(range(5))
          assert parr[2].as_py() is None
          assert parr[3].as_py() == [0]
  
      def test_column_of_lists(self):
          df, schema = dataframe_with_lists()
          _check_pandas_roundtrip(df, schema=schema, expected_schema=schema)
          table = pa.Table.from_pandas(df, schema=schema, preserve_index=False)
  
          # schema's metadata is generated by from_pandas conversion
          expected_schema = schema.add_metadata(table.schema.metadata)
          assert table.schema.equals(expected_schema)
  
          for column in df.columns:
              field = schema.field_by_name(column)
              _check_array_roundtrip(df[column], type=field.type)
  
      def test_column_of_lists_first_empty(self):
          # ARROW-2124
          num_lists = [[], [2, 3, 4], [3, 6, 7, 8], [], [2]]
          series = pd.Series([np.array(s, dtype=float) for s in num_lists])
          arr = pa.array(series)
          result = pd.Series(arr.to_pandas())
          tm.assert_series_equal(result, series)
  
      def test_column_of_lists_chunked(self):
          # ARROW-1357
          df = pd.DataFrame({
              'lists': np.array([
                  [1, 2],
                  None,
                  [2, 3],
                  [4, 5],
                  [6, 7],
                  [8, 9]
              ], dtype=object)
          })
  
          schema = pa.schema([
              pa.field('lists', pa.list_(pa.int64()))
          ])
  
          t1 = pa.Table.from_pandas(df[:2], schema=schema)
          t2 = pa.Table.from_pandas(df[2:], schema=schema)
  
          table = pa.concat_tables([t1, t2])
          result = table.to_pandas()
  
          tm.assert_frame_equal(result, df)
  
      def test_column_of_lists_chunked2(self):
          data1 = [[0, 1], [2, 3], [4, 5], [6, 7], [10, 11],
                   [12, 13], [14, 15], [16, 17]]
          data2 = [[8, 9], [18, 19]]
  
          a1 = pa.array(data1)
          a2 = pa.array(data2)
  
          t1 = pa.Table.from_arrays([a1], names=['a'])
          t2 = pa.Table.from_arrays([a2], names=['a'])
  
          concatenated = pa.concat_tables([t1, t2])
  
          result = concatenated.to_pandas()
          expected = pd.DataFrame({'a': data1 + data2})
  
          tm.assert_frame_equal(result, expected)
  
      def test_column_of_lists_strided(self):
          df, schema = dataframe_with_lists()
          df = pd.concat([df] * 6, ignore_index=True)
  
          arr = df['int64'].values[::3]
          assert arr.strides[0] != 8
  
          _check_array_roundtrip(arr)
  
      def test_nested_lists_all_none(self):
          data = np.array([[None, None], None], dtype=object)
  
          arr = pa.array(data)
          expected = pa.array(list(data))
          assert arr.equals(expected)
          assert arr.type == pa.list_(pa.null())
  
          data2 = np.array([None, None, [None, None],
                            np.array([None, None], dtype=object)],
                           dtype=object)
          arr = pa.array(data2)
          expected = pa.array([None, None, [None, None], [None, None]])
          assert arr.equals(expected)
  
      def test_nested_lists_all_empty(self):
          # ARROW-2128
          data = pd.Series([[], [], []])
          arr = pa.array(data)
          expected = pa.array(list(data))
          assert arr.equals(expected)
          assert arr.type == pa.list_(pa.null())
  
      def test_nested_smaller_ints(self):
          # ARROW-1345, ARROW-2008, there were some type inference bugs happening
          # before
          data = pd.Series([np.array([1, 2, 3], dtype='i1'), None])
          result = pa.array(data)
          result2 = pa.array(data.values)
          expected = pa.array([[1, 2, 3], None], type=pa.list_(pa.int8()))
          assert result.equals(expected)
          assert result2.equals(expected)
  
          data3 = pd.Series([np.array([1, 2, 3], dtype='f4'), None])
          result3 = pa.array(data3)
          expected3 = pa.array([[1, 2, 3], None], type=pa.list_(pa.float32()))
          assert result3.equals(expected3)
  
      def test_infer_lists(self):
          data = OrderedDict([
              ('nan_ints', [[None, 1], [2, 3]]),
              ('ints', [[0, 1], [2, 3]]),
              ('strs', [[None, u'b'], [u'c', u'd']]),
              ('nested_strs', [[[None, u'b'], [u'c', u'd']], None])
          ])
          df = pd.DataFrame(data)
  
          expected_schema = pa.schema([
              pa.field('nan_ints', pa.list_(pa.int64())),
              pa.field('ints', pa.list_(pa.int64())),
              pa.field('strs', pa.list_(pa.string())),
              pa.field('nested_strs', pa.list_(pa.list_(pa.string())))
          ])
  
          _check_pandas_roundtrip(df, expected_schema=expected_schema)
  
      def test_infer_numpy_array(self):
          data = OrderedDict([
              ('ints', [
                  np.array([0, 1], dtype=np.int64),
                  np.array([2, 3], dtype=np.int64)
              ])
          ])
          df = pd.DataFrame(data)
          expected_schema = pa.schema([
              pa.field('ints', pa.list_(pa.int64()))
          ])
  
          _check_pandas_roundtrip(df, expected_schema=expected_schema)
  
      @pytest.mark.parametrize('t,data,expected', [
          (
              pa.int64,
              [[1, 2], [3], None],
              [None, [3], None]
          ),
          (
              pa.string,
              [[u'aaa', u'bb'], [u'c'], None],
              [None, [u'c'], None]
          ),
          (
              pa.null,
              [[None, None], [None], None],
              [None, [None], None]
          )
      ])
      def test_array_from_pandas_typed_array_with_mask(self, t, data, expected):
          m = np.array([True, False, True])
  
          s = pd.Series(data)
          result = pa.Array.from_pandas(s, mask=m, type=pa.list_(t()))
  
          assert pa.Array.from_pandas(expected,
                                      type=pa.list_(t())).equals(result)
  
      def test_empty_list_roundtrip(self):
          empty_list_array = np.empty((3,), dtype=object)
          empty_list_array.fill([])
  
          df = pd.DataFrame({'a': np.array(['1', '2', '3']),
                             'b': empty_list_array})
          tbl = pa.Table.from_pandas(df)
  
          result = tbl.to_pandas()
  
          tm.assert_frame_equal(result, df)
  
      def test_array_from_nested_arrays(self):
          df, schema = dataframe_with_arrays()
          for field in schema:
              arr = df[field.name].values
              expected = pa.array(list(arr), type=field.type)
              result = pa.array(arr)
              assert result.type == field.type  # == list<scalar>
              assert result.equals(expected)
  
  
  class TestConvertStructTypes(object):
      """
      Conversion tests for struct types.
      """
  
      def test_to_pandas(self):
          ints = pa.array([None, 2, 3], type=pa.int64())
          strs = pa.array([u'a', None, u'c'], type=pa.string())
          bools = pa.array([True, False, None], type=pa.bool_())
          arr = pa.StructArray.from_arrays(
              [ints, strs, bools],
              ['ints', 'strs', 'bools'])
  
          expected = pd.Series([
              {'ints': None, 'strs': u'a', 'bools': True},
              {'ints': 2, 'strs': None, 'bools': False},
              {'ints': 3, 'strs': u'c', 'bools': None},
          ])
  
          series = pd.Series(arr.to_pandas())
          tm.assert_series_equal(series, expected)
  
      def test_from_numpy(self):
          dt = np.dtype([('x', np.int32),
                         (('y_title', 'y'), np.bool_)])
          ty = pa.struct([pa.field('x', pa.int32()),
                          pa.field('y', pa.bool_())])
  
          data = np.array([], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == []
  
          data = np.array([(42, True), (43, False)], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == [{'x': 42, 'y': True},
                                     {'x': 43, 'y': False}]
  
          # With mask
          arr = pa.array(data, mask=np.bool_([False, True]), type=ty)
          assert arr.to_pylist() == [{'x': 42, 'y': True}, None]
  
          # Trivial struct type
          dt = np.dtype([])
          ty = pa.struct([])
  
          data = np.array([], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == []
  
          data = np.array([(), ()], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == [{}, {}]
  
      def test_from_numpy_nested(self):
          dt = np.dtype([('x', np.dtype([('xx', np.int8),
                                         ('yy', np.bool_)])),
                         ('y', np.int16)])
          ty = pa.struct([pa.field('x', pa.struct([pa.field('xx', pa.int8()),
                                                   pa.field('yy', pa.bool_())])),
                          pa.field('y', pa.int16())])
  
          data = np.array([], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == []
  
          data = np.array([((1, True), 2), ((3, False), 4)], dtype=dt)
          arr = pa.array(data, type=ty)
          assert arr.to_pylist() == [{'x': {'xx': 1, 'yy': True}, 'y': 2},
                                     {'x': {'xx': 3, 'yy': False}, 'y': 4}]
  
      @pytest.mark.large_memory
      def test_from_numpy_large(self):
          # Exercise rechunking + nulls
          target_size = 3 * 1024**3  # 4GB
          dt = np.dtype([('x', np.float64), ('y', 'object')])
          bs = 65536 - dt.itemsize
          block = b'.' * bs
          n = target_size // (bs + dt.itemsize)
          data = np.zeros(n, dtype=dt)
          data['x'] = np.random.random_sample(n)
          data['y'] = block
          # Add implicit nulls
          data['x'][data['x'] < 0.2] = np.nan
  
          ty = pa.struct([pa.field('x', pa.float64()),
                          pa.field('y', pa.binary(bs))])
          arr = pa.array(data, type=ty, from_pandas=True)
          assert arr.num_chunks == 2
  
          def iter_chunked_array(arr):
              for chunk in arr.iterchunks():
                  for item in chunk:
                      yield item
  
          def check(arr, data, mask=None):
              assert len(arr) == len(data)
              xs = data['x']
              ys = data['y']
              for i, obj in enumerate(iter_chunked_array(arr)):
                  try:
                      d = obj.as_py()
                      if mask is not None and mask[i]:
                          assert d is None
                      else:
                          x = xs[i]
                          if np.isnan(x):
                              assert d['x'] is None
                          else:
                              assert d['x'] == x
                          assert d['y'] == ys[i]
                  except Exception:
                      print("Failed at index", i)
                      raise
  
          check(arr, data)
          del arr
  
          # Now with explicit mask
          mask = np.random.random_sample(n) < 0.2
          arr = pa.array(data, type=ty, mask=mask, from_pandas=True)
          assert arr.num_chunks == 2
  
          check(arr, data, mask)
          del arr
  
      def test_from_numpy_bad_input(self):
          ty = pa.struct([pa.field('x', pa.int32()),
                          pa.field('y', pa.bool_())])
          dt = np.dtype([('x', np.int32),
                         ('z', np.bool_)])
  
          data = np.array([], dtype=dt)
          with pytest.raises(TypeError,
                             match="Missing field 'y'"):
              pa.array(data, type=ty)
          data = np.int32([])
          with pytest.raises(TypeError,
                             match="Expected struct array"):
              pa.array(data, type=ty)
  
  
  class TestZeroCopyConversion(object):
      """
      Tests that zero-copy conversion works with some types.
      """
  
      def test_zero_copy_success(self):
          result = pa.array([0, 1, 2]).to_pandas(zero_copy_only=True)
          npt.assert_array_equal(result, [0, 1, 2])
  
      def test_zero_copy_dictionaries(self):
          arr = pa.DictionaryArray.from_arrays(
              np.array([0, 0]),
              np.array([5]))
  
          result = arr.to_pandas(zero_copy_only=True)
          values = pd.Categorical([5, 5])
  
          tm.assert_series_equal(pd.Series(result), pd.Series(values),
                                 check_names=False)
  
      def check_zero_copy_failure(self, arr):
          with pytest.raises(pa.ArrowInvalid):
              arr.to_pandas(zero_copy_only=True)
  
      def test_zero_copy_failure_on_object_types(self):
          self.check_zero_copy_failure(pa.array(['A', 'B', 'C']))
  
      def test_zero_copy_failure_with_int_when_nulls(self):
          self.check_zero_copy_failure(pa.array([0, 1, None]))
  
      def test_zero_copy_failure_with_float_when_nulls(self):
          self.check_zero_copy_failure(pa.array([0.0, 1.0, None]))
  
      def test_zero_copy_failure_on_bool_types(self):
          self.check_zero_copy_failure(pa.array([True, False]))
  
      def test_zero_copy_failure_on_list_types(self):
          arr = pa.array([[1, 2], [8, 9]], type=pa.list_(pa.int64()))
          self.check_zero_copy_failure(arr)
  
      def test_zero_copy_failure_on_timestamp_types(self):
          arr = np.array(['2007-07-13'], dtype='datetime64[ns]')
          self.check_zero_copy_failure(pa.array(arr))
  
  
  class TestConvertMisc(object):
      """
      Miscellaneous conversion tests.
      """
  
      type_pairs = [
          (np.int8, pa.int8()),
          (np.int16, pa.int16()),
          (np.int32, pa.int32()),
          (np.int64, pa.int64()),
          (np.uint8, pa.uint8()),
          (np.uint16, pa.uint16()),
          (np.uint32, pa.uint32()),
          (np.uint64, pa.uint64()),
          (np.float16, pa.float16()),
          (np.float32, pa.float32()),
          (np.float64, pa.float64()),
          # XXX unsupported
          # (np.dtype([('a', 'i2')]), pa.struct([pa.field('a', pa.int16())])),
          (np.object, pa.string()),
          (np.object, pa.binary()),
          (np.object, pa.binary(10)),
          (np.object, pa.list_(pa.int64())),
      ]
  
      def test_all_none_objects(self):
          df = pd.DataFrame({'a': [None, None, None]})
          _check_pandas_roundtrip(df)
  
      def test_all_none_category(self):
          df = pd.DataFrame({'a': [None, None, None]})
          df['a'] = df['a'].astype('category')
          _check_pandas_roundtrip(df)
  
      def test_empty_arrays(self):
          for dtype, pa_type in self.type_pairs:
              arr = np.array([], dtype=dtype)
              _check_array_roundtrip(arr, type=pa_type)
  
      def test_threaded_conversion(self):
          df = _alltypes_example()
          _check_pandas_roundtrip(df, use_threads=True)
          _check_pandas_roundtrip(df, use_threads=True, as_batch=True)
  
      def test_category(self):
          repeats = 5
          v1 = ['foo', None, 'bar', 'qux', np.nan]
          v2 = [4, 5, 6, 7, 8]
          v3 = [b'foo', None, b'bar', b'qux', np.nan]
          df = pd.DataFrame({'cat_strings': pd.Categorical(v1 * repeats),
                             'cat_ints': pd.Categorical(v2 * repeats),
                             'cat_binary': pd.Categorical(v3 * repeats),
                             'cat_strings_ordered': pd.Categorical(
                                 v1 * repeats, categories=['bar', 'qux', 'foo'],
                                 ordered=True),
                             'ints': v2 * repeats,
                             'ints2': v2 * repeats,
                             'strings': v1 * repeats,
                             'strings2': v1 * repeats,
                             'strings3': v3 * repeats})
          _check_pandas_roundtrip(df)
  
          arrays = [
              pd.Categorical(v1 * repeats),
              pd.Categorical(v2 * repeats),
              pd.Categorical(v3 * repeats)
          ]
          for values in arrays:
              _check_array_roundtrip(values)
  
      def test_empty_category(self):
          # ARROW-2443
          df = pd.DataFrame({'cat': pd.Categorical([])})
          _check_pandas_roundtrip(df)
  
      def test_mixed_types_fails(self):
          data = pd.DataFrame({'a': ['a', 1, 2.0]})
          with pytest.raises(pa.ArrowTypeError):
              pa.Table.from_pandas(data)
  
          data = pd.DataFrame({'a': [1, True]})
          with pytest.raises(pa.ArrowTypeError):
              pa.Table.from_pandas(data)
  
      def test_strided_data_import(self):
          cases = []
  
          columns = ['a', 'b', 'c']
          N, K = 100, 3
          random_numbers = np.random.randn(N, K).copy() * 100
  
          numeric_dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8',
                            'f4', 'f8']
  
          for type_name in numeric_dtypes:
              cases.append(random_numbers.astype(type_name))
  
          # strings
          cases.append(np.array([tm.rands(10) for i in range(N * K)],
                                dtype=object)
                       .reshape(N, K).copy())
  
          # booleans
          boolean_objects = (np.array([True, False, True] * N, dtype=object)
                             .reshape(N, K).copy())
  
          # add some nulls, so dtype comes back as objects
          boolean_objects[5] = None
          cases.append(boolean_objects)
  
          cases.append(np.arange("2016-01-01T00:00:00.001", N * K,
                                 dtype='datetime64[ms]')
                       .reshape(N, K).copy())
  
          strided_mask = (random_numbers > 0).astype(bool)[:, 0]
  
          for case in cases:
              df = pd.DataFrame(case, columns=columns)
              col = df['a']
  
              _check_pandas_roundtrip(df)
              _check_array_roundtrip(col)
              _check_array_roundtrip(col, mask=strided_mask)
  
      def test_all_nones(self):
          def _check_series(s):
              converted = pa.array(s)
              assert isinstance(converted, pa.NullArray)
              assert len(converted) == 3
              assert converted.null_count == 3
              assert converted[0] is pa.NA
  
          _check_series(pd.Series([None] * 3, dtype=object))
          _check_series(pd.Series([np.nan] * 3, dtype=object))
          _check_series(pd.Series([np.sqrt(-1)] * 3, dtype=object))
  
      def test_partial_schema(self):
          data = OrderedDict([
              ('a', [0, 1, 2, 3, 4]),
              ('b', np.array([-10, -5, 0, 5, 10], dtype=np.int32)),
              ('c', [-10, -5, 0, 5, 10])
          ])
          df = pd.DataFrame(data)
  
          partial_schema = pa.schema([
              pa.field('a', pa.int64()),
              pa.field('b', pa.int32())
          ])
  
          expected_schema = pa.schema([
              pa.field('a', pa.int64()),
              pa.field('b', pa.int32()),
              pa.field('c', pa.int64())
          ])
  
          _check_pandas_roundtrip(df, schema=partial_schema,
                                  expected_schema=expected_schema)
  
      def test_table_batch_empty_dataframe(self):
          df = pd.DataFrame({})
          _check_pandas_roundtrip(df)
          _check_pandas_roundtrip(df, as_batch=True)
  
          df2 = pd.DataFrame({}, index=[0, 1, 2])
          _check_pandas_roundtrip(df2, preserve_index=True)
          _check_pandas_roundtrip(df2, as_batch=True, preserve_index=True)
  
      def test_convert_empty_table(self):
          arr = pa.array([], type=pa.int64())
          tm.assert_almost_equal(arr.to_pandas(), np.array([], dtype=np.int64))
          arr = pa.array([], type=pa.string())
          tm.assert_almost_equal(arr.to_pandas(), np.array([], dtype=object))
          arr = pa.array([], type=pa.list_(pa.int64()))
          tm.assert_almost_equal(arr.to_pandas(), np.array([], dtype=object))
          arr = pa.array([], type=pa.struct([pa.field('a', pa.int64())]))
          tm.assert_almost_equal(arr.to_pandas(), np.array([], dtype=object))
  
      def test_non_natural_stride(self):
          """
          ARROW-2172: converting from a Numpy array with a stride that's
          not a multiple of itemsize.
          """
          dtype = np.dtype([('x', np.int32), ('y', np.int16)])
          data = np.array([(42, -1), (-43, 2)], dtype=dtype)
          assert data.strides == (6,)
          arr = pa.array(data['x'], type=pa.int32())
          assert arr.to_pylist() == [42, -43]
          arr = pa.array(data['y'], type=pa.int16())
          assert arr.to_pylist() == [-1, 2]
  
      def test_mixed_integer_columns(self):
          row = [[], []]
          df = pd.DataFrame(data=[row], columns=['foo', 123])
          expected_df = pd.DataFrame(data=[row], columns=['foo', '123'])
          _check_pandas_roundtrip(df, expected=expected_df, preserve_index=True)
  
  
  def _fully_loaded_dataframe_example():
      from distutils.version import LooseVersion
  
      index = pd.MultiIndex.from_arrays([
          pd.date_range('2000-01-01', periods=5).repeat(2),
          np.tile(np.array(['foo', 'bar'], dtype=object), 5)
      ])
  
      c1 = pd.date_range('2000-01-01', periods=10)
      data = {
          0: c1,
          1: c1.tz_localize('utc'),
          2: c1.tz_localize('US/Eastern'),
          3: c1[::2].tz_localize('utc').repeat(2).astype('category'),
          4: ['foo', 'bar'] * 5,
          5: pd.Series(['foo', 'bar'] * 5).astype('category').values,
          6: [True, False] * 5,
          7: np.random.randn(10),
          8: np.random.randint(0, 100, size=10),
          9: pd.period_range('2013', periods=10, freq='M')
      }
  
      if LooseVersion(pd.__version__) >= '0.21':
          # There is an issue with pickling IntervalIndex in pandas 0.20.x
          data[10] = pd.interval_range(start=1, freq=1, periods=10)
  
      return pd.DataFrame(data, index=index)
  
  
  @pytest.mark.parametrize('columns', ([b'foo'], ['foo']))
  def test_roundtrip_with_bytes_unicode(columns):
      df = pd.DataFrame(columns=columns)
      table1 = pa.Table.from_pandas(df)
      table2 = pa.Table.from_pandas(table1.to_pandas())
      assert table1.equals(table2)
      assert table1.schema.equals(table2.schema)
      assert table1.schema.metadata == table2.schema.metadata
  
  
  def _check_serialize_components_roundtrip(df):
      ctx = pa.default_serialization_context()
  
      components = ctx.serialize(df).to_components()
      deserialized = ctx.deserialize_components(components)
  
      tm.assert_frame_equal(df, deserialized)
  
  
  def test_serialize_deserialize_pandas():
      # ARROW-1784, serialize and deserialize DataFrame by decomposing
      # BlockManager
      df = _fully_loaded_dataframe_example()
      _check_serialize_components_roundtrip(df)
  
  
  def _pytime_from_micros(val):
      microseconds = val % 1000000
      val //= 1000000
      seconds = val % 60
      val //= 60
      minutes = val % 60
      hours = val // 60
      return time(hours, minutes, seconds, microseconds)
  
  
  def _pytime_to_micros(pytime):
      return (pytime.hour * 3600000000 +
              pytime.minute * 60000000 +
              pytime.second * 1000000 +
              pytime.microsecond)
  
apis: numpy.ones pandas.Series numpy.testing.assert_equal numpy.dtype numpy.random.seed pandas.Categorical numpy.int64 pandas.util.testing.assert_series_equal numpy.float64 pandas.period_range numpy.ma.masked_array pandas.to_numeric numpy.testing.assert_array_equal numpy.bool_ pandas.util.testing.rands pandas.to_datetime numpy.isnan pandas.isnull pandas.Timestamp numpy.random.randint numpy.sqrt pandas.date_range numpy.zeros numpy.arange numpy.int32 pandas.interval_range pandas.concat pandas.Index numpy.random.random_sample pandas.DatetimeIndex numpy.empty pandas.MultiIndex.from_arrays pandas.DataFrame numpy.random.randn numpy.iinfo numpy.array pandas.util.testing.assert_frame_equal