# 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. from collections import OrderedDict from collections.abc import Iterable import pickle import sys import weakref import numpy as np import pytest import pyarrow as pa def test_chunked_array_basics(): data = pa.chunked_array([], type=pa.string()) assert data.type == pa.string() assert data.to_pylist() == [] data.validate() data2 = pa.chunked_array([], type='binary') assert data2.type == pa.binary() with pytest.raises(ValueError): pa.chunked_array([]) data = pa.chunked_array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) assert isinstance(data.chunks, list) assert all(isinstance(c, pa.lib.Int64Array) for c in data.chunks) assert all(isinstance(c, pa.lib.Int64Array) for c in data.iterchunks()) assert len(data.chunks) == 3 assert data.nbytes == sum(c.nbytes for c in data.iterchunks()) assert sys.getsizeof(data) >= object.__sizeof__(data) + data.nbytes data.validate() wr = weakref.ref(data) assert wr() is not None del data assert wr() is None def test_chunked_array_construction(): arr = pa.chunked_array([ [1, 2, 3], [4, 5, 6], [7, 8, 9], ]) assert arr.type == pa.int64() assert len(arr) == 9 assert len(arr.chunks) == 3 arr = pa.chunked_array([ [1, 2, 3], [4., 5., 6.], [7, 8, 9], ]) assert arr.type == pa.int64() assert len(arr) == 9 assert len(arr.chunks) == 3 arr = pa.chunked_array([ [1, 2, 3], [4., 5., 6.], [7, 8, 9], ], type=pa.int8()) assert arr.type == pa.int8() assert len(arr) == 9 assert len(arr.chunks) == 3 arr = pa.chunked_array([ [1, 2, 3], [] ]) assert arr.type == pa.int64() assert len(arr) == 3 assert len(arr.chunks) == 2 msg = ( "When passing an empty collection of arrays you must also pass the " "data type" ) with pytest.raises(ValueError, match=msg): assert pa.chunked_array([]) assert pa.chunked_array([], type=pa.string()).type == pa.string() assert pa.chunked_array([[]]).type == pa.null() assert pa.chunked_array([[]], type=pa.string()).type == pa.string() def test_combine_chunks(): # ARROW-77363 arr = pa.array([1, 2]) chunked_arr = pa.chunked_array([arr, arr]) res = chunked_arr.combine_chunks() expected = pa.array([1, 2, 1, 2]) assert res.equals(expected) def test_chunked_array_to_numpy(): data = pa.chunked_array([ [1, 2, 3], [4, 5, 6], [] ]) arr1 = np.asarray(data) arr2 = data.to_numpy() assert isinstance(arr2, np.ndarray) assert arr2.shape == (6,) assert np.array_equal(arr1, arr2) def test_chunked_array_mismatch_types(): with pytest.raises(TypeError): # Given array types are different pa.chunked_array([ pa.array([1, 2, 3]), pa.array([1., 2., 3.]) ]) with pytest.raises(TypeError): # Given array type is different from explicit type argument pa.chunked_array([pa.array([1, 2, 3])], type=pa.float64()) def test_chunked_array_str(): data = [ pa.array([1, 2, 3]), pa.array([4, 5, 6]) ] data = pa.chunked_array(data) assert str(data) == """[ [ 1, 2, 3 ], [ 4, 5, 6 ] ]""" def test_chunked_array_getitem(): data = [ pa.array([1, 2, 3]), pa.array([4, 5, 6]) ] data = pa.chunked_array(data) assert data[1].as_py() == 2 assert data[-1].as_py() == 6 assert data[-6].as_py() == 1 with pytest.raises(IndexError): data[6] with pytest.raises(IndexError): data[-7] # Ensure this works with numpy scalars assert data[np.int32(1)].as_py() == 2 data_slice = data[2:4] assert data_slice.to_pylist() == [3, 4] data_slice = data[4:-1] assert data_slice.to_pylist() == [5] data_slice = data[99:99] assert data_slice.type == data.type assert data_slice.to_pylist() == [] def test_chunked_array_slice(): data = [ pa.array([1, 2, 3]), pa.array([4, 5, 6]) ] data = pa.chunked_array(data) data_slice = data.slice(len(data)) assert data_slice.type == data.type assert data_slice.to_pylist() == [] data_slice = data.slice(len(data) + 10) assert data_slice.type == data.type assert data_slice.to_pylist() == [] table = pa.Table.from_arrays([data], names=["a"]) table_slice = table.slice(len(table)) assert len(table_slice) == 0 table = pa.Table.from_arrays([data], names=["a"]) table_slice = table.slice(len(table) + 10) assert len(table_slice) == 0 def test_chunked_array_iter(): data = [ pa.array([0]), pa.array([1, 2, 3]), pa.array([4, 5, 6]), pa.array([7, 8, 9]) ] arr = pa.chunked_array(data) for i, j in zip(range(10), arr): assert i == j.as_py() assert isinstance(arr, Iterable) def test_chunked_array_equals(): def eq(xarrs, yarrs): if isinstance(xarrs, pa.ChunkedArray): x = xarrs else: x = pa.chunked_array(xarrs) if isinstance(yarrs, pa.ChunkedArray): y = yarrs else: y = pa.chunked_array(yarrs) assert x.equals(y) assert y.equals(x) assert x == y assert x != str(y) def ne(xarrs, yarrs): if isinstance(xarrs, pa.ChunkedArray): x = xarrs else: x = pa.chunked_array(xarrs) if isinstance(yarrs, pa.ChunkedArray): y = yarrs else: y = pa.chunked_array(yarrs) assert not x.equals(y) assert not y.equals(x) assert x != y eq(pa.chunked_array([], type=pa.int32()), pa.chunked_array([], type=pa.int32())) ne(pa.chunked_array([], type=pa.int32()), pa.chunked_array([], type=pa.int64())) a = pa.array([0, 2], type=pa.int32()) b = pa.array([0, 2], type=pa.int64()) c = pa.array([0, 3], type=pa.int32()) d = pa.array([0, 2, 0, 3], type=pa.int32()) eq([a], [a]) ne([a], [b]) eq([a, c], [a, c]) eq([a, c], [d]) ne([c, a], [a, c]) # ARROW-4822 assert not pa.chunked_array([], type=pa.int32()).equals(None) @pytest.mark.parametrize( ('data', 'typ'), [ ([True, False, True, True], pa.bool_()), ([1, 2, 4, 6], pa.int64()), ([1.0, 2.5, None], pa.float64()), (['a', None, 'b'], pa.string()), ([], pa.list_(pa.uint8())), ([[1, 2], [3]], pa.list_(pa.int64())), ([['a'], None, ['b', 'c']], pa.list_(pa.string())), ([(1, 'a'), (2, 'c'), None], pa.struct([pa.field('a', pa.int64()), pa.field('b', pa.string())])) ] ) def test_chunked_array_pickle(data, typ): arrays = [] while data: arrays.append(pa.array(data[:2], type=typ)) data = data[2:] array = pa.chunked_array(arrays, type=typ) array.validate() result = pickle.loads(pickle.dumps(array)) result.validate() assert result.equals(array) @pytest.mark.pandas def test_chunked_array_to_pandas(): import pandas as pd data = [ pa.array([-10, -5, 0, 5, 10]) ] table = pa.table(data, names=['a']) col = table.column(0) assert isinstance(col, pa.ChunkedArray) series = col.to_pandas() assert isinstance(series, pd.Series) assert series.shape == (5,) assert series[0] == -10 assert series.name == 'a' @pytest.mark.pandas def test_chunked_array_to_pandas_preserve_name(): # https://issues.apache.org/jira/browse/ARROW-7709 import pandas as pd import pandas.testing as tm for data in [ pa.array([1, 2, 3]), pa.array(pd.Categorical(["a", "b", "a"])), pa.array(pd.date_range("2012", periods=3)), pa.array(pd.date_range("2012", periods=3, tz="Europe/Brussels")), pa.array([1, 2, 3], pa.timestamp("ms")), pa.array([1, 2, 3], pa.timestamp("ms", "Europe/Brussels"))]: table = pa.table({"name": data}) result = table.column("name").to_pandas() assert result.name == "name" expected = pd.Series(data.to_pandas(), name="name") tm.assert_series_equal(result, expected) @pytest.mark.pandas @pytest.mark.nopandas def test_chunked_array_asarray(): # ensure this is tested both when pandas is present or not (ARROW-6564) data = [ pa.array([0]), pa.array([1, 2, 3]) ] chunked_arr = pa.chunked_array(data) np_arr = np.asarray(chunked_arr) assert np_arr.tolist() == [0, 1, 2, 3] assert np_arr.dtype == np.dtype('int64') # An optional type can be specified when calling np.asarray np_arr = np.asarray(chunked_arr, dtype='str') assert np_arr.tolist() == ['0', '1', '2', '3'] # Types are modified when there are nulls data = [ pa.array([1, None]), pa.array([1, 2, 3]) ] chunked_arr = pa.chunked_array(data) np_arr = np.asarray(chunked_arr) elements = np_arr.tolist() assert elements[0] == 1. assert np.isnan(elements[1]) assert elements[2:] == [1., 2., 3.] assert np_arr.dtype == np.dtype('float64') # DictionaryType data will be converted to dense numpy array arr = pa.DictionaryArray.from_arrays( pa.array([0, 1, 2, 0, 1]), pa.array(['a', 'b', 'c'])) chunked_arr = pa.chunked_array([arr, arr]) np_arr = np.asarray(chunked_arr) assert np_arr.dtype == np.dtype('object') assert np_arr.tolist() == ['a', 'b', 'c', 'a', 'b'] * 2 def test_chunked_array_flatten(): ty = pa.struct([pa.field('x', pa.int16()), pa.field('y', pa.float32())]) a = pa.array([(1, 2.5), (3, 4.5), (5, 6.5)], type=ty) carr = pa.chunked_array(a) x, y = carr.flatten() assert x.equals(pa.chunked_array(pa.array([1, 3, 5], type=pa.int16()))) assert y.equals(pa.chunked_array(pa.array([2.5, 4.5, 6.5], type=pa.float32()))) # Empty column a = pa.array([], type=ty) carr = pa.chunked_array(a) x, y = carr.flatten() assert x.equals(pa.chunked_array(pa.array([], type=pa.int16()))) assert y.equals(pa.chunked_array(pa.array([], type=pa.float32()))) def test_chunked_array_unify_dictionaries(): arr = pa.chunked_array([ pa.array(["foo", "bar", None, "foo"]).dictionary_encode(), pa.array(["quux", None, "foo"]).dictionary_encode(), ]) assert arr.chunk(0).dictionary.equals(pa.array(["foo", "bar"])) assert arr.chunk(1).dictionary.equals(pa.array(["quux", "foo"])) arr = arr.unify_dictionaries() expected_dict = pa.array(["foo", "bar", "quux"]) assert arr.chunk(0).dictionary.equals(expected_dict) assert arr.chunk(1).dictionary.equals(expected_dict) assert arr.to_pylist() == ["foo", "bar", None, "foo", "quux", None, "foo"] def test_recordbatch_basics(): data = [ pa.array(range(5), type='int16'), pa.array([-10, -5, 0, None, 10], type='int32') ] batch = pa.record_batch(data, ['c0', 'c1']) assert not batch.schema.metadata assert len(batch) == 5 assert batch.num_rows == 5 assert batch.num_columns == len(data) # (only the second array has a null bitmap) assert batch.nbytes == (5 * 2) + (5 * 4 + 1) assert sys.getsizeof(batch) >= object.__sizeof__(batch) + batch.nbytes pydict = batch.to_pydict() assert pydict == OrderedDict([ ('c0', [0, 1, 2, 3, 4]), ('c1', [-10, -5, 0, None, 10]) ]) if sys.version_info >= (3, 7): assert type(pydict) == dict else: assert type(pydict) == OrderedDict with pytest.raises(IndexError): # bounds checking batch[2] # Schema passed explicitly schema = pa.schema([pa.field('c0', pa.int16(), metadata={'key': 'value'}), pa.field('c1', pa.int32())], metadata={b'foo': b'bar'}) batch = pa.record_batch(data, schema=schema) assert batch.schema == schema # schema as first positional argument batch = pa.record_batch(data, schema) assert batch.schema == schema assert str(batch) == """pyarrow.RecordBatch c0: int16 c1: int32""" assert batch.to_string(show_metadata=True) == """\ pyarrow.RecordBatch c0: int16 -- field metadata -- key: 'value' c1: int32 -- schema metadata -- foo: 'bar'""" wr = weakref.ref(batch) assert wr() is not None del batch assert wr() is None def test_recordbatch_equals(): data1 = [ pa.array(range(5), type='int16'), pa.array([-10, -5, 0, None, 10], type='int32') ] data2 = [ pa.array(['a', 'b', 'c']), pa.array([['d'], ['e'], ['f']]), ] column_names = ['c0', 'c1'] batch = pa.record_batch(data1, column_names) assert batch == pa.record_batch(data1, column_names) assert batch.equals(pa.record_batch(data1, column_names)) assert batch != pa.record_batch(data2, column_names) assert not batch.equals(pa.record_batch(data2, column_names)) batch_meta = pa.record_batch(data1, names=column_names, metadata={'key': 'value'}) assert batch_meta.equals(batch) assert not batch_meta.equals(batch, check_metadata=True) # ARROW-8889 assert not batch.equals(None) assert batch != "foo" def test_recordbatch_take(): batch = pa.record_batch( [pa.array([1, 2, 3, None, 5]), pa.array(['a', 'b', 'c', 'd', 'e'])], ['f1', 'f2']) assert batch.take(pa.array([2, 3])).equals(batch.slice(2, 2)) assert batch.take(pa.array([2, None])).equals( pa.record_batch([pa.array([3, None]), pa.array(['c', None])], ['f1', 'f2'])) def test_recordbatch_column_sets_private_name(): # ARROW-6429 rb = pa.record_batch([pa.array([1, 2, 3, 4])], names=['a0']) assert rb[0]._name == 'a0' def test_recordbatch_from_arrays_validate_schema(): # ARROW-6263 arr = pa.array([1, 2]) schema = pa.schema([pa.field('f0', pa.list_(pa.utf8()))]) with pytest.raises(NotImplementedError): pa.record_batch([arr], schema=schema) def test_recordbatch_from_arrays_validate_lengths(): # ARROW-2820 data = [pa.array([1]), pa.array(["tokyo", "like", "happy"]), pa.array(["derek"])] with pytest.raises(ValueError): pa.record_batch(data, ['id', 'tags', 'name']) def test_recordbatch_no_fields(): batch = pa.record_batch([], []) assert len(batch) == 0 assert batch.num_rows == 0 assert batch.num_columns == 0 def test_recordbatch_from_arrays_invalid_names(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]) ] with pytest.raises(ValueError): pa.record_batch(data, names=['a', 'b', 'c']) with pytest.raises(ValueError): pa.record_batch(data, names=['a']) def test_recordbatch_empty_metadata(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]) ] batch = pa.record_batch(data, ['c0', 'c1']) assert batch.schema.metadata is None def test_recordbatch_pickle(): data = [ pa.array(range(5), type='int8'), pa.array([-10, -5, 0, 5, 10], type='float32') ] fields = [ pa.field('ints', pa.int8()), pa.field('floats', pa.float32()), ] schema = pa.schema(fields, metadata={b'foo': b'bar'}) batch = pa.record_batch(data, schema=schema) result = pickle.loads(pickle.dumps(batch)) assert result.equals(batch) assert result.schema == schema def test_recordbatch_get_field(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array(range(5, 10)) ] batch = pa.RecordBatch.from_arrays(data, names=('a', 'b', 'c')) assert batch.field('a').equals(batch.schema.field('a')) assert batch.field(0).equals(batch.schema.field('a')) with pytest.raises(KeyError): batch.field('d') with pytest.raises(TypeError): batch.field(None) with pytest.raises(IndexError): batch.field(4) def test_recordbatch_select_column(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array(range(5, 10)) ] batch = pa.RecordBatch.from_arrays(data, names=('a', 'b', 'c')) assert batch.column('a').equals(batch.column(0)) with pytest.raises( KeyError, match='Field "d" does not exist in record batch schema'): batch.column('d') with pytest.raises(TypeError): batch.column(None) with pytest.raises(IndexError): batch.column(4) def test_recordbatch_from_struct_array_invalid(): with pytest.raises(TypeError): pa.RecordBatch.from_struct_array(pa.array(range(5))) def test_recordbatch_from_struct_array(): struct_array = pa.array( [{"ints": 1}, {"floats": 1.0}], type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())]), ) result = pa.RecordBatch.from_struct_array(struct_array) assert result.equals(pa.RecordBatch.from_arrays( [ pa.array([1, None], type=pa.int32()), pa.array([None, 1.0], type=pa.float32()), ], ["ints", "floats"] )) def _table_like_slice_tests(factory): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]) ] names = ['c0', 'c1'] obj = factory(data, names=names) sliced = obj.slice(2) assert sliced.num_rows == 3 expected = factory([x.slice(2) for x in data], names=names) assert sliced.equals(expected) sliced2 = obj.slice(2, 2) expected2 = factory([x.slice(2, 2) for x in data], names=names) assert sliced2.equals(expected2) # 0 offset assert obj.slice(0).equals(obj) # Slice past end of array assert len(obj.slice(len(obj))) == 0 with pytest.raises(IndexError): obj.slice(-1) # Check __getitem__-based slicing assert obj.slice(0, 0).equals(obj[:0]) assert obj.slice(0, 2).equals(obj[:2]) assert obj.slice(2, 2).equals(obj[2:4]) assert obj.slice(2, len(obj) - 2).equals(obj[2:]) assert obj.slice(len(obj) - 2, 2).equals(obj[-2:]) assert obj.slice(len(obj) - 4, 2).equals(obj[-4:-2]) def test_recordbatch_slice_getitem(): return _table_like_slice_tests(pa.RecordBatch.from_arrays) def test_table_slice_getitem(): return _table_like_slice_tests(pa.table) @pytest.mark.pandas def test_slice_zero_length_table(): # ARROW-7907: a segfault on this code was fixed after 0.16.0 table = pa.table({'a': pa.array([], type=pa.timestamp('us'))}) table_slice = table.slice(0, 0) table_slice.to_pandas() table = pa.table({'a': pa.chunked_array([], type=pa.string())}) table.to_pandas() def test_recordbatchlist_schema_equals(): a1 = np.array([1], dtype='uint32') a2 = np.array([4.0, 5.0], dtype='float64') batch1 = pa.record_batch([pa.array(a1)], ['c1']) batch2 = pa.record_batch([pa.array(a2)], ['c1']) with pytest.raises(pa.ArrowInvalid): pa.Table.from_batches([batch1, batch2]) def test_table_column_sets_private_name(): # ARROW-6429 t = pa.table([pa.array([1, 2, 3, 4])], names=['a0']) assert t[0]._name == 'a0' def test_table_equals(): table = pa.Table.from_arrays([], names=[]) assert table.equals(table) # ARROW-4822 assert not table.equals(None) other = pa.Table.from_arrays([], names=[], metadata={'key': 'value'}) assert not table.equals(other, check_metadata=True) assert table.equals(other) def test_table_from_batches_and_schema(): schema = pa.schema([ pa.field('a', pa.int64()), pa.field('b', pa.float64()), ]) batch = pa.record_batch([pa.array([1]), pa.array([3.14])], names=['a', 'b']) table = pa.Table.from_batches([batch], schema) assert table.schema.equals(schema) assert table.column(0) == pa.chunked_array([[1]]) assert table.column(1) == pa.chunked_array([[3.14]]) incompatible_schema = pa.schema([pa.field('a', pa.int64())]) with pytest.raises(pa.ArrowInvalid): pa.Table.from_batches([batch], incompatible_schema) incompatible_batch = pa.record_batch([pa.array([1])], ['a']) with pytest.raises(pa.ArrowInvalid): pa.Table.from_batches([incompatible_batch], schema) @pytest.mark.pandas def test_table_to_batches(): from pandas.testing import assert_frame_equal import pandas as pd df1 = pd.DataFrame({'a': list(range(10))}) df2 = pd.DataFrame({'a': list(range(10, 30))}) batch1 = pa.RecordBatch.from_pandas(df1, preserve_index=False) batch2 = pa.RecordBatch.from_pandas(df2, preserve_index=False) table = pa.Table.from_batches([batch1, batch2, batch1]) expected_df = pd.concat([df1, df2, df1], ignore_index=True) batches = table.to_batches() assert len(batches) == 3 assert_frame_equal(pa.Table.from_batches(batches).to_pandas(), expected_df) batches = table.to_batches(max_chunksize=15) assert list(map(len, batches)) == [10, 15, 5, 10] assert_frame_equal(table.to_pandas(), expected_df) assert_frame_equal(pa.Table.from_batches(batches).to_pandas(), expected_df) table_from_iter = pa.Table.from_batches(iter([batch1, batch2, batch1])) assert table.equals(table_from_iter) def test_table_basics(): data = [ pa.array(range(5), type='int64'), pa.array([-10, -5, 0, 5, 10], type='int64') ] table = pa.table(data, names=('a', 'b')) table.validate() assert len(table) == 5 assert table.num_rows == 5 assert table.num_columns == 2 assert table.shape == (5, 2) assert table.nbytes == 2 * (5 * 8) assert sys.getsizeof(table) >= object.__sizeof__(table) + table.nbytes pydict = table.to_pydict() assert pydict == OrderedDict([ ('a', [0, 1, 2, 3, 4]), ('b', [-10, -5, 0, 5, 10]) ]) if sys.version_info >= (3, 7): assert type(pydict) == dict else: assert type(pydict) == OrderedDict columns = [] for col in table.itercolumns(): columns.append(col) for chunk in col.iterchunks(): assert chunk is not None with pytest.raises(IndexError): col.chunk(-1) with pytest.raises(IndexError): col.chunk(col.num_chunks) assert table.columns == columns assert table == pa.table(columns, names=table.column_names) assert table != pa.table(columns[1:], names=table.column_names[1:]) assert table != columns wr = weakref.ref(table) assert wr() is not None del table assert wr() is None def test_table_from_arrays_preserves_column_metadata(): # Added to test https://issues.apache.org/jira/browse/ARROW-3866 arr0 = pa.array([1, 2]) arr1 = pa.array([3, 4]) field0 = pa.field('field1', pa.int64(), metadata=dict(a="A", b="B")) field1 = pa.field('field2', pa.int64(), nullable=False) table = pa.Table.from_arrays([arr0, arr1], schema=pa.schema([field0, field1])) assert b"a" in table.field(0).metadata assert table.field(1).nullable is False def test_table_from_arrays_invalid_names(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]) ] with pytest.raises(ValueError): pa.Table.from_arrays(data, names=['a', 'b', 'c']) with pytest.raises(ValueError): pa.Table.from_arrays(data, names=['a']) def test_table_from_lists(): data = [ list(range(5)), [-10, -5, 0, 5, 10] ] result = pa.table(data, names=['a', 'b']) expected = pa.Table.from_arrays(data, names=['a', 'b']) assert result.equals(expected) schema = pa.schema([ pa.field('a', pa.uint16()), pa.field('b', pa.int64()) ]) result = pa.table(data, schema=schema) expected = pa.Table.from_arrays(data, schema=schema) assert result.equals(expected) def test_table_pickle(): data = [ pa.chunked_array([[1, 2], [3, 4]], type=pa.uint32()), pa.chunked_array([["some", "strings", None, ""]], type=pa.string()), ] schema = pa.schema([pa.field('ints', pa.uint32()), pa.field('strs', pa.string())], metadata={b'foo': b'bar'}) table = pa.Table.from_arrays(data, schema=schema) result = pickle.loads(pickle.dumps(table)) result.validate() assert result.equals(table) def test_table_get_field(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array(range(5, 10)) ] table = pa.Table.from_arrays(data, names=('a', 'b', 'c')) assert table.field('a').equals(table.schema.field('a')) assert table.field(0).equals(table.schema.field('a')) with pytest.raises(KeyError): table.field('d') with pytest.raises(TypeError): table.field(None) with pytest.raises(IndexError): table.field(4) def test_table_select_column(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array(range(5, 10)) ] table = pa.Table.from_arrays(data, names=('a', 'b', 'c')) assert table.column('a').equals(table.column(0)) with pytest.raises(KeyError, match='Field "d" does not exist in table schema'): table.column('d') with pytest.raises(TypeError): table.column(None) with pytest.raises(IndexError): table.column(4) def test_table_column_with_duplicates(): # ARROW-8209 table = pa.table([pa.array([1, 2, 3]), pa.array([4, 5, 6]), pa.array([7, 8, 9])], names=['a', 'b', 'a']) with pytest.raises(KeyError, match='Field "a" exists 2 times in table schema'): table.column('a') def test_table_add_column(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array(range(5, 10)) ] table = pa.Table.from_arrays(data, names=('a', 'b', 'c')) new_field = pa.field('d', data[1].type) t2 = table.add_column(3, new_field, data[1]) t3 = table.append_column(new_field, data[1]) expected = pa.Table.from_arrays(data + [data[1]], names=('a', 'b', 'c', 'd')) assert t2.equals(expected) assert t3.equals(expected) t4 = table.add_column(0, new_field, data[1]) expected = pa.Table.from_arrays([data[1]] + data, names=('d', 'a', 'b', 'c')) assert t4.equals(expected) def test_table_set_column(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array(range(5, 10)) ] table = pa.Table.from_arrays(data, names=('a', 'b', 'c')) new_field = pa.field('d', data[1].type) t2 = table.set_column(0, new_field, data[1]) expected_data = list(data) expected_data[0] = data[1] expected = pa.Table.from_arrays(expected_data, names=('d', 'b', 'c')) assert t2.equals(expected) def test_table_drop(): """ drop one or more columns given labels""" a = pa.array(range(5)) b = pa.array([-10, -5, 0, 5, 10]) c = pa.array(range(5, 10)) table = pa.Table.from_arrays([a, b, c], names=('a', 'b', 'c')) t2 = table.drop(['a', 'b']) exp = pa.Table.from_arrays([c], names=('c',)) assert exp.equals(t2) # -- raise KeyError if column not in Table with pytest.raises(KeyError, match="Column 'd' not found"): table.drop(['d']) def test_table_remove_column(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array(range(5, 10)) ] table = pa.Table.from_arrays(data, names=('a', 'b', 'c')) t2 = table.remove_column(0) t2.validate() expected = pa.Table.from_arrays(data[1:], names=('b', 'c')) assert t2.equals(expected) def test_table_remove_column_empty(): # ARROW-1865 data = [ pa.array(range(5)), ] table = pa.Table.from_arrays(data, names=['a']) t2 = table.remove_column(0) t2.validate() assert len(t2) == len(table) t3 = t2.add_column(0, table.field(0), table[0]) t3.validate() assert t3.equals(table) def test_empty_table_with_names(): # ARROW-13784 data = [] names = ["a", "b"] message = ( 'Length of names [(]2[)] does not match length of arrays [(]0[)]') with pytest.raises(ValueError, match=message): pa.Table.from_arrays(data, names=names) def test_empty_table(): table = pa.table([]) assert table.column_names == [] assert table.equals(pa.Table.from_arrays([], [])) def test_table_rename_columns(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array(range(5, 10)) ] table = pa.Table.from_arrays(data, names=['a', 'b', 'c']) assert table.column_names == ['a', 'b', 'c'] t2 = table.rename_columns(['eh', 'bee', 'sea']) t2.validate() assert t2.column_names == ['eh', 'bee', 'sea'] expected = pa.Table.from_arrays(data, names=['eh', 'bee', 'sea']) assert t2.equals(expected) def test_table_flatten(): ty1 = pa.struct([pa.field('x', pa.int16()), pa.field('y', pa.float32())]) ty2 = pa.struct([pa.field('nest', ty1)]) a = pa.array([(1, 2.5), (3, 4.5)], type=ty1) b = pa.array([((11, 12.5),), ((13, 14.5),)], type=ty2) c = pa.array([False, True], type=pa.bool_()) table = pa.Table.from_arrays([a, b, c], names=['a', 'b', 'c']) t2 = table.flatten() t2.validate() expected = pa.Table.from_arrays([ pa.array([1, 3], type=pa.int16()), pa.array([2.5, 4.5], type=pa.float32()), pa.array([(11, 12.5), (13, 14.5)], type=ty1), c], names=['a.x', 'a.y', 'b.nest', 'c']) assert t2.equals(expected) def test_table_combine_chunks(): batch1 = pa.record_batch([pa.array([1]), pa.array(["a"])], names=['f1', 'f2']) batch2 = pa.record_batch([pa.array([2]), pa.array(["b"])], names=['f1', 'f2']) table = pa.Table.from_batches([batch1, batch2]) combined = table.combine_chunks() combined.validate() assert combined.equals(table) for c in combined.columns: assert c.num_chunks == 1 def test_table_unify_dictionaries(): batch1 = pa.record_batch([ pa.array(["foo", "bar", None, "foo"]).dictionary_encode(), pa.array([123, 456, 456, 789]).dictionary_encode(), pa.array([True, False, None, None])], names=['a', 'b', 'c']) batch2 = pa.record_batch([ pa.array(["quux", "foo", None, "quux"]).dictionary_encode(), pa.array([456, 789, 789, None]).dictionary_encode(), pa.array([False, None, None, True])], names=['a', 'b', 'c']) table = pa.Table.from_batches([batch1, batch2]) table = table.replace_schema_metadata({b"key1": b"value1"}) assert table.column(0).chunk(0).dictionary.equals( pa.array(["foo", "bar"])) assert table.column(0).chunk(1).dictionary.equals( pa.array(["quux", "foo"])) assert table.column(1).chunk(0).dictionary.equals( pa.array([123, 456, 789])) assert table.column(1).chunk(1).dictionary.equals( pa.array([456, 789])) table = table.unify_dictionaries(pa.default_memory_pool()) expected_dict_0 = pa.array(["foo", "bar", "quux"]) expected_dict_1 = pa.array([123, 456, 789]) assert table.column(0).chunk(0).dictionary.equals(expected_dict_0) assert table.column(0).chunk(1).dictionary.equals(expected_dict_0) assert table.column(1).chunk(0).dictionary.equals(expected_dict_1) assert table.column(1).chunk(1).dictionary.equals(expected_dict_1) assert table.to_pydict() == { 'a': ["foo", "bar", None, "foo", "quux", "foo", None, "quux"], 'b': [123, 456, 456, 789, 456, 789, 789, None], 'c': [True, False, None, None, False, None, None, True], } assert table.schema.metadata == {b"key1": b"value1"} def test_concat_tables(): data = [ list(range(5)), [-10., -5., 0., 5., 10.] ] data2 = [ list(range(5, 10)), [1., 2., 3., 4., 5.] ] t1 = pa.Table.from_arrays([pa.array(x) for x in data], names=('a', 'b')) t2 = pa.Table.from_arrays([pa.array(x) for x in data2], names=('a', 'b')) result = pa.concat_tables([t1, t2]) result.validate() assert len(result) == 10 expected = pa.Table.from_arrays([pa.array(x + y) for x, y in zip(data, data2)], names=('a', 'b')) assert result.equals(expected) def test_concat_tables_none_table(): # ARROW-11997 with pytest.raises(AttributeError): pa.concat_tables([None]) @pytest.mark.pandas def test_concat_tables_with_different_schema_metadata(): import pandas as pd schema = pa.schema([ pa.field('a', pa.string()), pa.field('b', pa.string()), ]) values = list('abcdefgh') df1 = pd.DataFrame({'a': values, 'b': values}) df2 = pd.DataFrame({'a': [np.nan] * 8, 'b': values}) table1 = pa.Table.from_pandas(df1, schema=schema, preserve_index=False) table2 = pa.Table.from_pandas(df2, schema=schema, preserve_index=False) assert table1.schema.equals(table2.schema) assert not table1.schema.equals(table2.schema, check_metadata=True) table3 = pa.concat_tables([table1, table2]) assert table1.schema.equals(table3.schema, check_metadata=True) assert table2.schema.equals(table3.schema) def test_concat_tables_with_promotion(): t1 = pa.Table.from_arrays( [pa.array([1, 2], type=pa.int64())], ["int64_field"]) t2 = pa.Table.from_arrays( [pa.array([1.0, 2.0], type=pa.float32())], ["float_field"]) result = pa.concat_tables([t1, t2], promote=True) assert result.equals(pa.Table.from_arrays([ pa.array([1, 2, None, None], type=pa.int64()), pa.array([None, None, 1.0, 2.0], type=pa.float32()), ], ["int64_field", "float_field"])) def test_concat_tables_with_promotion_error(): t1 = pa.Table.from_arrays( [pa.array([1, 2], type=pa.int64())], ["f"]) t2 = pa.Table.from_arrays( [pa.array([1, 2], type=pa.float32())], ["f"]) with pytest.raises(pa.ArrowInvalid): pa.concat_tables([t1, t2], promote=True) def test_table_negative_indexing(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), pa.array([1.0, 2.0, 3.0, 4.0, 5.0]), pa.array(['ab', 'bc', 'cd', 'de', 'ef']), ] table = pa.Table.from_arrays(data, names=tuple('abcd')) assert table[-1].equals(table[3]) assert table[-2].equals(table[2]) assert table[-3].equals(table[1]) assert table[-4].equals(table[0]) with pytest.raises(IndexError): table[-5] with pytest.raises(IndexError): table[4] def test_table_cast_to_incompatible_schema(): data = [ pa.array(range(5)), pa.array([-10, -5, 0, 5, 10]), ] table = pa.Table.from_arrays(data, names=tuple('ab')) target_schema1 = pa.schema([ pa.field('A', pa.int32()), pa.field('b', pa.int16()), ]) target_schema2 = pa.schema([ pa.field('a', pa.int32()), ]) message = ("Target schema's field names are not matching the table's " "field names:.*") with pytest.raises(ValueError, match=message): table.cast(target_schema1) with pytest.raises(ValueError, match=message): table.cast(target_schema2) def test_table_safe_casting(): data = [ pa.array(range(5), type=pa.int64()), pa.array([-10, -5, 0, 5, 10], type=pa.int32()), pa.array([1.0, 2.0, 3.0, 4.0, 5.0], type=pa.float64()), pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string()) ] table = pa.Table.from_arrays(data, names=tuple('abcd')) expected_data = [ pa.array(range(5), type=pa.int32()), pa.array([-10, -5, 0, 5, 10], type=pa.int16()), pa.array([1, 2, 3, 4, 5], type=pa.int64()), pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string()) ] expected_table = pa.Table.from_arrays(expected_data, names=tuple('abcd')) target_schema = pa.schema([ pa.field('a', pa.int32()), pa.field('b', pa.int16()), pa.field('c', pa.int64()), pa.field('d', pa.string()) ]) casted_table = table.cast(target_schema) assert casted_table.equals(expected_table) def test_table_unsafe_casting(): data = [ pa.array(range(5), type=pa.int64()), pa.array([-10, -5, 0, 5, 10], type=pa.int32()), pa.array([1.1, 2.2, 3.3, 4.4, 5.5], type=pa.float64()), pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string()) ] table = pa.Table.from_arrays(data, names=tuple('abcd')) expected_data = [ pa.array(range(5), type=pa.int32()), pa.array([-10, -5, 0, 5, 10], type=pa.int16()), pa.array([1, 2, 3, 4, 5], type=pa.int64()), pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string()) ] expected_table = pa.Table.from_arrays(expected_data, names=tuple('abcd')) target_schema = pa.schema([ pa.field('a', pa.int32()), pa.field('b', pa.int16()), pa.field('c', pa.int64()), pa.field('d', pa.string()) ]) with pytest.raises(pa.ArrowInvalid, match='truncated'): table.cast(target_schema) casted_table = table.cast(target_schema, safe=False) assert casted_table.equals(expected_table) def test_invalid_table_construct(): array = np.array([0, 1], dtype=np.uint8) u8 = pa.uint8() arrays = [pa.array(array, type=u8), pa.array(array[1:], type=u8)] with pytest.raises(pa.lib.ArrowInvalid): pa.Table.from_arrays(arrays, names=["a1", "a2"]) @pytest.mark.parametrize('data, klass', [ ((['', 'foo', 'bar'], [4.5, 5, None]), list), ((['', 'foo', 'bar'], [4.5, 5, None]), pa.array), (([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array), ]) def test_from_arrays_schema(data, klass): data = [klass(data[0]), klass(data[1])] schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())]) table = pa.Table.from_arrays(data, schema=schema) assert table.num_columns == 2 assert table.num_rows == 3 assert table.schema == schema # length of data and schema not matching schema = pa.schema([('strs', pa.utf8())]) with pytest.raises(ValueError): pa.Table.from_arrays(data, schema=schema) # with different but compatible schema schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())]) table = pa.Table.from_arrays(data, schema=schema) assert pa.types.is_float32(table.column('floats').type) assert table.num_columns == 2 assert table.num_rows == 3 assert table.schema == schema # with different and incompatible schema schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))]) with pytest.raises((NotImplementedError, TypeError)): pa.Table.from_pydict(data, schema=schema) # Cannot pass both schema and metadata / names with pytest.raises(ValueError): pa.Table.from_arrays(data, schema=schema, names=['strs', 'floats']) with pytest.raises(ValueError): pa.Table.from_arrays(data, schema=schema, metadata={b'foo': b'bar'}) @pytest.mark.parametrize( ('cls'), [ (pa.Table), (pa.RecordBatch) ] ) def test_table_from_pydict(cls): table = cls.from_pydict({}) assert table.num_columns == 0 assert table.num_rows == 0 assert table.schema == pa.schema([]) assert table.to_pydict() == {} schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())]) # With lists as values data = OrderedDict([('strs', ['', 'foo', 'bar']), ('floats', [4.5, 5, None])]) table = cls.from_pydict(data) assert table.num_columns == 2 assert table.num_rows == 3 assert table.schema == schema assert table.to_pydict() == data # With metadata and inferred schema metadata = {b'foo': b'bar'} schema = schema.with_metadata(metadata) table = cls.from_pydict(data, metadata=metadata) assert table.schema == schema assert table.schema.metadata == metadata assert table.to_pydict() == data # With explicit schema table = cls.from_pydict(data, schema=schema) assert table.schema == schema assert table.schema.metadata == metadata assert table.to_pydict() == data # Cannot pass both schema and metadata with pytest.raises(ValueError): cls.from_pydict(data, schema=schema, metadata=metadata) # Non-convertible values given schema with pytest.raises(TypeError): cls.from_pydict({'c0': [0, 1, 2]}, schema=pa.schema([("c0", pa.string())])) # Missing schema fields from the passed mapping with pytest.raises(KeyError, match="doesn\'t contain.* c, d"): cls.from_pydict( {'a': [1, 2, 3], 'b': [3, 4, 5]}, schema=pa.schema([ ('a', pa.int64()), ('c', pa.int32()), ('d', pa.int16()) ]) ) # Passed wrong schema type with pytest.raises(TypeError): cls.from_pydict({'a': [1, 2, 3]}, schema={}) @pytest.mark.parametrize('data, klass', [ ((['', 'foo', 'bar'], [4.5, 5, None]), pa.array), (([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array), ]) def test_table_from_pydict_arrow_arrays(data, klass): data = OrderedDict([('strs', klass(data[0])), ('floats', klass(data[1]))]) schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())]) # With arrays as values table = pa.Table.from_pydict(data) assert table.num_columns == 2 assert table.num_rows == 3 assert table.schema == schema # With explicit (matching) schema table = pa.Table.from_pydict(data, schema=schema) assert table.num_columns == 2 assert table.num_rows == 3 assert table.schema == schema # with different but compatible schema schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())]) table = pa.Table.from_pydict(data, schema=schema) assert pa.types.is_float32(table.column('floats').type) assert table.num_columns == 2 assert table.num_rows == 3 assert table.schema == schema # with different and incompatible schema schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))]) with pytest.raises((NotImplementedError, TypeError)): pa.Table.from_pydict(data, schema=schema) @pytest.mark.parametrize('data, klass', [ ((['', 'foo', 'bar'], [4.5, 5, None]), list), ((['', 'foo', 'bar'], [4.5, 5, None]), pa.array), (([[''], ['foo', 'bar']], [[4.5], [5., None]]), pa.chunked_array), ]) def test_table_from_pydict_schema(data, klass): # passed schema is source of truth for the columns data = OrderedDict([('strs', klass(data[0])), ('floats', klass(data[1]))]) # schema has columns not present in data -> error schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64()), ('ints', pa.int64())]) with pytest.raises(KeyError, match='ints'): pa.Table.from_pydict(data, schema=schema) # data has columns not present in schema -> ignored schema = pa.schema([('strs', pa.utf8())]) table = pa.Table.from_pydict(data, schema=schema) assert table.num_columns == 1 assert table.schema == schema assert table.column_names == ['strs'] @pytest.mark.pandas def test_table_from_pandas_schema(): # passed schema is source of truth for the columns import pandas as pd df = pd.DataFrame(OrderedDict([('strs', ['', 'foo', 'bar']), ('floats', [4.5, 5, None])])) # with different but compatible schema schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float32())]) table = pa.Table.from_pandas(df, schema=schema) assert pa.types.is_float32(table.column('floats').type) assert table.schema.remove_metadata() == schema # with different and incompatible schema schema = pa.schema([('strs', pa.utf8()), ('floats', pa.timestamp('s'))]) with pytest.raises((NotImplementedError, TypeError)): pa.Table.from_pandas(df, schema=schema) # schema has columns not present in data -> error schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64()), ('ints', pa.int64())]) with pytest.raises(KeyError, match='ints'): pa.Table.from_pandas(df, schema=schema) # data has columns not present in schema -> ignored schema = pa.schema([('strs', pa.utf8())]) table = pa.Table.from_pandas(df, schema=schema) assert table.num_columns == 1 assert table.schema.remove_metadata() == schema assert table.column_names == ['strs'] @pytest.mark.pandas def test_table_factory_function(): import pandas as pd # Put in wrong order to make sure that lines up with schema d = OrderedDict([('b', ['a', 'b', 'c']), ('a', [1, 2, 3])]) d_explicit = {'b': pa.array(['a', 'b', 'c'], type='string'), 'a': pa.array([1, 2, 3], type='int32')} schema = pa.schema([('a', pa.int32()), ('b', pa.string())]) df = pd.DataFrame(d) table1 = pa.table(df) table2 = pa.Table.from_pandas(df) assert table1.equals(table2) table1 = pa.table(df, schema=schema) table2 = pa.Table.from_pandas(df, schema=schema) assert table1.equals(table2) table1 = pa.table(d_explicit) table2 = pa.Table.from_pydict(d_explicit) assert table1.equals(table2) # schema coerces type table1 = pa.table(d, schema=schema) table2 = pa.Table.from_pydict(d, schema=schema) assert table1.equals(table2) def test_table_factory_function_args(): # from_pydict not accepting names: with pytest.raises(ValueError): pa.table({'a': [1, 2, 3]}, names=['a']) # backwards compatibility for schema as first positional argument schema = pa.schema([('a', pa.int32())]) table = pa.table({'a': pa.array([1, 2, 3], type=pa.int64())}, schema) assert table.column('a').type == pa.int32() # from_arrays: accept both names and schema as positional first argument data = [pa.array([1, 2, 3], type='int64')] names = ['a'] table = pa.table(data, names) assert table.column_names == names schema = pa.schema([('a', pa.int64())]) table = pa.table(data, schema) assert table.column_names == names @pytest.mark.pandas def test_table_factory_function_args_pandas(): import pandas as pd # from_pandas not accepting names or metadata: with pytest.raises(ValueError): pa.table(pd.DataFrame({'a': [1, 2, 3]}), names=['a']) with pytest.raises(ValueError): pa.table(pd.DataFrame({'a': [1, 2, 3]}), metadata={b'foo': b'bar'}) # backwards compatibility for schema as first positional argument schema = pa.schema([('a', pa.int32())]) table = pa.table(pd.DataFrame({'a': [1, 2, 3]}), schema) assert table.column('a').type == pa.int32() def test_factory_functions_invalid_input(): with pytest.raises(TypeError, match="Expected pandas DataFrame, python"): pa.table("invalid input") with pytest.raises(TypeError, match="Expected pandas DataFrame"): pa.record_batch("invalid input") def test_table_repr_to_string(): # Schema passed explicitly schema = pa.schema([pa.field('c0', pa.int16(), metadata={'key': 'value'}), pa.field('c1', pa.int32())], metadata={b'foo': b'bar'}) tab = pa.table([pa.array([1, 2, 3, 4], type='int16'), pa.array([10, 20, 30, 40], type='int32')], schema=schema) assert str(tab) == """pyarrow.Table c0: int16 c1: int32 ---- c0: [[1,2,3,4]] c1: [[10,20,30,40]]""" assert tab.to_string(show_metadata=True) == """\ pyarrow.Table c0: int16 -- field metadata -- key: 'value' c1: int32 -- schema metadata -- foo: 'bar'""" assert tab.to_string(preview_cols=5) == """\ pyarrow.Table c0: int16 c1: int32 ---- c0: [[1,2,3,4]] c1: [[10,20,30,40]]""" assert tab.to_string(preview_cols=1) == """\ pyarrow.Table c0: int16 c1: int32 ---- c0: [[1,2,3,4]] ...""" def test_table_repr_to_string_ellipsis(): # Schema passed explicitly schema = pa.schema([pa.field('c0', pa.int16(), metadata={'key': 'value'}), pa.field('c1', pa.int32())], metadata={b'foo': b'bar'}) tab = pa.table([pa.array([1, 2, 3, 4]*10, type='int16'), pa.array([10, 20, 30, 40]*10, type='int32')], schema=schema) assert str(tab) == """pyarrow.Table c0: int16 c1: int32 ---- c0: [[1,2,3,4,1,2,3,4,1,2,...,3,4,1,2,3,4,1,2,3,4]] c1: [[10,20,30,40,10,20,30,40,10,20,...,30,40,10,20,30,40,10,20,30,40]]""" def test_table_function_unicode_schema(): col_a = "äääh" col_b = "öööf" # Put in wrong order to make sure that lines up with schema d = OrderedDict([(col_b, ['a', 'b', 'c']), (col_a, [1, 2, 3])]) schema = pa.schema([(col_a, pa.int32()), (col_b, pa.string())]) result = pa.table(d, schema=schema) assert result[0].chunk(0).equals(pa.array([1, 2, 3], type='int32')) assert result[1].chunk(0).equals(pa.array(['a', 'b', 'c'], type='string')) def test_table_take_vanilla_functionality(): table = pa.table( [pa.array([1, 2, 3, None, 5]), pa.array(['a', 'b', 'c', 'd', 'e'])], ['f1', 'f2']) assert table.take(pa.array([2, 3])).equals(table.slice(2, 2)) def test_table_take_null_index(): table = pa.table( [pa.array([1, 2, 3, None, 5]), pa.array(['a', 'b', 'c', 'd', 'e'])], ['f1', 'f2']) result_with_null_index = pa.table( [pa.array([1, None]), pa.array(['a', None])], ['f1', 'f2']) assert table.take(pa.array([0, None])).equals(result_with_null_index) def test_table_take_non_consecutive(): table = pa.table( [pa.array([1, 2, 3, None, 5]), pa.array(['a', 'b', 'c', 'd', 'e'])], ['f1', 'f2']) result_non_consecutive = pa.table( [pa.array([2, None]), pa.array(['b', 'd'])], ['f1', 'f2']) assert table.take(pa.array([1, 3])).equals(result_non_consecutive) def test_table_select(): a1 = pa.array([1, 2, 3, None, 5]) a2 = pa.array(['a', 'b', 'c', 'd', 'e']) a3 = pa.array([[1, 2], [3, 4], [5, 6], None, [9, 10]]) table = pa.table([a1, a2, a3], ['f1', 'f2', 'f3']) # selecting with string names result = table.select(['f1']) expected = pa.table([a1], ['f1']) assert result.equals(expected) result = table.select(['f3', 'f2']) expected = pa.table([a3, a2], ['f3', 'f2']) assert result.equals(expected) # selecting with integer indices result = table.select([0]) expected = pa.table([a1], ['f1']) assert result.equals(expected) result = table.select([2, 1]) expected = pa.table([a3, a2], ['f3', 'f2']) assert result.equals(expected) # preserve metadata table2 = table.replace_schema_metadata({"a": "test"}) result = table2.select(["f1", "f2"]) assert b"a" in result.schema.metadata # selecting non-existing column raises with pytest.raises(KeyError, match='Field "f5" does not exist'): table.select(['f5']) with pytest.raises(IndexError, match="index out of bounds"): table.select([5]) # duplicate selection gives duplicated names in resulting table result = table.select(['f2', 'f2']) expected = pa.table([a2, a2], ['f2', 'f2']) assert result.equals(expected) # selection duplicated column raises table = pa.table([a1, a2, a3], ['f1', 'f2', 'f1']) with pytest.raises(KeyError, match='Field "f1" exists 2 times'): table.select(['f1']) result = table.select(['f2']) expected = pa.table([a2], ['f2']) assert result.equals(expected)