| # 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 |
| import pickle |
| import sys |
| |
| import numpy as np |
| import pytest |
| import pyarrow as pa |
| from pyarrow import compat |
| |
| |
| def test_chunked_array_basics(): |
| data = pa.chunked_array([], type=pa.string()) |
| assert data.type == pa.string() |
| assert data.to_pylist() == [] |
| |
| 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 |
| |
| |
| def test_chunked_array_mismatch_types(): |
| with pytest.raises(pa.ArrowInvalid): |
| pa.chunked_array([pa.array([1, 2]), pa.array(['foo', 'bar'])]) |
| |
| |
| 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] |
| |
| 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_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 |
| |
| assert isinstance(arr, compat.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]) |
| |
| 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) |
| result = pickle.loads(pickle.dumps(array)) |
| assert result.equals(array) |
| |
| |
| @pytest.mark.pandas |
| def test_chunked_array_to_pandas(): |
| data = [ |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| table = pa.Table.from_arrays(data, names=['a']) |
| chunked_arr = table.column(0).data |
| assert isinstance(chunked_arr, pa.ChunkedArray) |
| array = chunked_arr.to_pandas() |
| assert array.shape == (5,) |
| assert array[0] == -10 |
| |
| |
| def test_chunked_array_asarray(): |
| 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') |
| |
| |
| def test_column_basics(): |
| data = [ |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| table = pa.Table.from_arrays(data, names=['a']) |
| column = table.column(0) |
| assert column.name == 'a' |
| assert column.length() == 5 |
| assert len(column) == 5 |
| assert column.shape == (5,) |
| assert column.to_pylist() == [-10, -5, 0, 5, 10] |
| assert column == pa.Column.from_array("a", column.data) |
| assert column != pa.Column.from_array("b", column.data) |
| assert column != column.data |
| assert not column.equals(None) |
| |
| |
| def test_column_factory_function(): |
| # ARROW-1575 |
| arr = pa.array([0, 1, 2, 3, 4]) |
| arr2 = pa.array([5, 6, 7, 8]) |
| |
| col1 = pa.Column.from_array('foo', arr) |
| col2 = pa.Column.from_array(pa.field('foo', arr.type), arr) |
| |
| assert col1.equals(col2) |
| |
| col3 = pa.column('foo', [arr, arr2]) |
| chunked_arr = pa.chunked_array([arr, arr2]) |
| col4 = pa.column('foo', chunked_arr) |
| assert col3.equals(col4) |
| |
| col5 = pa.column('foo', arr.to_pandas()) |
| assert col5.equals(pa.column('foo', arr)) |
| |
| # Type mismatch |
| with pytest.raises(ValueError): |
| pa.Column.from_array(pa.field('foo', pa.string()), arr) |
| |
| |
| def test_column_pickle(): |
| arr = pa.chunked_array([[1, 2], [5, 6, 7]], type=pa.int16()) |
| field = pa.field("ints", pa.int16()).add_metadata({b"foo": b"bar"}) |
| col = pa.column(field, arr) |
| |
| result = pickle.loads(pickle.dumps(col)) |
| assert result.equals(col) |
| assert result.data.num_chunks == 2 |
| assert result.field == field |
| |
| |
| @pytest.mark.pandas |
| def test_column_to_pandas(): |
| data = [ |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| table = pa.Table.from_arrays(data, names=['a']) |
| column = table.column(0) |
| series = column.to_pandas() |
| assert series.name == 'a' |
| assert series.shape == (5,) |
| assert series.iloc[0] == -10 |
| |
| |
| def test_column_asarray(): |
| data = [ |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| table = pa.Table.from_arrays(data, names=['a']) |
| column = table.column(0) |
| |
| np_arr = np.asarray(column) |
| assert np_arr.tolist() == [-10, -5, 0, 5, 10] |
| assert np_arr.dtype == np.dtype('int64') |
| |
| # An optional type can be specified when calling np.asarray |
| np_arr = np.asarray(column, dtype='str') |
| assert np_arr.tolist() == ['-10', '-5', '0', '5', '10'] |
| |
| |
| def test_column_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) |
| col = pa.Column.from_array('foo', a) |
| x, y = col.flatten() |
| assert x == pa.column('foo.x', pa.array([1, 3, 5], type=pa.int16())) |
| assert y == pa.column('foo.y', pa.array([2.5, 4.5, 6.5], |
| type=pa.float32())) |
| # Empty column |
| a = pa.array([], type=ty) |
| col = pa.Column.from_array('foo', a) |
| x, y = col.flatten() |
| assert x == pa.column('foo.x', pa.array([], type=pa.int16())) |
| assert y == pa.column('foo.y', pa.array([], type=pa.float32())) |
| |
| |
| def test_column_getitem(): |
| arr = pa.array([1, 2, 3, 4, 5, 6]) |
| col = pa.column('ints', arr) |
| |
| assert col[1].as_py() == 2 |
| assert col[-1].as_py() == 6 |
| assert col[-6].as_py() == 1 |
| with pytest.raises(IndexError): |
| col[6] |
| with pytest.raises(IndexError): |
| col[-7] |
| |
| data_slice = col[2:4] |
| assert data_slice.to_pylist() == [3, 4] |
| |
| data_slice = col[4:-1] |
| assert data_slice.to_pylist() == [5] |
| |
| data_slice = col[99:99] |
| assert data_slice.type == col.type |
| assert data_slice.to_pylist() == [] |
| |
| |
| def test_recordbatch_basics(): |
| data = [ |
| pa.array(range(5)), |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| |
| batch = pa.RecordBatch.from_arrays(data, ['c0', 'c1']) |
| assert not batch.schema.metadata |
| |
| assert len(batch) == 5 |
| assert batch.num_rows == 5 |
| assert batch.num_columns == len(data) |
| pydict = batch.to_pydict() |
| assert pydict == OrderedDict([ |
| ('c0', [0, 1, 2, 3, 4]), |
| ('c1', [-10, -5, 0, 5, 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()), |
| pa.field('c1', pa.int32())], |
| metadata={b'foo': b'bar'}) |
| batch = pa.RecordBatch.from_arrays(data, schema) |
| assert batch.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.RecordBatch.from_arrays(data, ['id', 'tags', 'name']) |
| |
| |
| def test_recordbatch_no_fields(): |
| batch = pa.RecordBatch.from_arrays([], []) |
| |
| 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.RecordBatch.from_arrays(data, names=['a', 'b', 'c']) |
| |
| with pytest.raises(ValueError): |
| pa.RecordBatch.from_arrays(data, names=['a']) |
| |
| |
| def test_recordbatch_empty_metadata(): |
| data = [ |
| pa.array(range(5)), |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| |
| batch = pa.RecordBatch.from_arrays(data, ['c0', 'c1']) |
| assert batch.schema.metadata is None |
| |
| |
| def test_recordbatch_pickle(): |
| data = [ |
| pa.array(range(5)), |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| schema = pa.schema([pa.field('ints', pa.int8()), |
| pa.field('floats', pa.float32()), |
| ]).add_metadata({b'foo': b'bar'}) |
| batch = pa.RecordBatch.from_arrays(data, schema) |
| |
| result = pickle.loads(pickle.dumps(batch)) |
| assert result.equals(batch) |
| assert result.schema == schema |
| |
| |
| def test_recordbatch_slice_getitem(): |
| data = [ |
| pa.array(range(5)), |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| names = ['c0', 'c1'] |
| |
| batch = pa.RecordBatch.from_arrays(data, names) |
| |
| sliced = batch.slice(2) |
| assert sliced.num_rows == 3 |
| |
| expected = pa.RecordBatch.from_arrays( |
| [x.slice(2) for x in data], names) |
| assert sliced.equals(expected) |
| |
| sliced2 = batch.slice(2, 2) |
| expected2 = pa.RecordBatch.from_arrays( |
| [x.slice(2, 2) for x in data], names) |
| assert sliced2.equals(expected2) |
| |
| # 0 offset |
| assert batch.slice(0).equals(batch) |
| |
| # Slice past end of array |
| assert len(batch.slice(len(batch))) == 0 |
| |
| with pytest.raises(IndexError): |
| batch.slice(-1) |
| |
| # Check __getitem__-based slicing |
| assert batch.slice(0, 0).equals(batch[:0]) |
| assert batch.slice(0, 2).equals(batch[:2]) |
| assert batch.slice(2, 2).equals(batch[2:4]) |
| assert batch.slice(2, len(batch) - 2).equals(batch[2:]) |
| assert batch.slice(len(batch) - 2, 2).equals(batch[-2:]) |
| assert batch.slice(len(batch) - 4, 2).equals(batch[-4:-2]) |
| |
| |
| def test_recordbatchlist_schema_equals(): |
| a1 = np.array([1], dtype='uint32') |
| a2 = np.array([4.0, 5.0], dtype='float64') |
| batch1 = pa.RecordBatch.from_arrays([pa.array(a1)], ['c1']) |
| batch2 = pa.RecordBatch.from_arrays([pa.array(a2)], ['c1']) |
| |
| with pytest.raises(pa.ArrowInvalid): |
| pa.Table.from_batches([batch1, batch2]) |
| |
| |
| def test_table_equals(): |
| table = pa.Table.from_arrays([]) |
| |
| assert table.equals(table) |
| # ARROW-4822 |
| assert not table.equals(None) |
| |
| |
| def test_table_from_batches_and_schema(): |
| schema = pa.schema([ |
| pa.field('a', pa.int64()), |
| pa.field('b', pa.float64()), |
| ]) |
| batch = pa.RecordBatch.from_arrays([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.column('a', pa.array([1])) |
| assert table.column(1) == pa.column('b', pa.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.RecordBatch.from_arrays([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.util.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(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)), |
| pa.array([-10, -5, 0, 5, 10]) |
| ] |
| table = pa.Table.from_arrays(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) |
| 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.data.iterchunks(): |
| assert chunk is not None |
| |
| with pytest.raises(IndexError): |
| col.data.chunk(-1) |
| |
| with pytest.raises(IndexError): |
| col.data.chunk(col.data.num_chunks) |
| |
| assert table.columns == columns |
| assert table == pa.Table.from_arrays(columns) |
| assert table != pa.Table.from_arrays(columns[1:]) |
| assert table != columns |
| |
| |
| 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) |
| columns = [ |
| pa.column(field0, arr0), |
| pa.column(field1, arr1) |
| ] |
| table = pa.Table.from_arrays(columns) |
| assert b"a" in table.column(0).field.metadata |
| assert table.column(1).field.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_raises(): |
| data = [ |
| list(range(5)), |
| [-10, -5, 0, 5, 10] |
| ] |
| |
| with pytest.raises(TypeError): |
| pa.Table.from_arrays(data, names=['a', 'b']) |
| |
| schema = pa.schema([ |
| pa.field('a', pa.uint16()), |
| pa.field('b', pa.int64()) |
| ]) |
| with pytest.raises(TypeError): |
| pa.Table.from_arrays(data, schema=schema) |
| |
| |
| 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_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): |
| table.column('d') |
| |
| with pytest.raises(TypeError): |
| table.column(None) |
| |
| |
| 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')) |
| |
| col = pa.Column.from_array('d', data[1]) |
| t2 = table.add_column(3, col) |
| t3 = table.append_column(col) |
| |
| 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, col) |
| 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')) |
| |
| col = pa.Column.from_array('d', data[1]) |
| t2 = table.set_column(0, col) |
| |
| 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[0]) |
| t3._validate() |
| assert t3.equals(table) |
| |
| |
| 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.RecordBatch.from_arrays([pa.array([1]), pa.array(["a"])], |
| names=['f1', 'f2']) |
| batch2 = pa.RecordBatch.from_arrays([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.data.num_chunks == 1 |
| |
| |
| 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) |
| |
| |
| @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, check_metadata=False) |
| 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, check_metadata=False) |
| |
| |
| 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='Floating point value 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"]) |
| |
| |
| def test_table_from_pydict(): |
| table = pa.Table.from_pydict({}) |
| assert table.num_columns == 0 |
| assert table.num_rows == 0 |
| assert table.schema == pa.schema([]) |
| assert table.to_pydict() == {} |
| |
| # With arrays as values |
| data = OrderedDict([('strs', pa.array([u'', u'foo', u'bar'])), |
| ('floats', pa.array([4.5, 5, None]))]) |
| schema = pa.schema([('strs', pa.utf8()), ('floats', pa.float64())]) |
| table = pa.Table.from_pydict(data) |
| assert table.num_columns == 2 |
| assert table.num_rows == 3 |
| assert table.schema == schema |
| |
| # With chunked arrays as values |
| data = OrderedDict([('strs', pa.chunked_array([[u''], [u'foo', u'bar']])), |
| ('floats', pa.chunked_array([[4.5], [5., None]]))]) |
| table = pa.Table.from_pydict(data) |
| assert table.num_columns == 2 |
| assert table.num_rows == 3 |
| assert table.schema == schema |
| |
| # With lists as values |
| data = OrderedDict([('strs', [u'', u'foo', u'bar']), |
| ('floats', [4.5, 5, None])]) |
| table = pa.Table.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.add_metadata(metadata) |
| table = pa.Table.from_pydict(data, metadata=metadata) |
| assert table.schema == schema |
| assert table.schema.metadata == metadata |
| assert table.to_pydict() == data |
| |
| # With explicit schema |
| table = pa.Table.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): |
| pa.Table.from_pydict(data, schema=schema, metadata=metadata) |
| |
| |
| @pytest.mark.pandas |
| def test_table_factory_function(): |
| import pandas as pd |
| |
| d = {'a': [1, 2, 3], 'b': ['a', 'b', 'c']} |
| 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) |
| table2 = pa.Table.from_pydict(d) |
| assert table1.equals(table2) |
| table1 = pa.table(d, schema=schema) |
| table2 = pa.Table.from_pydict(d, schema=schema) |
| assert table1.equals(table2) |
