/pandas/tests/test_tseries.py
Python | 358 lines | 260 code | 85 blank | 13 comment | 4 complexity | 8576b26e3ea00617b9facedd7f4f590b MD5 | raw file
Possible License(s): BSD-3-Clause, Apache-2.0
- import unittest
- import numpy as np
- from pandas import Index
- from pandas.util.testing import assert_almost_equal
- import pandas.util.testing as common
- import pandas._tseries as lib
- class TestTseriesUtil(unittest.TestCase):
- def test_combineFunc(self):
- pass
- def test_reindex(self):
- pass
- def test_isnull(self):
- pass
- def test_groupby(self):
- pass
- def test_groupby_withnull(self):
- pass
- def test_merge_indexer(self):
- old = Index([1, 5, 10])
- new = Index(range(12))
- filler = lib.merge_indexer_int64(new, old.indexMap)
- expect_filler = [-1, 0, -1, -1, -1, 1, -1, -1, -1, -1, 2, -1]
- self.assert_(np.array_equal(filler, expect_filler))
- # corner case
- old = Index([1, 4])
- new = Index(range(5, 10))
- filler = lib.merge_indexer_int64(new, old.indexMap)
- expect_filler = [-1, -1, -1, -1, -1]
- self.assert_(np.array_equal(filler, expect_filler))
- def test_backfill(self):
- old = Index([1, 5, 10])
- new = Index(range(12))
- filler = lib.backfill_int64(old, new, old.indexMap, new.indexMap)
- expect_filler = [0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, -1]
- self.assert_(np.array_equal(filler, expect_filler))
- # corner case
- old = Index([1, 4])
- new = Index(range(5, 10))
- filler = lib.backfill_int64(old, new, old.indexMap, new.indexMap)
- expect_filler = [-1, -1, -1, -1, -1]
- self.assert_(np.array_equal(filler, expect_filler))
- def test_pad(self):
- old = Index([1, 5, 10])
- new = Index(range(12))
- filler = lib.pad_int64(old, new, old.indexMap, new.indexMap)
- expect_filler = [-1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2]
- self.assert_(np.array_equal(filler, expect_filler))
- # corner case
- old = Index([5, 10])
- new = Index(range(5))
- filler = lib.pad_int64(old, new, old.indexMap, new.indexMap)
- expect_filler = [-1, -1, -1, -1, -1]
- self.assert_(np.array_equal(filler, expect_filler))
- def test_left_join_indexer():
- a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
- b = np.array([2, 2, 3, 4, 4], dtype=np.int64)
- result = lib.left_join_indexer_int64(b, a)
- expected = np.array([1, 1, 2, 3, 3], dtype='i4')
- assert(np.array_equal(result, expected))
- def test_left_outer_join_bug():
- left = np.array([0, 1, 0, 1, 1, 2, 3, 1, 0, 2, 1, 2, 0, 1, 1, 2, 3, 2, 3,
- 2, 1, 1, 3, 0, 3, 2, 3, 0, 0, 2, 3, 2, 0, 3, 1, 3, 0, 1,
- 3, 0, 0, 1, 0, 3, 1, 0, 1, 0, 1, 1, 0, 2, 2, 2, 2, 2, 0,
- 3, 1, 2, 0, 0, 3, 1, 3, 2, 2, 0, 1, 3, 0, 2, 3, 2, 3, 3,
- 2, 3, 3, 1, 3, 2, 0, 0, 3, 1, 1, 1, 0, 2, 3, 3, 1, 2, 0,
- 3, 1, 2, 0, 2], dtype=np.int32)
- right = np.array([3, 1], dtype=np.int32)
- max_groups = 4
- lidx, ridx = lib.left_outer_join(left, right, max_groups, sort=False)
- exp_lidx = np.arange(len(left))
- exp_ridx = -np.ones(len(left))
- exp_ridx[left == 1] = 1
- exp_ridx[left == 3] = 0
- assert(np.array_equal(lidx, exp_lidx))
- assert(np.array_equal(ridx, exp_ridx))
- def test_inner_join_indexer():
- a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
- b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
- index, ares, bres = lib.inner_join_indexer_int64(a, b)
- index_exp = np.array([3, 5], dtype=np.int64)
- assert_almost_equal(index, index_exp)
- aexp = np.array([2, 4])
- bexp = np.array([1, 2])
- assert_almost_equal(ares, aexp)
- assert_almost_equal(bres, bexp)
- def test_outer_join_indexer():
- a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
- b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
- index, ares, bres = lib.outer_join_indexer_int64(a, b)
- index_exp = np.array([0, 1, 2, 3, 4, 5, 7, 9], dtype=np.int64)
- assert_almost_equal(index, index_exp)
- aexp = np.array([-1, 0, 1, 2, 3, 4, -1, -1], dtype=np.int32)
- bexp = np.array([0, -1, -1, 1, -1, 2, 3, 4])
- assert_almost_equal(ares, aexp)
- assert_almost_equal(bres, bexp)
- def test_is_lexsorted():
- failure = [
- np.array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
- 3, 3,
- 3, 3,
- 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
- 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0]),
- np.array([30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16,
- 15, 14,
- 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 30, 29, 28,
- 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11,
- 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 30, 29, 28, 27, 26, 25,
- 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8,
- 7, 6, 5, 4, 3, 2, 1, 0, 30, 29, 28, 27, 26, 25, 24, 23, 22,
- 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5,
- 4, 3, 2, 1, 0])]
- assert(not lib.is_lexsorted(failure))
- # def test_get_group_index():
- # a = np.array([0, 1, 2, 0, 2, 1, 0, 0], dtype='i4')
- # b = np.array([1, 0, 3, 2, 0, 2, 3, 0], dtype='i4')
- # expected = np.array([1, 4, 11, 2, 8, 6, 3, 0], dtype='i4')
- # result = lib.get_group_index([a, b], (3, 4))
- # assert(np.array_equal(result, expected))
- def test_groupsort_indexer():
- a = np.random.randint(0, 1000, 100).astype('i4')
- b = np.random.randint(0, 1000, 100).astype('i4')
- result = lib.groupsort_indexer(a, 1000)[0]
- # need to use a stable sort
- expected = np.argsort(a, kind='mergesort')
- assert(np.array_equal(result, expected))
- # compare with lexsort
- key = a * 1000 + b
- result = lib.groupsort_indexer(key, 1000000)[0]
- expected = np.lexsort((b, a))
- assert(np.array_equal(result, expected))
- def test_duplicated_with_nas():
- keys = [0, 1, np.nan, 0, 2, np.nan]
- result = lib.duplicated(keys)
- expected = [False, False, False, True, False, True]
- assert(np.array_equal(result, expected))
- result = lib.duplicated(keys, take_last=True)
- expected = [True, False, True, False, False, False]
- assert(np.array_equal(result, expected))
- keys = [(0, 0), (0, np.nan), (np.nan, 0), (np.nan, np.nan)] * 2
- result = lib.duplicated(keys)
- falses = [False] * 4
- trues = [True] * 4
- expected = falses + trues
- assert(np.array_equal(result, expected))
- result = lib.duplicated(keys, take_last=True)
- expected = trues + falses
- assert(np.array_equal(result, expected))
- def test_convert_objects():
- arr = np.array(['a', 'b', np.nan, np.nan, 'd', 'e', 'f'], dtype='O')
- result = lib.maybe_convert_objects(arr)
- assert(result.dtype == np.object_)
- def test_convert_objects_ints():
- # test that we can detect many kinds of integers
- dtypes = ['i1', 'i2', 'i4', 'i8', 'u1', 'u2', 'u4', 'u8']
- for dtype_str in dtypes:
- arr = np.array(list(np.arange(20, dtype=dtype_str)), dtype='O')
- assert(arr[0].dtype == np.dtype(dtype_str))
- result = lib.maybe_convert_objects(arr)
- assert(issubclass(result.dtype.type, np.integer))
- def test_rank():
- from scipy.stats import rankdata
- from numpy import nan
- def _check(arr):
- mask = -np.isfinite(arr)
- arr = arr.copy()
- result = lib.rank_1d_float64(arr)
- arr[mask] = np.inf
- exp = rankdata(arr)
- exp[mask] = np.nan
- assert_almost_equal(result, exp)
- _check(np.array([nan, nan, 5., 5., 5., nan, 1, 2, 3, nan]))
- _check(np.array([4., nan, 5., 5., 5., nan, 1, 2, 4., nan]))
- def test_get_reverse_indexer():
- indexer = np.array([-1, -1, 1, 2, 0, -1, 3, 4], dtype='i4')
- result = lib.get_reverse_indexer(indexer, 5)
- expected = np.array([4, 2, 3, 6, 7], dtype='i4')
- assert(np.array_equal(result, expected))
- def test_pad_backfill_object_segfault():
- from datetime import datetime
- old = np.array([], dtype='O')
- new = np.array([datetime(2010, 12, 31)], dtype='O')
- result = lib.pad_object(old, new, lib.map_indices_object(old),
- lib.map_indices_object(new))
- expected = np.array([-1], dtype='i4')
- assert(np.array_equal(result, expected))
- result = lib.pad_object(new, old, lib.map_indices_object(new),
- lib.map_indices_object(old))
- expected = np.array([], dtype='i4')
- assert(np.array_equal(result, expected))
- result = lib.backfill_object(old, new, lib.map_indices_object(old),
- lib.map_indices_object(new))
- expected = np.array([-1], dtype='i4')
- assert(np.array_equal(result, expected))
- result = lib.backfill_object(new, old, lib.map_indices_object(new),
- lib.map_indices_object(old))
- expected = np.array([], dtype='i4')
- assert(np.array_equal(result, expected))
- def test_arrmap():
- values = np.array(['foo', 'foo', 'bar', 'bar', 'baz', 'qux'], dtype='O')
- result = lib.arrmap_object(values, lambda x: x in ['foo', 'bar'])
- assert(result.dtype == np.bool_)
- class TestTypeInference(unittest.TestCase):
- def test_length_zero(self):
- result = lib.infer_dtype(np.array([], dtype='i4'))
- self.assertEqual(result, 'empty')
- result = lib.infer_dtype(np.array([], dtype='O'))
- self.assertEqual(result, 'empty')
- def test_integers(self):
- arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype='O')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'integer')
- arr = np.array([1, 2, 3, np.int64(4), np.int32(5), 'foo'],
- dtype='O')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'mixed')
- arr = np.array([1, 2, 3, 4, 5], dtype='i4')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'integer')
- def test_bools(self):
- arr = np.array([True, False, True, True, True], dtype='O')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'boolean')
- arr = np.array([np.bool_(True), np.bool_(False)], dtype='O')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'boolean')
- arr = np.array([True, False, True, 'foo'], dtype='O')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'mixed')
- arr = np.array([True, False, True], dtype=bool)
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'boolean')
- def test_floats(self):
- arr = np.array([1., 2., 3., np.float64(4), np.float32(5)], dtype='O')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'floating')
- arr = np.array([1, 2, 3, np.float64(4), np.float32(5), 'foo'],
- dtype='O')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'mixed')
- arr = np.array([1, 2, 3, 4, 5], dtype='f4')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'floating')
- arr = np.array([1, 2, 3, 4, 5], dtype='f8')
- result = lib.infer_dtype(arr)
- self.assertEqual(result, 'floating')
- def test_string(self):
- pass
- def test_unicode(self):
- pass
- def test_datetime(self):
- pass
- def test_to_object_array_tuples(self):
- r = (5,6)
- values = [r]
- result = lib.to_object_array_tuples(values)
- try:
- # make sure record array works
- from collections import namedtuple
- record = namedtuple('record', 'x y')
- r = record(5, 6)
- values = [r]
- result = lib.to_object_array_tuples(values)
- except ImportError:
- pass
- class TestMoments(unittest.TestCase):
- pass
- if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'],
- exit=False)