Python base.InvalidIndexError方法代码示例

# 需要导入模块: from pandas.core.indexes import base [as 别名]
# 或者: from pandas.core.indexes.base import InvalidIndexError [as 别名]
def test_get_indexer_consistency(self):
        # See GH 16819
        for name, index in self.indices.items():
            if isinstance(index, IntervalIndex):
                continue

            if index.is_unique or isinstance(index, CategoricalIndex):
                indexer = index.get_indexer(index[0:2])
                assert isinstance(indexer, np.ndarray)
                assert indexer.dtype == np.intp
            else:
                e = "Reindexing only valid with uniquely valued Index objects"
                with pytest.raises(InvalidIndexError, match=e):
                    index.get_indexer(index[0:2])

            indexer, _ = index.get_indexer_non_unique(index[0:2])
            assert isinstance(indexer, np.ndarray)
            assert indexer.dtype == np.intp 

# 需要导入模块: from pandas.core.indexes import base [as 别名]
# 或者: from pandas.core.indexes.base import InvalidIndexError [as 别名]
def test_get_indexer_consistency(idx):
    # See GH 16819
    if isinstance(idx, IntervalIndex):
        pass

    if idx.is_unique or isinstance(idx, CategoricalIndex):
        indexer = idx.get_indexer(idx[0:2])
        assert isinstance(indexer, np.ndarray)
        assert indexer.dtype == np.intp
    else:
        e = "Reindexing only valid with uniquely valued Index objects"
        with pytest.raises(InvalidIndexError, match=e):
            idx.get_indexer(idx[0:2])

    indexer, _ = idx.get_indexer_non_unique(idx[0:2])
    assert isinstance(indexer, np.ndarray)
    assert indexer.dtype == np.intp 

# 需要导入模块: from pandas.core.indexes import base [as 别名]
# 或者: from pandas.core.indexes.base import InvalidIndexError [as 别名]
def test_get_indexer_consistency(self):
        # See GH 16819
        for name, index in self.indices.items():
            if isinstance(index, IntervalIndex):
                continue

            if index.is_unique or isinstance(index, CategoricalIndex):
                indexer = index.get_indexer(index[0:2])
                assert isinstance(indexer, np.ndarray)
                assert indexer.dtype == np.intp
            else:
                e = "Reindexing only valid with uniquely valued Index objects"
                with tm.assert_raises_regex(InvalidIndexError, e):
                    indexer = index.get_indexer(index[0:2])

            indexer, _ = index.get_indexer_non_unique(index[0:2])
            assert isinstance(indexer, np.ndarray)
            assert indexer.dtype == np.intp 

# 需要导入模块: from pandas.core.indexes import base [as 别名]
# 或者: from pandas.core.indexes.base import InvalidIndexError [as 别名]
def robust_lookup(df, indexer):
    """
    Robust way to apply pandas lookup
     (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.lookup.html)
     when indices are not unique

    Args:
        df (pdDataFrame):
        indexer (pdSeries): A Series with indexes either same or a subset of `df`
                            (BUT SHOULD NOT contain any index not found in `df`),
                            and whose values are values from `df.columns`.

    Returns:
        pdSeries
    """
    try:
        extracted = df.lookup(indexer.index, indexer)
    except InvalidIndexError:
        # Indices are not unique, fallback to using numpy-based indexing.
        # This assumes indexer's values are integers starting from 0.
        extracted = df.values[indexer.index, indexer]
    extracted = pdSeries(extracted, index=indexer.index)
    return extracted 

# 需要导入模块: from pandas.core.indexes import base [as 别名]
# 或者: from pandas.core.indexes.base import InvalidIndexError [as 别名]
def test_get_indexer():
    major_axis = Index(lrange(4))
    minor_axis = Index(lrange(2))

    major_codes = np.array([0, 0, 1, 2, 2, 3, 3], dtype=np.intp)
    minor_codes = np.array([0, 1, 0, 0, 1, 0, 1], dtype=np.intp)

    index = MultiIndex(levels=[major_axis, minor_axis],
                       codes=[major_codes, minor_codes])
    idx1 = index[:5]
    idx2 = index[[1, 3, 5]]

    r1 = idx1.get_indexer(idx2)
    assert_almost_equal(r1, np.array([1, 3, -1], dtype=np.intp))

    r1 = idx2.get_indexer(idx1, method='pad')
    e1 = np.array([-1, 0, 0, 1, 1], dtype=np.intp)
    assert_almost_equal(r1, e1)

    r2 = idx2.get_indexer(idx1[::-1], method='pad')
    assert_almost_equal(r2, e1[::-1])

    rffill1 = idx2.get_indexer(idx1, method='ffill')
    assert_almost_equal(r1, rffill1)

    r1 = idx2.get_indexer(idx1, method='backfill')
    e1 = np.array([0, 0, 1, 1, 2], dtype=np.intp)
    assert_almost_equal(r1, e1)

    r2 = idx2.get_indexer(idx1[::-1], method='backfill')
    assert_almost_equal(r2, e1[::-1])

    rbfill1 = idx2.get_indexer(idx1, method='bfill')
    assert_almost_equal(r1, rbfill1)

    # pass non-MultiIndex
    r1 = idx1.get_indexer(idx2.values)
    rexp1 = idx1.get_indexer(idx2)
    assert_almost_equal(r1, rexp1)

    r1 = idx1.get_indexer([1, 2, 3])
    assert (r1 == [-1, -1, -1]).all()

    # create index with duplicates
    idx1 = Index(lrange(10) + lrange(10))
    idx2 = Index(lrange(20))

    msg = "Reindexing only valid with uniquely valued Index objects"
    with pytest.raises(InvalidIndexError, match=msg):
        idx1.get_indexer(idx2) 

# 需要导入模块: from pandas.core.indexes import base [as 别名]
# 或者: from pandas.core.indexes.base import InvalidIndexError [as 别名]
def test_get_indexer(self):
        major_axis = Index(lrange(4))
        minor_axis = Index(lrange(2))

        major_labels = np.array([0, 0, 1, 2, 2, 3, 3], dtype=np.intp)
        minor_labels = np.array([0, 1, 0, 0, 1, 0, 1], dtype=np.intp)

        index = MultiIndex(levels=[major_axis, minor_axis],
                           labels=[major_labels, minor_labels])
        idx1 = index[:5]
        idx2 = index[[1, 3, 5]]

        r1 = idx1.get_indexer(idx2)
        assert_almost_equal(r1, np.array([1, 3, -1], dtype=np.intp))

        r1 = idx2.get_indexer(idx1, method='pad')
        e1 = np.array([-1, 0, 0, 1, 1], dtype=np.intp)
        assert_almost_equal(r1, e1)

        r2 = idx2.get_indexer(idx1[::-1], method='pad')
        assert_almost_equal(r2, e1[::-1])

        rffill1 = idx2.get_indexer(idx1, method='ffill')
        assert_almost_equal(r1, rffill1)

        r1 = idx2.get_indexer(idx1, method='backfill')
        e1 = np.array([0, 0, 1, 1, 2], dtype=np.intp)
        assert_almost_equal(r1, e1)

        r2 = idx2.get_indexer(idx1[::-1], method='backfill')
        assert_almost_equal(r2, e1[::-1])

        rbfill1 = idx2.get_indexer(idx1, method='bfill')
        assert_almost_equal(r1, rbfill1)

        # pass non-MultiIndex
        r1 = idx1.get_indexer(idx2.values)
        rexp1 = idx1.get_indexer(idx2)
        assert_almost_equal(r1, rexp1)

        r1 = idx1.get_indexer([1, 2, 3])
        assert (r1 == [-1, -1, -1]).all()

        # create index with duplicates
        idx1 = Index(lrange(10) + lrange(10))
        idx2 = Index(lrange(20))

        msg = "Reindexing only valid with uniquely valued Index objects"
        with tm.assert_raises_regex(InvalidIndexError, msg):
            idx1.get_indexer(idx2)