def test_dataframe_hash_partition(nrows, nparts, nkeys):
np.random.seed(123)
gdf = DataFrame()
keycols = []
for i in range(nkeys):
keyname = 'key{}'.format(i)
gdf[keyname] = np.random.randint(0, 7 - i, nrows)
keycols.append(keyname)
gdf['val1'] = np.random.randint(0, nrows * 2, nrows)
got = gdf.partition_by_hash(keycols, nparts=nparts)
# Must return a list
assert isinstance(got, list)
# Must have correct number of partitions
assert len(got) == nparts
# All partitions must be DataFrame type
assert all(isinstance(p, DataFrame) for p in got)
# Check that all partitions have unique keys
part_unique_keys = set()
for p in got:
if len(p):
# Take rows of the keycolums and build a set of the key-values
unique_keys = set(map(tuple, p.as_matrix(columns=keycols)))
# Ensure that none of the key-values have occurred in other groups
assert not (unique_keys & part_unique_keys)
part_unique_keys |= unique_keys
assert len(part_unique_keys)
def test_dataframe_hash_partition_masked_value(nrows):
gdf = DataFrame()
gdf['key'] = np.arange(nrows)
gdf['val'] = np.arange(nrows) + 100
bitmask = utils.random_bitmask(nrows)
bytemask = utils.expand_bits_to_bytes(bitmask)
gdf['val'] = gdf['val'].set_mask(bitmask)
parted = gdf.partition_by_hash(['key'], nparts=3)
# Verify that the valid mask is correct
for p in parted:
df = p.to_pandas()
for row in df.itertuples():
valid = bool(bytemask[row.key])
expected_value = row.key + 100 if valid else -1
got_value = row.val
assert expected_value == got_value
