def test_applymap_round(nelem, masked):
# Generate data
np.random.seed(0)
data = np.random.random(nelem) * 100
if masked:
# Make mask
bitmask = utils.random_bitmask(nelem)
boolmask = np.asarray(utils.expand_bits_to_bytes(bitmask),
dtype=np.bool)[:nelem]
data[~boolmask] = np.nan
sr = Series(data)
if masked:
# Mask the Series
sr = sr.set_mask(bitmask)
# Call applymap
out = sr.applymap(lambda x: (floor(x) + 1
if x - floor(x) >= 0.5 else floor(x)))
if masked:
# Fill masked values
out = out.fillna(np.nan)
# Check
expect = np.round(data)
got = out.to_array()
np.testing.assert_array_almost_equal(expect, got)
def test_applymap_python_lambda(dtype, udf, testfunc):
size = 500
lhs_arr = np.random.random(size).astype(dtype)
lhs_ser = Series(lhs_arr)
out_ser = lhs_ser.applymap(udf)
result = testfunc(lhs_arr)
np.testing.assert_almost_equal(result, out_ser.to_array())
def test_applymap_change_out_dtype():
# Test for changing the out_dtype using applymap
data = list(range(10))
sr = Series(data)
out = sr.applymap(lambda x: float(x), out_dtype=float)
# Check
expect = np.array(data, dtype=float)
got = out.to_array()
np.testing.assert_array_equal(expect, got)
