def pd_multi_index_from_product_impl(cls,
iterables,
sortorder=None,
names=None):
# TO-DO: support indexes.unique() method and use it here
levels_factorized = sdc_tuple_map(factorize_level, iterables)
levels_names = sdc_tuple_map(sdc_indexes_get_name, iterables)
index_levels = sdc_tuple_map(lambda x: fix_df_index(list(x[0])),
levels_factorized)
temp_cumprod_sizes = [
1,
]
codes_info = sdc_tuple_map(next_codes_info, levels_factorized,
temp_cumprod_sizes)
res_index_size = temp_cumprod_sizes[-1]
index_codes = sdc_tuple_map(next_codes_array, codes_info,
res_index_size)
res = sdc_pandas_multi_index_ctor(index_levels,
index_codes,
name=levels_names)
return res
def sdc_unify_index_types_impl(left, right):
if index_dtypes_match == True: # noqa
return left
else:
if is_left_index_cached == True: # noqa
index_data = left.values if is_left_index_array == False else left # noqa
elif is_right_index_cached == True: # noqa
index_data = right.values if is_right_index_array == False else right # noqa
else:
# using numpy_like.astype but not index.astype since latter works differently
index_data = numpy_like.astype(left, numba_index_common_dtype)
return fix_df_index(index_data)
def _multi_index_append_level_impl(A, codes_A, B, codes_B):
appender_map = _appender_build_map(A, B)
res_size = len(codes_A) + len(codes_B)
res_level = fix_df_index(list(appender_map.keys()))
res_codes = np.empty(res_size, dtype=np.int64)
A_size = len(codes_A)
for i in prange(res_size):
if i < A_size:
res_codes[i] = codes_A[i]
else:
res_codes[i] = appender_map[B[codes_B[i - A_size]]]
return (res_level, res_codes)
def pd_int64_index_ctor_impl(data, dtype=None, copy=False, name=None):
if not (dtype is None
or dtype_is_numpy_signed_int
or dtype_is_unicode_str and dtype in ('int8', 'int16', 'int32', 'int64')):
raise ValueError("Incorrect `dtype` passed: expected signed integer")
if is_data_array == True: # noqa
_data = data
elif is_data_index == True: # noqa
_data = data.values
else:
_data = fix_df_index(data)._data
if data_dtype_is_int64 == False: # noqa
_data = numpy_like.astype(_data, dtype=types.int64)
else:
if copy:
_data = np.copy(_data)
return init_int64_index(_data, name)
def _multi_index_create_levels_and_codes_impl(level_data, codes_data,
name):
level_data_fixed = fix_df_index(level_data)
level = sdc_indexes_rename(level_data_fixed, name)
codes = fix_df_array(codes_data)
# to avoid additional overload make data verification checks inplace
# these checks repeat those in MultiIndex::_verify_integrity
if len(codes) and np.max(codes) >= len(level):
raise ValueError(
"On one of the levels code max >= length of level. "
"NOTE: this index is in an inconsistent state")
if len(codes) and np.min(codes) < -1:
raise ValueError("On one of the levels code value < -1")
# TO-DO: support is_unique for all indexes and use it here
indexer_map = sdc_indexes_build_map_positions(level)
if len(level) != len(indexer_map):
raise ValueError("Level values must be unique")
return (level, codes)
def _multi_index_create_level_impl(index_data, name):
index = fix_df_index(index_data)
return sdc_indexes_rename(index, name)