def hpat_pandas_stringmethods_casefold_impl(self):
mask = get_nan_mask(self._data._data)
item_count = len(self._data)
res_list = [''] * item_count
for idx in numba.prange(item_count):
res_list[idx] = self._data._data[idx].casefold()
str_arr = create_str_arr_from_list(res_list)
result = str_arr_set_na_by_mask(str_arr, mask)
return pandas.Series(result, self._data._index, name=self._data._name)
def test_impl(x):
# make native encoded unicode string via StringArrayType
data_as_str_arr = create_str_arr_from_list([x, ])
data_ptr, size = get_str_view_data(data_as_str_arr, 0)
# actual test: create string view pointing to data and convert it back to unicode
str_view = string_view_create_with_data(data_ptr, size)
as_unicode = str(str_view)
check_equal = as_unicode == data_as_str_arr[0] # for extending the lifetime of str_arr
return as_unicode, check_equal
def sdc_reindex_series_impl(arr, index, name, by_index):
# no reindexing is needed if indexes are equal
if range_indexes == True: # noqa
equal_indexes = numpy_like.array_equal(index, by_index)
elif int64_indexes == True: # noqa
equal_indexes = numpy_like.array_equal(index, by_index)
else:
equal_indexes = False
if (index is by_index or equal_indexes):
return pandas.Series(data=arr, index=by_index, name=name)
if data_is_str_arr == True: # noqa
_res_data = [''] * len(by_index)
res_data_nan_mask = numpy.zeros(len(by_index), dtype=types.bool_)
else:
_res_data = numpy.empty(len(by_index), dtype=data_dtype)
# build a dict of self.index values to their positions:
map_index_to_position = Dict.empty(key_type=index_dtype,
value_type=types.int32)
for i, value in enumerate(index):
if value in map_index_to_position:
raise ValueError("cannot reindex from a duplicate axis")
else:
map_index_to_position[value] = i
index_mismatch = 0
for i in numba.prange(len(by_index)):
val = by_index[i]
if val in map_index_to_position:
pos_in_self = map_index_to_position[val]
_res_data[i] = arr[pos_in_self]
if data_is_str_arr == True: # noqa
res_data_nan_mask[i] = isna(arr, i)
else:
index_mismatch += 1
if index_mismatch:
msg = "Unalignable boolean Series provided as indexer " + \
"(index of the boolean Series and of the indexed object do not match)."
raise IndexingError(msg)
if data_is_str_arr == True: # noqa
res_data = create_str_arr_from_list(_res_data)
str_arr_set_na_by_mask(res_data, res_data_nan_mask)
else:
res_data = _res_data
return pandas.Series(data=res_data, index=by_index, name=name)
def hpat_pandas_stringmethods_upper_impl(self):
mask = get_nan_mask(self._data._data)
item_count = len(self._data)
result = [''] * item_count
for it in numba.prange(item_count):
item = self._data._data[it]
if len(item) > 0:
result[it] = item.upper()
else:
result[it] = item
str_arr = create_str_arr_from_list(result)
result = str_arr_set_na_by_mask(str_arr, mask)
return pandas.Series(result, self._data._index, name=self._data._name)
def impl(self, to_strip=None):
mask = get_nan_mask(self._data._data)
item_count = len(self._data)
res_list = [''] * item_count
for it in range(item_count):
item = self._data._data[it]
if len(item) > 0:
res_list[it] = usecase(item, to_strip)
else:
res_list[it] = item
str_arr = create_str_arr_from_list(res_list)
result = str_arr_set_na_by_mask(str_arr, mask)
return pandas.Series(result, self._data._index, name=self._data._name)
def getitem_by_mask_impl(arr, idx):
chunks = parallel_chunks(len(arr))
arr_len = numpy.empty(len(chunks), dtype=numpy.int64)
length = 0
for i in prange(len(chunks)):
chunk = chunks[i]
res = 0
for j in range(chunk.start, chunk.stop):
if idx[j]:
res += 1
length += res
arr_len[i] = res
if is_str_arr == True: # noqa
result_data = [''] * length
result_nan_mask = numpy.empty(shape=length, dtype=types.bool_)
else:
result_data = numpy.empty(shape=length, dtype=res_dtype)
for i in prange(len(chunks)):
chunk = chunks[i]
new_start = int(sum(arr_len[0:i]))
current_pos = new_start
for j in range(chunk.start, chunk.stop):
if idx[j]:
if is_range == True: # noqa
value = arr.start + arr.step * j
else:
value = arr[j]
result_data[current_pos] = value
if is_str_arr == True: # noqa
result_nan_mask[current_pos] = isna(arr, j)
current_pos += 1
if is_str_arr == True: # noqa
result_data_as_str_arr = create_str_arr_from_list(result_data)
str_arr_set_na_by_mask(result_data_as_str_arr, result_nan_mask)
return result_data_as_str_arr
else:
return result_data
def _sdc_asarray_impl(data):
return create_str_arr_from_list(data)
