def _hash_categories(categories, ordered=True):
from pandas.core.util.hashing import (hash_array, _combine_hash_arrays,
hash_tuples)
from pandas.core.dtypes.common import is_datetime64tz_dtype, _NS_DTYPE
if len(categories) and isinstance(categories[0], tuple):
# assumes if any individual category is a tuple, then all our. ATM
# I don't really want to support just some of the categories being
# tuples.
categories = list(categories) # breaks if a np.array of categories
cat_array = hash_tuples(categories)
else:
if categories.dtype == 'O':
if len({type(x) for x in categories}) != 1:
# TODO: hash_array doesn't handle mixed types. It casts
# everything to a str first, which means we treat
# {'1', '2'} the same as {'1', 2}
# find a better solution
hashed = hash((tuple(categories), ordered))
return hashed
if is_datetime64tz_dtype(categories.dtype):
# Avoid future warning.
categories = categories.astype(_NS_DTYPE)
cat_array = hash_array(np.asarray(categories), categorize=False)
if ordered:
cat_array = np.vstack(
[cat_array,
np.arange(len(cat_array), dtype=cat_array.dtype)])
else:
cat_array = [cat_array]
hashed = _combine_hash_arrays(iter(cat_array),
num_items=len(cat_array))
return np.bitwise_xor.reduce(hashed)
def _hash_categories(categories, ordered=True):
from pandas.core.util.hashing import (
hash_array, _combine_hash_arrays, hash_tuples
)
if len(categories) and isinstance(categories[0], tuple):
# assumes if any individual category is a tuple, then all our. ATM
# I don't really want to support just some of the categories being
# tuples.
categories = list(categories) # breaks if a np.array of categories
cat_array = hash_tuples(categories)
else:
if categories.dtype == 'O':
types = [type(x) for x in categories]
if not len(set(types)) == 1:
# TODO: hash_array doesn't handle mixed types. It casts
# everything to a str first, which means we treat
# {'1', '2'} the same as {'1', 2}
# find a better solution
hashed = hash((tuple(categories), ordered))
return hashed
cat_array = hash_array(np.asarray(categories), categorize=False)
if ordered:
cat_array = np.vstack([
cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)
])
else:
cat_array = [cat_array]
hashed = _combine_hash_arrays(iter(cat_array),
num_items=len(cat_array))
return np.bitwise_xor.reduce(hashed)
def _hash_categories(categories, ordered=True):
from pandas.core.util.hashing import (hash_array, _combine_hash_arrays,
hash_tuples)
if len(categories) and isinstance(categories[0], tuple):
# assumes if any individual category is a tuple, then all our. ATM
# I don't really want to support just some of the categories being
# tuples.
categories = list(categories) # breaks if a np.array of categories
cat_array = hash_tuples(categories)
else:
if categories.dtype == 'O':
types = [type(x) for x in categories]
if not len(set(types)) == 1:
# TODO: hash_array doesn't handle mixed types. It casts
# everything to a str first, which means we treat
# {'1', '2'} the same as {'1', 2}
# find a better solution
hashed = hash((tuple(categories), ordered))
return hashed
cat_array = hash_array(np.asarray(categories), categorize=False)
if ordered:
cat_array = np.vstack(
[cat_array,
np.arange(len(cat_array), dtype=cat_array.dtype)])
else:
cat_array = [cat_array]
hashed = _combine_hash_arrays(iter(cat_array),
num_items=len(cat_array))
if len(hashed) == 0:
# bug in Numpy<1.12 for length 0 arrays. Just return the correct
# value of 0
return 0
else:
return np.bitwise_xor.reduce(hashed)
def _hash_dataframe_rows_no_categorize(df):
""" returns a sequence of hashes, for each row, as in original `hash_pandas_object` """
# hashing whole df was too slow, as it uses categorize=True for each series --> adapted pandas code
hashes = (hash_array(series._values, categorize=False)
for _, series in df.items())
num_items = len(df.columns)
h = combine_hash_arrays(hashes, num_items)
return h
def _hash_categories(self) -> int:
from pandas.core.util.hashing import (
combine_hash_arrays,
hash_array,
hash_tuples,
)
categories = self.categories
ordered = self.ordered
if len(categories) and isinstance(categories[0], tuple):
# assumes if any individual category is a tuple, then all our. ATM
# I don't really want to support just some of the categories being
# tuples.
cat_list = list(categories) # breaks if a np.array of categories
cat_array = hash_tuples(cat_list)
else:
if categories.dtype == "O" and len({type(x)
for x in categories}) != 1:
# TODO: hash_array doesn't handle mixed types. It casts
# everything to a str first, which means we treat
# {'1', '2'} the same as {'1', 2}
# find a better solution
hashed = hash((tuple(categories), ordered))
return hashed
if DatetimeTZDtype.is_dtype(categories.dtype):
# Avoid future warning.
categories = categories.view("datetime64[ns]")
cat_array = hash_array(np.asarray(categories), categorize=False)
if ordered:
cat_array = np.vstack(
[cat_array,
np.arange(len(cat_array), dtype=cat_array.dtype)])
else:
# error: Incompatible types in assignment (expression has type
# "List[ndarray]", variable has type "ndarray")
cat_array = [cat_array] # type: ignore[assignment]
# error: Incompatible types in assignment (expression has type "ndarray",
# variable has type "int")
hashed = combine_hash_arrays( # type: ignore[assignment]
iter(cat_array),
num_items=len(cat_array))
return np.bitwise_xor.reduce(hashed)
