def mode(values):
"""Returns the mode or mode(s) of the passed Series or ndarray (sorted)"""
# must sort because hash order isn't necessarily defined.
from pandas.core.series import Series
if isinstance(values, Series):
constructor = values._constructor
values = values.values
else:
values = np.asanyarray(values)
constructor = Series
dtype = values.dtype
if com.is_integer_dtype(values):
values = com._ensure_int64(values)
result = constructor(sorted(htable.mode_int64(values)), dtype=dtype)
elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)):
dtype = values.dtype
values = values.view(np.int64)
result = constructor(sorted(htable.mode_int64(values)), dtype=dtype)
elif com.is_categorical_dtype(values):
result = constructor(values.mode())
else:
mask = com.isnull(values)
values = com._ensure_object(values)
res = htable.mode_object(values, mask)
try:
res = sorted(res)
except TypeError as e:
warn("Unable to sort modes: %s" % e)
result = constructor(res, dtype=dtype)
return result
def mode(values):
"""Returns the mode or mode(s) of the passed Series or ndarray (sorted)"""
# must sort because hash order isn't necessarily defined.
from pandas.core.series import Series
if isinstance(values, Series):
constructor = values._constructor
values = values.values
else:
values = np.asanyarray(values)
constructor = Series
dtype = values.dtype
if com.is_integer_dtype(values.dtype):
values = com._ensure_int64(values)
result = constructor(sorted(htable.mode_int64(values)), dtype=dtype)
elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)):
dtype = values.dtype
values = values.view(np.int64)
result = constructor(sorted(htable.mode_int64(values)), dtype=dtype)
else:
mask = com.isnull(values)
values = com._ensure_object(values)
res = htable.mode_object(values, mask)
try:
res = sorted(res)
except TypeError as e:
warn("Unable to sort modes: %s" % e)
result = constructor(res, dtype=dtype)
return result
def mode(values):
"""
Returns the mode(s) of an array.
Parameters
----------
values : array-like
Array over which to check for duplicate values.
Returns
-------
mode : Series
"""
# must sort because hash order isn't necessarily defined.
from pandas.core.series import Series
if isinstance(values, Series):
constructor = values._constructor
values = values.values
else:
values = np.asanyarray(values)
constructor = Series
dtype = values.dtype
if is_signed_integer_dtype(values):
values = _ensure_int64(values)
result = constructor(np.sort(htable.mode_int64(values)), dtype=dtype)
elif is_unsigned_integer_dtype(values):
values = _ensure_uint64(values)
result = constructor(np.sort(htable.mode_uint64(values)), dtype=dtype)
elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)):
dtype = values.dtype
values = values.view(np.int64)
result = constructor(np.sort(htable.mode_int64(values)), dtype=dtype)
elif is_categorical_dtype(values):
result = constructor(values.mode())
else:
values = _ensure_object(values)
res = htable.mode_object(values)
try:
res = np.sort(res)
except TypeError as e:
warn("Unable to sort modes: %s" % e)
result = constructor(res, dtype=dtype)
return result
def mode(values):
"""
Returns the mode(s) of an array.
Parameters
----------
values : array-like
Array over which to check for duplicate values.
Returns
-------
mode : Series
"""
# must sort because hash order isn't necessarily defined.
from pandas.core.series import Series
if isinstance(values, Series):
constructor = values._constructor
values = values.values
else:
values = np.asanyarray(values)
constructor = Series
dtype = values.dtype
if is_signed_integer_dtype(values):
values = _ensure_int64(values)
result = constructor(np.sort(htable.mode_int64(values)), dtype=dtype)
elif is_unsigned_integer_dtype(values):
values = _ensure_uint64(values)
result = constructor(np.sort(htable.mode_uint64(values)), dtype=dtype)
elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)):
dtype = values.dtype
values = values.view(np.int64)
result = constructor(np.sort(htable.mode_int64(values)), dtype=dtype)
elif is_categorical_dtype(values):
result = constructor(values.mode())
else:
values = _ensure_object(values)
res = htable.mode_object(values)
try:
res = np.sort(res)
except TypeError as e:
warn("Unable to sort modes: %s" % e)
result = constructor(res, dtype=dtype)
return result
def mode(self):
"""
Returns the mode(s) of the Categorical.
Empty if nothing occurs at least 2 times. Always returns `Categorical` even
if only one value.
Returns
-------
modes : `Categorical` (sorted)
"""
import pandas.hashtable as htable
good = self._codes != -1
result = Categorical(sorted(htable.mode_int64(com._ensure_int64(self._codes[good]))),
categories=self.categories,ordered=self.ordered, name=self.name,
fastpath=True)
return result
def mode(self):
"""
Returns the mode(s) of the Categorical.
Empty if nothing occurs at least 2 times. Always returns `Categorical` even
if only one value.
Returns
-------
modes : `Categorical` (sorted)
"""
import pandas.hashtable as htable
good = self._codes != -1
result = Categorical(sorted(htable.mode_int64(com._ensure_int64(self._codes[good]))),
categories=self.categories,ordered=self.ordered, name=self.name,
fastpath=True)
return result
