def __init__(self, values, categories=None, ordered=None, name=None, fastpath=False,
levels=None):
if fastpath:
# fast path
self._codes = values
self.name = name
self.categories = categories
self.ordered = ordered
return
if name is None:
name = getattr(values, 'name', None)
# TODO: Remove after deprecation period in 2017/ after 0.18
if not levels is None:
warn("Creating a 'Categorical' with 'levels' is deprecated, use 'categories' instead",
FutureWarning)
if categories is None:
categories = levels
else:
raise ValueError("Cannot pass in both 'categories' and (deprecated) 'levels', "
"use only 'categories'")
# sanitize input
if com.is_categorical_dtype(values):
# we are either a Series or a Categorical
cat = values
if isinstance(values, com.ABCSeries):
cat = values.values
if categories is None:
categories = cat.categories
if ordered is None:
ordered = cat.ordered
values = values.__array__()
elif isinstance(values, Index):
pass
else:
# on numpy < 1.6 datetimelike get inferred to all i8 by _sanitize_array
# which is fine, but since factorize does this correctly no need here
# this is an issue because _sanitize_array also coerces np.nan to a string
# under certain versions of numpy as well
values = com._possibly_infer_to_datetimelike(values, convert_dates=True)
if not isinstance(values, np.ndarray):
values = _convert_to_list_like(values)
from pandas.core.series import _sanitize_array
# On list with NaNs, int values will be converted to float. Use "object" dtype
# to prevent this. In the end objects will be casted to int/... in the category
# assignment step.
dtype = 'object' if isnull(values).any() else None
values = _sanitize_array(values, None, dtype=dtype)
if categories is None:
try:
codes, categories = factorize(values, sort=True)
# If the underlying data structure was sortable, and the user doesn't want to
# "forget" this order, the categorical also is sorted/ordered
if ordered is None:
ordered = True
except TypeError:
codes, categories = factorize(values, sort=False)
if ordered:
# raise, as we don't have a sortable data structure and so the user should
# give us one by specifying categories
raise TypeError("'values' is not ordered, please explicitly specify the "
"categories order by passing in a categories argument.")
else:
# there were two ways if categories are present
# - the old one, where each value is a int pointer to the levels array -> not anymore
# possible, but code outside of pandas could call us like that, so make some checks
# - the new one, where each value is also in the categories array (or np.nan)
# make sure that we always have the same type here, no matter what we get passed in
categories = self._validate_categories(categories)
codes = _get_codes_for_values(values, categories)
# TODO: check for old style usage. These warnings should be removes after 0.18/ in 2016
if com.is_integer_dtype(values) and not com.is_integer_dtype(categories):
warn("Values and categories have different dtypes. Did you mean to use\n"
"'Categorical.from_codes(codes, categories)'?", RuntimeWarning)
if com.is_integer_dtype(values) and (codes == -1).all():
warn("None of the categories were found in values. Did you mean to use\n"
"'Categorical.from_codes(codes, categories)'?", RuntimeWarning)
# if we got categories, we can assume that the order is intended
# if ordered is unspecified
if ordered is None:
ordered = True
self.ordered = False if ordered is None else ordered
self._codes = codes
self.categories = categories
self.name = name
def __init__(self, values, levels=None, ordered=None, name=None, fastpath=False, compat=False):
if fastpath:
# fast path
self._codes = values
self.name = name
self.levels = levels
self.ordered = ordered
return
if name is None:
name = getattr(values, 'name', None)
# sanitize input
if com.is_categorical_dtype(values):
# we are either a Series or a Categorical
cat = values
if isinstance(values, com.ABCSeries):
cat = values.values
if levels is None:
levels = cat.levels
if ordered is None:
ordered = cat.ordered
values = values.__array__()
elif isinstance(values, Index):
pass
else:
# on numpy < 1.6 datetimelike get inferred to all i8 by _sanitize_array
# which is fine, but since factorize does this correctly no need here
# this is an issue because _sanitize_array also coerces np.nan to a string
# under certain versions of numpy as well
values = com._possibly_infer_to_datetimelike(values)
if not isinstance(values, np.ndarray):
values = _convert_to_list_like(values)
from pandas.core.series import _sanitize_array
# On list with NaNs, int values will be converted to float. Use "object" dtype
# to prevent this. In the end objects will be casted to int/... in the level
# assignment step.
dtype = 'object' if com.isnull(values).any() else None
values = _sanitize_array(values, None, dtype=dtype)
if levels is None:
try:
codes, levels = factorize(values, sort=True)
# If the underlying data structure was sortable, and the user doesn't want to
# "forget" this order, the categorical also is sorted/ordered
if ordered is None:
ordered = True
except TypeError:
codes, levels = factorize(values, sort=False)
if ordered:
# raise, as we don't have a sortable data structure and so the usershould
# give us one by specifying levels
raise TypeError("'values' is not ordered, please explicitly specify the level "
"order by passing in a level argument.")
else:
# there are two ways if levels are present
# the old one, where each value is a int pointer to the levels array
# the new one, where each value is also in the level array (or np.nan)
# make sure that we always have the same type here, no matter what we get passed in
levels = self._validate_levels(levels)
# There can be two ways: the old which passed in codes and levels directly
# and values have to be inferred and the new one, which passes in values and levels
# and _codes have to be inferred.
# min and max can be higher and lower if not all levels are in the values
if compat and (com.is_integer_dtype(values) and
(np.min(values) >= -1) and (np.max(values) < len(levels))):
warn("Using 'values' as codes is deprecated.\n"
"'Categorical(... , compat=True)' is only there for historical reasons and "
"should not be used in new code!\n"
"See https://github.com/pydata/pandas/pull/7217", FutureWarning)
codes = values
else:
codes = _get_codes_for_values(values, levels)
# if we got levels, we can assume that the order is intended
# if ordered is unspecified
if ordered is None:
ordered = True
self.ordered = False if ordered is None else ordered
self._codes = codes
self.levels = levels
self.name = name
def __init__(self,
values,
levels=None,
ordered=None,
name=None,
fastpath=False,
compat=False):
if fastpath:
# fast path
self._codes = values
self.name = name
self.levels = levels
self.ordered = ordered
return
if name is None:
name = getattr(values, 'name', None)
# sanitize input
if com.is_categorical_dtype(values):
# we are either a Series or a Categorical
cat = values
if isinstance(values, com.ABCSeries):
cat = values.values
if levels is None:
levels = cat.levels
if ordered is None:
ordered = cat.ordered
values = values.__array__()
elif isinstance(values, Index):
pass
else:
# on numpy < 1.6 datetimelike get inferred to all i8 by _sanitize_array
# which is fine, but since factorize does this correctly no need here
# this is an issue because _sanitize_array also coerces np.nan to a string
# under certain versions of numpy as well
values = com._possibly_infer_to_datetimelike(values,
convert_dates=True)
if not isinstance(values, np.ndarray):
values = _convert_to_list_like(values)
from pandas.core.series import _sanitize_array
# On list with NaNs, int values will be converted to float. Use "object" dtype
# to prevent this. In the end objects will be casted to int/... in the level
# assignment step.
dtype = 'object' if isnull(values).any() else None
values = _sanitize_array(values, None, dtype=dtype)
if levels is None:
try:
codes, levels = factorize(values, sort=True)
# If the underlying data structure was sortable, and the user doesn't want to
# "forget" this order, the categorical also is sorted/ordered
if ordered is None:
ordered = True
except TypeError:
codes, levels = factorize(values, sort=False)
if ordered:
# raise, as we don't have a sortable data structure and so the usershould
# give us one by specifying levels
raise TypeError(
"'values' is not ordered, please explicitly specify the level "
"order by passing in a level argument.")
else:
# there are two ways if levels are present
# the old one, where each value is a int pointer to the levels array
# the new one, where each value is also in the level array (or np.nan)
# make sure that we always have the same type here, no matter what we get passed in
levels = self._validate_levels(levels)
# There can be two ways: the old which passed in codes and levels directly
# and values have to be inferred and the new one, which passes in values and levels
# and _codes have to be inferred.
# min and max can be higher and lower if not all levels are in the values
if compat and (com.is_integer_dtype(values) and
(np.min(values) >= -1) and
(np.max(values) < len(levels))):
warn(
"Using 'values' as codes is deprecated.\n"
"'Categorical(... , compat=True)' is only there for historical reasons and "
"should not be used in new code!\n"
"See https://github.com/pydata/pandas/pull/7217",
FutureWarning)
codes = values
else:
codes = _get_codes_for_values(values, levels)
# if we got levels, we can assume that the order is intended
# if ordered is unspecified
if ordered is None:
ordered = True
self.ordered = False if ordered is None else ordered
self._codes = codes
self.levels = levels
self.name = name
def __init__(self, values, categories=None, ordered=None, name=None, fastpath=False,
levels=None):
if fastpath:
# fast path
self._codes = values
self.name = name
self.categories = categories
self.ordered = ordered
return
if name is None:
name = getattr(values, 'name', None)
# TODO: Remove after deprecation period in 2017/ after 0.18
if not levels is None:
warn("Creating a 'Categorical' with 'levels' is deprecated, use 'categories' instead",
FutureWarning)
if categories is None:
categories = levels
else:
raise ValueError("Cannot pass in both 'categories' and (deprecated) 'levels', "
"use only 'categories'")
# sanitize input
if com.is_categorical_dtype(values):
# we are either a Series or a Categorical
cat = values
if isinstance(values, com.ABCSeries):
cat = values.values
if categories is None:
categories = cat.categories
if ordered is None:
ordered = cat.ordered
values = values.__array__()
elif isinstance(values, Index):
pass
else:
# on numpy < 1.6 datetimelike get inferred to all i8 by _sanitize_array
# which is fine, but since factorize does this correctly no need here
# this is an issue because _sanitize_array also coerces np.nan to a string
# under certain versions of numpy as well
values = com._possibly_infer_to_datetimelike(values, convert_dates=True)
if not isinstance(values, np.ndarray):
values = _convert_to_list_like(values)
from pandas.core.series import _sanitize_array
# On list with NaNs, int values will be converted to float. Use "object" dtype
# to prevent this. In the end objects will be casted to int/... in the category
# assignment step.
dtype = 'object' if isnull(values).any() else None
values = _sanitize_array(values, None, dtype=dtype)
if categories is None:
try:
codes, categories = factorize(values, sort=True)
# If the underlying data structure was sortable, and the user doesn't want to
# "forget" this order, the categorical also is sorted/ordered
if ordered is None:
ordered = True
except TypeError:
codes, categories = factorize(values, sort=False)
if ordered:
# raise, as we don't have a sortable data structure and so the user should
# give us one by specifying categories
raise TypeError("'values' is not ordered, please explicitly specify the "
"categories order by passing in a categories argument.")
else:
# there were two ways if categories are present
# - the old one, where each value is a int pointer to the levels array -> not anymore
# possible, but code outside of pandas could call us like that, so make some checks
# - the new one, where each value is also in the categories array (or np.nan)
# make sure that we always have the same type here, no matter what we get passed in
categories = self._validate_categories(categories)
codes = _get_codes_for_values(values, categories)
# TODO: check for old style usage. These warnings should be removes after 0.18/ in 2016
if com.is_integer_dtype(values) and not com.is_integer_dtype(categories):
warn("Values and categories have different dtypes. Did you mean to use\n"
"'Categorical.from_codes(codes, categories)'?", RuntimeWarning)
if com.is_integer_dtype(values) and (codes == -1).all():
warn("None of the categories were found in values. Did you mean to use\n"
"'Categorical.from_codes(codes, categories)'?", RuntimeWarning)
# if we got categories, we can assume that the order is intended
# if ordered is unspecified
if ordered is None:
ordered = True
self.ordered = False if ordered is None else ordered
self._codes = codes
self.categories = categories
self.name = name
def __init__(self, data, sparse_index=None, index=None, fill_value=None,
kind='integer', dtype=None, copy=False):
from pandas.core.internals import SingleBlockManager
if isinstance(data, SingleBlockManager):
data = data.internal_values()
if fill_value is None and isinstance(dtype, SparseDtype):
fill_value = dtype.fill_value
if isinstance(data, (type(self), ABCSparseSeries)):
# disable normal inference on dtype, sparse_index, & fill_value
if sparse_index is None:
sparse_index = data.sp_index
if fill_value is None:
fill_value = data.fill_value
if dtype is None:
dtype = data.dtype
# TODO: make kind=None, and use data.kind?
data = data.sp_values
# Handle use-provided dtype
if isinstance(dtype, compat.string_types):
# Two options: dtype='int', regular numpy dtype
# or dtype='Sparse[int]', a sparse dtype
try:
dtype = SparseDtype.construct_from_string(dtype)
except TypeError:
dtype = pandas_dtype(dtype)
if isinstance(dtype, SparseDtype):
if fill_value is None:
fill_value = dtype.fill_value
dtype = dtype.subtype
if index is not None and not is_scalar(data):
raise Exception("must only pass scalars with an index ")
if is_scalar(data):
if index is not None:
if data is None:
data = np.nan
if index is not None:
npoints = len(index)
elif sparse_index is None:
npoints = 1
else:
npoints = sparse_index.length
dtype = infer_dtype_from_scalar(data)[0]
data = construct_1d_arraylike_from_scalar(
data, npoints, dtype
)
if dtype is not None:
dtype = pandas_dtype(dtype)
# TODO: disentangle the fill_value dtype inference from
# dtype inference
if data is None:
# XXX: What should the empty dtype be? Object or float?
data = np.array([], dtype=dtype)
if not is_array_like(data):
try:
# probably shared code in sanitize_series
from pandas.core.series import _sanitize_array
data = _sanitize_array(data, index=None)
except ValueError:
# NumPy may raise a ValueError on data like [1, []]
# we retry with object dtype here.
if dtype is None:
dtype = object
data = np.atleast_1d(np.asarray(data, dtype=dtype))
else:
raise
if copy:
# TODO: avoid double copy when dtype forces cast.
data = data.copy()
if fill_value is None:
fill_value_dtype = data.dtype if dtype is None else dtype
if fill_value_dtype is None:
fill_value = np.nan
else:
fill_value = na_value_for_dtype(fill_value_dtype)
if isinstance(data, type(self)) and sparse_index is None:
sparse_index = data._sparse_index
sparse_values = np.asarray(data.sp_values, dtype=dtype)
elif sparse_index is None:
sparse_values, sparse_index, fill_value = make_sparse(
data, kind=kind, fill_value=fill_value, dtype=dtype
)
else:
sparse_values = np.asarray(data, dtype=dtype)
if len(sparse_values) != sparse_index.npoints:
raise AssertionError("Non array-like type {type} must "
"have the same length as the index"
.format(type=type(sparse_values)))
self._sparse_index = sparse_index
self._sparse_values = sparse_values
self._dtype = SparseDtype(sparse_values.dtype, fill_value)
def __init__(self,
data,
sparse_index=None,
index=None,
fill_value=None,
kind='integer',
dtype=None,
copy=False):
from pandas.core.internals import SingleBlockManager
if isinstance(data, SingleBlockManager):
data = data.internal_values()
if fill_value is None and isinstance(dtype, SparseDtype):
fill_value = dtype.fill_value
if isinstance(data, (type(self), ABCSparseSeries)):
# disable normal inference on dtype, sparse_index, & fill_value
if sparse_index is None:
sparse_index = data.sp_index
if fill_value is None:
fill_value = data.fill_value
if dtype is None:
dtype = data.dtype
# TODO: make kind=None, and use data.kind?
data = data.sp_values
# Handle use-provided dtype
if isinstance(dtype, compat.string_types):
# Two options: dtype='int', regular numpy dtype
# or dtype='Sparse[int]', a sparse dtype
try:
dtype = SparseDtype.construct_from_string(dtype)
except TypeError:
dtype = pandas_dtype(dtype)
if isinstance(dtype, SparseDtype):
if fill_value is None:
fill_value = dtype.fill_value
dtype = dtype.subtype
if index is not None and not is_scalar(data):
raise Exception("must only pass scalars with an index ")
if is_scalar(data):
if index is not None:
if data is None:
data = np.nan
if index is not None:
npoints = len(index)
elif sparse_index is None:
npoints = 1
else:
npoints = sparse_index.length
dtype = infer_dtype_from_scalar(data)[0]
data = construct_1d_arraylike_from_scalar(data, npoints, dtype)
if dtype is not None:
dtype = pandas_dtype(dtype)
# TODO: disentangle the fill_value dtype inference from
# dtype inference
if data is None:
# XXX: What should the empty dtype be? Object or float?
data = np.array([], dtype=dtype)
if not is_array_like(data):
try:
# probably shared code in sanitize_series
from pandas.core.series import _sanitize_array
data = _sanitize_array(data, index=None)
except ValueError:
# NumPy may raise a ValueError on data like [1, []]
# we retry with object dtype here.
if dtype is None:
dtype = object
data = np.atleast_1d(np.asarray(data, dtype=dtype))
else:
raise
if copy:
# TODO: avoid double copy when dtype forces cast.
data = data.copy()
if fill_value is None:
fill_value_dtype = data.dtype if dtype is None else dtype
if fill_value_dtype is None:
fill_value = np.nan
else:
fill_value = na_value_for_dtype(fill_value_dtype)
if isinstance(data, type(self)) and sparse_index is None:
sparse_index = data._sparse_index
sparse_values = np.asarray(data.sp_values, dtype=dtype)
elif sparse_index is None:
sparse_values, sparse_index, fill_value = make_sparse(
data, kind=kind, fill_value=fill_value, dtype=dtype)
else:
sparse_values = np.asarray(data, dtype=dtype)
if len(sparse_values) != sparse_index.npoints:
raise AssertionError(
"Non array-like type {type} must "
"have the same length as the index".format(
type=type(sparse_values)))
self._sparse_index = sparse_index
self._sparse_values = sparse_values
self._dtype = SparseDtype(sparse_values.dtype, fill_value)
