本文整理汇总了Python中pandas.core.series.Series.unique方法的典型用法代码示例。如果您正苦于以下问题：Python Series.unique方法的具体用法？Python Series.unique怎么用？Python Series.unique使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pandas.core.series.Series的用法示例。
在下文中一共展示了Series.unique方法的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: from_array
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def from_array(cls, data, **kwargs):
"""
.. deprecated:: 0.19.0
Use ``Categorical`` instead.
Make a Categorical type from a single array-like object.
For internal compatibility with numpy arrays.
Parameters
----------
data : array-like
Can be an Index or array-like. The categories are assumed to be
the unique values of `data`.
"""
warn("Categorical.from_array is deprecated, use Categorical instead",
FutureWarning, stacklevel=2)
return cls(data, **kwargs) 示例2: categories
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def categories(self):
"""
The categories of this categorical.
Setting assigns new values to each category (effectively a rename of
each individual category).
The assigned value has to be a list-like object. All items must be
unique and the number of items in the new categories must be the same
as the number of items in the old categories.
Assigning to `categories` is a inplace operation!
Raises
------
ValueError
If the new categories do not validate as categories or if the
number of new categories is unequal the number of old categories
See Also
--------
rename_categories
reorder_categories
add_categories
remove_categories
remove_unused_categories
set_categories
"""
return self.dtype.categories 示例3: remove_unused_categories
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def remove_unused_categories(self, inplace=False):
"""
Removes categories which are not used.
Parameters
----------
inplace : boolean (default: False)
Whether or not to drop unused categories inplace or return a copy of
this categorical with unused categories dropped.
Returns
-------
cat : Categorical with unused categories dropped or None if inplace.
See Also
--------
rename_categories
reorder_categories
add_categories
remove_categories
set_categories
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
cat = self if inplace else self.copy()
idx, inv = np.unique(cat._codes, return_inverse=True)
if idx.size != 0 and idx[0] == -1: # na sentinel
idx, inv = idx[1:], inv - 1
new_categories = cat.dtype.categories.take(idx)
new_dtype = CategoricalDtype._from_fastpath(new_categories,
ordered=self.ordered)
cat._dtype = new_dtype
cat._codes = coerce_indexer_dtype(inv, new_dtype.categories)
if not inplace:
return cat 示例4: categories
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def categories(self):
"""The categories of this categorical.
Setting assigns new values to each category (effectively a rename of
each individual category).
The assigned value has to be a list-like object. All items must be
unique and the number of items in the new categories must be the same
as the number of items in the old categories.
Assigning to `categories` is a inplace operation!
Raises
------
ValueError
If the new categories do not validate as categories or if the
number of new categories is unequal the number of old categories
See also
--------
rename_categories
reorder_categories
add_categories
remove_categories
remove_unused_categories
set_categories
"""
return self.dtype.categories 示例5: from_codes
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def from_codes(cls, codes, categories, ordered=False):
"""
Make a Categorical type from codes and categories arrays.
This constructor is useful if you already have codes and categories and
so do not need the (computation intensive) factorization step, which is
usually done on the constructor.
If your data does not follow this convention, please use the normal
constructor.
Parameters
----------
codes : array-like, integers
An integer array, where each integer points to a category in
categories or -1 for NaN
categories : index-like
The categories for the categorical. Items need to be unique.
ordered : boolean, (default False)
Whether or not this categorical is treated as a ordered
categorical. If not given, the resulting categorical will be
unordered.
"""
try:
codes = coerce_indexer_dtype(np.asarray(codes), categories)
except (ValueError, TypeError):
raise ValueError(
"codes need to be convertible to an arrays of integers")
categories = CategoricalDtype.validate_categories(categories)
if len(codes) and (codes.max() >= len(categories) or codes.min() < -1):
raise ValueError("codes need to be between -1 and "
"len(categories)-1")
return cls(codes, categories=categories, ordered=ordered,
fastpath=True) 示例6: remove_unused_categories
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def remove_unused_categories(self, inplace=False):
""" Removes categories which are not used.
Parameters
----------
inplace : boolean (default: False)
Whether or not to drop unused categories inplace or return a copy of
this categorical with unused categories dropped.
Returns
-------
cat : Categorical with unused categories dropped or None if inplace.
See also
--------
rename_categories
reorder_categories
add_categories
remove_categories
set_categories
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
cat = self if inplace else self.copy()
idx, inv = np.unique(cat._codes, return_inverse=True)
if idx.size != 0 and idx[0] == -1: # na sentinel
idx, inv = idx[1:], inv - 1
new_categories = cat.dtype.categories.take(idx)
new_dtype = CategoricalDtype._from_fastpath(new_categories,
ordered=self.ordered)
cat._dtype = new_dtype
cat._codes = coerce_indexer_dtype(inv, new_dtype.categories)
if not inplace:
return cat 示例7: from_codes
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def from_codes(cls, codes, categories, ordered=False):
"""
Make a Categorical type from codes and categories arrays.
This constructor is useful if you already have codes and categories and
so do not need the (computation intensive) factorization step, which is
usually done on the constructor.
If your data does not follow this convention, please use the normal
constructor.
Parameters
----------
codes : array-like, integers
An integer array, where each integer points to a category in
categories or -1 for NaN
categories : index-like
The categories for the categorical. Items need to be unique.
ordered : boolean, (default False)
Whether or not this categorical is treated as a ordered
categorical. If not given, the resulting categorical will be
unordered.
"""
try:
codes = np.asarray(codes, np.int64)
except:
raise ValueError(
"codes need to be convertible to an arrays of integers")
categories = CategoricalDtype._validate_categories(categories)
if len(codes) and (codes.max() >= len(categories) or codes.min() < -1):
raise ValueError("codes need to be between -1 and "
"len(categories)-1")
return cls(codes, categories=categories, ordered=ordered,
fastpath=True) 示例8: _codes_for_groupby
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def _codes_for_groupby(self, sort):
"""
If sort=False, return a copy of self, coded with categories as
returned by .unique(), followed by any categories not appearing in
the data. If sort=True, return self.
This method is needed solely to ensure the categorical index of the
GroupBy result has categories in the order of appearance in the data
(GH-8868).
Parameters
----------
sort : boolean
The value of the sort paramter groupby was called with.
Returns
-------
Categorical
If sort=False, the new categories are set to the order of
appearance in codes (unless ordered=True, in which case the
original order is preserved), followed by any unrepresented
categories in the original order.
"""
# Already sorted according to self.categories; all is fine
if sort:
return self
# sort=False should order groups in as-encountered order (GH-8868)
cat = self.unique()
# But for groupby to work, all categories should be present,
# including those missing from the data (GH-13179), which .unique()
# above dropped
cat.add_categories(
self.categories[~self.categories.isin(cat.categories)],
inplace=True)
return self.reorder_categories(cat.categories) 示例9: unique
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import unique [as 别名]
def unique(self):
"""
Return the ``Categorical`` which ``categories`` and ``codes`` are
unique. Unused categories are NOT returned.
- unordered category: values and categories are sorted by appearance
order.
- ordered category: values are sorted by appearance order, categories
keeps existing order.
Returns
-------
unique values : ``Categorical``
Examples
--------
An unordered Categorical will return categories in the
order of appearance.
>>> pd.Categorical(list('baabc'))
[b, a, c]
Categories (3, object): [b, a, c]
>>> pd.Categorical(list('baabc'), categories=list('abc'))
[b, a, c]
Categories (3, object): [b, a, c]
An ordered Categorical preserves the category ordering.
>>> pd.Categorical(list('baabc'),
... categories=list('abc'),
... ordered=True)
[b, a, c]
Categories (3, object): [a < b < c]
See Also
--------
unique
CategoricalIndex.unique
Series.unique
"""
# unlike np.unique, unique1d does not sort
unique_codes = unique1d(self.codes)
cat = self.copy()
# keep nan in codes
cat._codes = unique_codes
# exclude nan from indexer for categories
take_codes = unique_codes[unique_codes != -1]
if self.ordered:
take_codes = np.sort(take_codes)
return cat.set_categories(cat.categories.take(take_codes))