Python pandas.core.series.Series.unique() Examples

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) 

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) 

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 

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 

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) 

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) 

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 

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 

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) 

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) 

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 

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 

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 

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 

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) 

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 

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 

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)) 

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)) 

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))