Python pandas.core.dtypes.generic.ABCSeries() Examples

def __getitem__(self, key):
        if self._selection is not None:
            raise IndexError('Column(s) {selection} already selected'
                             .format(selection=self._selection))

        if isinstance(key, (list, tuple, ABCSeries, ABCIndexClass,
                            np.ndarray)):
            if len(self.obj.columns.intersection(key)) != len(key):
                bad_keys = list(set(key).difference(self.obj.columns))
                raise KeyError("Columns not found: {missing}"
                               .format(missing=str(bad_keys)[1:-1]))
            return self._gotitem(list(key), ndim=2)

        elif not getattr(self, 'as_index', False):
            if key not in self.obj.columns:
                raise KeyError("Column not found: {key}".format(key=key))
            return self._gotitem(key, ndim=2)

        else:
            if key not in self.obj:
                raise KeyError("Column not found: {key}".format(key=key))
            return self._gotitem(key, ndim=1) 

def _get_rename_function(mapper):
    """
    Returns a function that will map names/labels, dependent if mapper
    is a dict, Series or just a function.
    """
    if isinstance(mapper, (compat.Mapping, ABCSeries)):

        def f(x):
            if x in mapper:
                return mapper[x]
            else:
                return x
    else:
        f = mapper

    return f 

def check_bool_indexer(ax, key):
    # boolean indexing, need to check that the data are aligned, otherwise
    # disallowed

    # this function assumes that is_bool_indexer(key) == True

    result = key
    if isinstance(key, ABCSeries) and not key.index.equals(ax):
        result = result.reindex(ax)
        mask = isna(result._values)
        if mask.any():
            raise IndexingError('Unalignable boolean Series provided as '
                                'indexer (index of the boolean Series and of '
                                'the indexed object do not match')
        result = result.astype(bool)._values
    elif is_sparse(result):
        result = result.to_dense()
        result = np.asarray(result, dtype=bool)
    else:
        # is_bool_indexer has already checked for nulls in the case of an
        # object array key, so no check needed here
        result = np.asarray(result, dtype=bool)

    return result 

def is_bool_indexer(key):
    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)):
        if key.dtype == np.object_:
            key = np.asarray(_values_from_object(key))

            if not lib.is_bool_array(key):
                if isna(key).any():
                    raise ValueError('cannot index with vector containing '
                                     'NA / NaN values')
                return False
            return True
        elif key.dtype == np.bool_:
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False 

def check_bool_indexer(ax, key):
    # boolean indexing, need to check that the data are aligned, otherwise
    # disallowed

    # this function assumes that is_bool_indexer(key) == True

    result = key
    if isinstance(key, ABCSeries) and not key.index.equals(ax):
        result = result.reindex(ax)
        mask = isna(result._values)
        if mask.any():
            raise IndexingError('Unalignable boolean Series provided as '
                                'indexer (index of the boolean Series and of '
                                'the indexed object do not match')
        result = result.astype(bool)._values
    elif is_sparse(result):
        result = result.to_dense()
        result = np.asarray(result, dtype=bool)
    else:
        # is_bool_indexer has already checked for nulls in the case of an
        # object array key, so no check needed here
        result = np.asarray(result, dtype=bool)

    return result 

def __getitem__(self, key):
        if self._selection is not None:
            raise Exception('Column(s) {selection} already selected'
                            .format(selection=self._selection))

        if isinstance(key, (list, tuple, ABCSeries, ABCIndexClass,
                            np.ndarray)):
            if len(self.obj.columns.intersection(key)) != len(key):
                bad_keys = list(set(key).difference(self.obj.columns))
                raise KeyError("Columns not found: {missing}"
                               .format(missing=str(bad_keys)[1:-1]))
            return self._gotitem(list(key), ndim=2)

        elif not getattr(self, 'as_index', False):
            if key not in self.obj.columns:
                raise KeyError("Column not found: {key}".format(key=key))
            return self._gotitem(key, ndim=2)

        else:
            if key not in self.obj:
                raise KeyError("Column not found: {key}".format(key=key))
            return self._gotitem(key, ndim=1) 

def __getitem__(self, key):
        if self._selection is not None:
            raise IndexError('Column(s) {selection} already selected'
                             .format(selection=self._selection))

        if isinstance(key, (list, tuple, ABCSeries, ABCIndexClass,
                            np.ndarray)):
            if len(self.obj.columns.intersection(key)) != len(key):
                bad_keys = list(set(key).difference(self.obj.columns))
                raise KeyError("Columns not found: {missing}"
                               .format(missing=str(bad_keys)[1:-1]))
            return self._gotitem(list(key), ndim=2)

        elif not getattr(self, 'as_index', False):
            if key not in self.obj.columns:
                raise KeyError("Column not found: {key}".format(key=key))
            return self._gotitem(key, ndim=2)

        else:
            if key not in self.obj:
                raise KeyError("Column not found: {key}".format(key=key))
            return self._gotitem(key, ndim=1) 

def wrap_results_for_axis(self):
        """ return the results for the columns """
        results = self.results

        # we have requested to expand
        if self.result_type == 'expand':
            result = self.infer_to_same_shape()

        # we have a non-series and don't want inference
        elif not isinstance(results[0], ABCSeries):
            from pandas import Series
            result = Series(results)
            result.index = self.res_index

        # we may want to infer results
        else:
            result = self.infer_to_same_shape()

        return result 

def wrap_results_for_axis(self):
        """ return the results for the rows """

        results = self.results
        result = self.obj._constructor(data=results)

        if not isinstance(results[0], ABCSeries):
            try:
                result.index = self.res_columns
            except ValueError:
                pass

        try:
            result.columns = self.res_index
        except ValueError:
            pass

        return result 

def wrap_results_for_axis(self):
        """ return the results for the columns """
        results = self.results

        # we have requested to expand
        if self.result_type == 'expand':
            result = self.infer_to_same_shape()

        # we have a non-series and don't want inference
        elif not isinstance(results[0], ABCSeries):
            from pandas import Series
            result = Series(results)
            result.index = self.res_index

        # we may want to infer results
        else:
            result = self.infer_to_same_shape()

        return result 

def wrap_results_for_axis(self):
        """ return the results for the rows """

        results = self.results
        result = self.obj._constructor(data=results)

        if not isinstance(results[0], ABCSeries):
            try:
                result.index = self.res_columns
            except ValueError:
                pass

        try:
            result.columns = self.res_index
        except ValueError:
            pass

        return result 

def _stack_arrays(tuples, dtype):

    # fml
    def _asarray_compat(x):
        if isinstance(x, ABCSeries):
            return x._values
        else:
            return np.asarray(x)

    def _shape_compat(x):
        if isinstance(x, ABCSeries):
            return len(x),
        else:
            return x.shape

    placement, names, arrays = zip(*tuples)

    first = arrays[0]
    shape = (len(arrays),) + _shape_compat(first)

    stacked = np.empty(shape, dtype=dtype)
    for i, arr in enumerate(arrays):
        stacked[i] = _asarray_compat(arr)

    return stacked, placement 

def _create_arithmetic_method(cls, op):
        def arithmetic_method(self, other):
            if isinstance(other, (ABCIndexClass, ABCSeries)):
                return NotImplemented

            elif isinstance(other, cls):
                other = other._ndarray

            with np.errstate(all="ignore"):
                result = op(self._ndarray, other)

            if op is divmod:
                a, b = result
                return cls(a), cls(b)

            return cls(result)

        return compat.set_function_name(arithmetic_method,
                                        "__{}__".format(op.__name__),
                                        cls) 

def _join_i8_wrapper(joinf, dtype, with_indexers=True):
        """
        Create the join wrapper methods.
        """
        from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin

        @staticmethod
        def wrapper(left, right):
            if isinstance(left, (np.ndarray, ABCIndex, ABCSeries,
                                 DatetimeLikeArrayMixin)):
                left = left.view('i8')
            if isinstance(right, (np.ndarray, ABCIndex, ABCSeries,
                                  DatetimeLikeArrayMixin)):
                right = right.view('i8')
            results = joinf(left, right)
            if with_indexers:
                join_index, left_indexer, right_indexer = results
                join_index = join_index.view(dtype)
                return join_index, left_indexer, right_indexer
            return results

        return wrapper 

def wrap_results_for_axis(self):
        """ return the results for the columns """
        results = self.results

        # we have requested to expand
        if self.result_type == 'expand':
            result = self.infer_to_same_shape()

        # we have a non-series and don't want inference
        elif not isinstance(results[0], ABCSeries):
            from pandas import Series
            result = Series(results)
            result.index = self.res_index

        # we may want to infer results
        else:
            result = self.infer_to_same_shape()

        return result 

def wrap_results_for_axis(self):
        """ return the results for the rows """

        results = self.results
        result = self.obj._constructor(data=results)

        if not isinstance(results[0], ABCSeries):
            try:
                result.index = self.res_columns
            except ValueError:
                pass

        try:
            result.columns = self.res_index
        except ValueError:
            pass

        return result 

def _stack_arrays(tuples, dtype):

    # fml
    def _asarray_compat(x):
        if isinstance(x, ABCSeries):
            return x._values
        else:
            return np.asarray(x)

    def _shape_compat(x):
        if isinstance(x, ABCSeries):
            return len(x),
        else:
            return x.shape

    placement, names, arrays = zip(*tuples)

    first = arrays[0]
    shape = (len(arrays),) + _shape_compat(first)

    stacked = np.empty(shape, dtype=dtype)
    for i, arr in enumerate(arrays):
        stacked[i] = _asarray_compat(arr)

    return stacked, placement 

def _evaluate_with_timedelta_like(self, other, op):
        if isinstance(other, ABCSeries):
            # GH#19042
            return NotImplemented

        opstr = '__{opname}__'.format(opname=op.__name__).replace('__r', '__')
        # allow division by a timedelta
        if opstr in ['__div__', '__truediv__', '__floordiv__']:
            if _is_convertible_to_td(other):
                other = Timedelta(other)
                if isna(other):
                    raise NotImplementedError(
                        "division by pd.NaT not implemented")

                i8 = self.asi8
                left, right = i8, other.value

                if opstr in ['__floordiv__']:
                    result = op(left, right)
                else:
                    result = op(left, np.float64(right))
                result = self._maybe_mask_results(result, convert='float64')
                return Index(result, name=self.name, copy=False)

        return NotImplemented 

def _create_arithmetic_method(cls, op):
        def arithmetic_method(self, other):
            if isinstance(other, (ABCIndexClass, ABCSeries)):
                return NotImplemented

            elif isinstance(other, cls):
                other = other._ndarray

            with np.errstate(all="ignore"):
                result = op(self._ndarray, other)

            if op is divmod:
                a, b = result
                return cls(a), cls(b)

            return cls(result)

        return compat.set_function_name(arithmetic_method,
                                        "__{}__".format(op.__name__),
                                        cls) 

def _join_i8_wrapper(joinf, dtype, with_indexers=True):
        """
        Create the join wrapper methods.
        """
        from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin

        @staticmethod
        def wrapper(left, right):
            if isinstance(left, (np.ndarray, ABCIndex, ABCSeries,
                                 DatetimeLikeArrayMixin)):
                left = left.view('i8')
            if isinstance(right, (np.ndarray, ABCIndex, ABCSeries,
                                  DatetimeLikeArrayMixin)):
                right = right.view('i8')
            results = joinf(left, right)
            if with_indexers:
                join_index, left_indexer, right_indexer = results
                join_index = join_index.view(dtype)
                return join_index, left_indexer, right_indexer
            return results

        return wrapper 

def _create_comparison_method(cls, op):
        """
        Create a comparison method that dispatches to ``cls.values``.
        """
        def wrapper(self, other):
            if isinstance(other, ABCSeries):
                # the arrays defer to Series for comparison ops but the indexes
                #  don't, so we have to unwrap here.
                other = other._values

            result = op(self._data, maybe_unwrap_index(other))
            return result

        wrapper.__doc__ = op.__doc__
        wrapper.__name__ = '__{}__'.format(op.__name__)
        return wrapper 

def _create_comparison_method(cls, op):
        """
        Create a comparison method that dispatches to ``cls.values``.
        """
        def wrapper(self, other):
            if isinstance(other, ABCSeries):
                # the arrays defer to Series for comparison ops but the indexes
                #  don't, so we have to unwrap here.
                other = other._values

            result = op(self._data, maybe_unwrap_index(other))
            return result

        wrapper.__doc__ = op.__doc__
        wrapper.__name__ = '__{}__'.format(op.__name__)
        return wrapper 

def _convert_by(by):
    if by is None:
        by = []
    elif (is_scalar(by) or
          isinstance(by, (np.ndarray, Index, ABCSeries, Grouper)) or
          hasattr(by, '__call__')):
        by = [by]
    else:
        by = list(by)
    return by 

def __init__(self, data, orig):
        if not isinstance(data, ABCSeries):
            raise TypeError("cannot convert an object of type {0} to a "
                            "datetimelike index".format(type(data)))

        self.values = data
        self.orig = orig
        self.name = getattr(data, 'name', None)
        self.index = getattr(data, 'index', None)
        self._freeze() 

def _selected_obj(self):

        if self._selection is None or isinstance(self.obj, ABCSeries):
            return self.obj
        else:
            return self.obj[self._selection] 

def _get_names(arrs, names, prefix='row'):
    if names is None:
        names = []
        for i, arr in enumerate(arrs):
            if isinstance(arr, ABCSeries) and arr.name is not None:
                names.append(arr.name)
            else:
                names.append('{prefix}_{i}'.format(prefix=prefix, i=i))
    else:
        if len(names) != len(arrs):
            raise AssertionError('arrays and names must have the same length')
        if not isinstance(names, list):
            names = list(names)

    return names 

def length_of_indexer(indexer, target=None):
    """return the length of a single non-tuple indexer which could be a slice
    """
    if target is not None and isinstance(indexer, slice):
        l = len(target)
        start = indexer.start
        stop = indexer.stop
        step = indexer.step
        if start is None:
            start = 0
        elif start < 0:
            start += l
        if stop is None or stop > l:
            stop = l
        elif stop < 0:
            stop += l
        if step is None:
            step = 1
        elif step < 0:
            step = -step
        return (stop - start + step - 1) // step
    elif isinstance(indexer, (ABCSeries, Index, np.ndarray, list)):
        return len(indexer)
    elif not is_list_like_indexer(indexer):
        return 1
    raise AssertionError("cannot find the length of the indexer") 

def _create_categorical(self, data, categories=None, ordered=None,
                            dtype=None):
        """
        *this is an internal non-public method*

        create the correct categorical from data and the properties

        Parameters
        ----------
        data : data for new Categorical
        categories : optional categories, defaults to existing
        ordered : optional ordered attribute, defaults to existing
        dtype : CategoricalDtype, defaults to existing

        Returns
        -------
        Categorical
        """
        if (isinstance(data, (ABCSeries, type(self))) and
                is_categorical_dtype(data)):
            data = data.values

        if not isinstance(data, ABCCategorical):
            if ordered is None and dtype is None:
                ordered = False
            from pandas.core.arrays import Categorical
            data = Categorical(data, categories=categories, ordered=ordered,
                               dtype=dtype)
        else:
            if categories is not None:
                data = data.set_categories(categories, ordered=ordered)
            elif ordered is not None and ordered != data.ordered:
                data = data.set_ordered(ordered)
            if isinstance(dtype, CategoricalDtype):
                # we want to silently ignore dtype='category'
                data = data._set_dtype(dtype)
        return data 

def _dt_to_float_ordinal(dt):
    """
    Convert :mod:`datetime` to the Gregorian date as UTC float days,
    preserving hours, minutes, seconds and microseconds.  Return value
    is a :func:`float`.
    """
    if (isinstance(dt, (np.ndarray, Index, ABCSeries)
                   ) and is_datetime64_ns_dtype(dt)):
        base = dates.epoch2num(dt.asi8 / 1.0E9)
    else:
        base = dates.date2num(dt)
    return base


# Datetime Conversion 

def _convert_by(by):
    if by is None:
        by = []
    elif (is_scalar(by) or
          isinstance(by, (np.ndarray, Index, ABCSeries, Grouper)) or
          hasattr(by, '__call__')):
        by = [by]
    else:
        by = list(by)
    return by