Python pandas.core.generic.NDFrame() Examples

def get_iterator(self, data, axis=0):
        """
        Groupby iterator

        Returns
        -------
        Generator yielding sequence of (name, subsetted object)
        for each group
        """
        if isinstance(data, NDFrame):
            slicer = lambda start, edge: data._slice(
                slice(start, edge), axis=axis)
            length = len(data.axes[axis])
        else:
            slicer = lambda start, edge: data[slice(start, edge)]
            length = len(data)

        start = 0
        for edge, label in zip(self.bins, self.binlabels):
            if label is not NaT:
                yield label, slicer(start, edge)
            start = edge

        if start < length:
            yield self.binlabels[-1], slicer(start, None) 

def _multi_take_opportunity(self, tup):
        from pandas.core.generic import NDFrame

        # ugly hack for GH #836
        if not isinstance(self.obj, NDFrame):
            return False

        if not all(is_list_like_indexer(x) for x in tup):
            return False

        # just too complicated
        for indexer, ax in zip(tup, self.obj._data.axes):
            if isinstance(ax, MultiIndex):
                return False
            elif is_bool_indexer(indexer):
                return False
            elif not ax.is_unique:
                return False

        return True 

def _choose_chart_class(data):
        """
        Tries to guess the appropriate chart class based on the data.
        """
        # In case the data has an index and the first entry is a datetime type, return a stock chart
        # specialized for viewing time series.
        if isinstance(data, pd.Series):
            if isinstance(data.index[0], (np.datetime64, datetime)):
                return "StockChart"
        elif isinstance(data, NDFrame):
            for labels in data.axes:
                if isinstance(labels[0], (np.datetime64, datetime)):
                    return "StockChart"
        elif hasattr(data, "__getitem__"):
            try:
                if isinstance(data[0], Plot):
                    return data[0]._chart_cls
                if (len(data[0]) > 1) \
                        and isinstance(data[0], (list, tuple)) \
                        and isinstance(data[0][0], (np.datetime64, datetime)):
                    return "StockChart"
            except TypeError:
                pass

        return "Chart" 

def _flatten_data(data, chart_cfg, switch_zy=False):
        plot_axes_def = [(0, XAxis), (1, YAxis)]

        # Inject categories into the axis definitions of the plot
        if isinstance(data, NDFrame):
            for i, plot_axis in plot_axes_def[:data.ndim]:
                categories = data.axes[i]
                # Skip numeric indices
                if not categories.is_numeric():
                    chart_cfg = chart_cfg.inherit_many(plot_axis(categories=list(categories)))

        data = [list(index) + [value] for index, value in list(np.ndenumerate(data))]

        if switch_zy:
            for i in range(len(data)):
                tmp = data[i][-1]
                data[i][-1] = data[i][-2]
                data[i][-2] = tmp

        return data, chart_cfg 

def get_group(self, name, obj=None):
        """
        Constructs NDFrame from group with provided name

        Parameters
        ----------
        name : object
            the name of the group to get as a DataFrame
        obj : NDFrame, default None
            the NDFrame to take the DataFrame out of.  If
            it is None, the object groupby was called on will
            be used

        Returns
        -------
        group : type of obj
        """
        if obj is None:
            obj = self._selected_obj

        inds = self._get_index(name)
        if not len(inds):
            raise KeyError(name)

        return obj._take(inds, axis=self.axis, convert=False) 

def get_iterator(self, data, axis=0):
        """
        Groupby iterator

        Returns
        -------
        Generator yielding sequence of (name, subsetted object)
        for each group
        """
        if isinstance(data, NDFrame):
            slicer = lambda start, edge: data._slice(
                slice(start, edge), axis=axis)
            length = len(data.axes[axis])
        else:
            slicer = lambda start, edge: data[slice(start, edge)]
            length = len(data)

        start = 0
        for edge, label in zip(self.bins, self.binlabels):
            if label is not NaT:
                yield label, slicer(start, edge)
            start = edge

        if start < length:
            yield self.binlabels[-1], slicer(start, None) 

def get_iterator(self, data, axis=0):
        """
        Groupby iterator

        Returns
        -------
        Generator yielding sequence of (name, subsetted object)
        for each group
        """
        if isinstance(data, NDFrame):
            slicer = lambda start, edge: data._slice(
                slice(start, edge), axis=axis)
            length = len(data.axes[axis])
        else:
            slicer = lambda start, edge: data[slice(start, edge)]
            length = len(data)

        start = 0
        for edge, label in zip(self.bins, self.binlabels):
            if label is not NaT:
                yield label, slicer(start, edge)
            start = edge

        if start < length:
            yield self.binlabels[-1], slicer(start, None) 

def __init__(self, ndFrame=None, nodeName='', fileName='', standAlone=True,
                 iconColor=_defaultIconColor):
        """ Constructor

            The NDFrame is not part of Pandas' documented API, although it mentions this
            inheritance. Therefore it is not checked the ndFrame is actually of type NDFrame.

            :param ndFrame: the underlying pandas object. May be undefined (None)
            :type ndFrame: pandas.core.generic.NDFrame
            :param standAlone: True if the NDFrame is a stand-alone object, False if it is part of
                another higher-dimensional, NDFrame. This influences the array. Furthermore, if
                standAlone is True the index of the NDFrame will be included when the children
                are fetched and included in the tree (as a PandasIndexRti)
        """
        super(AbstractPandasNDFrameRti, self).__init__(nodeName=nodeName, fileName=fileName)
        check_class(ndFrame, NDFrame, allow_none=True)

        self._ndFrame = ndFrame
        self._iconColor = iconColor
        self._standAlone = standAlone 

def get_group(self, name, obj=None):
        """
        Constructs NDFrame from group with provided name.

        Parameters
        ----------
        name : object
            the name of the group to get as a DataFrame
        obj : NDFrame, default None
            the NDFrame to take the DataFrame out of.  If
            it is None, the object groupby was called on will
            be used

        Returns
        -------
        group : same type as obj
        """
        if obj is None:
            obj = self._selected_obj

        inds = self._get_index(name)
        if not len(inds):
            raise KeyError(name)

        return obj._take(inds, axis=self.axis) 

def __getattr__(self, item):
        if self._pandas_only:
            raise SkipTest("empyrical.%s expects pandas-only inputs that have "
                           "dt indices/labels" % item)

        func = super(ConvertPandasEmpyricalProxy, self).__getattr__(item)

        @wraps(func)
        def convert_args(*args, **kwargs):
            args = [self._convert(arg) if isinstance(arg, NDFrame) else arg
                    for arg in args]
            kwargs = {
                k: self._convert(v) if isinstance(v, NDFrame) else v
                for k, v in iteritems(kwargs)
            }
            return func(*args, **kwargs)

        return convert_args 

def get_group(self, name, obj=None):
        """
        Constructs NDFrame from group with provided name

        Parameters
        ----------
        name : object
            the name of the group to get as a DataFrame
        obj : NDFrame, default None
            the NDFrame to take the DataFrame out of.  If
            it is None, the object groupby was called on will
            be used

        Returns
        -------
        group : type of obj
        """
        if obj is None:
            obj = self.obj

        inds = self._get_index(name)
        return obj.take(inds, axis=self.axis, convert=False) 

def _multi_take_opportunity(self, tup):
        from pandas.core.generic import NDFrame

        # ugly hack for GH #836
        if not isinstance(self.obj, NDFrame):
            return False

        if not all(is_list_like_indexer(x) for x in tup):
            return False

        # just too complicated
        for indexer, ax in zip(tup, self.obj._data.axes):
            if isinstance(ax, MultiIndex):
                return False
            elif is_bool_indexer(indexer):
                return False
            elif not ax.is_unique:
                return False

        return True 

def _multi_take_opportunity(self, tup):
        from pandas.core.generic import NDFrame

        # ugly hack for GH #836
        if not isinstance(self.obj, NDFrame):
            return False

        if not all(_is_list_like(x) for x in tup):
            return False

        # just too complicated
        for indexer, ax in zip(tup, self.obj._data.axes):
            if isinstance(ax, MultiIndex):
                return False
            elif com._is_bool_indexer(indexer):
                return False

        return True 

def get_iterator(self, data, axis=0):
        """
        Groupby iterator

        Returns
        -------
        Generator yielding sequence of (name, subsetted object)
        for each group
        """
        if isinstance(data, NDFrame):
            slicer = lambda start, edge: data._slice(
                slice(start, edge), axis=axis)
            length = len(data.axes[axis])
        else:
            slicer = lambda start, edge: data[slice(start, edge)]
            length = len(data)

        start = 0
        for edge, label in zip(self.bins, self.binlabels):
            if label is not NaT:
                yield label, slicer(start, edge)
            start = edge

        if start < length:
            yield self.binlabels[-1], slicer(start, None) 

def get_group(self, name, obj=None):
        """
        Constructs NDFrame from group with provided name

        Parameters
        ----------
        name : object
            the name of the group to get as a DataFrame
        obj : NDFrame, default None
            the NDFrame to take the DataFrame out of.  If
            it is None, the object groupby was called on will
            be used

        Returns
        -------
        group : type of obj
        """
        if obj is None:
            obj = self._selected_obj

        inds = self._get_index(name)
        if not len(inds):
            raise KeyError(name)

        return obj._take(inds, axis=self.axis) 

def get_group(self, name, obj=None):
        """
        Constructs NDFrame from group with provided name

        Parameters
        ----------
        name : object
            the name of the group to get as a DataFrame
        obj : NDFrame, default None
            the NDFrame to take the DataFrame out of.  If
            it is None, the object groupby was called on will
            be used

        Returns
        -------
        group : type of obj
        """
        if obj is None:
            obj = self._selected_obj

        inds = self._get_index(name)
        if not len(inds):
            raise KeyError(name)

        return obj._take(inds, axis=self.axis, convert=False) 

def _multi_take_opportunity(self, tup):
        from pandas.core.generic import NDFrame

        # ugly hack for GH #836
        if not isinstance(self.obj, NDFrame):
            return False

        if not all(is_list_like_indexer(x) for x in tup):
            return False

        # just too complicated
        for indexer, ax in zip(tup, self.obj._data.axes):
            if isinstance(ax, MultiIndex):
                return False
            elif com.is_bool_indexer(indexer):
                return False
            elif not ax.is_unique:
                return False

        return True 

def get_iterator(self, data, axis=0):
        """
        Groupby iterator

        Returns
        -------
        Generator yielding sequence of (name, subsetted object)
        for each group
        """
        if isinstance(data, NDFrame):
            slicer = lambda start, edge: data._slice(
                slice(start, edge), axis=axis)
            length = len(data.axes[axis])
        else:
            slicer = lambda start, edge: data[slice(start, edge)]
            length = len(data)

        start = 0
        for edge, label in zip(self.bins, self.binlabels):
            if label is not NaT:
                yield label, slicer(start, edge)
            start = edge

        if start < length:
            yield self.binlabels[-1], slicer(start, None) 

def get_group(self, name, obj=None):
        """
        Constructs NDFrame from group with provided name.

        Parameters
        ----------
        name : object
            the name of the group to get as a DataFrame
        obj : NDFrame, default None
            the NDFrame to take the DataFrame out of.  If
            it is None, the object groupby was called on will
            be used

        Returns
        -------
        group : same type as obj
        """
        if obj is None:
            obj = self._selected_obj

        inds = self._get_index(name)
        if not len(inds):
            raise KeyError(name)

        return obj._take(inds, axis=self.axis) 

def __getitem__(self, index):
        """ Called when using the RTI with an index (e.g. rti[0]).
            Passes the index through to the values property of the underlying NDFrame.
        """
        assert self.isSliceable, "No underlying pandas object: self._ndFrame is None"
        return self._ndFrame.values.__getitem__(index) 

def __init__(self, obj, keys=None, axis=0, level=None,
                 grouper=None, exclusions=None, selection=None, as_index=True,
                 sort=True, group_keys=True, squeeze=False, **kwargs):

        self._selection = selection

        if isinstance(obj, NDFrame):
            obj._consolidate_inplace()

        self.level = level

        if not as_index:
            if not isinstance(obj, DataFrame):
                raise TypeError('as_index=False only valid with DataFrame')
            if axis != 0:
                raise ValueError('as_index=False only valid for axis=0')

        self.as_index = as_index
        self.keys = keys
        self.sort = sort
        self.group_keys = group_keys
        self.squeeze = squeeze
        self.mutated = kwargs.pop('mutated', False)

        if grouper is None:
            grouper, exclusions, obj = _get_grouper(obj, keys,
                                                    axis=axis,
                                                    level=level,
                                                    sort=sort,
                                                    mutated=self.mutated)

        self.obj = obj
        self.axis = obj._get_axis_number(axis)
        self.grouper = grouper
        self.exclusions = set(exclusions) if exclusions else set()

        # we accept no other args
        validate_kwargs('group', kwargs, {}) 

def _check_input_not_mutated(self, func):
        @wraps(func)
        def check_not_mutated(*args, **kwargs):
            # Copy inputs to compare them to originals later.
            arg_copies = [(i, arg.copy()) for i, arg in enumerate(args)
                          if isinstance(arg, (NDFrame, np.ndarray))]
            kwarg_copies = {
                k: v.copy() for k, v in iteritems(kwargs)
                if isinstance(v, (NDFrame, np.ndarray))
            }

            result = func(*args, **kwargs)

            # Check that inputs weren't mutated by func.
            for i, arg_copy in arg_copies:
                assert_allclose(
                    args[i],
                    arg_copy,
                    atol=0.5 * 10 ** (-DECIMAL_PLACES),
                    err_msg="Input 'arg %s' mutated by %s"
                            % (i, func.__name__),
                )
            for kwarg_name, kwarg_copy in iteritems(kwarg_copies):
                assert_allclose(
                    kwargs[kwarg_name],
                    kwarg_copy,
                    atol=0.5 * 10 ** (-DECIMAL_PLACES),
                    err_msg="Input '%s' mutated by %s"
                            % (kwarg_name, func.__name__),
                )

            return result

        return check_not_mutated 

def _update_inplace(self, result, **kwargs):
        # we want to call the generic version and not the IndexOpsMixin
        return generic.NDFrame._update_inplace(self, result, **kwargs) 

def _update_inplace(self, result, **kwargs):
        # we want to call the generic version and not the IndexOpsMixin
        return generic.NDFrame._update_inplace(self, result, **kwargs) 

def __init__(self, obj, keys=None, axis=0, level=None,
                 grouper=None, exclusions=None, selection=None, as_index=True,
                 sort=True, group_keys=True, squeeze=False, **kwargs):

        self._selection = selection

        if isinstance(obj, NDFrame):
            obj._consolidate_inplace()

        self.level = level

        if not as_index:
            if not isinstance(obj, DataFrame):
                raise TypeError('as_index=False only valid with DataFrame')
            if axis != 0:
                raise ValueError('as_index=False only valid for axis=0')

        self.as_index = as_index
        self.keys = keys
        self.sort = sort
        self.group_keys = group_keys
        self.squeeze = squeeze
        self.mutated = kwargs.pop('mutated', False)

        if grouper is None:
            grouper, exclusions, obj = _get_grouper(obj, keys,
                                                    axis=axis,
                                                    level=level,
                                                    sort=sort,
                                                    mutated=self.mutated)

        self.obj = obj
        self.axis = obj._get_axis_number(axis)
        self.grouper = grouper
        self.exclusions = set(exclusions) if exclusions else set()

        # we accept no other args
        validate_kwargs('group', kwargs, {}) 

def dimensionNames(self):
        """ Returns a list with names that correspond to every NDFrame dimension.
            For DataFrames this is: ['items', 'major_axis', 'minor_axis'], which correspond to the
            data frames itself, the rows and the columns of the underlying DataFrames.
        """
        return ['items', 'major_axis', 'minor_axis'] 

def dimensionNames(self):
        """ Returns a list with names that correspond to every NDFrame dimension.
            For DataFrames this is: ['index', 'columns']
        """
        return ['index', 'columns'] 

def dimensionNames(self):
        """ Returns a list with names that correspond to every NDFrame dimension.
            For Series this is: ['index']
        """
        return ['index'] 

def _update_inplace(self, result, **kwargs):
        # we want to call the generic version and not the IndexOpsMixin
        return generic.NDFrame._update_inplace(self, result, **kwargs) 

def __init__(self, obj, keys=None, axis=0, level=None,
                 grouper=None, exclusions=None, selection=None, as_index=True,
                 sort=True, group_keys=True, squeeze=False):
        self._selection = selection

        if isinstance(obj, NDFrame):
            obj._consolidate_inplace()

        self.obj = obj
        self.axis = obj._get_axis_number(axis)
        self.level = level

        if not as_index:
            if not isinstance(obj, DataFrame):
                raise TypeError('as_index=False only valid with DataFrame')
            if axis != 0:
                raise ValueError('as_index=False only valid for axis=0')

        self.as_index = as_index
        self.keys = keys
        self.sort = sort
        self.group_keys = group_keys
        self.squeeze = squeeze

        if grouper is None:
            grouper, exclusions = _get_grouper(obj, keys, axis=axis,
                                               level=level, sort=sort)

        self.grouper = grouper
        self.exclusions = set(exclusions) if exclusions else set()