Python generic.ABCDataFrame方法代碼示例

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
    """
    Helper function to convert DataFrame and Series to matplotlib.table

    Parameters
    ----------
    ax : Matplotlib axes object
    data : DataFrame or Series
        data for table contents
    kwargs : keywords, optional
        keyword arguments which passed to matplotlib.table.table.
        If `rowLabels` or `colLabels` is not specified, data index or column
        name will be used.

    Returns
    -------
    matplotlib table object
    """
    if isinstance(data, ABCSeries):
        data = data.to_frame()
    elif isinstance(data, ABCDataFrame):
        pass
    else:
        raise ValueError('Input data must be DataFrame or Series')

    if rowLabels is None:
        rowLabels = data.index

    if colLabels is None:
        colLabels = data.columns

    cellText = data.values

    import matplotlib.table
    table = matplotlib.table.table(ax, cellText=cellText,
                                   rowLabels=rowLabels,
                                   colLabels=colLabels, **kwargs)
    return table 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def test_abc_types(self):
        assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
        assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
        assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
        assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
        assert isinstance(self.multi_index, gt.ABCMultiIndex)
        assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
        assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
        assert isinstance(self.period_index, gt.ABCPeriodIndex)
        assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
        assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
        assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
        assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
        assert isinstance(self.df, gt.ABCDataFrame)
        with catch_warnings(record=True):
            simplefilter('ignore', FutureWarning)
            assert isinstance(self.df.to_panel(), gt.ABCPanel)
        assert isinstance(self.sparse_series, gt.ABCSparseSeries)
        assert isinstance(self.sparse_array, gt.ABCSparseArray)
        assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
        assert isinstance(self.categorical, gt.ABCCategorical)
        assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)

        assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
        assert isinstance(pd.Period('2012', freq='A-DEC').freq,
                          gt.ABCDateOffset)
        assert not isinstance(pd.Period('2012', freq='A-DEC'),
                              gt.ABCDateOffset)
        assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
        assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)

        assert isinstance(self.datetime_array, gt.ABCDatetimeArray)
        assert not isinstance(self.datetime_index, gt.ABCDatetimeArray)

        assert isinstance(self.timedelta_array, gt.ABCTimedeltaArray)
        assert not isinstance(self.timedelta_index, gt.ABCTimedeltaArray) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def _has_bool_dtype(x):
    try:
        if isinstance(x, ABCDataFrame):
            return 'bool' in x.dtypes
        else:
            return x.dtype == bool
    except AttributeError:
        return isinstance(x, (bool, np.bool_)) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def _obj_with_exclusions(self):
        if self._selection is not None and isinstance(self.obj,
                                                      ABCDataFrame):
            return self.obj.reindex(columns=self._selection_list)

        if len(self.exclusions) > 0:
            return self.obj.drop(self.exclusions, axis=1)
        else:
            return self.obj 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
        """
        Re-evaluate the obj with a groupby aggregation.
        """

        if grouper is None:
            self._set_binner()
            grouper = self.grouper

        obj = self._selected_obj

        try:
            grouped = groupby(obj, by=None, grouper=grouper, axis=self.axis)
        except TypeError:

            # panel grouper
            grouped = PanelGroupBy(obj, grouper=grouper, axis=self.axis)

        try:
            if isinstance(obj, ABCDataFrame) and compat.callable(how):
                # Check if the function is reducing or not.
                result = grouped._aggregate_item_by_item(how, *args, **kwargs)
            else:
                result = grouped.aggregate(how, *args, **kwargs)
        except Exception:

            # we have a non-reducing function
            # try to evaluate
            result = grouped.apply(how, *args, **kwargs)

        result = self._apply_loffset(result)
        return self._wrap_result(result) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def size(self):
        # It's a special case as higher level does return
        # a copy of 0-len objects. GH14962
        result = self._downsample('size')
        if not len(self.ax) and isinstance(self._selected_obj, ABCDataFrame):
            result = pd.Series([], index=result.index, dtype='int64')
        return result 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def test_abc_types(self):
        assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
        assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
        assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
        assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
        assert isinstance(self.multi_index, gt.ABCMultiIndex)
        assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
        assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
        assert isinstance(self.period_index, gt.ABCPeriodIndex)
        assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
        assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
        assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
        assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
        assert isinstance(self.df, gt.ABCDataFrame)
        with catch_warnings(record=True):
            assert isinstance(self.df.to_panel(), gt.ABCPanel)
        assert isinstance(self.sparse_series, gt.ABCSparseSeries)
        assert isinstance(self.sparse_array, gt.ABCSparseArray)
        assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
        assert isinstance(self.categorical, gt.ABCCategorical)
        assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)

        assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
        assert isinstance(pd.Period('2012', freq='A-DEC').freq,
                          gt.ABCDateOffset)
        assert not isinstance(pd.Period('2012', freq='A-DEC'),
                              gt.ABCDateOffset)
        assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
        assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
        """ re-evaluate the obj with a groupby aggregation """

        if grouper is None:
            self._set_binner()
            grouper = self.grouper

        obj = self._selected_obj

        try:
            grouped = groupby(obj, by=None, grouper=grouper, axis=self.axis)
        except TypeError:

            # panel grouper
            grouped = PanelGroupBy(obj, grouper=grouper, axis=self.axis)

        try:
            if isinstance(obj, ABCDataFrame) and compat.callable(how):
                # Check if the function is reducing or not.
                result = grouped._aggregate_item_by_item(how, *args, **kwargs)
            else:
                result = grouped.aggregate(how, *args, **kwargs)
        except Exception:

            # we have a non-reducing function
            # try to evaluate
            result = grouped.apply(how, *args, **kwargs)

        result = self._apply_loffset(result)
        return self._wrap_result(result) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def test_abc_types(self):
        assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
        assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
        assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
        assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
        assert isinstance(self.multi_index, gt.ABCMultiIndex)
        assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
        assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
        assert isinstance(self.period_index, gt.ABCPeriodIndex)
        assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
        assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
        assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
        assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
        assert isinstance(self.df, gt.ABCDataFrame)
        with catch_warnings(record=True):
            assert isinstance(self.df.to_panel(), gt.ABCPanel)
        assert isinstance(self.sparse_series, gt.ABCSparseSeries)
        assert isinstance(self.sparse_array, gt.ABCSparseArray)
        assert isinstance(self.categorical, gt.ABCCategorical)
        assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)

        assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
        assert isinstance(pd.Period('2012', freq='A-DEC').freq,
                          gt.ABCDateOffset)
        assert not isinstance(pd.Period('2012', freq='A-DEC'),
                              gt.ABCDateOffset) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def __add__(self, other):
        other = lib.item_from_zerodim(other)
        if isinstance(other, (ABCSeries, ABCDataFrame)):
            return NotImplemented

        # scalar others
        elif other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_delta(other)
        elif isinstance(other, DateOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
            result = self._add_datetimelike_scalar(other)
        elif lib.is_integer(other):
            # This check must come after the check for np.timedelta64
            # as is_integer returns True for these
            if not is_period_dtype(self):
                maybe_integer_op_deprecated(self)
            result = self._time_shift(other)

        # array-like others
        elif is_timedelta64_dtype(other):
            # TimedeltaIndex, ndarray[timedelta64]
            result = self._add_delta(other)
        elif is_offsetlike(other):
            # Array/Index of DateOffset objects
            result = self._addsub_offset_array(other, operator.add)
        elif is_datetime64_dtype(other) or is_datetime64tz_dtype(other):
            # DatetimeIndex, ndarray[datetime64]
            return self._add_datetime_arraylike(other)
        elif is_integer_dtype(other):
            if not is_period_dtype(self):
                maybe_integer_op_deprecated(self)
            result = self._addsub_int_array(other, operator.add)
        elif is_float_dtype(other):
            # Explicitly catch invalid dtypes
            raise TypeError("cannot add {dtype}-dtype to {cls}"
                            .format(dtype=other.dtype,
                                    cls=type(self).__name__))
        elif is_period_dtype(other):
            # if self is a TimedeltaArray and other is a PeriodArray with
            #  a timedelta-like (i.e. Tick) freq, this operation is valid.
            #  Defer to the PeriodArray implementation.
            # In remaining cases, this will end up raising TypeError.
            return NotImplemented
        elif is_extension_array_dtype(other):
            # Categorical op will raise; defer explicitly
            return NotImplemented
        else:  # pragma: no cover
            return NotImplemented

        if is_timedelta64_dtype(result) and isinstance(result, np.ndarray):
            from pandas.core.arrays import TimedeltaArray
            # TODO: infer freq?
            return TimedeltaArray(result)
        return result 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def _getitem_axis(self, key, axis=None):
        if axis is None:
            axis = self.axis or 0

        if is_iterator(key):
            key = list(key)

        labels = self.obj._get_axis(axis)
        key = self._get_partial_string_timestamp_match_key(key, labels)

        if isinstance(key, slice):
            self._validate_key(key, axis)
            return self._get_slice_axis(key, axis=axis)
        elif com.is_bool_indexer(key):
            return self._getbool_axis(key, axis=axis)
        elif is_list_like_indexer(key):

            # convert various list-like indexers
            # to a list of keys
            # we will use the *values* of the object
            # and NOT the index if its a PandasObject
            if isinstance(labels, MultiIndex):

                if isinstance(key, (ABCSeries, np.ndarray)) and key.ndim <= 1:
                    # Series, or 0,1 ndim ndarray
                    # GH 14730
                    key = list(key)
                elif isinstance(key, ABCDataFrame):
                    # GH 15438
                    raise NotImplementedError("Indexing a MultiIndex with a "
                                              "DataFrame key is not "
                                              "implemented")
                elif hasattr(key, 'ndim') and key.ndim > 1:
                    raise NotImplementedError("Indexing a MultiIndex with a "
                                              "multidimensional key is not "
                                              "implemented")

                if (not isinstance(key, tuple) and len(key) > 1 and
                        not isinstance(key[0], tuple)):
                    key = tuple([key])

            # an iterable multi-selection
            if not (isinstance(key, tuple) and isinstance(labels, MultiIndex)):

                if hasattr(key, 'ndim') and key.ndim > 1:
                    raise ValueError('Cannot index with multidimensional key')

                return self._getitem_iterable(key, axis=axis)

            # nested tuple slicing
            if is_nested_tuple(key, labels):
                locs = labels.get_locs(key)
                indexer = [slice(None)] * self.ndim
                indexer[axis] = locs
                return self.obj.iloc[tuple(indexer)]

        # fall thru to straight lookup
        self._validate_key(key, axis)
        return self._get_label(key, axis=axis) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def to_clipboard(obj, excel=True, sep=None, **kwargs):  # pragma: no cover
    """
    Attempt to write text representation of object to the system clipboard
    The clipboard can be then pasted into Excel for example.

    Parameters
    ----------
    obj : the object to write to the clipboard
    excel : boolean, defaults to True
            if True, use the provided separator, writing in a csv
            format for allowing easy pasting into excel.
            if False, write a string representation of the object
            to the clipboard
    sep : optional, defaults to tab
    other keywords are passed to to_csv

    Notes
    -----
    Requirements for your platform
      - Linux: xclip, or xsel (with gtk or PyQt4 modules)
      - Windows:
      - OS X:
    """
    encoding = kwargs.pop('encoding', 'utf-8')

    # testing if an invalid encoding is passed to clipboard
    if encoding is not None and encoding.lower().replace('-', '') != 'utf8':
        raise ValueError('clipboard only supports utf-8 encoding')

    from pandas.io.clipboard import clipboard_set
    if excel is None:
        excel = True

    if excel:
        try:
            if sep is None:
                sep = '\t'
            buf = StringIO()
            # clipboard_set (pyperclip) expects unicode
            obj.to_csv(buf, sep=sep, encoding='utf-8', **kwargs)
            text = buf.getvalue()
            if PY2:
                text = text.decode('utf-8')
            clipboard_set(text)
            return
        except TypeError:
            warnings.warn('to_clipboard in excel mode requires a single '
                          'character separator.')
    elif sep is not None:
        warnings.warn('to_clipboard with excel=False ignores the sep argument')

    if isinstance(obj, ABCDataFrame):
        # str(df) has various unhelpful defaults, like truncation
        with option_context('display.max_colwidth', 999999):
            objstr = obj.to_string(**kwargs)
    else:
        objstr = str(obj)
    clipboard_set(objstr) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def _getitem_lowerdim(self, tup):

        # we can directly get the axis result since the axis is specified
        if self.axis is not None:
            axis = self.obj._get_axis_number(self.axis)
            return self._getitem_axis(tup, axis=axis)

        # we may have a nested tuples indexer here
        if self._is_nested_tuple_indexer(tup):
            return self._getitem_nested_tuple(tup)

        # we maybe be using a tuple to represent multiple dimensions here
        ax0 = self.obj._get_axis(0)
        # ...but iloc should handle the tuple as simple integer-location
        # instead of checking it as multiindex representation (GH 13797)
        if isinstance(ax0, MultiIndex) and self.name != 'iloc':
            result = self._handle_lowerdim_multi_index_axis0(tup)
            if result is not None:
                return result

        if len(tup) > self.obj.ndim:
            raise IndexingError("Too many indexers. handle elsewhere")

        # to avoid wasted computation
        # df.ix[d1:d2, 0] -> columns first (True)
        # df.ix[0, ['C', 'B', A']] -> rows first (False)
        for i, key in enumerate(tup):
            if is_label_like(key) or isinstance(key, tuple):
                section = self._getitem_axis(key, axis=i)

                # we have yielded a scalar ?
                if not is_list_like_indexer(section):
                    return section

                elif section.ndim == self.ndim:
                    # we're in the middle of slicing through a MultiIndex
                    # revise the key wrt to `section` by inserting an _NS
                    new_key = tup[:i] + (_NS,) + tup[i + 1:]

                else:
                    new_key = tup[:i] + tup[i + 1:]

                    # unfortunately need an odious kludge here because of
                    # DataFrame transposing convention
                    if (isinstance(section, ABCDataFrame) and i > 0 and
                            len(new_key) == 2):
                        a, b = new_key
                        new_key = b, a

                    if len(new_key) == 1:
                        new_key, = new_key

                # Slices should return views, but calling iloc/loc with a null
                # slice returns a new object.
                if com.is_null_slice(new_key):
                    return section
                # This is an elided recursive call to iloc/loc/etc'
                return getattr(section, self.name)[new_key]

        raise IndexingError('not applicable') 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCDataFrame [as 別名]
def _getitem_lowerdim(self, tup):

        # we can directly get the axis result since the axis is specified
        if self.axis is not None:
            axis = self.obj._get_axis_number(self.axis)
            return self._getitem_axis(tup, axis=axis)

        # we may have a nested tuples indexer here
        if self._is_nested_tuple_indexer(tup):
            return self._getitem_nested_tuple(tup)

        # we maybe be using a tuple to represent multiple dimensions here
        ax0 = self.obj._get_axis(0)
        # ...but iloc should handle the tuple as simple integer-location
        # instead of checking it as multiindex representation (GH 13797)
        if isinstance(ax0, MultiIndex) and self.name != 'iloc':
            result = self._handle_lowerdim_multi_index_axis0(tup)
            if result is not None:
                return result

        if len(tup) > self.obj.ndim:
            raise IndexingError("Too many indexers. handle elsewhere")

        # to avoid wasted computation
        # df.ix[d1:d2, 0] -> columns first (True)
        # df.ix[0, ['C', 'B', A']] -> rows first (False)
        for i, key in enumerate(tup):
            if is_label_like(key) or isinstance(key, tuple):
                section = self._getitem_axis(key, axis=i)

                # we have yielded a scalar ?
                if not is_list_like_indexer(section):
                    return section

                elif section.ndim == self.ndim:
                    # we're in the middle of slicing through a MultiIndex
                    # revise the key wrt to `section` by inserting an _NS
                    new_key = tup[:i] + (_NS,) + tup[i + 1:]

                else:
                    new_key = tup[:i] + tup[i + 1:]

                    # unfortunately need an odious kludge here because of
                    # DataFrame transposing convention
                    if (isinstance(section, ABCDataFrame) and i > 0 and
                            len(new_key) == 2):
                        a, b = new_key
                        new_key = b, a

                    if len(new_key) == 1:
                        new_key, = new_key

                # Slices should return views, but calling iloc/loc with a null
                # slice returns a new object.
                if is_null_slice(new_key):
                    return section
                # This is an elided recursive call to iloc/loc/etc'
                return getattr(section, self.name)[new_key]

        raise IndexingError('not applicable')