Python index.MultiIndex方法代碼示例

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def var(self, ddof=1, *args, **kwargs):
        """
        Compute variance of groups, excluding missing values.

        For multiple groupings, the result index will be a MultiIndex.

        Parameters
        ----------
        ddof : integer, default 1
            degrees of freedom
        """
        nv.validate_groupby_func('var', args, kwargs)
        if ddof == 1:
            try:
                return self._cython_agg_general('var', **kwargs)
            except Exception:
                f = lambda x: x.var(ddof=ddof, **kwargs)
                with _group_selection_context(self):
                    return self._python_agg_general(f)
        else:
            f = lambda x: x.var(ddof=ddof, **kwargs)
            with _group_selection_context(self):
                return self._python_agg_general(f) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def test_reorder_levels(self):
        index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
                           labels=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
                                   [0, 1, 0, 1, 0, 1]],
                           names=['L0', 'L1', 'L2'])
        s = Series(np.arange(6), index=index)

        # no change, position
        result = s.reorder_levels([0, 1, 2])
        assert_series_equal(s, result)

        # no change, labels
        result = s.reorder_levels(['L0', 'L1', 'L2'])
        assert_series_equal(s, result)

        # rotate, position
        result = s.reorder_levels([1, 2, 0])
        e_idx = MultiIndex(levels=[['one', 'two', 'three'], [0, 1], ['bar']],
                           labels=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1],
                                   [0, 0, 0, 0, 0, 0]],
                           names=['L1', 'L2', 'L0'])
        expected = Series(np.arange(6), index=e_idx)
        assert_series_equal(result, expected) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
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 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def _is_scalar_access(self, key):
        # this is a shortcut accessor to both .loc and .iloc
        # that provide the equivalent access of .at and .iat
        # a) avoid getting things via sections and (to minimize dtype changes)
        # b) provide a performant path
        if not hasattr(key, '__len__'):
            return False

        if len(key) != self.ndim:
            return False

        for i, k in enumerate(key):
            if not is_scalar(k):
                return False

            ax = self.obj.axes[i]
            if isinstance(ax, MultiIndex):
                return False

            if not ax.is_unique:
                return False

        return True 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def _get_partial_string_timestamp_match_key(self, key, labels):
        """Translate any partial string timestamp matches in key, returning the
        new key (GH 10331)"""
        if isinstance(labels, MultiIndex):
            if isinstance(key, compat.string_types) and \
                    labels.levels[0].is_all_dates:
                # Convert key '2016-01-01' to
                # ('2016-01-01'[, slice(None, None, None)]+)
                key = tuple([key] + [slice(None)] * (len(labels.levels) - 1))

            if isinstance(key, tuple):
                # Convert (..., '2016-01-01', ...) in tuple to
                # (..., slice('2016-01-01', '2016-01-01', None), ...)
                new_key = []
                for i, component in enumerate(key):
                    if isinstance(component, compat.string_types) and \
                            labels.levels[i].is_all_dates:
                        new_key.append(slice(component, component, None))
                    else:
                        new_key.append(component)
                key = tuple(new_key)

        return key 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def _write_body(self, indent):
        self.write('<tbody>', indent)
        indent += self.indent_delta

        fmt_values = {}
        for i in range(min(len(self.columns), self.max_cols)):
            fmt_values[i] = self.fmt._format_col(i)

        # write values
        if self.fmt.index:
            if isinstance(self.frame.index, MultiIndex):
                self._write_hierarchical_rows(fmt_values, indent)
            else:
                self._write_regular_rows(fmt_values, indent)
        else:
            for i in range(min(len(self.frame), self.max_rows)):
                row = [fmt_values[j][i] for j in range(len(self.columns))]
                self.write_tr(row, indent, self.indent_delta, tags=None)

        indent -= self.indent_delta
        self.write('</tbody>', indent)
        indent -= self.indent_delta

        return indent 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def __setitem__(self, key, value):
        # kludgetastic
        ax = self.obj._get_axis(0)
        if isinstance(ax, MultiIndex):
            try:
                indexer = ax.get_loc(key)
                self._setitem_with_indexer(indexer, value)
                return
            except Exception:
                pass

        if isinstance(key, tuple):
            if len(key) > self.ndim:
                raise IndexingError('only tuples of length <= %d supported' %
                                    self.ndim)
            indexer = self._convert_tuple(key, is_setter=True)
        else:
            indexer = self._convert_to_indexer(key, is_setter=True)

        self._setitem_with_indexer(indexer, value) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
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 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def _write_body(self, indent):
        self.write('<tbody>', indent)
        indent += self.indent_delta

        fmt_values = {}
        for i in range(min(len(self.columns), self.max_cols)):
            fmt_values[i] = self.fmt._format_col(i)
        truncated = (len(self.columns) > self.max_cols)

        # write values
        if self.fmt.index:
            if isinstance(self.frame.index, MultiIndex):
                self._write_hierarchical_rows(fmt_values, indent)
            else:
                self._write_regular_rows(fmt_values, indent, truncated)
        else:
            for i in range(len(self.frame)):
                row = [fmt_values[j][i] for j in range(len(self.columns))]
                self.write_tr(row, indent, self.indent_delta, tags=None)

        indent -= self.indent_delta
        self.write('</tbody>', indent)
        indent -= self.indent_delta

        return indent 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def _format_header_regular(self):
        has_aliases = isinstance(self.header, (tuple, list, np.ndarray))
        if has_aliases or self.header:
            coloffset = 0

            if self.index:
                coloffset = 1
                if isinstance(self.df.index, MultiIndex):
                    coloffset = len(self.df.index[0])

            colnames = self.columns
            if has_aliases:
                if len(self.header) != len(self.columns):
                    raise ValueError(('Writing %d cols but got %d aliases'
                                      % (len(self.columns), len(self.header))))
                else:
                    colnames = self.header

            for colindex, colname in enumerate(colnames):
                yield ExcelCell(self.rowcounter, colindex + coloffset, colname,
                                header_style) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
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 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def test_multilevel_name_print(self):
        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
                                                                  'three']],
                           codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                  [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])
        s = Series(lrange(0, len(index)), index=index, name='sth')
        expected = ["first  second", "foo    one       0",
                    "       two       1", "       three     2",
                    "bar    one       3", "       two       4",
                    "baz    two       5", "       three     6",
                    "qux    one       7", "       two       8",
                    "       three     9", "Name: sth, dtype: int64"]
        expected = "\n".join(expected)
        assert repr(s) == expected 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def stack_sparse_frame(frame):
    """
    Only makes sense when fill_value is NaN
    """
    lengths = [s.sp_index.npoints for _, s in compat.iteritems(frame)]
    nobs = sum(lengths)

    # this is pretty fast
    minor_codes = np.repeat(np.arange(len(frame.columns)), lengths)

    inds_to_concat = []
    vals_to_concat = []
    # TODO: Figure out whether this can be reached.
    # I think this currently can't be reached because you can't build a
    # SparseDataFrame with a non-np.NaN fill value (fails earlier).
    for _, series in compat.iteritems(frame):
        if not np.isnan(series.fill_value):
            raise TypeError('This routine assumes NaN fill value')

        int_index = series.sp_index.to_int_index()
        inds_to_concat.append(int_index.indices)
        vals_to_concat.append(series.sp_values)

    major_codes = np.concatenate(inds_to_concat)
    stacked_values = np.concatenate(vals_to_concat)
    index = MultiIndex(levels=[frame.index, frame.columns],
                       codes=[major_codes, minor_codes],
                       verify_integrity=False)

    lp = DataFrame(stacked_values.reshape((nobs, 1)), index=index,
                   columns=['foo'])
    return lp.sort_index(level=0) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def result_index(self):
        if not self.compressed and len(self.groupings) == 1:
            return self.groupings[0].result_index.rename(self.names[0])

        codes = self.recons_labels
        levels = [ping.result_index for ping in self.groupings]
        result = MultiIndex(levels=levels,
                            codes=codes,
                            verify_integrity=False,
                            names=self.names)
        return result 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import MultiIndex [as 別名]
def std(self, ddof=1, *args, **kwargs):
        """
        Compute standard deviation of groups, excluding missing values.

        For multiple groupings, the result index will be a MultiIndex.

        Parameters
        ----------
        ddof : integer, default 1
            degrees of freedom
        """

        # TODO: implement at Cython level?
        nv.validate_groupby_func('std', args, kwargs)
        return np.sqrt(self.var(ddof=ddof, **kwargs))