Python compat.zip方法代碼示例

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def _get_errorbars(self, label=None, index=None, xerr=True, yerr=True):
        errors = {}

        for kw, flag in zip(['xerr', 'yerr'], [xerr, yerr]):
            if flag:
                err = self.errors[kw]
                # user provided label-matched dataframe of errors
                if isinstance(err, (ABCDataFrame, dict)):
                    if label is not None and label in err.keys():
                        err = err[label]
                    else:
                        err = None
                elif index is not None and err is not None:
                    err = err[index]

                if err is not None:
                    errors[kw] = err
        return errors 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def test_no_pairwise_with_other(self, f):

        # DataFrame with another DataFrame, pairwise=False
        results = [f(df, self.df2) if df.columns.is_unique else None
                   for df in self.df1s]
        for (df, result) in zip(self.df1s, results):
            if result is not None:
                with catch_warnings(record=True):
                    warnings.simplefilter("ignore", RuntimeWarning)
                    # we can have int and str columns
                    expected_index = df.index.union(self.df2.index)
                    expected_columns = df.columns.union(self.df2.columns)
                tm.assert_index_equal(result.index, expected_index)
                tm.assert_index_equal(result.columns, expected_columns)
            else:
                with pytest.raises(ValueError,
                                   match="'arg1' columns are not unique"):
                    f(df, self.df2)
                with pytest.raises(ValueError,
                                   match="'arg2' columns are not unique"):
                    f(self.df2, df) 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def test_copy(self, mgr):
        cp = mgr.copy(deep=False)
        for blk, cp_blk in zip(mgr.blocks, cp.blocks):

            # view assertion
            assert cp_blk.equals(blk)
            if isinstance(blk.values, np.ndarray):
                assert cp_blk.values.base is blk.values.base
            else:
                # DatetimeTZBlock has DatetimeIndex values
                assert cp_blk.values._data.base is blk.values._data.base

        cp = mgr.copy(deep=True)
        for blk, cp_blk in zip(mgr.blocks, cp.blocks):

            # copy assertion we either have a None for a base or in case of
            # some blocks it is an array (e.g. datetimetz), but was copied
            assert cp_blk.equals(blk)
            if not isinstance(cp_blk.values, np.ndarray):
                assert cp_blk.values._data.base is not blk.values._data.base
            else:
                assert cp_blk.values.base is None and blk.values.base is None 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def _check_data(self, xp, rs):
        """
        Check each axes has identical lines

        Parameters
        ----------
        xp : matplotlib Axes object
        rs : matplotlib Axes object
        """
        xp_lines = xp.get_lines()
        rs_lines = rs.get_lines()

        def check_line(xpl, rsl):
            xpdata = xpl.get_xydata()
            rsdata = rsl.get_xydata()
            tm.assert_almost_equal(xpdata, rsdata)

        assert len(xp_lines) == len(rs_lines)
        [check_line(xpl, rsl) for xpl, rsl in zip(xp_lines, rs_lines)]
        tm.close() 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def test_uhf(self):
        import pandas.plotting._converter as conv
        idx = date_range('2012-6-22 21:59:51.960928', freq='L', periods=500)
        df = DataFrame(np.random.randn(len(idx), 2), idx)

        _, ax = self.plt.subplots()
        df.plot(ax=ax)
        axis = ax.get_xaxis()

        tlocs = axis.get_ticklocs()
        tlabels = axis.get_ticklabels()
        for loc, label in zip(tlocs, tlabels):
            xp = conv._from_ordinal(loc).strftime('%H:%M:%S.%f')
            rs = str(label.get_text())
            if len(rs):
                assert xp == rs 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def test_get_default(self):

        # GH 7725
        d0 = "a", "b", "c", "d"
        d1 = np.arange(4, dtype='int64')
        others = "e", 10

        for data, index in ((d0, d1), (d1, d0)):
            s = Series(data, index=index)
            for i, d in zip(index, data):
                assert s.get(i) == d
                assert s.get(i, d) == d
                assert s.get(i, "z") == d
                for other in others:
                    assert s.get(other, "z") == "z"
                    assert s.get(other, other) == other 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def test_rename(self, datetime_series):
        ts = datetime_series
        renamer = lambda x: x.strftime('%Y%m%d')
        renamed = ts.rename(renamer)
        assert renamed.index[0] == renamer(ts.index[0])

        # dict
        rename_dict = dict(zip(ts.index, renamed.index))
        renamed2 = ts.rename(rename_dict)
        tm.assert_series_equal(renamed, renamed2)

        # partial dict
        s = Series(np.arange(4), index=['a', 'b', 'c', 'd'], dtype='int64')
        renamed = s.rename({'b': 'foo', 'd': 'bar'})
        tm.assert_index_equal(renamed.index, Index(['a', 'foo', 'c', 'bar']))

        # index with name
        renamer = Series(np.arange(4),
                         index=Index(['a', 'b', 'c', 'd'], name='name'),
                         dtype='int64')
        renamed = renamer.rename({})
        assert renamed.index.name == renamer.index.name 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def test_constructor_dict_datetime64_index(self):
        # GH 9456

        dates_as_str = ['1984-02-19', '1988-11-06', '1989-12-03', '1990-03-15']
        values = [42544017.198965244, 1234565, 40512335.181958228, -1]

        def create_data(constructor):
            return dict(zip((constructor(x) for x in dates_as_str), values))

        data_datetime64 = create_data(np.datetime64)
        data_datetime = create_data(lambda x: datetime.strptime(x, '%Y-%m-%d'))
        data_Timestamp = create_data(Timestamp)

        expected = Series(values, (Timestamp(x) for x in dates_as_str))

        result_datetime64 = Series(data_datetime64)
        result_datetime = Series(data_datetime)
        result_Timestamp = Series(data_Timestamp)

        assert_series_equal(result_datetime64, expected)
        assert_series_equal(result_datetime, expected)
        assert_series_equal(result_Timestamp, expected) 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def equals(self, other):
        self_axes, other_axes = self.axes, other.axes
        if len(self_axes) != len(other_axes):
            return False
        if not all(ax1.equals(ax2) for ax1, ax2 in zip(self_axes, other_axes)):
            return False
        self._consolidate_inplace()
        other._consolidate_inplace()
        if len(self.blocks) != len(other.blocks):
            return False

        # canonicalize block order, using a tuple combining the type
        # name and then mgr_locs because there might be unconsolidated
        # blocks (say, Categorical) which can only be distinguished by
        # the iteration order
        def canonicalize(block):
            return (block.dtype.name, block.mgr_locs.as_array.tolist())

        self_blocks = sorted(self.blocks, key=canonicalize)
        other_blocks = sorted(other.blocks, key=canonicalize)
        return all(block.equals(oblock)
                   for block, oblock in zip(self_blocks, other_blocks)) 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
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 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def _unstack(self, unstacker_func, new_columns, n_rows, fill_value):
        # ExtensionArray-safe unstack.
        # We override ObjectBlock._unstack, which unstacks directly on the
        # values of the array. For EA-backed blocks, this would require
        # converting to a 2-D ndarray of objects.
        # Instead, we unstack an ndarray of integer positions, followed by
        # a `take` on the actual values.
        dummy_arr = np.arange(n_rows)
        dummy_unstacker = functools.partial(unstacker_func, fill_value=-1)
        unstacker = dummy_unstacker(dummy_arr)

        new_placement, new_values, mask = self._get_unstack_items(
            unstacker, new_columns
        )

        blocks = [
            self.make_block_same_class(
                self.values.take(indices, allow_fill=True,
                                 fill_value=fill_value),
                [place])
            for indices, place in zip(new_values.T, new_placement)
        ]
        return blocks, mask 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def _format_native_types(self, na_rep='nan', **kwargs):
        new_levels = []
        new_codes = []

        # go through the levels and format them
        for level, level_codes in zip(self.levels, self.codes):
            level = level._format_native_types(na_rep=na_rep, **kwargs)
            # add nan values, if there are any
            mask = (level_codes == -1)
            if mask.any():
                nan_index = len(level)
                level = np.append(level, na_rep)
                level_codes = level_codes.values()
                level_codes[mask] = nan_index
            new_levels.append(level)
            new_codes.append(level_codes)

        if len(new_levels) == 1:
            return Index(new_levels[0])._format_native_types()
        else:
            # reconstruct the multi-index
            mi = MultiIndex(levels=new_levels, codes=new_codes,
                            names=self.names, sortorder=self.sortorder,
                            verify_integrity=False)
            return mi.values 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def _multi_take(self, tup):
        """
        Create the indexers for the passed tuple of keys, and execute the take
        operation. This allows the take operation to be executed all at once -
        rather than once for each dimension - improving efficiency.

        Parameters
        ----------
        tup : tuple
            Tuple of indexers, one per axis

        Returns
        -------
        values: same type as the object being indexed
        """
        # GH 836
        o = self.obj
        d = {axis: self._get_listlike_indexer(key, axis)
             for (key, axis) in zip(tup, o._AXIS_ORDERS)}
        return o._reindex_with_indexers(d, copy=True, allow_dups=True) 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
def _convert_key(self, key, is_setter=False):
        """ require they keys to be the same type as the index (so we don't
        fallback)
        """

        # allow arbitrary setting
        if is_setter:
            return list(key)

        for ax, i in zip(self.obj.axes, key):
            if ax.is_integer():
                if not is_integer(i):
                    raise ValueError("At based indexing on an integer index "
                                     "can only have integer indexers")
            else:
                if is_integer(i) and not ax.holds_integer():
                    raise ValueError("At based indexing on an non-integer "
                                     "index can only have non-integer "
                                     "indexers")
        return key 

# 需要導入模塊: from pandas import compat [as 別名]
# 或者: from pandas.compat import zip [as 別名]
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)