Python compat.string_types方法代码示例

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def _convert_1d(values, units, axis):
        if not hasattr(axis, 'freq'):
            raise TypeError('Axis must have `freq` set to convert to Periods')
        valid_types = (compat.string_types, datetime,
                       Period, pydt.date, pydt.time, np.datetime64)
        if (isinstance(values, valid_types) or is_integer(values) or
                is_float(values)):
            return get_datevalue(values, axis.freq)
        elif isinstance(values, PeriodIndex):
            return values.asfreq(axis.freq)._ndarray_values
        elif isinstance(values, Index):
            return values.map(lambda x: get_datevalue(x, axis.freq))
        elif lib.infer_dtype(values, skipna=False) == 'period':
            # https://github.com/pandas-dev/pandas/issues/24304
            # convert ndarray[period] -> PeriodIndex
            return PeriodIndex(values, freq=axis.freq)._ndarray_values
        elif isinstance(values, (list, tuple, np.ndarray, Index)):
            return [get_datevalue(x, axis.freq) for x in values]
        return values 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def get_finder(freq):
    if isinstance(freq, compat.string_types):
        freq = get_freq(freq)
    fgroup = resolution.get_freq_group(freq)

    if fgroup == FreqGroup.FR_ANN:
        return _annual_finder
    elif fgroup == FreqGroup.FR_QTR:
        return _quarterly_finder
    elif freq == FreqGroup.FR_MTH:
        return _monthly_finder
    elif ((freq >= FreqGroup.FR_BUS) or fgroup == FreqGroup.FR_WK):
        return _daily_finder
    else:  # pragma: no cover
        errmsg = "Unsupported frequency: {freq}".format(freq=freq)
        raise NotImplementedError(errmsg) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def __eq__(self, other):
        if isinstance(other, compat.string_types):
            from pandas.tseries.frequencies import to_offset
            try:
                # GH#23524 if to_offset fails, we are dealing with an
                #  incomparable type so == is False and != is True
                other = to_offset(other)
            except ValueError:
                # e.g. "infer"
                return False

        if isinstance(other, Tick):
            return self.delta == other.delta
        else:
            return False

    # This is identical to DateOffset.__hash__, but has to be redefined here
    # for Python 3, because we've redefined __eq__. 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def test_iter(self):
        # GH3638
        strs = 'google', 'wikimedia', 'wikipedia', 'wikitravel'
        ds = Series(strs)

        for s in ds.str:
            # iter must yield a Series
            assert isinstance(s, Series)

            # indices of each yielded Series should be equal to the index of
            # the original Series
            tm.assert_index_equal(s.index, ds.index)

            for el in s:
                # each element of the series is either a basestring/str or nan
                assert isinstance(el, compat.string_types) or isna(el)

        # desired behavior is to iterate until everything would be nan on the
        # next iter so make sure the last element of the iterator was 'l' in
        # this case since 'wikitravel' is the longest string
        assert s.dropna().values.item() == 'l' 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def test_encoding(self, version):

        # GH 4626, proper encoding handling
        raw = read_stata(self.dta_encoding)
        with tm.assert_produces_warning(FutureWarning):
            encoded = read_stata(self.dta_encoding, encoding='latin-1')
        result = encoded.kreis1849[0]

        expected = raw.kreis1849[0]
        assert result == expected
        assert isinstance(result, compat.string_types)

        with tm.ensure_clean() as path:
            with tm.assert_produces_warning(FutureWarning):
                encoded.to_stata(path, write_index=False, version=version,
                                 encoding='latin-1')
            reread_encoded = read_stata(path)
            tm.assert_frame_equal(encoded, reread_encoded) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def test_to_html(biggie_df_fixture):
    # TODO: split this test
    df = biggie_df_fixture
    s = df.to_html()

    buf = StringIO()
    retval = df.to_html(buf=buf)
    assert retval is None
    assert buf.getvalue() == s

    assert isinstance(s, compat.string_types)

    df.to_html(columns=['B', 'A'], col_space=17)
    df.to_html(columns=['B', 'A'],
               formatters={'A': lambda x: '{x:.1f}'.format(x=x)})

    df.to_html(columns=['B', 'A'], float_format=str)
    df.to_html(columns=['B', 'A'], col_space=12, float_format=str) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def test_datetime(self):
        df = DataFrame({'A': date_range('2013-01-01 09:00:00', periods=3),
                        'B': np.arange(3.0)})
        df.to_sql('test_datetime', self.conn)

        # with read_table -> type information from schema used
        result = sql.read_sql_table('test_datetime', self.conn)
        result = result.drop('index', axis=1)
        tm.assert_frame_equal(result, df)

        # with read_sql -> no type information -> sqlite has no native
        result = sql.read_sql_query('SELECT * FROM test_datetime', self.conn)
        result = result.drop('index', axis=1)
        if self.flavor == 'sqlite':
            assert isinstance(result.loc[0, 'A'], string_types)
            result['A'] = to_datetime(result['A'])
            tm.assert_frame_equal(result, df)
        else:
            tm.assert_frame_equal(result, df) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def validate(self):
        super(Window, self).validate()

        window = self.window
        if isinstance(window, (list, tuple, np.ndarray)):
            pass
        elif is_integer(window):
            if window <= 0:
                raise ValueError("window must be > 0 ")
            try:
                import scipy.signal as sig
            except ImportError:
                raise ImportError('Please install scipy to generate window '
                                  'weight')

            if not isinstance(self.win_type, compat.string_types):
                raise ValueError('Invalid win_type {0}'.format(self.win_type))
            if getattr(sig, self.win_type, None) is None:
                raise ValueError('Invalid win_type {0}'.format(self.win_type))
        else:
            raise ValueError('Invalid window {0}'.format(window)) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def _apply(self, func, name, window=None, center=None,
               check_minp=None, **kwargs):
        """
        Dispatch to apply; we are stripping all of the _apply kwargs and
        performing the original function call on the grouped object.
        """

        def f(x, name=name, *args):
            x = self._shallow_copy(x)

            if isinstance(name, compat.string_types):
                return getattr(x, name)(*args, **kwargs)

            return x.apply(name, *args, **kwargs)

        return self._groupby.apply(f) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def searchsorted(self, value, side='left', sorter=None):
        if isinstance(value, Period):
            if value.freq != self.freq:
                msg = DIFFERENT_FREQ.format(cls=type(self).__name__,
                                            own_freq=self.freqstr,
                                            other_freq=value.freqstr)
                raise IncompatibleFrequency(msg)
            value = value.ordinal
        elif isinstance(value, compat.string_types):
            try:
                value = Period(value, freq=self.freq).ordinal
            except DateParseError:
                raise KeyError("Cannot interpret '{}' as period".format(value))

        return self._ndarray_values.searchsorted(value, side=side,
                                                 sorter=sorter) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def _validate_where(w):
    """
    Validate that the where statement is of the right type.

    The type may either be String, Expr, or list-like of Exprs.

    Parameters
    ----------
    w : String term expression, Expr, or list-like of Exprs.

    Returns
    -------
    where : The original where clause if the check was successful.

    Raises
    ------
    TypeError : An invalid data type was passed in for w (e.g. dict).
    """

    if not (isinstance(w, (Expr, string_types)) or is_list_like(w)):
        raise TypeError("where must be passed as a string, Expr, "
                        "or list-like of Exprs")

    return w 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def update(self, value):
        """
        search order for local (i.e., @variable) variables:

        scope, key_variable
        [('locals', 'local_name'),
         ('globals', 'local_name'),
         ('locals', 'key'),
         ('globals', 'key')]
        """
        key = self.name

        # if it's a variable name (otherwise a constant)
        if isinstance(key, string_types):
            self.env.swapkey(self.local_name, key, new_value=value)

        self.value = value 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
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 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def convert_to_index_sliceable(obj, key):
    """
    if we are index sliceable, then return my slicer, otherwise return None
    """
    idx = obj.index
    if isinstance(key, slice):
        return idx._convert_slice_indexer(key, kind='getitem')

    elif isinstance(key, compat.string_types):

        # we are an actual column
        if obj._data.items.contains(key):
            return None

        # We might have a datetimelike string that we can translate to a
        # slice here via partial string indexing
        if idx.is_all_dates:
            try:
                return idx._get_string_slice(key)
            except (KeyError, ValueError, NotImplementedError):
                return None

    return None 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import string_types [as 别名]
def aggregate(self, arg, *args, **kwargs):
        """
        Aggregate using input function or dict of {column -> function}

        Parameters
        ----------
        arg : function or dict
            Function to use for aggregating groups. If a function, must either
            work when passed a Panel or when passed to Panel.apply. If
            pass a dict, the keys must be DataFrame column names

        Returns
        -------
        aggregated : Panel
        """
        if isinstance(arg, compat.string_types):
            return getattr(self, arg)(*args, **kwargs)

        return self._aggregate_generic(arg, *args, **kwargs)