Python index.Index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 Index [as 別名]
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 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def _unpickle_sparse_frame_compat(self, state):
        """ original pickle format """
        series, cols, idx, fv, kind = state

        if not isinstance(cols, Index):  # pragma: no cover
            from pandas.io.pickle import _unpickle_array
            columns = _unpickle_array(cols)
        else:
            columns = cols

        if not isinstance(idx, Index):  # pragma: no cover
            from pandas.io.pickle import _unpickle_array
            index = _unpickle_array(idx)
        else:
            index = idx

        series_dict = DataFrame()
        for col, (sp_index, sp_values) in compat.iteritems(series):
            series_dict[col] = SparseSeries(sp_values, sparse_index=sp_index,
                                            fill_value=fv)

        self._data = to_manager(series_dict, columns, index)
        self._default_fill_value = fv
        self._default_kind = kind 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def _ixs(self, i, axis=0):
        """
        Return the i-th value or values in the SparseSeries by location

        Parameters
        ----------
        i : int, slice, or sequence of integers

        Returns
        -------
        value : scalar (int) or Series (slice, sequence)
        """
        label = self.index[i]
        if isinstance(label, Index):
            return self.take(i, axis=axis)
        else:
            return self._get_val_at(i) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def _aggregate_series_pure_python(self, obj, func):

        group_index, _, ngroups = self.group_info

        counts = np.zeros(ngroups, dtype=int)
        result = None

        splitter = get_splitter(obj, group_index, ngroups, axis=self.axis)

        for label, group in splitter:
            res = func(group)
            if result is None:
                if (isinstance(res, (Series, Index, np.ndarray))):
                    raise ValueError('Function does not reduce')
                result = np.empty(ngroups, dtype='O')

            counts[label] = group.shape[0]
            result[label] = res

        result = lib.maybe_convert_objects(result, try_float=0)
        return result, counts 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def str_strip(arr, to_strip=None, side='both'):
    """
    Strip whitespace (including newlines) from each string in the
    Series/Index.

    Parameters
    ----------
    to_strip : str or unicode
    side : {'left', 'right', 'both'}, default 'both'

    Returns
    -------
    stripped : Series/Index of objects
    """
    if side == 'both':
        f = lambda x: x.strip(to_strip)
    elif side == 'left':
        f = lambda x: x.lstrip(to_strip)
    elif side == 'right':
        f = lambda x: x.rstrip(to_strip)
    else:  # pragma: no cover
        raise ValueError('Invalid side')
    return _na_map(f, arr) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def str_decode(arr, encoding, errors="strict"):
    """
    Decode character string in the Series/Index using indicated encoding.
    Equivalent to :meth:`str.decode` in python2 and :meth:`bytes.decode` in
    python3.

    Parameters
    ----------
    encoding : str
    errors : str, optional

    Returns
    -------
    decoded : Series/Index of objects
    """
    if encoding in _cpython_optimized_decoders:
        # CPython optimized implementation
        f = lambda x: x.decode(encoding, errors)
    else:
        decoder = codecs.getdecoder(encoding)
        f = lambda x: decoder(x, errors)[0]
    return _na_map(f, arr) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def normalize(self, form):
        """
        Return the Unicode normal form for the strings in the Series/Index.
        For more information on the forms, see the
        :func:`unicodedata.normalize`.

        Parameters
        ----------
        form : {'NFC', 'NFKC', 'NFD', 'NFKD'}
            Unicode form

        Returns
        -------
        normalized : Series/Index of objects
        """
        import unicodedata
        f = lambda x: unicodedata.normalize(form, compat.u_safe(x))
        result = _na_map(f, self._parent)
        return self._wrap_result(result) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds):
        mask = isna(y)
        if mask.any():
            y = np.ma.array(y)
            y = np.ma.masked_where(mask, y)

        if isinstance(x, Index):
            x = x._mpl_repr()

        if is_errorbar:
            if 'xerr' in kwds:
                kwds['xerr'] = np.array(kwds.get('xerr'))
            if 'yerr' in kwds:
                kwds['yerr'] = np.array(kwds.get('yerr'))
            return ax.errorbar(x, y, **kwds)
        else:
            # prevent style kwarg from going to errorbar, where it is
            # unsupported
            if style is not None:
                args = (x, y, style)
            else:
                args = (x, y)
            return ax.plot(*args, **kwds) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
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)
        if isinstance(values, PeriodIndex):
            return values.asfreq(axis.freq)._ndarray_values
        if isinstance(values, Index):
            return values.map(lambda x: get_datevalue(x, axis.freq))
        if is_period_arraylike(values):
            return PeriodIndex(values, freq=axis.freq)._ndarray_values
        if isinstance(values, (list, tuple, np.ndarray, Index)):
            return [get_datevalue(x, axis.freq) for x in values]
        return values 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def _handle_lowerdim_multi_index_axis0(self, tup):
        # we have an axis0 multi-index, handle or raise

        try:
            # fast path for series or for tup devoid of slices
            return self._get_label(tup, axis=self.axis)
        except TypeError:
            # slices are unhashable
            pass
        except Exception as e1:
            if isinstance(tup[0], (slice, Index)):
                raise IndexingError("Handle elsewhere")

            # raise the error if we are not sorted
            ax0 = self.obj._get_axis(0)
            if not ax0.is_lexsorted_for_tuple(tup):
                raise e1

        return None 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def _index_labels_to_array(labels, dtype=None):
    """
    Transform label or iterable of labels to array, for use in Index.

    Parameters
    ----------
    dtype : dtype
        If specified, use as dtype of the resulting array, otherwise infer.

    Returns
    -------
    array
    """
    if isinstance(labels, (compat.string_types, tuple)):
        labels = [labels]

    if not isinstance(labels, (list, np.ndarray)):
        try:
            labels = list(labels)
        except TypeError:  # non-iterable
            labels = [labels]

    labels = _asarray_tuplesafe(labels, dtype=dtype)

    return labels 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def str_rsplit(arr, pat=None, n=None):
    """
    Split each string in the Series/Index by the given delimiter
    string, starting at the end of the string and working to the front.
    Equivalent to :meth:`str.rsplit`.

    Parameters
    ----------
    pat : string, default None
        Separator to split on. If None, splits on whitespace
    n : int, default -1 (all)
        None, 0 and -1 will be interpreted as return all splits
    expand : bool, default False
        * If True, return DataFrame/MultiIndex expanding dimensionality.
        * If False, return Series/Index.

    Returns
    -------
    split : Series/Index or DataFrame/MultiIndex of objects
    """
    if n is None or n == 0:
        n = -1
    f = lambda x: x.rsplit(pat, n)
    res = _na_map(f, arr)
    return res 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def str_slice(arr, start=None, stop=None, step=None):
    """
    Slice substrings from each element in the Series/Index

    Parameters
    ----------
    start : int or None
    stop : int or None
    step : int or None

    Returns
    -------
    sliced : Series/Index of objects
    """
    obj = slice(start, stop, step)
    f = lambda x: x[obj]
    return _na_map(f, arr) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def str_encode(arr, encoding, errors="strict"):
    """
    Encode character string in the Series/Index using indicated encoding.
    Equivalent to :meth:`str.encode`.

    Parameters
    ----------
    encoding : str
    errors : str, optional

    Returns
    -------
    encoded : Series/Index of objects
    """
    if encoding in _cpython_optimized_encoders:
        # CPython optimized implementation
        f = lambda x: x.encode(encoding, errors)
    else:
        encoder = codecs.getencoder(encoding)
        f = lambda x: encoder(x, errors)[0]
    return _na_map(f, arr) 

# 需要導入模塊: from pandas.core import index [as 別名]
# 或者: from pandas.core.index import Index [as 別名]
def zfill(self, width):
        """
        Filling left side of strings in the Series/Index with 0.
        Equivalent to :meth:`str.zfill`.

        Parameters
        ----------
        width : int
            Minimum width of resulting string; additional characters will be
            filled with 0

        Returns
        -------
        filled : Series/Index of objects
        """
        result = str_pad(self._data, width, side='left', fillchar='0')
        return self._wrap_result(result)