Python pandas.compat.itervalues() Examples

def __call__(self, f):
        @functools.wraps(f)
        def _f(*args, **kwargs):
            obj_iter = itertools.chain(args, compat.itervalues(kwargs))
            if any(self.check(obj) for obj in obj_iter):
                msg = 'reduction operation {name!r} not allowed for this dtype'
                raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
            try:
                with np.errstate(invalid='ignore'):
                    return f(*args, **kwargs)
            except ValueError as e:
                # we want to transform an object array
                # ValueError message to the more typical TypeError
                # e.g. this is normally a disallowed function on
                # object arrays that contain strings
                if is_object_dtype(args[0]):
                    raise TypeError(e)
                raise

        return _f 

def __call__(self, f):
        @functools.wraps(f)
        def _f(*args, **kwargs):
            obj_iter = itertools.chain(args, compat.itervalues(kwargs))
            if any(self.check(obj) for obj in obj_iter):
                msg = 'reduction operation {name!r} not allowed for this dtype'
                raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
            try:
                with np.errstate(invalid='ignore'):
                    return f(*args, **kwargs)
            except ValueError as e:
                # we want to transform an object array
                # ValueError message to the more typical TypeError
                # e.g. this is normally a disallowed function on
                # object arrays that contain strings
                if is_object_dtype(args[0]):
                    raise TypeError(e)
                raise

        return _f 

def _get_series_result_type(result, objs=None):
    """
    return appropriate class of Series concat
    input is either dict or array-like
    """
    from pandas import SparseSeries, SparseDataFrame, DataFrame

    # concat Series with axis 1
    if isinstance(result, dict):
        # concat Series with axis 1
        if all(isinstance(c, (SparseSeries, SparseDataFrame))
               for c in compat.itervalues(result)):
            return SparseDataFrame
        else:
            return DataFrame

    # otherwise it is a SingleBlockManager (axis = 0)
    if result._block.is_sparse:
        return SparseSeries
    else:
        return objs[0]._constructor 

def __call__(self, f):
        @functools.wraps(f)
        def _f(*args, **kwargs):
            obj_iter = itertools.chain(args, compat.itervalues(kwargs))
            if any(self.check(obj) for obj in obj_iter):
                msg = 'reduction operation {name!r} not allowed for this dtype'
                raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
            try:
                with np.errstate(invalid='ignore'):
                    return f(*args, **kwargs)
            except ValueError as e:
                # we want to transform an object array
                # ValueError message to the more typical TypeError
                # e.g. this is normally a disallowed function on
                # object arrays that contain strings
                if is_object_dtype(args[0]):
                    raise TypeError(e)
                raise

        return _f 

def _get_series_result_type(result, objs=None):
    """
    return appropriate class of Series concat
    input is either dict or array-like
    """
    # concat Series with axis 1
    if isinstance(result, dict):
        # concat Series with axis 1
        if all(is_sparse(c) for c in compat.itervalues(result)):
            from pandas.core.sparse.api import SparseDataFrame
            return SparseDataFrame
        else:
            from pandas.core.frame import DataFrame
            return DataFrame

    # otherwise it is a SingleBlockManager (axis = 0)
    if result._block.is_sparse:
        from pandas.core.sparse.api import SparseSeries
        return SparseSeries
    else:
        return objs[0]._constructor 

def __call__(self, f):
        @functools.wraps(f)
        def _f(*args, **kwargs):
            obj_iter = itertools.chain(args, compat.itervalues(kwargs))
            if any(self.check(obj) for obj in obj_iter):
                msg = 'reduction operation {name!r} not allowed for this dtype'
                raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
            try:
                with np.errstate(invalid='ignore'):
                    return f(*args, **kwargs)
            except ValueError as e:
                # we want to transform an object array
                # ValueError message to the more typical TypeError
                # e.g. this is normally a disallowed function on
                # object arrays that contain strings
                if is_object_dtype(args[0]):
                    raise TypeError(e)
                raise

        return _f 

def _get_series_result_type(result, objs=None):
    """
    return appropriate class of Series concat
    input is either dict or array-like
    """
    from pandas import SparseSeries, SparseDataFrame, DataFrame

    # concat Series with axis 1
    if isinstance(result, dict):
        # concat Series with axis 1
        if all(isinstance(c, (SparseSeries, SparseDataFrame))
               for c in compat.itervalues(result)):
            return SparseDataFrame
        else:
            return DataFrame

    # otherwise it is a SingleBlockManager (axis = 0)
    if result._block.is_sparse:
        return SparseSeries
    else:
        return objs[0]._constructor 

def __call__(self, f):
        @functools.wraps(f)
        def _f(*args, **kwargs):
            obj_iter = itertools.chain(args, compat.itervalues(kwargs))
            if any(self.check(obj) for obj in obj_iter):
                msg = 'reduction operation {name!r} not allowed for this dtype'
                raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
            try:
                with np.errstate(invalid='ignore'):
                    return f(*args, **kwargs)
            except ValueError as e:
                # we want to transform an object array
                # ValueError message to the more typical TypeError
                # e.g. this is normally a disallowed function on
                # object arrays that contain strings
                if is_object_dtype(args[0]):
                    raise TypeError(e)
                raise

        return _f 

def _construct_return_type(self, result, axes=None):
        """
        Return the type for the ndim of the result.
        """
        ndim = getattr(result, 'ndim', None)

        # need to assume they are the same
        if ndim is None:
            if isinstance(result, dict):
                ndim = getattr(list(compat.itervalues(result))[0], 'ndim', 0)

                # have a dict, so top-level is +1 dim
                if ndim != 0:
                    ndim += 1

        # scalar
        if ndim == 0:
            return Series(result)

        # same as self
        elif self.ndim == ndim:
            # return the construction dictionary for these axes
            if axes is None:
                return self._constructor(result)
            return self._constructor(result, **self._construct_axes_dict())

        # sliced
        elif self.ndim == ndim + 1:
            if axes is None:
                return self._constructor_sliced(result)
            return self._constructor_sliced(
                result, **self._extract_axes_for_slice(self, axes))

        raise ValueError('invalid _construct_return_type [self->{self}] '
                         '[result->{result}]'.format(self=self, result=result)) 

def _construct_return_type(self, result, axes=None):
        """ return the type for the ndim of the result """
        ndim = getattr(result, 'ndim', None)

        # need to assume they are the same
        if ndim is None:
            if isinstance(result, dict):
                ndim = getattr(list(compat.itervalues(result))[0], 'ndim', 0)

                # have a dict, so top-level is +1 dim
                if ndim != 0:
                    ndim += 1

        # scalar
        if ndim == 0:
            return Series(result)

        # same as self
        elif self.ndim == ndim:
            # return the construction dictionary for these axes
            if axes is None:
                return self._constructor(result)
            return self._constructor(result, **self._construct_axes_dict())

        # sliced
        elif self.ndim == ndim + 1:
            if axes is None:
                return self._constructor_sliced(result)
            return self._constructor_sliced(
                result, **self._extract_axes_for_slice(self, axes))

        raise ValueError('invalid _construct_return_type [self->%s] '
                         '[result->%s]' % (self, result)) 

def test_default_encoding(self):
        for frame in compat.itervalues(self.frame):
            result = frame.to_msgpack()
            expected = frame.to_msgpack(encoding='utf8')
            assert result == expected
            result = self.encode_decode(frame)
            assert_frame_equal(result, frame) 

def test_utf(self):
        # GH10581
        for encoding in self.utf_encodings:
            for frame in compat.itervalues(self.frame):
                result = self.encode_decode(frame, encoding=encoding)
                assert_frame_equal(result, frame) 

def _construct_return_type(self, result, axes=None):
        """ return the type for the ndim of the result """
        ndim = getattr(result, 'ndim', None)

        # need to assume they are the same
        if ndim is None:
            if isinstance(result, dict):
                ndim = getattr(list(compat.itervalues(result))[0], 'ndim', 0)

                # have a dict, so top-level is +1 dim
                if ndim != 0:
                    ndim += 1

        # scalar
        if ndim == 0:
            return Series(result)

        # same as self
        elif self.ndim == ndim:
            # return the construction dictionary for these axes
            if axes is None:
                return self._constructor(result)
            return self._constructor(result, **self._construct_axes_dict())

        # sliced
        elif self.ndim == ndim + 1:
            if axes is None:
                return self._constructor_sliced(result)
            return self._constructor_sliced(
                result, **self._extract_axes_for_slice(self, axes))

        raise ValueError('invalid _construct_return_type [self->%s] '
                         '[result->%s]' % (self, result)) 

def _construct_return_type(self, result, axes=None):
        """
        Return the type for the ndim of the result.
        """
        ndim = getattr(result, 'ndim', None)

        # need to assume they are the same
        if ndim is None:
            if isinstance(result, dict):
                ndim = getattr(list(compat.itervalues(result))[0], 'ndim', 0)

                # have a dict, so top-level is +1 dim
                if ndim != 0:
                    ndim += 1

        # scalar
        if ndim == 0:
            return Series(result)

        # same as self
        elif self.ndim == ndim:
            # return the construction dictionary for these axes
            if axes is None:
                return self._constructor(result)
            return self._constructor(result, **self._construct_axes_dict())

        # sliced
        elif self.ndim == ndim + 1:
            if axes is None:
                return self._constructor_sliced(result)
            return self._constructor_sliced(
                result, **self._extract_axes_for_slice(self, axes))

        raise ValueError('invalid _construct_return_type [self->{self}] '
                         '[result->{result}]'.format(self=self, result=result)) 

def get_quote_yahoo(symbols):
    """
    Get current yahoo quote

    Returns a DataFrame
    """
    if isinstance(symbols, compat.string_types):
        sym_list = symbols
    else:
        sym_list = '+'.join(symbols)

    # for codes see: http://www.gummy-stuff.org/Yahoo-data.htm
    request = ''.join(compat.itervalues(_yahoo_codes))  # code request string
    header = list(_yahoo_codes.keys())

    data = defaultdict(list)

    url_str = _YAHOO_QUOTE_URL + 's=%s&f=%s' % (sym_list, request)

    with urlopen(url_str) as url:
        lines = url.readlines()

    for line in lines:
        fields = line.decode('utf-8').strip().split(',')
        for i, field in enumerate(fields):
            if field[-2:] == '%"':
                v = float(field.strip('"%'))
            elif field[0] == '"':
                v = field.strip('"')
            else:
                try:
                    v = float(field)
                except ValueError:
                    v = np.nan
            data[header[i]].append(v)

    idx = data.pop('symbol')
    return DataFrame(data, index=idx) 

def __call__(self, f):
        @functools.wraps(f)
        def _f(*args, **kwargs):
            obj_iter = itertools.chain(args, compat.itervalues(kwargs))
            if any(self.check(obj) for obj in obj_iter):
                raise TypeError('reduction operation {0!r} not allowed for '
                                'this dtype'.format(f.__name__.replace('nan',
                                                                       '')))
            return f(*args, **kwargs)
        return _f 

def _construct_return_type(self, result, axes=None, **kwargs):
        """ return the type for the ndim of the result """
        ndim = getattr(result,'ndim',None)

        # need to assume they are the same
        if ndim is None:
            if isinstance(result,dict):
                ndim = getattr(list(compat.itervalues(result))[0],'ndim',None)

                # a saclar result
                if ndim is None:
                    ndim = 0

                # have a dict, so top-level is +1 dim
                else:
                    ndim += 1

        # scalar
        if ndim == 0:
            return Series(result)

        # same as self
        elif self.ndim == ndim:
            """ return the construction dictionary for these axes """
            if axes is None:
                return self._constructor(result)
            return self._constructor(result, **self._construct_axes_dict())

        # sliced
        elif self.ndim == ndim + 1:
            if axes is None:
                return self._constructor_sliced(result)
            return self._constructor_sliced(
                result, **self._extract_axes_for_slice(self, axes))

        raise PandasError('invalid _construct_return_type [self->%s] '
                          '[result->%s]' % (self, result)) 

def __set__(self, obj, value):
        value = _ensure_index(value)

        if isinstance(value, MultiIndex):
            raise NotImplementedError

        for v in compat.itervalues(obj._frames):
            setattr(v, self.frame_attr, value)

        setattr(obj, self.cache_field, value) 

def _construct_return_type(self, result, axes=None):
        """ return the type for the ndim of the result """
        ndim = getattr(result, 'ndim', None)

        # need to assume they are the same
        if ndim is None:
            if isinstance(result, dict):
                ndim = getattr(list(compat.itervalues(result))[0], 'ndim', 0)

                # have a dict, so top-level is +1 dim
                if ndim != 0:
                    ndim += 1

        # scalar
        if ndim == 0:
            return Series(result)

        # same as self
        elif self.ndim == ndim:
            # return the construction dictionary for these axes
            if axes is None:
                return self._constructor(result)
            return self._constructor(result, **self._construct_axes_dict())

        # sliced
        elif self.ndim == ndim + 1:
            if axes is None:
                return self._constructor_sliced(result)
            return self._constructor_sliced(
                result, **self._extract_axes_for_slice(self, axes))

        raise ValueError('invalid _construct_return_type [self->{self}] '
                         '[result->{result}]'.format(self=self, result=result)) 

def test_default_encoding(self):
        for frame in compat.itervalues(self.frame):
            result = frame.to_msgpack()
            expected = frame.to_msgpack(encoding='utf8')
            assert result == expected
            result = self.encode_decode(frame)
            assert_frame_equal(result, frame) 

def test_utf(self):
        # GH10581
        for encoding in self.utf_encodings:
            for frame in compat.itervalues(self.frame):
                result = self.encode_decode(frame, encoding=encoding)
                assert_frame_equal(result, frame) 

def _aggregate_multiple_funcs(self, arg, _level):
        if isinstance(arg, dict):

            # show the deprecation, but only if we
            # have not shown a higher level one
            # GH 15931
            if isinstance(self._selected_obj, Series) and _level <= 1:
                warnings.warn(
                    ("using a dict on a Series for aggregation\n"
                     "is deprecated and will be removed in a future "
                     "version"),
                    FutureWarning, stacklevel=3)

            columns = list(arg.keys())
            arg = list(arg.items())
        elif any(isinstance(x, (tuple, list)) for x in arg):
            arg = [(x, x) if not isinstance(x, (tuple, list)) else x
                   for x in arg]

            # indicated column order
            columns = lzip(*arg)[0]
        else:
            # list of functions / function names
            columns = []
            for f in arg:
                if isinstance(f, compat.string_types):
                    columns.append(f)
                else:
                    # protect against callables without names
                    columns.append(com.get_callable_name(f))
            arg = lzip(columns, arg)

        results = {}
        for name, func in arg:
            obj = self
            if name in results:
                raise SpecificationError(
                    'Function names must be unique, found multiple named '
                    '{}'.format(name))

            # reset the cache so that we
            # only include the named selection
            if name in self._selected_obj:
                obj = copy.copy(obj)
                obj._reset_cache()
                obj._selection = name
            results[name] = obj.aggregate(func)

        if any(isinstance(x, DataFrame) for x in compat.itervalues(results)):
            # let higher level handle
            if _level:
                return results

        return DataFrame(results, columns=columns) 

def _aggregate_multiple_funcs(self, arg, _level):
        if isinstance(arg, dict):

            # show the deprecation, but only if we
            # have not shown a higher level one
            # GH 15931
            if isinstance(self._selected_obj, Series) and _level <= 1:
                warnings.warn(
                    ("using a dict on a Series for aggregation\n"
                     "is deprecated and will be removed in a future "
                     "version"),
                    FutureWarning, stacklevel=3)

            columns = list(arg.keys())
            arg = list(arg.items())
        elif any(isinstance(x, (tuple, list)) for x in arg):
            arg = [(x, x) if not isinstance(x, (tuple, list)) else x
                   for x in arg]

            # indicated column order
            columns = lzip(*arg)[0]
        else:
            # list of functions / function names
            columns = []
            for f in arg:
                if isinstance(f, compat.string_types):
                    columns.append(f)
                else:
                    # protect against callables without names
                    columns.append(_get_callable_name(f))
            arg = lzip(columns, arg)

        results = {}
        for name, func in arg:
            obj = self
            if name in results:
                raise SpecificationError('Function names must be unique, '
                                         'found multiple named %s' % name)

            # reset the cache so that we
            # only include the named selection
            if name in self._selected_obj:
                obj = copy.copy(obj)
                obj._reset_cache()
                obj._selection = name
            results[name] = obj.aggregate(func)

        if isinstance(list(compat.itervalues(results))[0],
                      DataFrame):

            # let higher level handle
            if _level:
                return results
            return list(compat.itervalues(results))[0]
        return DataFrame(results, columns=columns) 

def _aggregate_multiple_funcs(self, arg, _level):
        if isinstance(arg, dict):

            # show the deprecation, but only if we
            # have not shown a higher level one
            # GH 15931
            if isinstance(self._selected_obj, Series) and _level <= 1:
                warnings.warn(
                    ("using a dict on a Series for aggregation\n"
                     "is deprecated and will be removed in a future "
                     "version"),
                    FutureWarning, stacklevel=3)

            columns = list(arg.keys())
            arg = list(arg.items())
        elif any(isinstance(x, (tuple, list)) for x in arg):
            arg = [(x, x) if not isinstance(x, (tuple, list)) else x
                   for x in arg]

            # indicated column order
            columns = lzip(*arg)[0]
        else:
            # list of functions / function names
            columns = []
            for f in arg:
                if isinstance(f, compat.string_types):
                    columns.append(f)
                else:
                    # protect against callables without names
                    columns.append(com._get_callable_name(f))
            arg = lzip(columns, arg)

        results = {}
        for name, func in arg:
            obj = self
            if name in results:
                raise SpecificationError('Function names must be unique, '
                                         'found multiple named %s' % name)

            # reset the cache so that we
            # only include the named selection
            if name in self._selected_obj:
                obj = copy.copy(obj)
                obj._reset_cache()
                obj._selection = name
            results[name] = obj.aggregate(func)

        if isinstance(list(compat.itervalues(results))[0],
                      DataFrame):

            # let higher level handle
            if _level:
                return results
            return list(compat.itervalues(results))[0]
        return DataFrame(results, columns=columns) 

def _aggregate_multiple_funcs(self, arg, _level):
        if isinstance(arg, dict):

            # show the deprecation, but only if we
            # have not shown a higher level one
            # GH 15931
            if isinstance(self._selected_obj, Series) and _level <= 1:
                warnings.warn(
                    ("using a dict on a Series for aggregation\n"
                     "is deprecated and will be removed in a future "
                     "version"),
                    FutureWarning, stacklevel=3)

            columns = list(arg.keys())
            arg = list(arg.items())
        elif any(isinstance(x, (tuple, list)) for x in arg):
            arg = [(x, x) if not isinstance(x, (tuple, list)) else x
                   for x in arg]

            # indicated column order
            columns = lzip(*arg)[0]
        else:
            # list of functions / function names
            columns = []
            for f in arg:
                if isinstance(f, compat.string_types):
                    columns.append(f)
                else:
                    # protect against callables without names
                    columns.append(com.get_callable_name(f))
            arg = lzip(columns, arg)

        results = {}
        for name, func in arg:
            obj = self
            if name in results:
                raise SpecificationError(
                    'Function names must be unique, found multiple named '
                    '{}'.format(name))

            # reset the cache so that we
            # only include the named selection
            if name in self._selected_obj:
                obj = copy.copy(obj)
                obj._reset_cache()
                obj._selection = name
            results[name] = obj.aggregate(func)

        if any(isinstance(x, DataFrame) for x in compat.itervalues(results)):
            # let higher level handle
            if _level:
                return results

        return DataFrame(results, columns=columns) 

def _aggregate_multiple_funcs(self, arg, _level):
        if isinstance(arg, dict):

            # show the deprecation, but only if we
            # have not shown a higher level one
            # GH 15931
            if isinstance(self._selected_obj, Series) and _level <= 1:
                warnings.warn(
                    ("using a dict on a Series for aggregation\n"
                     "is deprecated and will be removed in a future "
                     "version"),
                    FutureWarning, stacklevel=3)

            columns = list(arg.keys())
            arg = list(arg.items())
        elif any(isinstance(x, (tuple, list)) for x in arg):
            arg = [(x, x) if not isinstance(x, (tuple, list)) else x
                   for x in arg]

            # indicated column order
            columns = lzip(*arg)[0]
        else:
            # list of functions / function names
            columns = []
            for f in arg:
                if isinstance(f, compat.string_types):
                    columns.append(f)
                else:
                    # protect against callables without names
                    columns.append(_get_callable_name(f))
            arg = lzip(columns, arg)

        results = {}
        for name, func in arg:
            obj = self
            if name in results:
                raise SpecificationError('Function names must be unique, '
                                         'found multiple named %s' % name)

            # reset the cache so that we
            # only include the named selection
            if name in self._selected_obj:
                obj = copy.copy(obj)
                obj._reset_cache()
                obj._selection = name
            results[name] = obj.aggregate(func)

        if isinstance(list(compat.itervalues(results))[0],
                      DataFrame):

            # let higher level handle
            if _level:
                return results
            return list(compat.itervalues(results))[0]
        return DataFrame(results, columns=columns)