else:
data = self.fillna(method=fill_method, limit=limit)
rs = data / data.shift(periods=periods, freq=freq, **kwds) - 1
if freq is None:
mask = com.isnull(self.values)
np.putmask(rs.values, mask, np.nan)
return rs
def to_hdf(self, path_or_buf, key, **kwargs):
""" activate the HDFStore """
from pandas.io import pytables
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
# install the indexerse
for _name, _indexer in indexing.get_indexers_list():
PandasObject._create_indexer(_name, _indexer)
class NDFrame(PandasObject):
"""
N-dimensional analogue of DataFrame. Store multi-dimensional in a
size-mutable, labeled data structure
Parameters
----------
data : BlockManager
axes : list
copy : boolean, default False
"""
# kludge
Returns
-------
chg : Series or DataFrame
"""
if fill_method is None:
data = self
else:
data = self.fillna(method=fill_method, limit=limit)
rs = data / data.shift(periods=periods, freq=freq, **kwds) - 1
if freq is None:
mask = com.isnull(self.values)
np.putmask(rs.values, mask, np.nan)
return rs
# install the indexerse
for _name, _indexer in indexing.get_indexers_list():
PandasObject._create_indexer(_name,_indexer)
class NDFrame(PandasObject):
"""
N-dimensional analogue of DataFrame. Store multi-dimensional in a
size-mutable, labeled data structure
Parameters
----------
data : BlockManager
axes : list
copy : boolean, default False
"""
# kludge
_default_stat_axis = 0
