def _s_compress_size(self, aggresiveness=0, sparsify=False):
'''
Always returns a new Series as inplace type change is not allowed
'''
c = self.copy()
if c.is_numerical() or c.is_index():
if c.is_index() or str(c.dtype).startswith('int'):
c = c.astype(utils.get_optimal_numeric_type('int', min(c), max(c)))
return c if not sparsify else c.to_sparse(fill_value=int(c.mode()))
elif str(c.dtype).startswith('float'):
return c.astype(utils.get_optimal_numeric_type(c.dtype, min(c), max(c), aggresiveness=aggresiveness))
else:
raise Exception(str(c.name) + ' expected "int" or "float" type got: ', str(c.dtype))
else :
misc.dbg(c.name + ' is not supported, ignored during compression')
return c
def _s_compress_size(self, aggresiveness=0, sparsify=False):
'''
Always returns a new Series as inplace type change is not allowed
'''
c = self.copy()
if c.is_numerical() or c.is_index():
if c.is_index() or str(c.dtype).startswith('int'):
c = c.astype(utils.get_optimal_numeric_type('int', min(c), max(c)))
return c if not sparsify else c.to_sparse(fill_value=int(c.mode()))
elif str(c.dtype).startswith('float'):
return c.astype(utils.get_optimal_numeric_type(c.dtype, min(c), max(c), aggresiveness=aggresiveness))
else:
raise Exception(str(c.name) + ' expected "int" or "float" type got: ', str(c.dtype))
else :
misc.dbg(c.name + ' is not supported, ignored during compression')
return c
def _s_to_indexes(self):
cat = pd.Categorical.from_array(self)
lbls = cat.codes if hasattr(cat, 'codes') else cat.labels
return pd.Series(lbls,
index=self.index,
dtype=utils.get_optimal_numeric_type(
'int', 0,
len(lbls) + 1))
def _s_to_indexes(self):
cat = pd.Categorical.from_array(self)
lbls = cat.codes if hasattr(cat, 'codes') else cat.labels
return pd.Series(lbls, index=self.index, dtype=utils.get_optimal_numeric_type('int', 0, len(lbls) + 1))
