def delete_columns_array(frame):
# datatable supports a shape of n x 0 for non-zero n's, while
# python doesn't, so we never remove all the columns from a Frame.
if frame.ncols < 2:
return
s = random_array(frame.ncols - 1, positive=True)
frame.delete_columns(s)
def __init__(self, context):
super().__init__(context)
# datatable supports a shape of n x 0 for non-zero n's, while
# python doesn't, so we never remove all the columns from a Frame.
ncols = self.frame.ncols
nkeys = self.frame.nkeys
if ncols <= 1:
self.skipped = True
else:
self.array = random_array(ncols - 1, positive=True)
set_keys = set(range(nkeys))
set_delcols = set(self.array)
self.n_keys_to_remove = len(set_keys.intersection(set_delcols))
if (self.n_keys_to_remove > 0 and self.n_keys_to_remove < nkeys
and self.frame.nrows > 0):
self.raises = ValueError
self.error_message = "Cannot delete a column that is a part " \
"of a multi-column key"
def __init__(self, context):
super().__init__(context)
self.skipped = (self.frame.nrows == 0)
if not self.skipped:
self.array = random_array(self.frame.nrows, positive=True)
self.array = sorted(set(self.array))
def delete_rows_array(frame):
if frame.nrows == 0:
return
s = random_array(frame.nrows, positive=True)
s = sorted(set(s))
frame.delete_rows(s)
def select_rows_array(frame):
if frame.nrows == 0:
return
s = random_array(frame.nrows)
frame.slice_rows(s)
def __init__(self, context):
super().__init__(context)
if self.frame.nrows == 0:
self.skipped = True
else:
self.array = random_array(self.frame.nrows)