def _build_update_filter(df, update_q):
if type(update_q) is not list:
raise_malformed("Expressions must be lists", update_q)
if not update_q:
raise_malformed("Empty expression not allowed", update_q)
operator = update_q[0]
if operator == "isnull":
assert_len(update_q, 2, 'Invalid length of isnull query')
try:
return getattr(_prepare_arg(df, update_q[1]), 'isnull')()
except AttributeError:
raise_malformed("Unknown column for 'isnull'", update_q)
if operator == "in":
if len(update_q) != 3:
raise_malformed("Invalid length of 'in' query", update_q)
_, column, values = update_q
if column not in df:
raise_malformed("First argument to 'in' must be a column present in frame", update_q)
if not isinstance(values, (list, tuple)):
raise_malformed("Second argument to 'in' must be a list", update_q)
return getattr(df, column).isin([_prepare_arg(df, val) for val in values])
if operator in COMPARISON_OPERATORS:
arg1 = _prepare_arg(df, update_q[1])
arg2 = _prepare_arg(df, update_q[2])
return COMPARISON_OPERATORS[operator](arg1, arg2)
raise_malformed("Unknown operator '{operator}'".format(operator=operator), update_q)
def _like_filter(df, q):
assert_len(q, 3)
op, column, raw_expr = q
if not is_quoted(raw_expr):
raise_malformed("like expects a quoted string as second argument", q)
regexp = unquote(raw_expr)
if not regexp.startswith('%'):
regexp = '^' + regexp
else:
regexp = regexp[1:]
if not regexp.endswith('%'):
regexp += '$'
else:
regexp = regexp[:-1]
# 'like' is case sensitive, 'ilike' is case insensitive
case = op == 'like'
try:
return df[column].str.contains(regexp, case=case)
except AttributeError:
raise_malformed("Invalid column type for (i)like", q)
def _bitwise_filter(df, q):
assert_len(q, 3)
op, column, arg = q
if not isinstance(arg, (int, long)):
raise_malformed('Invalid argument type, must be an integer:'.format(t=type(arg)), q)
try:
series = df[column] & arg
if op == "any_bits":
return series > 0
return series == arg
except TypeError:
raise_malformed("Invalid column type, must be an integer", q)
def _build_filter(self, q):
result = None
if type(q) is not list:
return unicode(q)
if not q:
raise_malformed("Empty expression not allowed", q)
op = q[0]
if op == "!":
assert_len(q, 2, "! is a single arity operator, invalid number of arguments")
result = "not " + self._build_filter(q[1])
elif op == "isnull":
assert_len(q, 2, "isnull is a single arity operator, invalid number of arguments")
# Slightly hacky but the only way I've come up with so far.
result = "({arg} != {arg})".format(arg=q[1])
elif op in COMPARISON_OPERATORS:
assert_len(q, 3)
_, arg1, arg2 = q
result = self._build_filter(arg1) + " " + op + " " + self._build_filter(arg2)
elif op in JOINING_OPERATORS:
if len(q) < 2:
raise_malformed("Invalid number of arguments", q)
elif len(q) == 2:
# Conjunctions and disjunctions with only one clause are OK
result = self._build_filter(q[1])
else:
result = ' {op} '.format(op=op).join(self._build_filter(x) for x in q[1:])
elif op == 'in':
col_name, args = prepare_in_clause(q, FILTER_ENGINE_NUMEXPR)
var_name = self._insert_in_env(args)
result = '{col_name} in @env.{var_name}'.format(col_name=col_name, var_name=var_name)
else:
raise_malformed("Unknown operator", q)
return "({result})".format(result=result)
def _comparison_filter(df, q):
assert_len(q, 3)
op, col_name, arg = q
return COMPARISON_OPERATORS[op](df[col_name], _do_pandas_filter(df, arg))
def _isnull_filter(df, q):
assert_len(q, 2, "isnull is a single arity operator, invalid number of arguments")
# Slightly hacky but the only way I've come up with so far.
return df[q[1]] != df[q[1]]
def _not_filter(df, q):
assert_len(q, 2, "! is a single arity operator, invalid number of arguments")
return ~_do_pandas_filter(df, q[1])
