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bug fix: facilitate scalar check for more generic objects like datetime, sets as well #42

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bug fix: facilitate scalar check for more generic objects like datetime, sets as well #42

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18 changes: 9 additions & 9 deletions src/tidypandas/tidyframe_class.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1763,7 +1763,7 @@ def _mutate(self, dictionary, order_by = None, **kwargs):
keys = dictionary.keys()
# ensure new column names to be created are valid (do not start with '_')
keys_flattened = list(
np.concatenate([[x] if np.isscalar(x) else list(x) for x in keys])
np.concatenate([[x] if (np.ndim(x) == 0) else list(x) for x in keys])
)
assert np.all([_is_valid_colname(x) for x in keys_flattened]),\
(f"column names to be created/modified should be valid column names. "
Expand All @@ -1790,7 +1790,7 @@ def _mutate(self, dictionary, order_by = None, **kwargs):
keys = dictionary.keys()
# ensure new column names to be created are valid (do not start with '_')
keys_flattened = list(
np.concatenate([[x] if np.isscalar(x) else list(x) for x in keys])
np.concatenate([[x] if (np.ndim(x) == 0) else list(x) for x in keys])
)
assert np.all([_is_valid_colname(x) for x in keys_flattened]),\
(f"column names to be created/modified should be valid column names. "
Expand Down Expand Up @@ -1826,7 +1826,7 @@ def _mutate(self, dictionary, order_by = None, **kwargs):

# 1. direct assign
if isinstance(rhs, ((pd.Series, np.ndarray))
or np.isscalar(rhs)):
or (np.ndim(rhs) == 0)):
mutated[akey] = rhs

# 2. assign via function -- pandas style UDF
Expand Down Expand Up @@ -2266,7 +2266,7 @@ def mutate(self
keys = dictionary.keys()
keys_flattened = list(
np.concatenate(
[[x] if np.isscalar(x) else list(x) for x in keys]
[[x] if (np.ndim(x) == 0) else list(x) for x in keys]
)
)
# keys should not intersect with 'by' columns
Expand Down Expand Up @@ -2323,8 +2323,8 @@ def _summarise(self, dictionary, **kwargs):

def _validate_rhs_val(akey, rhs_val):
if not pd.isna(rhs_val):
if not np.isscalar(rhs_val):
if not (len(rhs_val) == 1 and np.isscalar(rhs_val[0])):
if not (np.ndim(rhs_val) == 0):
if not (len(rhs_val) == 1 and (np.ndim(rhs_val[0]) == 0)):
raise Exception((f"Summarised value for key {akey} does not"
" turn out to be a scalar or cannot be "
"converted to a scalar")
Expand Down Expand Up @@ -2678,7 +2678,7 @@ def summarise(self
keys = dictionary.keys()
keys_flattened = list(
np.concatenate(
[[x] if np.isscalar(x) else list(x) for x in keys]
[[x] if (np.ndim(x) == 0) else list(x) for x in keys]
)
)
assert len(set(by).intersection(keys_flattened)) == 0,\
Expand Down Expand Up @@ -3831,7 +3831,7 @@ def pivot_wider(self
"'names_from' or 'values_from'"
)
if values_fill is not None:
assert np.isscalar(values_fill),\
assert np.ndim(values_fill) == 0,\
"arg 'values_fill' should be a scalar"

assert (values_fn is None
Expand Down Expand Up @@ -5546,7 +5546,7 @@ def unnest(self, nest_column_name = 'data'):
def is_list_scalar_na(x):
if isinstance(x, (list, pd.core.arrays.floating.FloatingArray)):
res = True
elif np.isscalar(x):
elif np.ndim(x) == 0:
res = True
elif isinstance(pd.isna(x), bool) and pd.isna(x):
res = True
Expand Down