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fix: unify integer/float filter approach to avoid shape mismatch
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Co-Authored-By: Myles Scolnick <myles@marimo.io>
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devin-ai-integration[bot] and mscolnick committed Jan 17, 2025
1 parent bde19c6 commit f22f7ba
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Showing 2 changed files with 52 additions and 60 deletions.
90 changes: 46 additions & 44 deletions marimo/_plugins/ui/_impl/dataframes/transforms/handlers.py
Original file line number Diff line number Diff line change
Expand Up @@ -38,10 +38,12 @@ class PandasTransformHandler(TransformHandler["pd.DataFrame"]):
def handle_column_conversion(
df: "pd.DataFrame", transform: ColumnConversionTransform
) -> "pd.DataFrame":
import numpy as np
# Use numpy dtype for type conversion
df[transform.column_id] = df[transform.column_id].astype(
transform.data_type,
errors=transform.errors,
) # type: ignore[call-overload]
np.dtype(transform.data_type),
errors=transform.errors
)
return df

@staticmethod
Expand Down Expand Up @@ -73,56 +75,59 @@ def handle_filter_rows(

clauses: List[pd.Series[Any]] = []
for condition in transform.where:
try:
value = _coerce_value(
df[condition.column_id].dtype, condition.value
)
except Exception:
value = condition.value or ""
# Get column and value without type coercion
column = df[condition.column_id]
value = condition.value if condition.value is not None else ""

# Ensure string values for string operations
if condition.operator in ["contains", "regex", "starts_with", "ends_with"]:
value = str(value)
# Ensure list values for isin operation
elif condition.operator == "in":
value = list(value) if value else []

# Handle numeric comparisons consistently without type coercion
if condition.operator == "==":
df_filter = df[condition.column_id] == value
df_filter = column == value
elif condition.operator == "!=":
df_filter = df[condition.column_id] != value
df_filter = column != value
elif condition.operator == ">":
df_filter = df[condition.column_id] > value
df_filter = column > value
elif condition.operator == "<":
df_filter = df[condition.column_id] < value
df_filter = column < value
elif condition.operator == ">=":
df_filter = df[condition.column_id] >= value
df_filter = column >= value
elif condition.operator == "<=":
df_filter = df[condition.column_id] <= value
df_filter = column <= value
# Handle boolean operations
elif condition.operator == "is_true":
df_filter = df[condition.column_id].eq(True)
df_filter = column.eq(True)
elif condition.operator == "is_false":
df_filter = df[condition.column_id].eq(False)
df_filter = column.eq(False)
# Handle null checks
elif condition.operator == "is_nan":
df_filter = df[condition.column_id].isna()
df_filter = column.isna()
elif condition.operator == "is_not_nan":
df_filter = df[condition.column_id].notna()
df_filter = column.notna()
# Handle equality operations consistently with numeric comparisons
elif condition.operator == "equals":
df_filter = df[condition.column_id].eq(value)
df_filter = column == value
elif condition.operator == "does_not_equal":
df_filter = df[condition.column_id].ne(value)
df_filter = column != value
# Handle string operations
elif condition.operator == "contains":
df_filter = df[condition.column_id].str.contains(
value, regex=False, na=False
)
df_filter = column.str.contains(value, regex=False, na=False)
elif condition.operator == "regex":
df_filter = df[condition.column_id].str.contains(
value, regex=True, na=False
)
df_filter = column.str.contains(value, regex=True, na=False)
elif condition.operator == "starts_with":
df_filter = df[condition.column_id].str.startswith(
value, na=False
)
df_filter = column.str.startswith(value, na=False)
elif condition.operator == "ends_with":
df_filter = df[condition.column_id].str.endswith(
value, na=False
)
df_filter = column.str.endswith(value, na=False)
# Handle list operations
elif condition.operator == "in":
df_filter = df[condition.column_id].isin(value)
df_filter = column.isin(value)
else:
assert_never(condition.operator)
raise ValueError(f"Unknown operator: {condition.operator}")

clauses.append(df_filter)

Expand All @@ -131,7 +136,7 @@ def handle_filter_rows(
elif transform.operation == "remove_rows":
df = df[~pd.concat(clauses, axis=1).all(axis=1)]
else:
assert_never(transform.operation)
raise ValueError(f"Unknown operation: {transform.operation}")

return df

Expand All @@ -153,7 +158,7 @@ def handle_group_by(
elif transform.aggregation == "max":
return group.max()
else:
assert_never(transform.aggregation)
raise ValueError(f"Unknown aggregation: {transform.aggregation}")

@staticmethod
def handle_aggregate(
Expand Down Expand Up @@ -362,7 +367,7 @@ def handle_group_by(
elif agg_func == "max":
aggs.append(col(column_id).max().alias(f"{column_id}_max"))
else:
assert_never(agg_func)
raise ValueError(f"Unknown aggregation function: {agg_func}")

return df.group_by(transform.column_ids, maintain_order=True).agg(aggs)

Expand All @@ -388,7 +393,7 @@ def handle_aggregate(
elif agg_func == "max":
agg_df = selected_df.max()
else:
assert_never(agg_func)
raise ValueError(f"Unknown aggregation function: {agg_func}")

# Rename all
agg_df = agg_df.rename(
Expand Down Expand Up @@ -539,7 +544,7 @@ def handle_filter_rows(
elif condition.operator == "in":
filter_conditions.append(column.isin(value))
else:
assert_never(condition.operator)
raise ValueError(f"Unknown operator: {condition.operator}")

combined_condition = ibis.and_(*filter_conditions)

Expand Down Expand Up @@ -651,7 +656,4 @@ def as_sql_code(transformed_df: "ibis.Table") -> str | None:
return None


def _coerce_value(dtype: Any, value: Any) -> Any:
import numpy as np

return np.array([value]).astype(dtype)[0]
# Removed _coerce_value function as we now use direct comparisons without type coercion
22 changes: 6 additions & 16 deletions marimo/_plugins/ui/_impl/dataframes/transforms/print_code.py
Original file line number Diff line number Diff line change
Expand Up @@ -38,18 +38,14 @@ def generate_where_clause(df_name: str, where: Condition) -> str:
where.value,
)

if operator == "==":
return (
f"{df_name}[{_as_literal(column_id)}] == {_as_literal(value)}"
)
# Handle numeric comparisons consistently without type coercion
if operator in ["==", "!=", ">", "<", ">=", "<="]:
# Use direct comparison operators for all numeric comparisons
return f"{df_name}[{_as_literal(column_id)}] {operator} {_as_literal(value)}"
elif operator == "equals":
return (
f"{df_name}[{_as_literal(column_id)}].eq({_as_literal(value)})"
)
return f"{df_name}[{_as_literal(column_id)}] == {_as_literal(value)}"
elif operator == "does_not_equal":
return (
f"{df_name}[{_as_literal(column_id)}].ne({_as_literal(value)})"
)
return f"{df_name}[{_as_literal(column_id)}] != {_as_literal(value)}"
elif operator == "contains":
return f"{df_name}[{_as_literal(column_id)}].str.contains({_as_literal(value)})" # noqa: E501
elif operator == "regex":
Expand All @@ -60,12 +56,6 @@ def generate_where_clause(df_name: str, where: Condition) -> str:
return f"{df_name}[{_as_literal(column_id)}].str.endswith({_as_literal(value)})" # noqa: E501
elif operator == "in":
return f"{df_name}[{_as_literal(column_id)}].isin({_list_of_strings(value)})" # noqa: E501
elif operator == "!=":
return (
f"{df_name}[{_as_literal(column_id)}].ne({_as_literal(value)})"
)
elif operator in [">", ">=", "<", "<="]:
return f"{df_name}[{_as_literal(column_id)}] {operator} {_as_literal(value)}" # noqa: E501
elif operator == "is_nan":
return f"{df_name}[{_as_literal(column_id)}].isna()"
elif operator == "is_not_nan":
Expand Down

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