feat: enhance DataFrame Operations component with contains filter and modern UI (#8838)
* feat: enhance DataFrame Operations component with contains filter and modern UI - Add "contains" filter operator for partial string matching in DataFrame filters - Update UI to use SortableListInput with icons for consistent modern design - Add 7 filter operators: equals, not equals, contains, starts with, ends with, greater than, less than - Fix deselection handling to prevent "unhashable type: list" errors - Improve dynamic field visibility when operations are deselected - Add comprehensive test suite with 25 tests covering all operations and edge cases - Update placeholder text from "Select DataFrame Operation" to "Select Operation" 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * [autofix.ci] apply automated fixes * Update dataframe_operations.py --------- Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Edwin Jose <edwin.jose@datastax.com>
This commit is contained in:
Rodrigo Nader
GitHub
parent
f6a9e8fad4
commit
e0400f29eb
2 changed files with 504 additions and 79 deletions
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@ -1,4 +1,7 @@
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import pandas as pd
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from langflow.custom.custom_component.component import Component
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from langflow.inputs import SortableListInput
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from langflow.io import (
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BoolInput,
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DataFrameInput,
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@ -39,12 +42,25 @@ class DataFrameOperationsComponent(Component):
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info="The input DataFrame to operate on.",
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required=True,
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),
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DropdownInput(
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SortableListInput(
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name="operation",
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display_name="Operation",
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options=OPERATION_CHOICES,
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placeholder="Select Operation",
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info="Select the DataFrame operation to perform.",
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options=[
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{"name": "Add Column", "icon": "plus"},
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{"name": "Drop Column", "icon": "minus"},
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{"name": "Filter", "icon": "filter"},
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{"name": "Head", "icon": "arrow-up"},
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{"name": "Rename Column", "icon": "pencil"},
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{"name": "Replace Value", "icon": "replace"},
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{"name": "Select Columns", "icon": "columns"},
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{"name": "Sort", "icon": "arrow-up-down"},
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{"name": "Tail", "icon": "arrow-down"},
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{"name": "Drop Duplicates", "icon": "copy-x"},
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],
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real_time_refresh=True,
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limit=1,
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),
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StrInput(
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name="column_name",
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@ -60,6 +76,16 @@ class DataFrameOperationsComponent(Component):
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dynamic=True,
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show=False,
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),
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DropdownInput(
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name="filter_operator",
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display_name="Filter Operator",
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options=["equals", "not equals", "contains", "starts with", "ends with", "greater than", "less than"],
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value="equals",
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info="The operator to apply for filtering rows.",
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advanced=False,
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dynamic=True,
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show=False,
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),
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BoolInput(
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name="ascending",
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display_name="Sort Ascending",
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@ -126,6 +152,7 @@ class DataFrameOperationsComponent(Component):
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dynamic_fields = [
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"column_name",
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"filter_value",
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"filter_operator",
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"ascending",
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"new_column_name",
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"new_column_value",
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@ -138,36 +165,57 @@ class DataFrameOperationsComponent(Component):
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build_config[field]["show"] = False
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if field_name == "operation":
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if field_value == "Filter":
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# Handle SortableListInput format
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if isinstance(field_value, list):
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operation_name = field_value[0].get("name", "") if field_value else ""
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else:
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operation_name = field_value or ""
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# If no operation selected, all dynamic fields stay hidden (already set to False above)
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if not operation_name:
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return build_config
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if operation_name == "Filter":
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build_config["column_name"]["show"] = True
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build_config["filter_value"]["show"] = True
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elif field_value == "Sort":
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build_config["filter_operator"]["show"] = True
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elif operation_name == "Sort":
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build_config["column_name"]["show"] = True
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build_config["ascending"]["show"] = True
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elif field_value == "Drop Column":
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elif operation_name == "Drop Column":
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build_config["column_name"]["show"] = True
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elif field_value == "Rename Column":
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elif operation_name == "Rename Column":
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build_config["column_name"]["show"] = True
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build_config["new_column_name"]["show"] = True
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elif field_value == "Add Column":
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elif operation_name == "Add Column":
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build_config["new_column_name"]["show"] = True
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build_config["new_column_value"]["show"] = True
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elif field_value == "Select Columns":
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elif operation_name == "Select Columns":
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build_config["columns_to_select"]["show"] = True
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elif field_value in {"Head", "Tail"}:
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elif operation_name in {"Head", "Tail"}:
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build_config["num_rows"]["show"] = True
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elif field_value == "Replace Value":
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elif operation_name == "Replace Value":
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build_config["column_name"]["show"] = True
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build_config["replace_value"]["show"] = True
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build_config["replacement_value"]["show"] = True
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elif field_value == "Drop Duplicates":
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elif operation_name == "Drop Duplicates":
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build_config["column_name"]["show"] = True
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return build_config
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def perform_operation(self) -> DataFrame:
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df_copy = self.df.copy()
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op = self.operation
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# Handle SortableListInput format for operation
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operation_input = getattr(self, "operation", [])
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if isinstance(operation_input, list) and len(operation_input) > 0:
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op = operation_input[0].get("name", "")
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else:
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op = ""
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# If no operation selected, return original DataFrame
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if not op:
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return df_copy
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if op == "Filter":
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return self.filter_rows_by_value(df_copy)
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@ -194,7 +242,42 @@ class DataFrameOperationsComponent(Component):
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raise ValueError(msg)
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def filter_rows_by_value(self, df: DataFrame) -> DataFrame:
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return DataFrame(df[df[self.column_name] == self.filter_value])
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column = df[self.column_name]
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filter_value = self.filter_value
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# Handle regular DropdownInput format (just a string value)
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operator = getattr(self, "filter_operator", "equals") # Default to equals for backward compatibility
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if operator == "equals":
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mask = column == filter_value
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elif operator == "not equals":
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mask = column != filter_value
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elif operator == "contains":
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mask = column.astype(str).str.contains(str(filter_value), na=False)
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elif operator == "starts with":
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mask = column.astype(str).str.startswith(str(filter_value), na=False)
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elif operator == "ends with":
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mask = column.astype(str).str.endswith(str(filter_value), na=False)
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elif operator == "greater than":
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try:
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# Try to convert filter_value to numeric for comparison
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numeric_value = pd.to_numeric(filter_value)
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mask = column > numeric_value
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except (ValueError, TypeError):
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# If conversion fails, compare as strings
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mask = column.astype(str) > str(filter_value)
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elif operator == "less than":
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try:
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# Try to convert filter_value to numeric for comparison
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numeric_value = pd.to_numeric(filter_value)
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mask = column < numeric_value
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except (ValueError, TypeError):
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# If conversion fails, compare as strings
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mask = column.astype(str) < str(filter_value)
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else:
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mask = column == filter_value # Fallback to equals
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return DataFrame(df[mask])
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def sort_by_column(self, df: DataFrame) -> DataFrame:
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return DataFrame(df.sort_values(by=self.column_name, ascending=self.ascending))
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