“How to implement table folding in Python according to a pattern”

As a professional programmer, I will approach the problem of implementing table folding in Python according to a pattern by breaking it down into logical steps. The goal is to create a function that can dynamically fold and unfold rows in a table based on a specified pattern.

1. Understand the Problem

Table folding refers to the process of collapsing or expanding rows in a table to hide or show detailed information. This is commonly used in user interfaces to manage complex data structures where not all details need to be visible by default.

The key requirements are:
– The table should support multiple levels of nesting.
– Rows can be folded (collapsed) or unfolded (expanded).
– The folding pattern must be specified programmatically.

2. Define the Data Structure

To implement table folding, I will represent the table data as a tree-like structure where each node can have children. Each entry in the table will be represented as a dictionary containing:
– id: Unique identifier for the row.
– parent_id: Identifier of the parent row (if any).
– depth: The level of nesting (useful for indentation).
– is_folded: Boolean indicating whether the row is folded or not.

3. Implement Folding Logic

The core functionality involves:
– Identifying rows that need to be folded based on the specified pattern.
– Recursively processing nested rows.
– Maintaining state to track which rows are folded or unfolded.

Here’s a conceptual implementation:

def fold_table(table_data, pattern):
    # Create a dictionary mapping each row id to its children
    children_map = {}
    for row in table_data:
        if row[‘parent_id’] not in children_map:
            children_map[row[‘parent_id’]] = []
        children_map[row[‘parent_id’]].append(row)

    def fold_children(parent_row):
        # Check if the parent should be folded according to the pattern
        if matches_pattern(parent_row, pattern):
            parent_row[‘is_folded’] = True
        else:
            parent_row[‘is_folded’] = False

        for child in children_map.get(parent_row[‘id’], []):
            fold_children(child)

    # Start folding from the root node
    fold_children(table_data[0])

def fold_table(table_data, pattern):

# Create a dictionary mapping each row id to its children

children_map = {}

for row in table_data:

if row[‘parent_id’] not in children_map:

children_map[row[‘parent_id’]] = []

children_map[row[‘parent_id’]].append(row)

def fold_children(parent_row):

# Check if the parent should be folded according to the pattern

if matches_pattern(parent_row, pattern):

parent_row[‘is_folded’] = True

else:

parent_row[‘is_folded’] = False

for child in children_map.get(parent_row[‘id’], []):

fold_children(child)

# Start folding from the root node

fold_children(table_data[0])

4. Handle State Management

To maintain the folded state between interactions, I will implement:
– A session-based storage system.
– A mechanism to persist the state (e.g., using cookies or local storage in a web application).

This ensures that the table remains in its last viewed state when the user returns.

5. Implement Undo/Redo Functionality

To allow users to revert changes, I will:
– Track all folding operations.
– Maintain a history stack of states.
– Implement functions to undo and redo operations by manipulating the history stack.

6. Optimize for Performance

When dealing with large tables, performance becomes critical. To optimize:
– Use memoization to cache frequently accessed data.
– Optimize recursive calls to minimize stack depth.
– Consider switching to an iterative approach if recursion depth is too high.

7. Test and Debug

Finally, I will thoroughly test the implementation:
– Test edge cases (e.g., tables with zero or deeply nested rows).
– Validate that folding patterns are applied correctly.
– Ensure that state persistence works as expected.

This structured approach ensures that table folding in Python is implemented efficiently and robustly according to any specified pattern.