A Swift library for creating expandable and collapsible table view cells in iOS with easy customization and smooth animations.
ExpandableCell is a Swift library for iOS that enables developers to easily create expandable and collapsible table view cells. It solves the problem of manually managing `insertRows` and `deleteRows` by providing a streamlined delegate-based API. The library supports customizable animations, multiple expansion styles, and built-in UI effects like arrow icons.
iOS developers building apps with interactive table views, such as those creating settings menus, accordion lists, or detailed data displays.
Developers choose ExpandableCell for its simplicity, reliability, and extensive customization options compared to handling expandable cells manually or using less maintained alternatives.
✨ Awesome expandable, collapsible tableview cell for iOS written in Swift 5
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
By inheriting ExpandableDelegate, developers avoid the complexities of manually calling insertRows and deleteRows, as emphasized in the README for bug-free implementation.
Supports various UITableViewRowAnimation styles like .automatic for smooth opening and closing effects, enhancing user experience with minimal code.
Offers single, single per section, and multi expansion modes, allowing precise control over how cells expand based on app requirements.
Includes arrow icons and highlight animations with configurable margins via ExpandableCell class, reducing the need for additional UI boilerplate code.
Only compatible with UITableView in UIKit, making it unsuitable for SwiftUI projects or modern cross-platform frameworks without workarounds.
Requires classes to inherit from ExpandableDelegate and use ExpandableTableView, which can lead to tight coupling and conflict with existing codebases using protocols or other patterns.
Developers must implement methods like expandedCellsForRowAt to provide cell data, which can be error-prone and cumbersome for dynamic or complex datasets compared to data-binding solutions.