https://github.com/biocpy/compressed-lists
Memory-efficient container for list-like objects
https://github.com/biocpy/compressed-lists
Last synced: 2 months ago
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Memory-efficient container for list-like objects
- Host: GitHub
- URL: https://github.com/biocpy/compressed-lists
- Owner: BiocPy
- License: mit
- Created: 2025-04-13T05:34:44.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2026-02-16T16:29:45.000Z (2 months ago)
- Last Synced: 2026-02-16T23:56:56.771Z (2 months ago)
- Language: Python
- Homepage: https://biocpy.github.io/compressed-lists/
- Size: 282 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md
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README
[](https://pypi.org/project/compressed-lists/)
# CompressedList Implementation in Python
A Python implementation of the `CompressedList` class from R/Bioconductor for memory-efficient list-like objects.
`CompressedList` is a memory-efficient container for list-like objects. Instead of storing each list element separately, it concatenates all elements into a single vector-like object and maintains information about where each original element begins and ends. This approach is significantly more memory-efficient than standard lists, especially when dealing with many list elements.
## Install
To get started, install the package from [PyPI](https://pypi.org/project/compressed-lists/)
```bash
pip install compressed-lists
```
## Usage
```py
from compressed_lists import CompressedIntegerList, CompressedStringList, Partitioning
# Create a CompressedIntegerList
int_data = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]
names = ["A", "B", "C"]
int_list = CompressedIntegerList.from_list(int_data, names)
# Access elements
print(int_list[0]) # [1, 2, 3]
print(int_list["B"]) # [4, 5]
print(int_list[1:3]) # Slice of elements
# Apply a function to each element
squared = int_list.lapply(lambda x: [i**2 for i in x])
print(squared[0]) # [1, 4, 9]
# Convert to a regular Python list
regular_list = int_list.to_list()
# Create a CompressedStringList from lengths
import biocutils as ut
char_data = ut.StringList(["apple", "banana", "cherry", "date", "elderberry", "fig"])
char_list = CompressedStringList(char_data, partitioning=Partitioning.from_lengths([2,3,1]))
print(char_list)
```
### Partitioning
The `Partitioning` class handles the information about where each element begins and ends in the concatenated data. It allows for efficient extraction of elements without storing each element separately.
```python
from compressed_lists import Partitioning
# Create partitioning from end positions
ends = [3, 5, 10]
names = ["A", "B", "C"]
part = Partitioning(ends, names)
# Get partition range for an element
start, end = part[1] # Returns (3, 5)
```
> [!NOTE]
>
> Check out the [documentation](https://biocpy.github.io/compressed-lists) for available compressed list implementations and extending `CompressedLists` to custom data types.
## Note
This project has been set up using [BiocSetup](https://github.com/biocpy/biocsetup)
and [PyScaffold](https://pyscaffold.org/).