| Name | lontras JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | A lightweight, dictionary-based DataFrame implementation in pure Python. Designed for simplicity and ease of use. Perfect for small projects or when a full-fledged DataFrame library is overkill. |
| upload_time | 2025-01-25 17:18:17 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
dataframe
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Lontras
<img src="https://raw.githubusercontent.com/luxedo/lontras/refs/heads/main/docs/_static/lontra.png" height=400 alt="Lontras Logo"/>
[](https://pypi.org/project/lontras)
[](https://pypi.org/project/lontras)
[](https://opensource.org/license/mit)
[](https://results.pre-commit.ci/latest/github/luxedo/lontras/main)
---
> ### ⚠️ This library is under development and has not been released yet
> We love `pandas` and its siblings! They're the industry-standard tools for powerful data
> manipulation. However, for smaller projects or when minimizing dependencies is paramount,
> `lontras` offers a lightweight, pure-Python alternative built on simple dictionaries. Designed
> for ease of use and direct integration, lontras encourages you to copy and paste its core
> components into your code. It's the perfect stand mixer for baking smaller data tasks when
> bringing out the full industrial equipment of `pandas` is too much.
## TLDR;
- **🤏 Small-scale `DataFrame` operations? `lontras`!**
- **🚀 Fast `DataFrame` loading, no dependencies? `lontras`!**
- **⚙️ Embed `DataFrames` in [MicroPython](https://micropython.org/)? `lontras`!**
- **🌐 Use `DataFrames` in the browser ([PyScript](https://pyscript.net/))? `lontras`!**
- **🤝 High [Pandas](https://pandas.pydata.org/) compatibility? `lontras`!**
- **📦 1000x smaller! Pandas+Numpy ~ 120MB; `lontras` ~ 102KB!**
- **⏱️ Loads 20x faster! Pandas ~ 400ms; `lontras` ~ 20ms!** <small>[test script](tools/repo-info.sh)</small>
## Table of Contents
- [Installation](#installation)
- [Documentation](#documentation)
- [Core Functionalities](#core-functionalities)
- [License](#license)
## Installation
If you prefer to install lontras using pip for easy management and updates, you can do so with the
following command:
```console
pip install lontras
# or
uv pip install lontras
```
This will download and install lontras from the Python Package Index (PyPI).
For lightweight projects where you want to avoid external dependencies, you can simply copy the
source file [src/lontras/lontras.py](src/lontras/lontras.py) into your project directory. This
allows you to directly use the library functions from your code without any installation.
## Documentation
Check out the [API Docs](https://lontras.readthedocs.io/en/latest/).
## Core Functionalities:
Lontras prioritizes simplicity and minimal dependencies. It leverages Python's native dictionaries
(via [UserDict](https://docs.python.org/3/library/collections.html#collections.UserDict)) to offer
core `DataFrame` and `Series` functionalities without external libraries.
### Data Structures:
- `Series`: A one-dimensional array-like structure.
- `DataFrame`: A two-dimensional labeled data structure.
### Accessing Data:
- `loc` and `iloc`: Access data by label or by index.
- Label-based access: Access data using standard dictionary-like syntax (e.g., series['label']).
- Positional access (slicing): Use slices for location-based access (e.g., series[1:3]).
- Attribute-based access: For convenient access to all values for a given key, use attribute-based access using **getattr** that dynamically retrieves data based on provided keys or a list of keys.
### Modifying Data:
- Setting values: Modify existing values or add new entries using `loc` or `iloc` assignment (e.g., series.loc['label'] = value).
- Deleting values: Remove entries using del series['label'].
- Concatenation: Combine Series or DataFrames vertically or horizontally.
### Transforming Data:
- Mapping and applying functions: Apply functions element-wise using map or along axes/indices using apply.
- Sorting: Sort indexes and values using provided sorting functions.
- Basic operations: Use standard Python operators (+, -, \*, /, //, %, \*\*, comparisons) for element-wise operations.
### Data Aggregation and Combination:
- groupby: Group data based on a column and perform operations within each group (similar to pandas groupby).
- Join/Merge: Merge two DataFrames based on specific columns (similar to pandas join/merge operations).
- reduce: Apply a function cumulatively to the elements. Basic reduction functions like max, min, argmax, sum, etc.
- Leverages Python's built-in statistics module for basic statistical calculations.
### Limitations & Trade-offs: (Same as before)
- No `dtype`! You ask for a sum, `lontras` will try to sum and may raise an exception if an unexpcted value is found.
- Specialized Data Handling: Lontras focuses on core functionalities and doesn't include specialized functions for datetime, strings, or categorical data. However, users can achieve similar behavior through apply and map functions.
- Statistical Functions: Limited set of statistical functions. Lontras relies primarily on Python's built-in statistics module.
- Data Import/Export: Supports limited import/export formats. External libraries might be necessary for complex file handling.
- Missing Data Handling: Doesn't include dedicated functions for handling missing data. Users can implement their own logic using conditional statements or filtering.
- Multilevel Indexing: Lacks built-in support for multilevel indexing. However, tuple indexes can be used to achieve similar hierarchical structures.
- Plotting: Currently doesn't include plotting functionalities. External plotting libraries are recommended for visualization.
## License
`lontras` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
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"description": "# Lontras\n\n<img src=\"https://raw.githubusercontent.com/luxedo/lontras/refs/heads/main/docs/_static/lontra.png\" height=400 alt=\"Lontras Logo\"/>\n\n[](https://pypi.org/project/lontras)\n[](https://pypi.org/project/lontras)\n[](https://opensource.org/license/mit)\n\n[](https://results.pre-commit.ci/latest/github/luxedo/lontras/main)\n\n\n\n---\n\n> ### \u26a0\ufe0f This library is under development and has not been released yet\n\n> We love `pandas` and its siblings! They're the industry-standard tools for powerful data\n> manipulation. However, for smaller projects or when minimizing dependencies is paramount,\n> `lontras` offers a lightweight, pure-Python alternative built on simple dictionaries. Designed\n> for ease of use and direct integration, lontras encourages you to copy and paste its core\n> components into your code. It's the perfect stand mixer for baking smaller data tasks when\n> bringing out the full industrial equipment of `pandas` is too much.\n\n## TLDR;\n\n- **\ud83e\udd0f Small-scale `DataFrame` operations? `lontras`!**\n- **\ud83d\ude80 Fast `DataFrame` loading, no dependencies? `lontras`!**\n- **\u2699\ufe0f Embed `DataFrames` in [MicroPython](https://micropython.org/)? `lontras`!**\n- **\ud83c\udf10 Use `DataFrames` in the browser ([PyScript](https://pyscript.net/))? `lontras`!**\n- **\ud83e\udd1d High [Pandas](https://pandas.pydata.org/) compatibility? `lontras`!**\n- **\ud83d\udce6 1000x smaller! Pandas+Numpy ~ 120MB; `lontras` ~ 102KB!**\n- **\u23f1\ufe0f Loads 20x faster! Pandas ~ 400ms; `lontras` ~ 20ms!** <small>[test script](tools/repo-info.sh)</small>\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Documentation](#documentation)\n- [Core Functionalities](#core-functionalities)\n- [License](#license)\n\n## Installation\n\nIf you prefer to install lontras using pip for easy management and updates, you can do so with the\nfollowing command:\n\n```console\npip install lontras\n# or\nuv pip install lontras\n```\n\nThis will download and install lontras from the Python Package Index (PyPI).\n\nFor lightweight projects where you want to avoid external dependencies, you can simply copy the\nsource file [src/lontras/lontras.py](src/lontras/lontras.py) into your project directory. This\nallows you to directly use the library functions from your code without any installation.\n\n## Documentation\n\nCheck out the [API Docs](https://lontras.readthedocs.io/en/latest/).\n\n## Core Functionalities:\n\nLontras prioritizes simplicity and minimal dependencies. It leverages Python's native dictionaries\n(via [UserDict](https://docs.python.org/3/library/collections.html#collections.UserDict)) to offer\ncore `DataFrame` and `Series` functionalities without external libraries.\n\n### Data Structures:\n\n- `Series`: A one-dimensional array-like structure.\n- `DataFrame`: A two-dimensional labeled data structure.\n\n### Accessing Data:\n\n- `loc` and `iloc`: Access data by label or by index.\n- Label-based access: Access data using standard dictionary-like syntax (e.g., series['label']).\n- Positional access (slicing): Use slices for location-based access (e.g., series[1:3]).\n- Attribute-based access: For convenient access to all values for a given key, use attribute-based access using **getattr** that dynamically retrieves data based on provided keys or a list of keys.\n\n### Modifying Data:\n\n- Setting values: Modify existing values or add new entries using `loc` or `iloc` assignment (e.g., series.loc['label'] = value).\n- Deleting values: Remove entries using del series['label'].\n- Concatenation: Combine Series or DataFrames vertically or horizontally.\n\n### Transforming Data:\n\n- Mapping and applying functions: Apply functions element-wise using map or along axes/indices using apply.\n- Sorting: Sort indexes and values using provided sorting functions.\n- Basic operations: Use standard Python operators (+, -, \\*, /, //, %, \\*\\*, comparisons) for element-wise operations.\n\n### Data Aggregation and Combination:\n\n- groupby: Group data based on a column and perform operations within each group (similar to pandas groupby).\n- Join/Merge: Merge two DataFrames based on specific columns (similar to pandas join/merge operations).\n- reduce: Apply a function cumulatively to the elements. Basic reduction functions like max, min, argmax, sum, etc.\n- Leverages Python's built-in statistics module for basic statistical calculations.\n\n### Limitations & Trade-offs: (Same as before)\n\n- No `dtype`! You ask for a sum, `lontras` will try to sum and may raise an exception if an unexpcted value is found.\n- Specialized Data Handling: Lontras focuses on core functionalities and doesn't include specialized functions for datetime, strings, or categorical data. However, users can achieve similar behavior through apply and map functions.\n- Statistical Functions: Limited set of statistical functions. Lontras relies primarily on Python's built-in statistics module.\n- Data Import/Export: Supports limited import/export formats. External libraries might be necessary for complex file handling.\n- Missing Data Handling: Doesn't include dedicated functions for handling missing data. 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