| Name | tabeline JSON |
| Version |
0.6.2
JSON |
| download |
| home_page | None |
| Summary | A data frame and data grammar library |
| upload_time | 2025-10-14 03:31:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4.0,>=3.10 |
| license | None |
| keywords |
dataframe
datatable
datagrammar
dplyr
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Tabeline
Tabeline is a data frame and data grammar library. You write the expressions in strings and supply them to methods on the `DataFrame` class. The strings are parsed by Parsita and converted into Polars for execution.
Tabeline draws inspiration from dplyr, the data grammar of R's tidyverse, especially for its methods names. The `filter`, `mutate`, `group_by`, and `summarize` methods should all feel familiar. But Tabeline is as proper a Python library as can be, using methods instead of pipes, like is standard in R.
Tabeline uses Polars under the hood, but adds a lot of handling of edge cases from Polars, which otherwise result in crashes or behavior that is not type stable.
See the [Documentation](https://tabeline.drhagen.com) for the full user guide.
## Installation
It is recommended to install Tabeline from PyPI using `pip`.
```shell
pip install tabeline
```
## Motivating example
```python
from tabeline import DataFrame
# Construct a data frame using clean syntax
# from_csv, from_pandas, and from_polars are also available
df = DataFrame(
id=[0, 0, 0, 0, 1, 1, 1, 1, 1],
t=[0, 6, 12, 24, 0, 6, 12, 24, 48],
y=[0, 2, 3, 1, 0, 4, 3, 2, 1],
)
# Use data grammar methods and string expressions to define
# transformed data frames
analysis = (
df
.filter("t <= 24")
.group_by("id")
.summarize(auc="trapz(t, y)")
)
print(analysis)
# shape: (2, 2)
# ┌─────┬──────┐
# │ id ┆ auc │
# │ --- ┆ --- │
# │ i64 ┆ f64 │
# ╞═════╪══════╡
# │ 0 ┆ 45.0 │
# ├╌╌╌╌╌┼╌╌╌╌╌╌┤
# │ 1 ┆ 63.0 │
# └─────┴──────┘
```
Raw data
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