| Name | schemarrow JSON |
| Version |
0.1.0a0
JSON |
| download |
| home_page | |
| Summary | right out of the box mljar pipeline. |
| upload_time | 2024-03-06 15:35:33 |
| maintainer | |
| docs_url | None |
| author | DanielAvdar |
| requires_python | >=3.9,<3.13 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# SchemArrow
[](https://img.shields.io/pypi/v/SchemArrow)
[](https://pypi.org/project/SchemArrow/)
[](https://github.com/DanielAvdar/SchemArrow/actions/workflows/ci.yml)
[](https://github.com/DanielAvdar/SchemArrow/actions/workflows/code-checks.yml)
[](https://opensource.org/license/mit/)
[](https://pypi.org/project/SchemArrow/)
`SchemArrow` simplifies the conversion between pandas and Arrow DataFrames, allowing you to seamlessly switch back and forth.
**Get started:**
## Get started:
### Installation
To install the package use pip:
```bash
pip install schemarrow
```
### Usage
```python
import pandas as pd
from schemarrow import SchemArrow
# Create a pandas DataFrame
df = pd.DataFrame({
'A': [1, 2, 3],
'B': ['a', 'b', 'c'],
'C': [1.1, 2.2, 3.3],
'D': [True, False, True]
})
# Instantiate a SchemArrow object
arrow_schema = SchemArrow()
# Convert the pandas DataFrame dtypes to arrow dtypes
df_pa: pd.DataFrame = arrow_schema(df)
print(df_pa.dtypes)
```
outputs:
```
A int64[pyarrow]
B string[pyarrow]
C double[pyarrow]
D bool[pyarrow]
dtype: object
```
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