https://github.com/mayasmess/panorma
⚡️A lightweight Python package (just over 50 lines of code) that enables you to create typed models for Pandas DataFrames using classes.
https://github.com/mayasmess/panorma
Last synced: 8 months ago
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⚡️A lightweight Python package (just over 50 lines of code) that enables you to create typed models for Pandas DataFrames using classes.
- Host: GitHub
- URL: https://github.com/mayasmess/panorma
- Owner: MayasMess
- Created: 2023-06-03T21:51:48.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2024-05-08T10:23:39.000Z (about 2 years ago)
- Last Synced: 2025-06-26T01:05:40.725Z (12 months ago)
- Language: Python
- Homepage:
- Size: 102 KB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Panorma
A lightweight Python package (just over 50 lines of code) that enables you to create typed
models for Pandas DataFrames using classes.
You can easily define structured models, enforce column typing,
enjoy autocompletion, and catch invalid column errors early in your DataFrame operations.
Simplify your data modeling and enhance the reliability of your DataFrame workflows.
Installation:
-
```shell script
pip install panorma
```
or
```shell script
poetry add panorma
```
Example:
-
- Create some models:
```python
from panorma.fields import String, Int, Float, DateTime, Categorical
from panorma.frames import DataFrame
class Users(DataFrame):
name: String
age: Int
percentage: Float
birth_date: DateTime
class Cars(DataFrame):
car: String
mpg: Float
cylinders: Int
displacement: Float
horsepower: Float
weight: Float
acceleration: Float
model: Int
origin: Categorical
```
- Instantiate your models as you instantiate a simple pandas dataframe
```python
import pandas as pd
users = Users({
"name": ['john', 'kevin'],
"age": [99, 15],
"percentage": [0.8, 7.3],
"birth_date": [pd.Timestamp('20180310'), pd.Timestamp('20230910')],
})
cars = Cars(pd.read_csv('CAR_DATASET.csv'))
```
- You will get autocompletion for models columns and pandas at the same time
- If the columns of your data are not matching your model, you will get a NotMatchingFields exception:
- If a column cannot be cast to the type declared in the model, you will get a ParseError exception:
