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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:
pip install panorma
Example:
- Create some models:
from panorma.fields import StringDtype, Int16Dtype, Float32Dtype, Timestamp, CategoricalDtype
from panorma.frames import DataFrame
class Users(DataFrame):
name: StringDtype
age: Int16Dtype
percentage: Float32Dtype
birth_date: Timestamp
class Cars(DataFrame):
car: StringDtype
mpg: Float32Dtype
cylinders: Int16Dtype
displacement: Float32Dtype
horsepower: Float32Dtype
weight: Float32Dtype
acceleration: Float32Dtype
model: Int16Dtype
origin: CategoricalDtype
- Instantiate your models as you instantiate a simple pandas dataframe
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:
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