# Vrame
Vrame is a Python library designed to efficiently parse and validate all columns in pandas DataFrame. It leverages the vectorized operations of pandas to significantly speed up the validation process, making it a powerful tool for data validation in data science and machine learning projects.
## Features
- **Vectorized Validation**: Utilizes pandas' vectorized operations for fast and efficient data validation.
- **Similar Syntax to Pydantic**: Offers a familiar API for those who have used Pydantic, making it easy to adopt.
- **Custom Validators**: Allows for the definition of custom validation rules to meet specific data requirements.
## Installation
To install Vrame, use pip:
pip install vrame
## Usage
First, define a schema for your DataFrame using Vrame's syntax, which is similar to Pydantic's:
import pandas as pd
import numpy as np
from vrame.basemodel import BaseModel
from vrame.column_types import (
Integer,
Float,
Boolean,
Datetime,
String,
List,
Tuple,
Dictionary,
Set,
Object
)
class Model(BaseModel):
integer = Integer(lower=-1, upper=6, nullable=True)
float = Float(lower=-1.0, upper=6.0, nullable=True)
bool = Boolean(nullable=True)
datetime = Datetime(lower="2024-03-20", upper="2024-03-21", nullable=True)
list = List(nullable=True, min_items=1, max_items=3)
tuple = Tuple(nullable=True, min_items=1, max_items=3)
dictionary = Dictionary(nullable=True, min_items=1, max_items=3)
set = Set(nullable=True, min_items=1, max_items=3)
string = String(min_length=0, max_length=5, nullable=True)
object = Object(nullable=True)
if __name__ == "__main__":
df = pd.DataFrame(
{
'integer': [1, "2", 3, 4, np.nan],
'float': [1.0, "2.0", 3, "4", "5"],
'bool': [True, "False", "True", False, False],
'datetime': [
"2024-03-20",
"2024-03-21",
"2024-03-21",
"2024-03-21",
"2024-03-21"
],
'list': [[1, 2], "[3, 4]", [5, 6], [7, 8], [9, 10]],
'tuple': [(1, 2), "(3, 4)", (5, 6), (7, 8), (9, 10)],
'dictionary': [
{'a': 1, 'b': 2.1},
"{'e': 3, 'f': 4.0}",
{'a': 1, 'b': 2.1},
{'a': 1, 'b': 2.1},
{'a': 1, 'b': 2.1}
],
'set': [{1, 2}, "{1, 2}", {1, 2}, {1, 2}, {1, 2}],
'string': ["str1", "str2", "", "12345", "I"],
'object': [1, 2.0, False, np.nan, None]
}
)
m = Model(df)
df = m.parse_and_validate()
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"description": "# Vrame\r\n\r\nVrame is a Python library designed to efficiently parse and validate all columns in pandas DataFrame. It leverages the vectorized operations of pandas to significantly speed up the validation process, making it a powerful tool for data validation in data science and machine learning projects.\r\n\r\n## Features\r\n\r\n- **Vectorized Validation**: Utilizes pandas' vectorized operations for fast and efficient data validation.\r\n- **Similar Syntax to Pydantic**: Offers a familiar API for those who have used Pydantic, making it easy to adopt.\r\n- **Custom Validators**: Allows for the definition of custom validation rules to meet specific data requirements.\r\n\r\n## Installation\r\n\r\nTo install Vrame, use pip:\r\n\r\n pip install vrame\r\n\r\n\r\n## Usage\r\n\r\nFirst, define a schema for your DataFrame using Vrame's syntax, which is similar to Pydantic's:\r\n \r\n import pandas as pd\r\n import numpy as np\r\n from vrame.basemodel import BaseModel\r\n from vrame.column_types import (\r\n Integer,\r\n Float,\r\n Boolean,\r\n Datetime,\r\n String,\r\n List,\r\n Tuple,\r\n Dictionary,\r\n Set,\r\n Object\r\n )\r\n \r\n \r\n class Model(BaseModel):\r\n integer = Integer(lower=-1, upper=6, nullable=True)\r\n float = Float(lower=-1.0, upper=6.0, nullable=True)\r\n bool = Boolean(nullable=True)\r\n datetime = Datetime(lower=\"2024-03-20\", upper=\"2024-03-21\", nullable=True)\r\n list = List(nullable=True, min_items=1, max_items=3)\r\n tuple = Tuple(nullable=True, min_items=1, max_items=3)\r\n dictionary = Dictionary(nullable=True, min_items=1, max_items=3)\r\n set = Set(nullable=True, min_items=1, max_items=3)\r\n string = String(min_length=0, max_length=5, nullable=True)\r\n object = Object(nullable=True)\r\n \r\n \r\n if __name__ == \"__main__\":\r\n df = pd.DataFrame(\r\n {\r\n 'integer': [1, \"2\", 3, 4, np.nan],\r\n 'float': [1.0, \"2.0\", 3, \"4\", \"5\"],\r\n 'bool': [True, \"False\", \"True\", False, False],\r\n 'datetime': [\r\n \"2024-03-20\",\r\n \"2024-03-21\",\r\n \"2024-03-21\",\r\n \"2024-03-21\",\r\n \"2024-03-21\"\r\n ],\r\n 'list': [[1, 2], \"[3, 4]\", [5, 6], [7, 8], [9, 10]],\r\n 'tuple': [(1, 2), \"(3, 4)\", (5, 6), (7, 8), (9, 10)],\r\n 'dictionary': [\r\n {'a': 1, 'b': 2.1},\r\n \"{'e': 3, 'f': 4.0}\",\r\n {'a': 1, 'b': 2.1},\r\n {'a': 1, 'b': 2.1},\r\n {'a': 1, 'b': 2.1}\r\n ],\r\n 'set': [{1, 2}, \"{1, 2}\", {1, 2}, {1, 2}, {1, 2}],\r\n 'string': [\"str1\", \"str2\", \"\", \"12345\", \"I\"],\r\n 'object': [1, 2.0, False, np.nan, None]\r\n }\r\n )\r\n\r\n m = Model(df)\r\n df = m.parse_and_validate()\r\n",
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