sliq 0.1.0

Sliq - Data cleaning, made effortless. An AI-powered data cleaning library to clean your datasets with just a few lines of code.

Sliq Python Library

Data cleaning, made effortless.

Sliq is a Python library that provides a simple interface to clean your datasets using AI. With just a few lines of code, you can clean messy data, handle missing values, fix inconsistencies, and prepare your data for analysis or machine learning.

Installation

pip install sliq

To install Sliq locally for development

pip install -e "Sliq-python-lib"

Quick Start

Clean a file

import sliq

# Clean a CSV file and save the result
sliq.clean_from_file(
    api_key="your-api-key",
    dataset_path="path/to/your/data.csv",
    dataset_name="My Dataset",
    save_clean_file_path="path/to/output/",
)

Clean a DataFrame

import sliq
import pandas as pd

# Load your data
df = pd.read_csv("path/to/your/data.csv")

# Clean and get the result as a DataFrame
cleaned_df = sliq.clean_from_dataframe(
    api_key="your-api-key",
    dataframe=df,
    dataset_name="My Dataset",
    is_return_dataframe=True,
)

Features

• Simple API: Just two functions to clean any dataset
• Multiple formats: Supports CSV, JSON, JSONL, Excel, and Parquet files
• DataFrame support: Works with both Pandas and Polars DataFrames
• AI-powered: Uses advanced AI to understand and clean your data
• Customizable: Provide context about your data for better cleaning
• Secure: Your data is processed securely and deleted after cleaning

Supported File Formats

Format	Extension
CSV	.csv
JSON	.json
JSONL	.jsonl
Excel	.xlsx, .xls
Parquet	.parquet

API Reference

clean_from_file()

Clean a dataset from a file path.

sliq.clean_from_file(
    api_key: str,                          # Required: Your Sliq API key
    dataset_path: str,                     # Required: Path to your dataset file
    dataset_name: str,                     # Required: Name for your dataset
    save_clean_file_path: str = "",        # Path to save the cleaned file
    save_clean_file_name: str = "",        # Custom name for the saved file
    dataset_description: str = "",         # Description of your data
    dataset_purpose: str = "",             # What the data will be used for
    column_guide: dict[str, str] = None,   # Column name to description mapping (see below)
    data_source: str = "",                 # Where the data came from
    user_instructions: str = "",           # Special cleaning instructions
    is_feature_engineering: bool = False,  # Enable feature engineering
    is_detailed_report: bool = False,      # Generate detailed report
    verbose: bool = False,                 # Show progress messages
)

clean_from_dataframe()

Clean a Pandas or Polars DataFrame.

cleaned_df = sliq.clean_from_dataframe(
    api_key: str,                          # Required: Your Sliq API key
    dataframe: pd.DataFrame | pl.DataFrame,# Required: Your DataFrame
    dataset_name: str,                     # Required: Name for your dataset
    is_return_dataframe: bool = True,      # Return cleaned DataFrame
    dataset_description: str = "",         # Description of your data
    dataset_purpose: str = "",             # What the data will be used for
    column_guide: dict[str, str] = None,   # Column name to description mapping (see below)
    data_source: str = "",                 # Where the data came from
    user_instructions: str = "",           # Special cleaning instructions
    is_feature_engineering: bool = False,  # Enable feature engineering
    is_detailed_report: bool = False,      # Generate detailed report
    verbose: bool = False,                 # Show progress messages
)

Column Guide Validation

When providing a column_guide, the following rules apply:

1. All columns required: You must provide descriptions for all columns in your dataset
2. Column count must match: The number of keys in column_guide must equal the number of columns in your dataset
3. Names must match: Column names are validated against the dataset (case-insensitive, underscore-insensitive)

Example:

# For a dataset with columns: "first_name", "last_name", "Age"
column_guide = {
    "first_name": "Customer's first name",
    "last_name": "Customer's last name", 
    "age": "Customer's age in years",  # Case-insensitive: "age" matches "Age"
}

If column names don't match, you'll get a helpful error message listing the mismatched columns and the actual dataset columns.

## Getting an API Key

To use Sliq, you need an API key. Visit [sliqdata.com](https://sliqdata.com) to get your API key.

## Documentation

For full documentation, visit [sliqdata.com/docs](https://sliqdata.com/docs).

## License

MIT License - see [LICENSE](LICENSE) for details.