sanitipy 1.1.0

Sanitipy is a user-friendly Python library designed for data cleaning and preprocessing. It provides essential utilities to streamline the process of preparing datasets for analysis or modeling. With features such as duplicate removal, handling missing values, and automatic data type inference, sanitipy simplifies the data cleaning workflow, making it a useful tool for data scientists and analysts.

SanitiPy - Automatic Data Cleaner

SanitiPy automates the data cleaning process for your data science projects using Python.

Overview

SanitiPy is a user-friendly Python library designed to streamline the data cleaning and preprocessing workflow. It provides essential utilities to prepare datasets for analysis or modeling by handling common data quality issues such as duplicate entries, missing values, and inconsistent data types.

Features

• Remove Duplicates: Easily eliminate duplicate rows from your DataFrame to ensure data integrity.

• Handle Missing Values: Automatically identify and remove rows containing NaN (Not a Number) values.

• Infer Data Types: Intelligently detect and convert column data types, including:

  • Converting potential datetime columns based on a configurable ratio of valid dates.

  • Converting numeric-like values to proper numeric types.

  • Falling back to string type when type inference is unsuccessful.

• Automated Cleaning Process: The DataCleaner class orchestrates the cleaning steps, ensuring your data is ready for further analysis.

Installation

You can install SanitiPy using pip:

pip install sanitipy

Useage

Quick example on using the package with a Pandas DataFrame:

import pandas as pd
from sanitipy import DataCleaner

# Create a sample DataFrame with some common data issues
data = {
  'ID': [1, 2, 3, 1, 4, 5],
  'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'David', 'Eve'],
  'Value': [100, 200, None, 100, 400, 500],
  'Date': ['2023/01/01', '2023/01/02', '2023/01/03', '2023/01/01', 'invalid-date', '2023/01/05'],
  'Category': ['A', 'B', 'C', 'A', 'D', 'E']
}
df = pd.DataFrame(data)

# Initialize the DataCleaner
cleaner = DataCleaner(df)

# Clean the data
cleaned_df = cleaner.clean_data()

API Reference

DataCleaner Class

The main class for orchestrating the data cleaning process.

• __init__(self, data_frame: pd.DataFrame):

  • Initializes the DataCleaner with a pandas DataFrame

• clean_data(self) -> pd.DataFrame:

  • Performs a sequence of cleaning operations:

    • Removes duplicate rows.

    • Removes rows with missing values (if any are detected). Raises a ValueError if missing values persist after removal.

    • Infers and converts data types for columns with inconsistent types.

    • Resets the DataFrame index.

  • Returns the cleaned pandas DataFrame.

Preprocessor Class

Provides individual data transformation and cleaning utilities.

• remove_duplicates(self, data: pd.DataFrame) -> pd.DataFrame:

  • Removes duplicate rows from the input DataFrame.

• remove_na(self, data: pd.DataFrame) -> pd.DataFrame:

  • Removes rows containing any NaN values from the input DataFrame.

• infer_data_types(self, data_frame: pd.DataFrame, date_time_ratio: float = 0.5) -> pd.DataFrame:

  • Infers and converts data types for columns.
  • date_time_ratio: The threshold (0-1) for treating an object column as datetime. Default is 0.5 (50% valid date values required).

Validator Class

Used internally by DataCleaner to check data quality.

• check_missing_values(self, data: pd.DataFrame) -> int:

  • Retuns the total count of missing values in the DataFrame.

• validate_data_types(self) -> bool:

  • Checks if all columns in the DataFrame have consistent data types. Returns True if all columns have the same data type or if the DataFrame is empty, False otherwise.

Contributing

Constributions are welcome! If you have suggestions for improvements or new features, please open an issue or submit a pull request.

License

This project is licensed under the GNU License - see the LICENSE file for details.