Getting Started

The Datascrubber package is designed to address these challenges and empower data analysts and scientists with a robust set of tools for data preprocessing and exploratory data analysis. By automating and simplifying many of the common data cleaning tasks, the package aims to save time, enhance data quality, and ultimately enable more accurate and informed decision-making.

Installation

pip install Datascrubber

Getting started in python notebooks

Importing the package in your python code

from Datascrubber import Datacleaning

data_cleaner = Datacleaning() #data_cleaner can initialized as any variable name.

OR

from Datascrubber.datacleaning import Datacleaning

data_cleaner = Datacleaning() #data_cleaner can initialized as any variable name.

Mission

Our mission is to deliver an intuitive and potent tool for data preprocessing, empowering analysts and data enthusiasts to concentrate on the essence of their research, free from the intricacies of data wrangling. We firmly hold that pristine data underpins profound insights, and with the Datascrubber package, we pledge our commitment to elevating the data analysis journey.

Dependencies

It is very important to check and confirm whether all the dependencies are installed on your machine before running any code.

• pandas
• numpy
• matplotlib
• scipy
• seaborn
• missingno