Every data project starts with excitement.

Then comes:

missing values

duplicate rows

inconsistent column names

encoding

leakage checks

skew analysis

outlier handling

repetitive preprocessing pipelines

After rebuilding the same workflow across notebooks and projects, I decided to create something reusable.

So I built dfxpy — an open-source Python package focused on accelerating DataFrame workflows for machine learning, analytics, and research.

What dfxpy does

Automated Cleaning

smart type inference

missing value imputation

duplicate removal

snake_case normalization

currency/percentage/date detection

categorical encoding

ML Preparation

feature/target splitting

optional scaling

target encoding

date feature extraction

class balancing

Diagnostics & Research

leakage detection

skewness + multicollinearity audits

statistical profiling

dataset lineage hashing

publication-ready LaTeX exports

Workflow Utilities

reusable transformation pipelines

dataframe comparison tools

schema…