There are 0 repository under ydata-profiling topic.
This ETL (Extract, Transform, Load) project employs several Python libraries, including Airflow, Soda, Polars, YData Profiling, DuckDB, Requests, Loguru, and Google Cloud to streamline the extraction, transformation, and loading of CSV datasets from the U.S. government's data repository at https://catalog.data.gov.
Repositório para geração de relatórios exploratórios a partir de arquivos CSV utilizando a biblioteca ydata-profiling.
Comparison between several Python data profile libraries.
This repository contains interactive data dashboards generated for the Karnataka Police Datathon 2024. The analysis focuses on various aspects of accident data, including details about accused individuals, FIR insights, accident reports, complainant information, and victim insights.
This repository contains the code snippets, short and long scripts for EDA, and some useful libraries to save time.
The Exploratory Data Analysis (EDA) App is a Streamlit-based web application that allows users to perform comprehensive exploratory data analysis on their datasets. This app provides an intuitive and user-friendly interface for uploading CSV files, visualizing the input data, and generating an interactive profiling report.
Creating quick visualizations and summary statistics using python
Data Sweeper Pro+ is an advanced data cleaning and transformation platform built with Streamlit. It allows users to upload datasets, clean them, analyze them with interactive profiling reports, and export the cleaned data in multiple formats. The app is designed for both technical and non-technical users.
The model predicts household energy usage using historical data and weather factors to optimize consumption and promote sustainability.
Data Profiler is a Streamlit app designed to provide insightful data analysis and visualization. Users can upload their datasets in '.csv' or '.xlsx' format, and the app generates a comprehensive profiling report using the YData Profiling library.
This repository showcases my learning process of automating EDA using 'ydata-profiling'
A python project using Streamlit, Pycaret and Pandas to demo an automated data modelling🤖 workflow.
Data profiling y-data profile, Data staging (Staging tables), Talend for ETL jobs, MySQL validations Dimensional model (Target tables), Facts and Dimensions, Mapping document explaining the source column name and where it finally maps to target column, Stage to Target, Document all transformations if any