Popular GitHub Repositories for Dataframes

BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.

DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

?? RumbleDB 1.22.0 "Pyrenean oak" ? for Apache Spark | Run queries on your large-scale, messy JSON-like data (JSON, text, CSV, Parquet, ROOT, AVRO, SVM...) | No install required (just a jar to download) | Declarative Machine Learning and more

Build reliable AI and agentic applications with DataFrames

A crossplatform GUI to plot tabulated data from files (e.g. CSV, Excel, OpenFAST, HAWC2, Flex...), or python pandas dataframes

Declarative TabularData creation for Swift - Convert objects to DataFrames with type-safe, SwiftUI-like syntax

? GemInsights: Unleash Gemini AI on your data! ? Analyze dataframes for valuable insights, replacing traditional data analysis. ? A cutting-edge tool revolutionizes the way you analyze dataframes, offering a paradigm shift from conventional data analysis methods.

Term 1 Project 3 Design a Sign Language Recognition System by Luke Schoen for Udacity Artificial Intelligence Nanodegree (AIND)

A comprehensive collection of data analysis and machine learning projects, showcasing techniques and models for various data challenges. Dive in to explore code examples, analyses, and machine learning workflows.

The highly versatile synthetic data generator

DF Embedder is a high-performance Python library (with a Rust backend) for indexing and embedding Apache Arrow compatible DataFrames (like Polars or Pandas) into low latency vector databases based on Lance files.

In this tutorial, I explained SparkContext by using map and filter methods with Lambda functions in Python and created RDD from object and external files, transformations and actions on RDD and pair RDD, PySpark DataFrame from RDD and external files, used sql queries with DataFrames by using Spark SQL, used machine learning with PySpark MLlib.

LLMWorkbook is a Python package that integrates Large Language Models (LLMs) with tabular datatypes - workbooks and dataframes for seamless data analysis and automation.

OpenAI API for completions from GPT-3, BeautifulSoup for web scraping, Matplotlib for data visualization, Pandas for dataframes (spreadsheets or tables), Numpy for manipulation of arrays

This repository contains the implementation of a recurrent neural network (LSTM from keras library) with the purpose of forecasting target time series, given the targets historical records and covariates. The project uses a toy data set, while focusing on the data transformation tasks (pandas dataframes to 3D numpy arrays required by recurrent networks) and on the hyperparameters tuning tasks, taking advantage of keras_tuner package.

Machine Learning engine generates predictions given any dataset using regression

Fake news detection based on Naive Bayes classifier. Implemented in python using PySpark dataframes and machine learning.

Sumeh — Unified Data Quality Framework Sumeh is a unified data quality validation framework supporting multiple backends (PySpark, Dask, Polars, DuckDB, Pandas) with centralized rule configuration.

A Python library for exporting SQL queries and/or Pandas DataFrames to Excel, with support for chart generation and data visualization.