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- analytics
- charts
- statistics
- google-analytics
Repository Index
47,133 repositories indexed
1–19 of 19 · Most stars
A powerful 🚀 Android chart view / graph view library, supporting line- bar- pie- radar- bubble- and candlestick charts as well as scaling, panning and animations.
Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
- redash
- python
- visualization
- analytics
Beautiful charts for iOS/tvOS/OSX! The Apple side of the crossplatform MPAndroidChart.
This repository contains implementations and illustrative code to accompany DeepMind publications
Charts library for Android compatible with API 8+, several chart types with scaling, scrolling and animations 📊
the portable Python dataframe library
A scikit-learn compatible neural network library that wraps PyTorch
Browser compatibility data for Web technologies as displayed on MDN
- compatibility
- compat
- data
- dataset
Visualize and compare datasets, target values and associations, with one line of code.
- pandas-dataframe
- eda
- pandas
- data-exploration
An R package for causal inference in time series
Lightweight and extensible compatibility layer between dataframe libraries!
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
- forecasting
- scikit-learn
- machine-learning
- python
Multiple Pairwise Comparisons (Post Hoc) Tests in Python
- posthoc-comparisons
- posthoc-tests
- python
- scikit
🇪🇺 Warehouse-native Analytics compatible with Google Analytics and Matomo tracking protocols. Ingest into ClickHouse, BigQuery or CSV/Parquet on S3/GCS/local fs while maintaining complete control over your data.
- clickhouse
- ga4
- tracker
- bigquery
A pygame API-compatible reimplementation for running `pygame`-style code inside Jupyter notebooks (backed by ipycanvas)
Converts rfc7946 geojson polygons to d3 compatible ones
Blazingly fast data comparison tool for Python, powered by Rust. Compare massive CSV/Parquet datasets instantly.
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.