Danfo.js is heavily inspired by the Pandas library and provides a similar interface and API. This means users familiar with the Pandas API can easily use Danfo.js.

Main Features

• Danfo.js is fast. It is built on Tensorflow.js, and supports tensors out of the box. This means you can convert danfo data structure to Tensors.

• Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data

• Size mutability: columns can be inserted/deleted from DataFrame

• Automatic and explicit alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations

• Powerful, flexible groupby functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data

• Make it easy to convert Arrays, JSONs, List or Objects, Tensors and differently-indexed data structures into DataFrame objects

• Intelligent label-based slicing, fancy indexing, and querying of large data sets

• Intuitive merging and joining data sets

• Robust IO tools for loading data from flat-files (CSV and delimited) and JSON data format.

• Powerful, flexible and intutive API for plotting DataFrames and Series interactively.

• Timeseries-specific functionality: date range generation and date and time properties.

• Robust data preprocessing functions like OneHotEncoders, LabelEncoders, and scalers like StandardScaler and MinMaxScaler are supported on DataFrame and Series

Getting Started

New to danfo? Check out the getting started guides. They contain an introduction to danfo's main concepts and links to additional contents.

API Reference

The reference guide contains a detailed description of the danfo API. The reference describes how each function works and which parameters can be used.

Contributing Guide

Want to help improve our documentation and existing functionalities? The contributing guidelines will guide you through the process.