- Danfo.js is fast and supports Tensorflow.js's tensors out of the box. This means you can convert Danfo.js DataFrames to Tensors, and vice versa.
- Easy handling of missing data (represented as
NaN, undefined, or null) in data
- Size mutability: columns can be inserted/deleted from DataFrames
- 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
- 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
New to Danfo? Check out the getting started guides. It contains a quick introduction to D_anfo's_ main concepts and links to additional content.
The reference guide contains a detailed description of the danfo API. The reference describes how each function works and which parameters can be used.
Want to help improve our documentation and existing functionalities? The contributing guidelines will guide you through the process.