Danfo.js is a javascript package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It is heavily inspired by Pandas library, and provides a similar API. This means that users familiar with Pandas, can easily pick up danfo.js.
- Danfo.js is fast and supports Tensorflow.js 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, Json, Excel).
- 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
There are three ways to install and use Danfo.js in your application
- For Nodejs applications, you can install the danfojs-node version via package managers like yarn and/or npm:
npm install danfojs-node or yarn add danfojs-nodeFor client-side applications built with frameworks like React, Vue, Next.js, etc, you can install the danfojs version:
npm install danfojs or yarn add danfojsFor use directly in HTML files, you can add the latest script tag from JsDelivr to your HTML file:
<scriptsrc="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.js"></script>See all available versions here
- Danfojs with HTML and vanilla JavaScript on CodePen
- Danfojs with React on Code Sandbox
- Danfojs on ObservableHq
- Danfojs in Nodejs on Replit
<!DOCTYPE html><htmllang="en"><head><metacharset="UTF-8" /><metaname="viewport" content="width=device-width, initial-scale=1.0" /><scriptsrc="https://cdn.jsdelivr.net/npm/danfojs@1.1.2/lib/bundle.js"></script><title>Document</title></head><body><divid="div1"></div><divid="div2"></div><divid="div3"></div><script>dfd.readCSV("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv").then(df=>{df['AAPL.Open'].plot("div1").box()//makes a box plotdf.plot("div2").table()//display csv as tablenew_df=df.setIndex({column: "Date",drop: true});//resets the index to Date columnnew_df.head().print()//new_df.plot("div3").line({config: {columns: ["AAPL.Open","AAPL.High"]}})//makes a timeseries plot}).catch(err=>{console.log(err);})</script></body></html>Output in Browser:
constdfd=require("danfojs-node");constfile_url="https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv";dfd.readCSV(file_url).then((df)=>{//prints the first five columnsdf.head().print();// Calculate descriptive statistics for all numerical columnsdf.describe().print();//prints the shape of the dataconsole.log(df.shape);//prints all column namesconsole.log(df.columns);// //prints the inferred dtypes of each columndf.ctypes.print();//selecting a column by subsettingdf["Name"].print();//drop columns by namesletcols_2_remove=["Age","Pclass"];letdf_drop=df.drop({columns: cols_2_remove,axis: 1});df_drop.print();//select columns by dtypesletstr_cols=df_drop.selectDtypes(["string"]);letnum_cols=df_drop.selectDtypes(["int32","float32"]);str_cols.print();num_cols.print();//add new column to Dataframeletnew_vals=df["Fare"].round(1);df_drop.addColumn("fare_round",new_vals,{inplace: true});df_drop.print();df_drop["fare_round"].round(2).print(5);//prints the number of occurence each value in the columndf_drop["Survived"].valueCounts().print();//print the last ten elementa of a DataFramedf_drop.tail(10).print();//prints the number of missing values in a DataFramedf_drop.isNa().sum().print();}).catch((err)=>{console.log(err);});Output in Node Console:
- VsCode nodejs notebook extension now supports Danfo.js. See guide here
- ObservableHQ Notebooks. See example notebook here
The official documentation can be found here
Development discussions take place here.
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. A detailed overview on how to contribute can be found in the contributing guide.
