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:
<script src="https://cdn.jsdelivr.net/npm/[email protected]/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>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/bundle.js"></script>
<title>Document</title>
</head>
<body>
<div id="div1"></div>
<div id="div2"></div>
<div id="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 plot
df.plot("div2").table() //display csv as table
new_df = df.setIndex({ column: "Date", drop: true }); //resets the index to Date column
new_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:
const dfd = require("danfojs-node");
const file_url =
"https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv";
dfd
.readCSV(file_url)
.then((df) => {
//prints the first five columns
df.head().print();
// Calculate descriptive statistics for all numerical columns
df.describe().print();
//prints the shape of the data
console.log(df.shape);
//prints all column names
console.log(df.columns);
// //prints the inferred dtypes of each column
df.ctypes.print();
//selecting a column by subsetting
df["Name"].print();
//drop columns by names
let cols_2_remove = ["Age", "Pclass"];
let df_drop = df.drop({ columns: cols_2_remove, axis: 1 });
df_drop.print();
//select columns by dtypes
let str_cols = df_drop.selectDtypes(["string"]);
let num_cols = df_drop.selectDtypes(["int32", "float32"]);
str_cols.print();
num_cols.print();
//add new column to Dataframe
let new_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 column
df_drop["Survived"].valueCounts().print();
//print the last ten elementa of a DataFrame
df_drop.tail(10).print();
//prints the number of missing values in a DataFrame
df_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
We published a book titled "Building Data Driven Applications with Danfo.js". Read more about it 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.
