JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.
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.
NaN) in floating point as well as non-floating point dataSeries, DataFrame, etc. automatically
align the data for you in computationsTo use Danfo.js via script tags, copy and paste the CDN below to the body of your HTML file
<script src="https://cdn.jsdelivr.net/npm/danfojs@0.2.5/lib/bundle.min.js"></script>
See the example below in Code Sandbox
<!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.plot.ly/plotly-1.2.0.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/danfojs@0.2.5/lib/bundle.min.js"></script>
<title>Document</title>
</head>
<body>
<div id="div1"></div>
<div id="div2"></div>
<div id="div3"></div>
<script>
dfd.read_csv("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.set_index({ key: "Date" }) //resets the index to Date column
new_df.plot("div3").line({ columns: ["AAPL.Open", "AAPL.High"] }) //makes a timeseries plot
}).catch(err => {
console.log(err);
})
</script>
</body>
</html>
Output in Browser:
Danfo.js is hosted on NPM, and can installed via package managers like npm and yarn
npm install danfojs-node
const dfd = require("danfojs-node")
dfd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv")
.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.column_names);
//prints the inferred dtypes of each column
df.ctypes.print()
//selecting a column by subsetting
df['Name'].print()
//drop columns by names
cols_2_remove = ['Age', 'Pclass']
df_drop = df.drop({ columns: cols_2_remove, axis: 1 })
df_drop.print()
//select columns by dtypes
let str_cols = df_drop.select_dtypes(["string"])
let num_cols = df_drop.select_dtypes(["int32", "float32"])
str_cols.print()
num_cols.print()
//add new column to Dataframe
let new_vals = df['Fare'].round().values
df_drop.addColumn({ column: "fare_round", value: new_vals})
df_drop.print()
df_drop['fare_round'].print(5)
//prints the number of occurence each value in the column
df_drop['Survived'].value_counts().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:
If you want to use Danfo in frontend frameworks like React/Vue, read this guide
The official documentation can be found here
Development discussions take place on our issues tab.
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.
FAQs
JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.
The npm package danfojs receives a total of 2,096 weekly downloads. As such, danfojs popularity was classified as popular.
We found that danfojs demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 3 open source maintainers collaborating on the project.
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