https://github.com/patrickpontes44/tiny-panda
tiny-panda is a lightweight JavaScript library inspired by Python’s pandas. It provides a simple, intuitive API for working with tabular data in the browser or Node.js.
https://github.com/patrickpontes44/tiny-panda
data-science javascript javascript-library machine-learning pandas pandas-dataframe statistics typescript typescript-library
Last synced: 2 months ago
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tiny-panda is a lightweight JavaScript library inspired by Python’s pandas. It provides a simple, intuitive API for working with tabular data in the browser or Node.js.
- Host: GitHub
- URL: https://github.com/patrickpontes44/tiny-panda
- Owner: PatrickPontes44
- Created: 2025-08-20T03:45:21.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-28T23:56:32.000Z (10 months ago)
- Last Synced: 2025-08-29T03:32:54.379Z (10 months ago)
- Topics: data-science, javascript, javascript-library, machine-learning, pandas, pandas-dataframe, statistics, typescript, typescript-library
- Language: TypeScript
- Homepage:
- Size: 887 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
tiny-panda is a lightweight JavaScript library inspired by Python’s pandas.
It provides a simple, intuitive API for working with tabular data in the browser or Node.js.
---
## Features
- Easy-to-use DataFrame and Series objects
- Data filtering, selection, and transformation
- Basic statistics and summary methods
- Import/export to JSON and CSV
- Familiar pandas-like syntax
---
## Installation
```
npm install tiny-panda
```
---
## Usage
#### Series
A `Series` is a one-dimensional labeled array capable of holding any data type.
```
import { Series } from "tiny-panda";
const s = new Series([1, 3, 5, null, 6, 8]);
console.log(s.values);
// Output: [1, 3, 5, null, 6, 8]
// Get the length
console.log(s.length());
// Output: 6
// Get unique values
console.log(s.unique().values);
// Output: [1, 3, 5, null, 6, 8]
// Get value counts
const s2 = new Series(['a', 'b', 'a', 'c', 'a', 'd']);
console.log(s2.valueCounts());
// Output: { a: 3, b: 1, c: 1, d: 1 }
const numSeries = new Series([10, 20, 30, 40, 50]);
console.log(numSeries.sum()); // Output: 150
console.log(numSeries.mean()); // Output: 30
console.log(numSeries.median());// Output: 30
console.log(numSeries.min()); // Output: 10
console.log(numSeries.max()); // Output: 50
// Get a full summary
console.log(numSeries.describe());
/*
Output:
{
max: 50,
min: 10,
sum: 150,
mean: 30,
median: 30,
mode: 10
}
*/
const s = new Series([1, 2, 3, 4]);
const squared = s.apply(x => x * x);
console.log(squared.values);
// Output: [1, 4, 9, 16]
```
#### DataFrame
A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types.
```
import { DataFrame } from "tiny-panda";
// From an array of objects (rows)
const df1 = new DataFrame([
{ name: 'Alice', age: 25, city: 'New York' },
{ name: 'Bob', age: 30, city: 'Paris' },
{ name: 'Charlie', age: 35, city: 'London' }
]);
// From an object of arrays (columns)
const df2 = new DataFrame({
name: ['Alice', 'Bob', 'Charlie'],
age: [25, 30, 35],
city: ['New York', 'Paris', 'London']
});
// Display the first 2 rows
df1.head(2).printTable();
/*
Output:
name | age | city
--------|-----|---------
Alice | 25 | New York
Bob | 30 | Paris
*/
// Get the dimensions of the DataFrame
console.log(df1.shape());
// Output: [3, 3]
// Select the 'age' column (returns a Series)
const ages = (df1 as any).age;
console.log(ages.values);
// Output: [25, 30, 35]
// Get value by label (row index 1, column 'name')
console.log(df1.loc(1, 'name'));
// Output: 'Bob'
// Get value by position (row index 2, column index 2)
console.log(df1.iloc(2, 2));
// Output: 'London'
// Get an entire row by position
console.log(df1.iloc(0));
// Output: { name: 'Alice', age: 25, city: 'New York' }
const df = new DataFrame({
A: [1, 2, 3],
B: [10, 20, 30],
C: [100, 200, 300]
});
// Get the sum of each column
console.log(df.sum());
// Output: { A: 6, B: 60, C: 600 }
// Get a full statistical summary
console.log(df.describe());
/*
Output:
{
max: { A: 3, B: 30, C: 300 },
min: { A: 1, B: 10, C: 100 },
sum: { A: 6, B: 60, C: 600 },
mean: { A: 2, B: 20, C: 200 },
median: { A: 2, B: 20, C: 200 },
mode: { A: 1, B: 10, C: 100 }
}
*/
// Apply a function to each column (axis=0, default)
const columnMax = df.apply(series => series.max());
console.log(columnMax);
// Output: { A: 3, B: 30, C: 300 }
// Apply a function to each row (axis=1)
const rowSum = df.apply(series => series.sum(), 1);
console.log(rowSum);
// Output: [111, 222, 333]
```
---
## Roadmap
#### Future / Advanced Features
Add / drop columns (df["new"] = ..., df.drop("col")).
Rename columns (df.rename({ old: new })).
Sorting (df.sortBy("col")).
Filtering / boolean indexing (df[df["a"].values.map(x => x > 2)]).
Merge / join / concat multiple DataFrames.
Handling missing values (fillna(), dropna()).
GroupBy / aggregation (df.groupby("col").mean()).
---
## License
**MIT License**