API reference

Top-level functions

Functions available directly on the pd namespace (the default export), mirroring pandas top-level functions.

Name	Summary
`pd.concat`	Concatenate pandas objects along a particular axis.
`pd.merge`	Merge DataFrame or named Series objects with a database-style join.
`pd.date_range`	Return a fixed frequency DatetimeIndex.
`pd.to_datetime`	Convert argument to datetime.
`pd.read_csv`	Read a comma-separated values (CSV) file into a DataFrame.
`pd.isna`	Detect missing values. Returns boolean Series/DataFrame/scalar.
`pd.notna`	Detect non-missing values. Inverse of `isna`.
`pd.cut`	Bin values into discrete intervals.
`pd.qcut`	Quantile-based discretization function.
`pd.get_dummies`	Convert categorical variable into dummy/indicator variables.
`pd.crosstab`	Compute a cross-tabulation of two (or more) factors.
`pd.eval`	Evaluate an expression string. With a DataFrame, evaluates column expressions. W
`pd.infer_freq`	Infer the most likely frequency of a DatetimeIndex.
`pd.wide_to_long`	Wide panel to long format. Less flexible but more user-friendly than melt.
`pd.json_normalize`	Normalize semi-structured JSON data into a flat table.

`pd.concat`

pd.concat(objs, options?)

Concatenate pandas objects along a particular axis.

Returns

DataFrame | Series

Example

TypeScript

import pd from 'pandas-ts';
const a = new pd.DataFrame({ x: [1,2] });
const b = new pd.DataFrame({ x: [3,4] });
pd.concat([a, b]).shape;    // => [4, 1]

`pd.merge`

pd.merge(left, right, options?)

Merge DataFrame or named Series objects with a database-style join.

Returns

DataFrame

Example

TypeScript

const left = new pd.DataFrame({ id: [1,2], val: ['a','b'] });
const right = new pd.DataFrame({ id: [1,2], score: [10,20] });
pd.merge(left, right, { on: 'id' }).shape;    // => [2, 3]

`pd.date_range`

pd.date_range(start, options)

Return a fixed frequency DatetimeIndex.

Returns

DatetimeIndex

Example

TypeScript

pd.date_range('2024-01-01', { periods: 5, freq: 'D' }).length;    // => 5

`pd.to_datetime`

pd.to_datetime(arg, options?)

Convert argument to datetime.

Returns

Timestamp | DatetimeIndex

Example

TypeScript

pd.to_datetime('2024-01-15').year;    // => 2024

`pd.read_csv`

pd.read_csv(path, options?)

Read a comma-separated values (CSV) file into a DataFrame.

Returns

DataFrame

Example

TypeScript

// Node.js only
const df = pd.read_csv('data.csv');
df.columns;    // column names from the header row

`pd.isna`

pd.isna(obj)

Detect missing values. Returns boolean Series/DataFrame/scalar.

Returns

boolean | Series | DataFrame

Example

TypeScript

import { isna } from 'pandas-ts';
isna(null);        // => true
isna(NaN);         // => true
isna(0);           // => false

`pd.notna`

pd.notna(obj)

Detect non-missing values. Inverse of isna.

Returns

boolean | Series | DataFrame

Example

TypeScript

import { notna } from 'pandas-ts';
notna(1);       // => true
notna(NaN);     // => false

`pd.cut`

pd.cut(x, bins, options?)

Bin values into discrete intervals.

Returns

Series

Example

TypeScript

import { cut } from 'pandas-ts';
cut(new pd.Series([1,2,3,4,5]), 2).dtype.name;    // => 'category'

`pd.qcut`

pd.qcut(x, q, options?)

Quantile-based discretization function.

Returns

Series

Example

TypeScript

import { qcut } from 'pandas-ts';
qcut(new pd.Series([1,2,3,4,5,6,7,8,9,10]), 4).nunique();    // => 4

`pd.get_dummies`

pd.get_dummies(data, options?)

Convert categorical variable into dummy/indicator variables.

Returns

DataFrame

Example

TypeScript

pd.get_dummies(new pd.Series(['a','b','a'])).shape;    // => [3, 2]

`pd.crosstab`

pd.crosstab(index, columns, options?)

Compute a cross-tabulation of two (or more) factors.

Returns

DataFrame

Example

TypeScript

const idx = new pd.Series(['a','a','b','b']);
const col = new pd.Series([1,2,1,2]);
pd.crosstab(idx, col).shape;    // => [2, 2]

`pd.eval`

pd.eval(expr, df?)

Evaluate an expression string. With a DataFrame, evaluates column expressions. Without one, evaluates scalar math.

Returns

Series | number

Example

TypeScript

import { pdEval } from 'pandas-ts';
pdEval('2 + 3');    // => 5
pdEval('a + b', new pd.DataFrame({ a: [1,2], b: [3,4] })).to_list();    // => [4, 6]

`pd.infer_freq`

pd.infer_freq(dates)

Infer the most likely frequency of a DatetimeIndex.

Returns

string | null

Example

TypeScript

import { infer_freq } from 'pandas-ts';
infer_freq(['2024-01-01','2024-01-02','2024-01-03']);    // => 'D'
infer_freq(['2024-01-01','2024-02-01','2024-03-01']);    // => 'MS'

`pd.wide_to_long`

pd.wide_to_long(df, stubnames, i, j, options?)

Wide panel to long format. Less flexible but more user-friendly than melt.

Returns

DataFrame

Example

TypeScript

const df = new pd.DataFrame({
  id: [1, 2],
  sales_2023: [100, 200],
  sales_2024: [110, 220],
});
pd.wide_to_long(df, ['sales'], 'id', 'year').shape;    // => [4, 2]

`pd.json_normalize`

pd.json_normalize(data, options?)

Normalize semi-structured JSON data into a flat table.

Returns

DataFrame

Example

TypeScript

pd.json_normalize([{ a: 1, b: { c: 2 } }, { a: 3, b: { c: 4 } }]).columns;
// => ['a', 'b.c']