In this context it's a specific class, DataFrame, in the pandas library: https://pandas.pydata.org/pandas-docs/stable/reference/api/p...

> Two-dimensional, size-mutable, potentially heterogeneous tabular data.

> Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.

It’s a special data structure that holds a 2 dimensional array and allows you to perform very efficient “vector” (all rows/all columns) operations on it.

It’s used a lot in data science applications, particularly for matrix math.

It's a table. Think of a table from a database or a 2-dimensional csv file that exists as an object in your programming language of choice. The exact implementation is different in different languages and libraries. In Python the most popular one is the DataFrame class from the pandas library, R has data.frame built in, Julia has DataFrames.jl and there are other implementations too - spark, dask, data.table, tibble etc.

The easiest way for this is to click is open up Python or R and play around with a dataframe, it really isn't that complicated.

This article might help: "Is a Dataframe Just a Table?" https://drops.dagstuhl.de/opus/volltexte/2020/11960/pdf/OASI...

A dataframe is usually a list of columns. Columns are usually typed arrays that have a name. It's a bit like a struct of arrays (https://en.wikipedia.org/wiki/AoS_and_SoA) except that you can usually easily transform the dataframe (add columns, split columns, stuff like that).

I hate dataframes, but the definition is just a 2d array of numbers with names on the rows and columns.

They don't have to be numbers, you can have any data type but the type is always consistent along each column.