Dataframe

TL;DR

Organizes data in two dimensions: rows (observations) and columns (variables).
Makes it easy to store, manipulate, and analyze structured datasets.
Supports common analyses such as aggregations and filtering across columns and rows.

Definition

A dataframe is a two-dimensional data structure that consists of rows and columns. It is a powerful tool for organizing and analyzing data in a structured and organized manner.

Explanation

A dataframe represents a dataset where each row corresponds to a specific record and each column corresponds to a particular attribute or field. This tabular layout allows straightforward manipulation and analysis of data, such as computing aggregates, filtering records, and comparing values across columns.

Examples

Employee records example

Consider a dataset of employee records for a company. The dataframe for this dataset could include the following columns: employee name, employee ID, department, salary, and years of experience. Each row in the dataframe would represent the information for a specific employee. This dataframe would allow for easy analysis and manipulation of the data, such as calculating the average salary by department or identifying the employee with the most years of experience.

Retail sales example

A sales dataset for a retail store could be represented as a dataframe that includes columns for the date of the sale, the product name, the quantity sold, the price per unit, and the total sale amount. Each row in the dataframe would represent a single sale transaction. This dataframe would allow for analysis of the sales data, such as identifying the most popular products or calculating the total sales for a given time period.

Use cases

Calculating the average salary by department.
Identifying the employee with the most years of experience.
Identifying the most popular products from sales data.
Calculating total sales for a given time period.

Dataset
Row
Column