Fiter Data

The Filter node allows users to selectively extract or exclude data from a dataset based on specific criteria. It refines datasets by applying conditions like equality, comparison, range, or null checks to columns or fields, focusing on relevant data subsets. This functionality facilitates efficient analysis, reporting, and data manipulation tasks.

Configuration

Upon clicking the Filter node, users are presented with the following fields in the configuration section:

Add Rule: Define individual conditions to define segmentation criteria.
Add Group: Combine conditions together using logical operators like AND, OR, or NOT to create more complex segmentation rules.

Creating Conditions:

Field Selection: Select the column from dropdown within the dataset on which the condition will be applied.
Logic Condition: Choose from a range of logical operators such as
1. Equal to (==)
2. Not equal to (!=)
3. Greater than (>)
4. Greater than or equal to (>=)
5. Less than (<)
6. Less than or equal to (<=)
7. Between
8. Not between
9. Is null
Input Value: Specify the value against which the selected column will be evaluated.

Logic Representation

The SQL Query Display section provides users with a view of the SQL query generated based on the conditions they have created. This allows users to understand the underlying SQL logic behind their segmentation rules.

Example Usage

Problem Statement: Let's consider a scenario to pinpoint employees who fit particular age and salary ranges for performance assessment. Additionally, we aim to remove employees associated with the Finance department from consideration.

Dataset

Employee ID	Name	Age	Department	Salary
101	Alice	30	HR	60000
102	Bob	28	IT	55000
103	Charlie	35	Marketing	70000
104	David	32	Finance	62000
105	Emily	27	Operations	58000
106	Frank	31	HR	62000
107	Grace	29	Finance	54000
108	Henry	33	IT	64000
109	Irene	26	Marketing	56000
110	Jack	34	Operations	61000

Problem Statement Breakdown

Age: Employees aged between 25 and 30.
Salary: Employees with salaries greater than or equal to 55000.
Department: Exclude employees working in the Finance department.

We'll apply these filter conditions using the Filter Data component to segment the dataset and identify the employees meeting the specified criteria.

Add Rule:

Field Selection: Select "Age" column.
Logic Condition: Choose "Greater than or equal to (>=)" and "Less than or equal to (<=)". Alternatively, use the "Between" condition, which functions similarly.
Input Value: Input 25 as the lower limit and 30 as the upper limit.

Add Group:

Field Selection: Select "Salary" column.
Logic Condition: Choose "Greater than or equal to (>=)".
Input Value: Input 55000 as the threshold salary.

Additional Rule:

Field Selection: Select "Department" column.
Logic Condition: Choose "Not equal to (!=)".
Input Value: Input "Finance" to exclude employees in the Finance department.

Final Logic Representation

('Age' between 25 and 30 and ('Salary' >= 55000 and 'Department' != 'Finance'))

Resultant Output

By applying these filter conditions, we get the desired result.

Employee ID	Name	Age	Department	Salary
101	Alice	30	HR	60000
102	Bob	28	IT	55000
105	Emily	27	Operations	58000
109	Irene	26	Marketing	56000

Last modified: 21 February 2025