Filter on Falsy Values
Infoveave Data Automation — Filtering & Selection
Empty strings. Zeros. NaN. False. Undefined. Pick which ones matter and decide what to do with them — automatically.
Falsy values — zeros, empty strings, NaN, undefined — are some of the most common data quality problems in real-world datasets. Leave them in and your aggregations are wrong, your charts mislead, and your ML models pick up noise. Filter on Falsy Values detects and handles them automatically inside your workflow, so every downstream step starts with clean, semantically complete data.
What Filter on Falsy Values does
Detect and handle rows containing empty strings, zeros, NaN, false, or undefined in your Infoveave workflow. Keep, remove, or flag falsy data before it distorts aggregations and reports.
When to use Filter on Falsy Values
- You want to remove rows where a key metric column contains a zero or empty value that would skew aggregations
- Your source data has undefined or NaN values from failed lookups, missing sensor readings, or incomplete form submissions
- You need to flag potentially incomplete rows for a data quality review without removing them from the pipeline
- You are preprocessing data for a machine learning model that cannot handle NaN or empty inputs
When to avoid it
- Zero is a valid and meaningful value in your data — such as a product with zero sales in a period — removing it would lose real signal
- You want to replace missing values with a default rather than remove rows — use Replace Null Values or Fill Columns instead
- You are filtering on a semantic meaning rather than a raw falsy marker — use Filter on Bad Meaning for URL, IP, or date-in-wrong-field detection
Where it fits in your Infoveave automation
Filter on Falsy Values is one step inside a multi-step Infoveave workflow. Chain it with other activities — no code, no manual hand-offs.
Build this workflow visually in Infoveave Data Automation — drag, connect, and schedule with no infrastructure setup.
How teams use Filter on Falsy Values
Real scenarios where this transformation saves hours of manual work.
Remove Incomplete Patient Intake Records
A health analytics team removes patient records where critical fields — diagnosis code, admission date — are empty or undefined before feeding data to the reporting layer. Filter on Falsy Values flags and removes those rows automatically on each data load, preventing incomplete records from distorting care metrics.
Exclude Zero-Price Items Before Margin Calculation
A retail operations team needs to exclude product rows where the unit price is zero — typically test SKUs or data entry errors — before calculating average margin. The workflow filters on zero automatically, so the margin dashboard never includes meaningless rows.
Clean Sensor Readings of NaN Values
A factory's IoT pipeline receives sensor readings where failed reads come through as NaN. Filter on Falsy Values removes or flags those rows before the OEE calculation step, preventing corrupted readings from distorting shift performance metrics.
See Filter on Falsy Values in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
Value0, NaNFlag Rowsfalsy_flagInput Data
| Value | Description |
|---|---|
| 1 | data |
| 0 | more data |
| NaN | test |
| example | |
| 2 | content |
Output Data
| Value | Description | falsy_flag |
|---|---|---|
| 1 | data | 0 |
| 0 | more data | 1 |
| NaN | test | 1 |
| example | 0 | |
| 2 | content | 0 |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Column
The column to evaluate for falsy values. Select the column where empty, zero, or undefined entries would be problematic for your downstream steps.
Falsy Values
Choose which falsy types to detect. Supported options: false, 0, NaN, empty string, and undefined. You can select multiple types in one step — for example, flag both zeros and empty strings.
Actions
Keep Matching Rows retains only rows with falsy values (useful when inspecting bad data). Remove Matching Rows drops them (the most common choice for data cleaning). Flag Rows adds a 0/1 indicator column without changing row count — best when you want to audit the impact before committing to removal.
Flag Rows Column Name
Required when using Flag Rows action. Name the flag column something descriptive — like missing_value or falsy_flag — so downstream steps and quality reports can reference it clearly.
Frequently asked questions
Everything you need to know about Filter on Falsy Values in Infoveave.
Also in Filtering & Selection — and what runs before & after
Transformations in the same family as Filter on Falsy Values, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
80+ transformations. Zero manual steps.
Filter on Falsy Values is one of over 80 transformation activities available inside Infoveave workflows. Chain transformations together — no code, no exports, no waiting for IT.
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