# CSV Filter

Filter CSV data by column values with multiple conditions and operators. Supports 12 filter operators including equals, contains, greater_than, less_than, and empty value checks.

Additional Filters examples:
[{"column": "age", "operator": "greater_than", "value": "25"}]
[{"column": "status", "operator": "equals", "value": "active"}, {"column": "score", "operator": "greater_equal", "value": "80"}]
[{"column": "name", "operator": "contains", "value": "john"}, {"column": "email", "operator": "is_not_empty"}]

> Canonical page: https://elysiatools.com/en/tools/csv-filter

- **Category:** Data Processing

- **Keywords:** csv, filter, data, rows, conditions, query, spreadsheet

## Overview

The CSV Filter tool allows you to quickly refine large datasets by applying precise column-based conditions. With support for 12 operators including comparison, text matching, and empty value checks, you can extract specific rows from your CSV files without needing complex spreadsheet software.

## Inputs

- **CSV File** (file)
- **Filter Column** (text): Column name to filter on
- **Filter Operator** (select)
- **Filter Value** (text): Value to filter by (leave empty for is_empty/is_not_empty)
- **Additional Filters (JSON)** (textarea): Examples: \[{"column": "age", "operator": "greater_than", "value": "25"}\] \[{"column": "status", "operator": "equals", "value": "active"}, {"column": "score", "operator": "greater_equal", "value": "80"}\] \[{"column": "name", "operator": "contains", "value": "john"}, {"column": "email", "operator": "is_not_empty"}\]
- **Output Format** (select)
- **Include Header Row** (checkbox)
- **Case Sensitive** (checkbox)
- **Trim Whitespace** (checkbox)

## When to use

- When you need to extract specific subsets of data from a large CSV file based on column criteria.
- When you need to clean your dataset by removing rows with missing or empty values.
- When you want to perform multi-condition queries to isolate records that meet several requirements simultaneously.

## How it works

- Upload your CSV file and specify the primary column and filter operator you wish to apply.
- Define your filter value and toggle options like case sensitivity or whitespace trimming for accuracy.
- Use the Additional Filters field to add complex, multi-condition logic via JSON.
- Select your preferred output format and download the filtered results.

## Use cases

- Isolating high-value customer records from a master sales list based on purchase amount.
- Cleaning contact lists by filtering out entries that lack an email address or phone number.
- Extracting specific project tasks from a team export based on status and priority levels.

## Frequently asked questions

### What file formats are supported?

This tool is specifically designed for CSV files.

### Can I filter by multiple columns at once?

Yes, you can use the 'Additional Filters' field to define multiple conditions across different columns using JSON syntax.

### How do I handle empty cells?

You can select the 'is_empty' or 'is_not_empty' operators to filter rows based on whether a specific column contains data.

### Is the filtering case-sensitive?

By default, it is not, but you can enable the 'Case Sensitive' checkbox to ensure exact matching.

### What output formats can I choose?

You can export your results as a new CSV file, a JSON object, or a summary report.

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## Samples

- [CSV Samples](https://elysiatools.com/en/samples/csv-samples): Sample CSV files with various data types, sizes, and complexity levels
- [Python Samples](https://elysiatools.com/en/samples/python): Essential Python code examples and Hello World demonstrations
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- [Distributed Tracing Samples](https://elysiatools.com/en/samples/distributed-tracing-samples): Comprehensive distributed tracing examples using Jaeger, OpenTelemetry, and other modern observability tools for microservices architecture

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