CSV Row Filter

How to Use CSV Row Filter

The CSV Row Filter allows you to filter CSV data based on column values and conditions. Create focused datasets by keeping only rows that match your criteria - perfect for data analysis, cleaning, and creating subsets.

Quick Start Guide

1. Paste CSV Data: Copy and paste your CSV data into the input area
2. Add Filters: Click "Add Filter" to create filtering conditions
3. Configure Each Filter:
   • Select column to filter
   • Choose operator (equals, contains, greater than, etc.)
   • Enter value to match
4. Choose Match Type: Select "Match ALL" (AND) or "Match ANY" (OR)
5. Apply Filter: Click "Apply Filter" to see results
6. Copy Output: Click "Copy Output" to copy filtered data

Understanding Row Filtering

What is Row Filtering?

Row filtering selects specific rows from a dataset based on conditions you define.

Before Filtering:

id,name,status,age
1,Alice,active,28
2,Bob,inactive,35
3,Carol,active,42

Filter: status equals "active"

After Filtering:

id,name,status,age
1,Alice,active,28
3,Carol,active,42

Why Filter Rows?

• Focus on specific data
• Remove irrelevant records
• Create subsets for analysis
• Extract matching records
• Clean datasets
• Generate reports

Common Use Cases

1. Filter by Status

Input CSV:

order_id,customer,amount,status
1001,Alice,999.99,completed
1002,Bob,24.99,pending
1003,Carol,79.99,completed

Filter: status equals "completed"

Output:

order_id,customer,amount,status
1001,Alice,999.99,completed
1003,Carol,79.99,completed

Use Case: Get only completed orders.

2. Filter by Numeric Value

Input CSV:

product_id,name,price,stock
101,Laptop,999.99,15
102,Mouse,24.99,150
103,Keyboard,79.99,5

Filter: stock greater than 10

Output:

product_id,name,price,stock
101,Laptop,999.99,15
102,Mouse,24.99,150

Use Case: Find products with adequate stock.

3. Filter by Text Content

Input CSV:

id,email,name,city
1,alice@gmail.com,Alice,New York
2,bob@yahoo.com,Bob,Los Angeles
3,carol@gmail.com,Carol,Chicago

Filter: email contains "gmail"

Output:

id,email,name,city
1,alice@gmail.com,Alice,New York
3,carol@gmail.com,Carol,Chicago

Use Case: Find all Gmail users.

4. Multiple Conditions (AND)

Input CSV:

emp_id,name,department,salary
501,John,Engineering,95000
502,Jane,Marketing,75000
503,Mike,Engineering,102000

Filters (Match ALL):

• department equals "Engineering"
• salary greater than 100000

Output:

emp_id,name,department,salary
503,Mike,Engineering,102000

Use Case: Find high-earning engineers.

5. Multiple Conditions (OR)

Input CSV:

id,name,city,country
1,Alice,NYC,USA
2,Bob,London,UK
3,Carol,Paris,France

Filters (Match ANY):

• city equals "NYC"
• city equals "London"

Output:

id,name,city,country
1,Alice,NYC,USA
2,Bob,London,UK

Use Case: Get records from multiple cities.

6. Exclude Records

Input CSV:

user_id,username,status
1,alice,active
2,bob,banned
3,carol,active

Filter: status not_equals "banned"

Output:

user_id,username,status
1,alice,active
3,carol,active

Use Case: Remove banned users.

Filter Operators

Text Operators:

• equals: Exact match (case-insensitive)
• not_equals: Does not match exactly
• contains: Contains substring
• not_contains: Does not contain substring
• starts_with: Begins with text
• ends_with: Ends with text

Numeric Operators:

• greater: Greater than (>)
• less: Less than (<)
• equals: Equal to
• not_equals: Not equal to

Match Types

Match ALL (AND Logic):

All conditions must be true:

Filter 1: status = "active"
Filter 2: age > 25

Result: Rows where status is active AND age is greater than 25

Match ANY (OR Logic):

At least one condition must be true:

Filter 1: city = "NYC"
Filter 2: city = "LA"

Result: Rows where city is NYC OR city is LA

Features

Multiple Filters

Add unlimited filter conditions:

• Combine different columns
• Mix operator types
• Complex filtering logic
• Dynamic condition building

Flexible Operators

8 different operators:

• Text matching (equals, contains)
• Numeric comparison (greater, less)
• Pattern matching (starts/ends with)
• Negation (not equals, not contains)

AND/OR Logic

Choose how filters combine:

• Match ALL: All conditions true (AND)
• Match ANY: At least one true (OR)
• Switch between modes easily

Preserved Structure

Maintains data integrity:

• Header row always included
• Column order preserved
• Data formatting maintained
• Valid CSV output

Advanced Filtering

Date Filtering:

For ISO dates (YYYY-MM-DD):

Filter: date greater than "2024-01-01"
Filter: date starts_with "2024"

Case-Insensitive Matching:

All text comparisons ignore case:

"Active" equals "active" ✓
"PENDING" contains "end" ✓

Numeric String Handling:

Numeric operators parse strings:

"100" greater than "50" ✓
"1000.5" less than "2000" ✓

Empty Value Filtering:

Filter for empty/missing values:

Filter: column equals "" (empty)
Filter: column not_equals "" (not empty)

Wildcard Alternatives:

Use contains for partial matching:

Instead of: name = "*smith*"
Use: name contains "smith"

Best Practices

Filter Strategy:

1. Start Simple: Begin with one filter
2. Test Incrementally: Add filters one at a time
3. Verify Results: Check filtered row count
4. Combine Logically: Use AND/OR appropriately
5. Document Filters: Note filter criteria used

Choosing Operators:

For Exact Match:

• Use "equals" for exact values
• Use "not_equals" to exclude

For Partial Match:

• Use "contains" for substring search
• Use "starts_with" for prefixes
• Use "ends_with" for suffixes

For Numbers:

• Use "greater" for minimum threshold
• Use "less" for maximum threshold
• Convert to numeric for comparison

Performance Tips:

Fast Filtering:

• Filters 10,000+ rows instantly
• Multiple conditions supported
• Real-time preview
• Client-side processing

Large Datasets:

• Test filters on sample first
• Verify condition logic
• Check result count
• Copy incrementally

Common Filter Patterns

Active Records:

Filter: status equals "active"

Recent Data:

Filter: date starts_with "2024"

High Value:

Filter: amount greater than "1000"

Specific Category:

Filter: category equals "Electronics"

Email Domain:

Filter: email ends_with "@company.com"

Not Deleted:

Filter: deleted_at equals ""

Multiple Statuses (OR):

Filter 1: status equals "pending"
Filter 2: status equals "processing"
Match: ANY

Qualified Leads (AND):

Filter 1: score greater than "80"
Filter 2: status equals "qualified"
Match: ALL

Troubleshooting

Issue: No rows in output

Solution: Check that:

• Filter values match data exactly
• Operators are correct (equals vs contains)
• Match type is appropriate (ALL vs ANY)
• Column names are correct

Issue: Too many rows returned

Solution:

• Add more restrictive filters
• Use "Match ALL" instead of "Match ANY"
• Use "equals" instead of "contains"
• Check filter values for typos

Issue: Numbers not filtering correctly

Solution:

• Verify numeric values in CSV
• Remove quotes from numbers in CSV
• Use numeric operators (greater/less)
• Check decimal formatting

Issue: Case sensitivity problems

Solution: All text filtering is case-insensitive. "Active" matches "active", "ACTIVE", etc.

Issue: Dates not filtering

Solution:

• Use ISO format: YYYY-MM-DD
• Use "starts_with" for year/month
• Use "greater"/"less" for date ranges
• Ensure consistent date format

Integration Examples

Data Analysis:

1. Filter rows matching criteria
2. Export to analysis tool
3. Perform calculations
4. Generate insights

Report Generation:

1. Filter by date range
2. Filter by category
3. Copy filtered data
4. Create report

Data Cleaning:

1. Filter invalid records
2. Review filtered results
3. Remove or fix issues
4. Export clean data

Subset Creation:

1. Filter by multiple criteria
2. Verify subset accuracy
3. Save as new CSV
4. Use for specific purpose

Privacy & Security

Client-Side Processing:

All filtering happens in browser:

• No data uploaded to servers
• No data stored or logged
• Completely private
• Offline-capable

Safe for Sensitive Data:

Use with confidential data:

• Customer information
• Financial records
• Personal data (PII)
• Internal reports

Tips & Tricks

1. Use Examples: Load examples to see filtering in action
2. Start Broad: Begin with loose filters, narrow down
3. Test Operators: Try different operators to find matches
4. Check Count: Verify filtered row count makes sense
5. AND for Precision: Use "Match ALL" for specific results
6. OR for Coverage: Use "Match ANY" to include multiple values
7. Contains for Search: Use "contains" when exact value unknown
8. Empty Filters: No filters returns all rows
9. Combine Filters: Mix text and numeric filters
10. Document Logic: Note filter criteria for reproducibility

Common Filtering Scenarios

Sales Analysis:

Filter: date starts_with "2024-01"
Filter: amount greater than "100"
Match: ALL
Result: High-value sales in January 2024

Active Users:

Filter: status equals "active"
Filter: last_login starts_with "2024"
Match: ALL
Result: Recently active users

Product Inventory:

Filter: stock less than "10"
Filter: category not_equals "Discontinued"
Match: ALL
Result: Low stock products to reorder

Email Segmentation:

Filter: email ends_with "@gmail.com"
Filter: subscribed equals "true"
Match: ALL
Result: Subscribed Gmail users

Geographic Filtering:

Filter: country equals "USA"
Filter: city contains "New"
Match: ALL
Result: US cities with "New" in name