Filter Rows: Keep CSV, JSON or Dataset Rows by Condition

Keep only the CSV, JSON or dataset rows that match your conditions

Filter rows of a CSV, TSV, JSON or NDJSON file or an Apify dataset with plain conditions like country = US or price > 100. Paste a file link (or pick a dataset), write one condition per line, press Start: the matching rows land in a dataset you can download as CSV, Excel or JSON.

Try it free: leave the file empty and press Start — the conditions already in the form run on a built-in 20-row sample (not charged, rows carry _demo: true).

condition	keeps rows where
country = US	the country is US (text is compared without case)
price > 100	the price is more than 100 (numbers are compared as numbers)
signup_date >= 2025-06-01	the date is on or after 1 June 2025 (ISO dates)
email contains @gmail.com	the email contains that text
phone is empty	the phone column is blank
status in open, pending	the status is one of the listed values

• Price: $0.01 per 1,000 rows read, charged per row ($0.00001 a row) — 100,000 rows cost $1, whether 5 or 50,000 of them match. Failed runs and the demo are free. Store discounts apply automatically: BRONZE −10%, SILVER −20%, GOLD −30%.
• Big files are fine: CSV, TSV and NDJSON are read one row at a time, so a 3-million-row CSV uses about as little memory as a small one.

How to use

1. File URL (a public link to a CSV, TSV, JSON or NDJSON file) or an Apify dataset (for example the results of a scraper run).
2. Conditions, one per line: column operator value.
3. Keep rows that match: all conditions (AND, default) or any condition (OR).

{
  "fileUrl": "https://example.com/leads.csv",
  "conditions": ["country = US", "employees >= 50", "email is not empty"],
  "match": "all"
}

Operators

= != > >= < <= · contains · not contains · starts with · ends with · in (comma list) · not in · is empty · is not empty

• Text comparisons ignore case and surrounding spaces. 1,000 and 1000.0 both count as the number 1000.
• > and < compare numbers as numbers; other text (such as ISO dates 2025-06-01) is compared alphabetically, which is correct for ISO dates. Blank values never pass > or <.
• Nested fields of JSON or dataset rows: address.city = Berlin.
• A column name with spaces works as is (Monthly Spend >= 49); put it in quotes if it contains an operator ("a=b" = 1).
• Column names are matched without case. In a CSV, a column that does not exist stops the run before anything is charged and lists the real column names.

Output

• Dataset: every matching row with all its original columns and values, in the original order. CSV values stay exactly as in the file (a ZIP code 00123 stays 00123). Download it as CSV, Excel, JSON, XML or HTML from the run page.
• STATS record: rows read, rows matched, the conditions used, the rows charged and whether the run stopped early.

Limits (cannot be raised by input)

• Files up to 500 MB; JSON arrays up to 150 MB (an array has to be read as a whole — use NDJSON for bigger files); 5,000,000 rows read per run; 20 conditions per run
• Set a maximum charge per run to cap spend: the Actor stops cleanly when it is reached and keeps the rows found so far. maxOutputRows stops after that many matching rows
• Public files only (no login). Links to internal network addresses are blocked

Pricing

Pay per event: $0.00001 per row read ($0.01 per 1,000 rows), charged per row. Failed runs and the demo are free. On the Apify Free plan, the $5 of monthly platform credit covers about 500,000 rows a month — enough to try it on real data.

FAQ

Does it change my data? No. Rows are copied as they are; only the rows that do not match are left out. Excel files? Convert them first (for example with the Excel to JSON Actor from the same developer), or save the sheet as CSV.

If this Actor saved you time, a rating on the Store helps others find it.

More tools from the same developer

• CSV / XLSX Converter — convert CSV, TSV, Excel XLSX, JSON and NDJSON files or Apify datasets into each other
• Remove Duplicates — merge Apify datasets and remove duplicates by the fields you choose
• Dataset Splitter — split a dataset or CSV/JSON file into parts by rows, parts, column value or size, or take a random sample
• Google Sheets Export — export an Apify dataset to Google Sheets