Datablist is a CSV editor with a built-in tool to get distinct values from a CSV column. And you get the sum of occurrences for each value.

Example Distinct Values
Example Distinct Values

This tutorial shows you the step-by-step process to count and aggregate distinct values with a free online tool. No development or technical skills are required.

Get distinct values in a CSV file

First, load your CSV file into Datablist. The Distinct Values feature is available without registration. You can import a CSV file with a maximum of 10000 rows as an anonymous. For larger CSV files, just create an account for free. Registered users can load CSV files with up to 1.5 million rows.

Create a new collection ("+" button in the left sidebar) and then click "Import CSV" to load your file.

Import CSV file
Import CSV file

CSV columns and your CSV content is visible directly in Datablist in a few seconds. Click on the CSV column you want to analyze.

Open Calculations tool
Open Calculations tool

Select "Count distinct values" in the calculation options. Other options include Word Count, and Character Count.

Calculation Selection
Calculation Selection

Then run the process. Datablist analyses your data in real-time without sending the data to servers. You get results in a few seconds.

Distinct Value Results
Distinct Value Results

Distinct value algorithm explained

Datablist converts all values to lowercase before doing the aggregation. The algorithm is thus "case insensitive". Leading and trailing spaces are also removed.

The terms:

PARIS

and

      paRis

Are similar and will be aggregated.

When to use the distinct values feature?

This is great for aggregation of limited choice values (countries, status, etc.). For example if your CSV c

  • Leads Analysis - Get insights on your prospects list by running the distinct values counter on your prospect outreach status.
  • Geographical Analytics - Countries, Cities, and other location information can be aggregated to have a better understanding of your dataset.
  • Data Cleaning - Find outliers and irrelevant data by looking at the values with few occurrences. Learn more about Data Cleaning.