Argon DataViz helps you import CSV data and visualize it.
Easily import large data
Import large multi-GB CSV files without running into memory issues.
Includes an embedded database into which data is imported. Importing data into a database allows you to query and get the results back fast than if you were to search for the data directly in the CSV file.
Automatically identifies data types (string, integer, floating point, dates, etc.) by pre-parsing the first few hundred lines of data. This allows you to query data using expressions like “find all students aged 10 years or over”.
If the identified data types are not correct, you can choose the correct types before importing. For instance, an Amount column might include a $ sign before the number. You might want to remove this sign so the column can be queried and manipulated like a number.
You can also apply more extensive editing rules to the data before importing it.
Quickly search your data with powerful queries
After importing the data you can query it to isolate the data you are interested in. For example, find stores in a specified zip from a CSV file containing all the stores.
Extensive options for specifying search conditions including
Prefix search - search for text starting with a specified value
Phrase search - search for a word within a field.
Wildcard search - use wildcard characters such as
*and ? to build your search.
Regex Search - use a regular expression to match text content in a field.
Exact Search - use when you need to find text in a field exactly as specified.
Multiple-select - specify multiple field values to match a field.
Fuzzy search - find approximate matches for a specified field text.
Visualize your data with drag-and-drop
Once data is imported into the application, you can visualize it in a variety of ways. Most common chart types are supported.
Bar, column, area and line charts.
Stacked versions of these charts.
Pie and doughnut charts
Easily build pivot tables and charts
Pivot refers to a form of summarizing results by the unique values in two or more rows and columns. You can drag and drop fields into row and column buckets, and Argon will automatically aggregate results by unique values in these fields.
Pivot tables and charts are especially useful in detecting patterns in data.
Manage your data without writing code
Argon provides several ways of organizing and manipulating data to make it easier to work with.
Unpivot data - When the CSV dataset you have contains pivoted data, it might be easier to work with if you unpivot the data. This can also be viewed as a type of normalization. Argon provides ways to unpivot your data with a simplified GUI.
Join data - Using Argon, you can join CSV data with another (related) CSV file. For example, you can join Customer data with orders placed by the customer using a unique identifier.
You can export joined data to JSON in a way that preserves object hierarchy. For example, you can join customer data with order data and export the result to JSON in a format that each customer entry includes the orders placed by that customer.
Easily export results of a query to CSV.