Understand a dataset before you build a dashboard.
Upload CSV, XLSX, or JSON and Amridata profiles the dataset automatically: column types, descriptive statistics, missing values, duplicate rows, unique values, and useful starting points for analysis.
- Automatic column profiling
- Numeric + categorical summaries
- Quality checks without code
Less setup. More useful analysis.
Each feature is designed to move a real dataset forward rather than add another disconnected dashboard.
Know what is in the file
See row and column counts, inferred types, sample values, ranges, averages, and category frequencies without building formulas first.
Find data-quality friction early
Surface blanks, duplicate records, suspicious types, and columns that may need cleaning before you rely on a chart or report.
Move from inspection to action
Use the same dataset workspace to clean, visualize, export, or ask AI questions instead of moving between disconnected tools.
What Data Analysis actually does
Concrete product behavior—not placeholder features.
Type detection
Identifies likely number, date, category, text, boolean, and ID columns while still allowing a user override when the source is ambiguous.
Descriptive statistics
For numeric data, expose values such as minimum, maximum, mean, median, and standard deviation where available.
Category profiling
Understand unique-value counts, common values, and whether a text field behaves more like a category than free text.
Missing + duplicate checks
See where values are absent and whether exact duplicate rows exist before downstream analysis.
Rows workspace
Review data in a paginated table, change page size, and inspect records while keeping analysis context nearby.
Connected workflow
Open recommended charts, Custom Chart Builder, Clean Data, exports, AI Chat, and reports from the same dataset.
From raw file to next action
The feature stays connected to the same dataset, so you do not have to recreate context in a second tool.
-
1
Upload
Add a CSV, XLSX, or JSON file.
-
2
Profile
Amridata reads structure, types, statistics, and quality signals.
-
3
Inspect
Review columns and rows before making assumptions.
-
4
Act
Clean, chart, export, ask AI, or build a report.
Know what is included before you use it.
Amridata is most useful when the product is explicit about what it does, what it stores, and where a user should verify the result.
- Best for
- First-pass exploratory analysis, spreadsheet review, QA before reporting, and non-technical users who need a structured overview.
- Inputs
- CSV, XLSX, and JSON datasets supported by the upload workflow.
- Outputs
- Column profiles, quality observations, statistics, charts, cleaned data, exports, AI explanations, and reports.
- Control
- Users can rename columns, override column types, clean records, export data, save charts, and delete datasets they own.
Use it with the rest of Amridata
Analysis, cleaning, visualization, AI, and reporting are designed to work around the same dataset.
Data Analysis questions
Clear answers about the current product behavior.
Does Amridata require SQL or Python for data analysis?
No. The standard workflow is designed for browser-based analysis using uploaded files, automatic profiling, charts, cleaning controls, and optional AI assistance.
Can I correct a column type if automatic detection is wrong?
Yes. The dataset workspace supports user type overrides so you can correct an ambiguous field before charting or interpreting it.
Does analysis change my original file?
Amridata works on the imported dataset in the application. Cleaning actions change that dataset copy; your source file on your own computer is not modified.