Messy spreadsheet? Clean it in clicks — no Python, no Alteryx, no code.
Remove Duplicates, Fix Casing, Filter Rows — All Without Writing a Line of Code
Business analysts and Excel power users spend hours manually cleaning data — deleting duplicate rows, fixing inconsistent capitalization, removing empty cells, trimming trailing spaces. Diwadi does all of this automatically, locally on your computer, in seconds. Works with CSV, Excel, and Parquet files.
Common Data Quality Problems That Slow You Down
Before you can analyze data, you have to clean it. Studies find that data professionals spend 60–80% of their time on data preparation. Here are the most common problems in real-world spreadsheets:
Duplicate Rows
Duplicates are pervasive — industry studies estimate 10–30% of records in large datasets are duplicates. A CRM export might have the same customer 3 times with slightly different email addresses. A survey might have repeated submissions. Duplicates silently inflate counts, skew averages, and corrupt analysis.
Empty Rows and Missing Cells
Exported spreadsheets often contain blank rows between sections, empty header rows, or records with key fields missing. Formulas break on empty cells. Pivot tables miscount. Joins fail. Manual deletion is tedious and error-prone on files with thousands of rows.
Inconsistent Text Casing
"New York", "new york", "NEW YORK", and "New york" are treated as four different values by any database or formula. This breaks GROUP BY queries, VLOOKUP matching, and pivot table grouping. Cities, country names, product categories, and job titles are all common victims of inconsistent casing.
Extra Whitespace
A space before or after a word is invisible in a spreadsheet but breaks exact matching. "Apple " and "Apple" will not match in VLOOKUP, SQL JOIN, or deduplication. Imported data from forms, APIs, and legacy systems routinely includes leading and trailing spaces.
Mixed Date Formats
One column might contain "2024-01-15", "01/15/2024", "January 15 2024", and "15-Jan-24" — all representing the same date. Sorting fails, date arithmetic breaks, and filters don't work across mixed formats. This is especially common in data exported from multiple systems.
Special Characters and Encoding Issues
Names with accents, currency symbols, smart quotes, and non-breaking spaces cause import failures, broken formulas, and database errors. Data exported from older systems or different locales often has encoding artifacts that need to be stripped or standardized.
The Old Way vs. The Diwadi Way
| Tool | Skills Required | Annual Cost | Large Files | Data Privacy |
|---|---|---|---|---|
| Python / pandas | Coding knowledge required — pandas, Jupyter, environment setup | Free (but your time isn't) | Fast for large files | Local — data stays on your machine |
| Alteryx | Drag-and-drop but complex workflow builder | $5,195 / year minimum | Excellent — built for enterprise data | Depends on deployment (cloud vs. desktop) |
| Excel (manual) | Basic — but repetitive and error-prone | $99–$150 / year (Microsoft 365) | Crashes or slows on files over 100K rows | Local — data stays on your machine |
| OpenRefine | Moderate — complex UI, steep learning curve | Free | Handles large files but slow on very large datasets | Local — runs in browser via local server |
| Diwadi | None — point-and-click, plain English | Free tier available | Handles millions of rows efficiently | 100% local — data never leaves your computer |
Data Cleaning Operations Available in Diwadi
Every operation works on CSV, Excel (.xlsx), and Parquet files without any setup or configuration.
Remove Duplicates
Most UsedRemove exact duplicate rows, or deduplicate by specific columns (e.g., keep one record per email address, even if other fields differ). Choose which record to keep — first occurrence, last, or based on a condition.
Filter Rows
PowerfulKeep only rows matching your conditions — filter by value, range, contains text, starts with, ends with, or regex pattern. Chain multiple conditions with AND/OR logic. Works on any column type.
Trim Whitespace
operations.items.2.tagRemove leading and trailing spaces from all text columns in one click. Also removes non-breaking spaces and other invisible characters that cause matching failures.
Fix Text Casing
operations.items.3.tagStandardize text to UPPERCASE, lowercase, Title Case, or Sentence case. Apply to all text columns or specific ones. Instantly resolves "New York" vs "new york" vs "NEW YORK" inconsistencies across entire columns.
Remove Empty Rows
operations.items.4.tagDelete rows where any cell is blank, or rows where a specific key column (like email, ID, or name) is empty. Also removes fully blank rows inserted by export tools.
Search and Replace
Regex SupportFind and replace values across entire columns or the whole dataset. Supports plain text and regex patterns — useful for standardizing abbreviations ("NY" → "New York"), removing unwanted characters, or fixing systematic errors.
Extract and Reorder Columns
operations.items.6.tagSelect only the columns you need, reorder them, and rename headers — without touching the source data. Useful for creating standardized exports from raw data with 50+ columns.
Before and After: Real Data Cleaning Examples
Customer List — Before Cleaning
Raw export from CRM with 3 common issues
| First Name | City | Status | |
|---|---|---|---|
| john.smith@acme.com | john smith | new york | active |
| john.smith@acme.com | John Smith | New York | Active |
| sarah.j@corp.com_ | Sarah Jones | BOSTON | active |
| (empty) | Mike Brown | Chicago | inactive |
| mike.b@firm.com | mike brown | chicago_ | Inactive |
- Duplicate email (rows 1 & 2)
- Inconsistent casing on names and cities
- Trailing space in email (row 3)
- Empty email field (row 4)
- Duplicate customer (rows 4 & 5)
Customer List — After Cleaning
After deduplication, casing fix, trim, and empty row removal
| First Name | City | Status | |
|---|---|---|---|
| john.smith@acme.com | John Smith | New York | Active |
| sarah.j@corp.com | Sarah Jones | Boston | Active |
| mike.b@firm.com | Mike Brown | Chicago | Inactive |
Your Data Never Leaves Your Computer
Business data is sensitive — customer lists, sales figures, employee records, financial transactions. Many cloud-based data cleaning tools require you to upload your files to their servers. Diwadi is different.
100% Local Processing
Every cleaning operation happens on your computer using your CPU and memory. No data is transmitted over the internet at any point — not even anonymized metadata.
No Account Required
Download Diwadi and start cleaning immediately. No login, no account creation, no email verification. Your data cleaning activity is not tracked or logged anywhere.
GDPR and Data Compliance Friendly
If your data contains personal information (names, emails, phone numbers), keeping it local means you don't need to add a third-party processor to your data processing agreements.
Works Offline
Clean data on a plane, at a client site, or on a machine with no internet access. Diwadi requires no connectivity to function — ever.
How to Clean Your Data with Diwadi
Download and Open Diwadi
Install Diwadi on your Mac or Windows computer. Open the Data Tools section. No account or internet connection needed.
Load Your File
Drag and drop your CSV, Excel, or Parquet file into Diwadi. It previews the first rows instantly so you can see the structure and spot issues before cleaning.
Apply Cleaning Operations
Select the operations you need: remove duplicates, trim whitespace, fix casing, filter rows, or search and replace. Each operation shows a preview of what will change before you apply it.
Export the Cleaned File
Save the cleaned data as CSV, Excel, or Parquet. Your original file is unchanged — Diwadi always writes to a new file, so you can compare before and after.
Data Tools in Diwadi
Clean Data
Remove duplicates, trim whitespace, fix casing — all in one tool
Remove Duplicates
Deduplicate rows by all columns or specific key columns
Filter CSV
Filter rows by conditions, text matching, or regex
CSV to Excel
Convert cleaned CSV files to Excel format
Excel to CSV
Convert Excel files to CSV for processing and sharing
Frequently Asked Questions
Can I clean data without knowing Python or SQL?
Yes — that is exactly what Diwadi is built for. Every cleaning operation is point-and-click: select the operation, pick your columns, apply. You do not write any code, formulas, or queries. Business analysts, operations teams, and Excel power users use Diwadi to do in minutes what previously required a data engineer to script.
How does Diwadi handle very large CSV files that crash Excel?
Excel struggles with files over 100,000–200,000 rows and often crashes or hangs on files with a million rows. Diwadi uses efficient streaming processing and can handle files with millions of rows without loading everything into memory at once. If your file is too large for Excel, Diwadi is designed precisely for that use case.
Can I remove duplicates based on specific columns rather than the entire row?
Yes. Diwadi lets you choose which columns to use for deduplication. For example, you can remove rows where the email column matches a previous row — even if the name or phone number is different. You can also choose whether to keep the first or last occurrence of a duplicate.
How is Diwadi different from OpenRefine for data cleaning?
OpenRefine is a powerful tool but has a steep learning curve — it runs as a local server accessed via a browser, uses its own query language (GREL), and requires familiarity with its facet-based workflow. Diwadi is designed for non-technical users with a straightforward interface: pick an operation, set parameters, apply. For common cleaning tasks like deduplication, casing fixes, and whitespace trimming, Diwadi is significantly faster to use.
Is Diwadi really free for data cleaning?
Diwadi has a generous free tier that covers the core data cleaning operations — removing duplicates, filtering rows, trimming whitespace, fixing casing, and search/replace. You can clean real business data without paying. Advanced features and higher volume usage have paid options.
Can Diwadi clean Excel files (.xlsx) directly, or only CSV?
Diwadi works directly with Excel (.xlsx) files, CSV, and Parquet format. You do not need to convert Excel to CSV before cleaning. You can also export the cleaned result in any of these formats — for example, clean an Excel file and export as CSV, or vice versa.
How do I standardize inconsistent date formats in a column?
Use Diwadi's search and replace with regex to normalize common date patterns. For example, you can convert "DD/MM/YYYY" and "MM-DD-YYYY" patterns to ISO 8601 format ("YYYY-MM-DD") using regex replacement rules. For complex date normalization, the filter and transform operations support pattern-based replacement across entire columns.
Does data cleaning in Diwadi modify my original file?
No. Diwadi always writes the cleaned data to a new output file. Your original CSV or Excel file is never modified. This means you can always compare the original and cleaned versions, and there is no risk of accidentally overwriting source data.
How much does Alteryx cost, and is Diwadi a real alternative?
Alteryx Designer starts at approximately $5,195 per user per year — it is enterprise software built for large data pipelines and BI teams. Diwadi is not a full replacement for Alteryx in enterprise ETL scenarios. However, for individual business analysts who need to clean and prepare data for reports, Diwadi covers the most common tasks (deduplication, filtering, casing, whitespace) at a fraction of the cost, with no coding required.
Can I chain multiple cleaning operations together?
Yes. You can apply multiple operations in sequence: first trim whitespace, then fix casing, then remove duplicates, then filter out rows where a column is empty. Each operation updates the preview so you see the cumulative result before exporting. This lets you build a cleaning workflow for your specific dataset without scripting.
Stop Cleaning Data by Hand
Diwadi handles duplicates, casing, whitespace, and filters — entirely on your computer. No uploads, no code, no expensive subscriptions.