Ask questions about messy data in plain English. 

Upload a messy CSV or Excel file, describe what you want to know in plain English, and get a clean result table — plus the exact code that produced it. 

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messy_sales_q3.csv
Whisper Query
Show Q3 revenue by region
Clean Table
Fixed headers & dates
Python Code
pandas.groupby()
Whisper QueryPandas AI
PromptShow Q3 revenue by region
TransparencyFull Python Pandas
Execution time0.14s instant
Generated Python Code
df = df.groupby('region')
res = df['revenue'].sum()
out = res.sort_values(desc)
Cleanedprocessed in 140ms

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GitHub
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Figma
Notion
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Everything you need for messy exports

CSV Whisperer handles broken headers, mixed date formats, and conversational refinement so you get instant clean results without template setup. Upload a file to get started

No manual cleanup required

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Iterate conversationally

Conversational AI

Ask questions about messy data in plain English.

CSV Whisperer turns plain English questions into clean result tables. Upload any file, describe what you need, and keep refining conversationally.

Upload a file
Ask what you want to know in plain English...
Upload raw export.csv
Ask: 'Show top 5 sales regions in Q3'
Get clean table + Python code

Conversational data analysis

Describe what you want to know in plain English. CSV Whisperer cleans headers, fixes dates, and yields instant result tables.

Generated by CSV Whisperer
Input
messy_sales.csv
Query
Filter Q3 revenue by region
Output
Clean table + Pandas code
1 clean table, 100% transparent

Execution time: 0.14s. Ready to refine conversationally or export code.

Upload a file to get started

Full Code Transparency

See the real code behind every answer — full transparency, nothing is a black box. Copy or export anytime.

# Generated Python Pandas Code

"df": "pd.read_csv('export.csv')"

"result": "df.groupby('region')['sales'].sum()"

Export options
Python .pyClean .csv
No setup required

Works on any messy export out of the box

No broken headers, mixed date formats, or manual cleanup. Upload any spreadsheet export and ask questions right away.

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Upload raw_export.csv
Parsed
Fix broken headers
Unify date formats
Clean result table

Query History

Browse past queries, refine previous table transformations, and see every generated Python code snippet.

Recent queries

QUERY-20412s ago
QUERY-20405m ago
QUERY-203912m ago
QUERY-203815m ago

Query details

Inspect every step of the transformation. See cleaned column names, type conversions, and code output.

Source: messy_sales_q3.csv
Cleaned 14 columns
Transformation complete

QUERY-2041 completed in 0.14s

Executed 2s ago
Header Repair

Fixes multi-line headers, missing keys, and duplicate column names.

Date Standardization

Unifies ISO, US, and EU date formats across all rows automatically.

Conversational Edits

Iterate and refine analysis without starting over from scratch.

Transparent Code

Full Python Pandas code view — nothing is a hidden black box.

Use Cases & Examples

Built for Ops, Sales, and Finance

Upload any messy export, describe what you need in plain English, and get clean result tables with transparent code.

Upload CSV
→
Fix headers
→
Clean table
InputRaw sales_export.csv
Cleaned in 0.14s•••

Operations

Clean messy sales export & repair headers

Billing file
→
Fix dates
→
Reconciled
→
Pandas code
InputMixed ISO & US dates
Instant date conversion

Finance

Reconcile mixed date formats in billing

CRM dump
→
Query > $10k
→
Filtered list
QueryExtract leads > $10k
Instant filtering

Sales

Filter high-value leads from CRM dump

Survey dump
→
Repair rows
→
Clean grid
→
Summary
InputUnstructured headers
Header repair complete

Operations

Parse survey CSV with multi-line headers

Revenue file
→
GroupBy Region
→
Summary
QueryQ3 Revenue by Region
Grouped aggregation

Finance

Group Q3 revenue by region & product

Customer file
→
Refine query
→
Updated table
PromptFilter active churn risk
Refined in 0.1s

Analytics

Iterate conversationally on churn data

df.groupby('region').sum()

Developers

Export Python Pandas code for data pipeline

Stop fighting spreadsheets. Start asking questions.

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