Analysis Without the Headache
Get insights from your CSV files without Excel crashes or Python complexity.
Stop Fighting With CSV Files
If you're reading this, you're probably frustrated. Good. That means you care about getting work done.
The tools in this section are designed to solve one thing: your CSV problems.
No fluff. No "enterprise features" you'll never use. Just solutions to common, annoying problems.
What You'll Find Here
- Make Charts from Messy CSV - Create bar, line, and scatter charts from your broken CSV data because visualizing disasters is still useful.
- Excel Chart Extractor - Extract embedded charts from Excel files and recreate them as interactive visualizations.
- Online Pivot Disaster - Summarize broken data like in Excel but without Excel crashing every 5 minutes.
- Calculate Stats on Broken Data - Calculate Mean, Median, Mode, and Standard Deviation on your disaster data.
- Automated Disaster Report - Generate a full report showing how broken your data quality is with one painful click.
- Group and Aggregate Mess - Group broken rows by category and calculate sums or averages when nothing adds up correctly.
- Query Disaster CSV - Run SQL queries directly on your broken CSV files because flat files suck for analysis.
- Find Relationships in Broken Data - Find relationships between variables using Pearson Correlation even when your data is a mess.
- Count Broken Values - Count frequency of unique values in a broken column.
- Compare Two Broken CSVs - Find differences (added/removed rows) between two CSV disasters because versions never match.
- Guess Data Types from Mess - Try to infer data types and structure for SQL CREATE TABLE when everything is stored as text.
- Profile Broken Text Data - Analyze word frequency and text length in your broken text columns.
- Ask AI About Your Mess - Ask AI questions about your broken data and hope it understands the chaos.
- AI Data Transformer - Transform and edit your CSV using natural language commands. AI converts your instructions into code and applies changes instantly.
- Broken Forecasting Tool - Attempt to forecast future disasters using broken historical data.
- K-Means Grouping Hell - Automatically group similar broken data points that shouldn't be together.
- Linear Regression Hell - Model the relationship between two variables that don't actually relate.
- Find Data Anomalies - Find broken data points that don't fit the pattern using Z-Score because outliers break everything.
- Fix Outlier Disasters - Cap, remove, or flag anomalies that destroy your dataset statistics.
- Data Normalization Disaster - Scale broken numbers to 0-1 range or standardize them when nothing is standardized.
- One-Hot Encoding Disaster - Convert categorical text disasters into binary numbers for ML because machines hate text.
- Group Numbers into Broken Buckets - Group continuous numbers into arbitrary buckets (e.g. Age groups) because granularity is painful.
- Rolling Statistics Disaster - Smooth out volatile broken data using rolling windows that don't fix anything.
- Fill Missing by Group Hell - Fill missing values based on broken group averages because individual values are unknown.
- What-If Disaster Builder - Create "What-if" scenarios by modifying variables that never predict reality.
- Generate Fake Data - Generate synthetic fake data samples to expand tiny broken datasets.
Why These Tools Exist
Because Excel crashes. Because Google Sheets is slow. Because Python is overkill for simple tasks.
We built these tools out of frustration. Every feature exists because someone (probably you) kept running into the same annoying problem.
Your data stays on your computer. Always free. No nonsense.