Make input formats and validation rules explicit.
MyDataStories / What we solve
Automate data work without losing control
We use Python to clean, validate, combine and analyse data, with clear outputs and exception handling.
Run consistent transformations and scheduled jobs.
Log what ran, what changed and what needs review.
We agree measures and definitions with each organisation. Any demonstration charts use illustrative data, not client performance claims.
Discuss your data →Python & Automation
Automate analysis with reproducible Python
We use Python to clean data, build automated checks and develop transparent forecasting and machine-learning demonstrations. The example shows a calculated signal, not an AI-rendered picture.
- Pandas data preparation and automated validation
- Scheduled analysis and reproducible Python workflows
- Forecast evaluation and explainable machine-learning models
Python — water demand pattern
Daily consumption across a sample period
Key / units: Million litres per day; illustrative daily observations. Values shown in the chart are synthetic examples, not customer results.
Illustrative example using synthetic data; not a customer result.
Python-powered analytic models. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
What we solve
Data tasks that require too much manual effort or repetitive file handling.
We develop Python and pandas workflows to ingest, validate, transform, reconcile and export datasets, with useful error logs and checks.