Telecom Automation & Data Analytic

5 Easy Steps to Automate NDO with Python & Pandas

By Yosef Jatiwaseso • 6 min read • Published: June 3, 2026

Traditional RF optimization involved engineers manually downloading Excel sheets from OSS systems, matching cell IDs, and creating charts. In modern networks with tens of thousands of cells, this manual approach is too slow.

By applying Python and data analytics, Technical Managers and RF engineers can automate these flows. For example, a Python parser can connect to the NetAct OSS, extract KPI counters, and instantly flag any cell with a drop rate anomaly.

Libraries like Pandas are used for data wrangling, NumPy for mathematical analysis, and libraries like Dash or Streamlit to build interactive web dashboards for executive reporting.

Automation allows engineers to resolve network anomalies in minutes rather than hours, moving operations from a reactive state to proactive, automated optimization.