Scientific python programmers adore Pandas due to its many functionalities. In particular, for data manipulation and analysis it offers handy data structures and operations for numerical tables and time series. Combined with the rest of the SciPy stack and scikit-learn (e.g. for Machine Learning Analysis), multiple goals can be achieved. When it comes to on-line data analysis, interaction, or simple data navigation by multiple users, the SciPy stack can be stressed to its limits. It is here where the combination of InfluxDB as a data source and Grafana as a visualization tool have gained increasing awareness in recent years. Both methods of data analysis and visualization have their advantages and drawbacks. My talk will evidence some of them, as well as show what can be gained by combining python-based offline analysis with query language-based online analysis.