Monday

Presenting a convenient method of loading, storing and manipulating time-dependent datasets using the Python Pandas Series and DataFrame objects.
Scientific data is often time-dependent and, due to instrument constraints, this is often not binned using exactly equal time intervals. Pandas is an open source Python library designed for data analysis, it’s valuable tool for storing, processing and manipulating such datasets in a fast and efficient way.
Here the Pandas library is presented as a tool for both manipulating the data directly and as an ideal base for the creation of data holding classes such as the SunPy TimeSeries.
Time will be spent explaining how various file formats can easily be opened as a Pandas DataFrame, followed by a demonstration pre-processing and peak detection will show how this can be used on observational data to detect solar and stellar flares.