The Data Trinity – Practical NumPy, pandas and Matplotlib a workshop with Jakub Urban & Jan Pipek
Sunday, 16 June, 14:00 in room EB129
In this tutorial, you will get your hands on the basic toolbox of any Python data analyst or scientist. Based on publicly available data, you will learn how to:
- Compute effectively with numeric arrays with NumPy (covering topics such as indexing and slicing, matrix operations, vectorized functions, broadcasting)
- Perform data manipulation and queries in pandas (explaining data frames, data I/O, data cleaning, complex indexing, aggregation, grouping)
- Visualize your results with Matplotlib (showing various types of plots and backends, mentioning alternatives when useful)
Workshop will take 3 hours.
There will be maximum of 30 attendees.
We’re sorry but registration is not possible anymore.
Prerequisites
We recommend attending the NumPy, SciPy, Matplotlib workshop in the morning session for a more in-depth understanding of NumPy and Matplotlib as we will focus more on Pandas.
Requirements
It is necessary to bring your own computer. The session will use Jupyter notebooks for interactive work. A pre-installed Python (ideally Anaconda distribution) is highly recommended (we will not perform installation on stage).
The full list of requirements and instructions is available in this GitHub repository More instructions will be posted here in due time.
Jakub Urban
I am a data analyst with a long experience in computational science, a university lecturer of scientific programming in Python and a PyData Prague meetup co-organizer. I have been using and promoting Python in the plasma physics academic community since Python 2.5 was released. I have steered my career recently to Quantlane, a Python-powered algorithmic trading company.
Jan Pipek
I am a data scientist and engineer at Showmax, helping neural networks understand what happens in movies and building a video streaming platform for Africa. I only recently converted from Monte Carlo simulations in medical physics.
I've been using Python for more than ten years, with a strong inclination for data analysis and visualization (having written several useless and hopefully at least one useful library – physt), but also trying to enjoy the language in the broader sense.
I am both happy and fortunate to be one of the PyData Prague meetup organizers.
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