Analytics with 🐼 Pandas and Jupyterlab

Alexander CS Hendorf

Analytics Data Data Science Visualization

See in schedule

For Analytics Pandas is a de-facto industry standard and an established tool in the scientific community. Dealing with data-analysis is easy and simple - but there are some things you need to get your head around first as Data-Frames and Data-Series.

After this tutorial you will be able to work with Pandas and make simple data analytics incl. visualisations. Pandas is not only useful in data science it’s also a great tool for creating e.g. sales reports or any other data-driven report required in business. It’s easy to make fancy analytics while integrating with fellow co-workers used working with Excel.

This training will also get you started with Jupyter lab and Pandas.

PLEASE COME PREPARED: Follow the instructions here:
https://github.com/alanderex/pydata-pandas-workshop

This training is a good preparation for the "Get to grips with pandas and scikit-learn" training in the afternoon.
https://ep2019.europython.eu/talks/7PJy7GR-get-to-grips-with-pandas-and-scikit-learn/

Outline:

Part one: The Basics
- Working with pandas and Jupyter lab
reading and writing data across multiple formats (CSV, Excel, JSON, SQL, HTML,…)
- selecting and accessing data
- inner-mechanics of Pandas: Data-Frames, Data-Series
- boolean indexes
- summary and Q&A

Part two: Visualisation
- Pandas features directly accessible, powerful visualisations.
- data visualisation basics
- enhance visualisations / inner mechanics
- summary and Q&A

Part three: Data Analytics and Aggregation
- statistical data analysis and aggregation
- indexing
- data grouping and aggregation
- summary and Q&A

Part four: Data formats and scaling
- Limits of Pandas and how to scale with e.g Dask
- Speeding up by using optimised file formats as Parquet.

The workshop will be provided as Jupyter notebooks for the attendees to follow along.
The workshop does include exercises.

Type: Training (180 mins); Python level: Beginner; Domain level: Beginner