Python
- 10 tips for making the Django Admin more secure
- Django Migrations: A Primer
- How Numba and Cython speed up Python code
Linkdump #69
Linkdump #68
R
- A comparison between spaCy and UDPipe for Natural Language Processing for R users
- Some datasets for teaching data science
- The Postmortem Airtime Effect
- Clustering of Labour MPs based on their voting patterns
Python
- Shallow vs Deep Copying of Python Objects
- faiss – A library for efficient similarity search and clustering of dense vectors
- Distributed Pandas on a Cluster with Dask DataFrames
- A brief tour of Python 3.7 data classes
Interesting articles, projects and news
- Family fun with deepfakes. Or how I got my wife onto the Tonight Show
- Warnung von Ex-Mitarbeitern: Facebook, Google & Co. machen süchtig und gefährden die Gesellschaft
- Rechte und Trump-Anhänger teilen Fake News am häufigsten
- Citizen Science: Das begehrte Wissen der Hobby-Forscher
- A Good Life For All Within Planetary Boundaries (O’Neill, D.W., Fanning, A.L., Lamb, W.F., and Steinberger, J.K) – with interactive data visualizations
- Asking the Right Questions About AI
- “Rechte manipulieren stärker als Linke”: Soziale Medien und die politische Debatte
- “Heatmap” for social athlete’s app reveals secret bases, secret places
- Geschichtsforscher werten Archive mit maschinellem Lernen aus
Vectorization and parallelization in Python with NumPy and Pandas
Modern computers are equipped with processors that allow fast parallel computation at several levels: Vector or array operations, which allow to execute similar operations simultaneously on a bunch of data, and parallel computing, which allows to distribute data chunks on several CPU cores and process them in parallel. When working with large amounts of data, it is important to know how to exploit these features because this can reduce computation time drastically. Taking advantage of this usually requires some extra effort during implementation. With packages like NumPy and Python’s multiprocessing module the additional work is manageable and usually pays off when compared to the enormous waiting time that you may need when doing large-scale calculations inefficiently.
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