With the ongoing paradigm shift towards a data mesh that

About double that of nested np.where(), but not only does this save you from the table flipping parenthesis debugging, but also the absentminded choicelist change. I will be the first to bite the bullet, I have forgotten to update choicelist for way too many time that I have ended up spending more than quadruple the trying to debug my machine learning models. Trust me, np.select() with dict. It is worth it!

Notebooks, whether they’re used to run Spark jobs on top of a Hadoop cluster (Apache Zeppelin) or a Dash web application (JupyterDash), proved to be a viable option to turn data into insights in this data-driven era. And with metadata-based search engines like Lyft’s Amundsen and LinkedIn’s DataHub proving to be a powerful new asset for companies, benefitting from the flexibility of notebooks to add dashboarding capabilities to such tools would be a useful upgrade.

Numpy’s vectorised operations: If your code involves looping and evaluating unary functions, binary functions or functions that operate on sequences of numbers. You should definitely refactor your code by transforming your data into a numpy ndarray, and make full use of numpy’s vectorised operations to immensely speed up your script. Conclusion: This is a situational winner! Check out here at Numpy’s official documentation for examples of unary functions, binary functions or functions that operate on sequences of numbers!
numba.njit: Now this is real business, real vectorisation. When it comes to optimised loops, you simply cannot easily beat numba without super genius hacky moves. It tries to move any numpy evaluation to as close to C as possible to supercharge its efficiency. While it can light-speed your numeric calculation, it also restricts itself to numeric calculations, which means no pandas series, no string indexing, just numpy’s ndarray with types int, float, datetime, bool, and category. Conclusion: A real CHAMPION if you are comfortable with using only Numpy’s ndarray and translating your logic into numeric computations and numeric computations only. Learn more from here.
The application determines which chart is the most suitable for every dataset and then relies on the retrieved aggregates to populate a dashboard template (using a notebook)

This dynamically-built dashboard gives a big-picture overview of everything related to the query, while also allowing in-depth analysis by the user (leveraging notebooks’ capabilities)

As you can see from the above, you will need to update both condlist and choicelsit to make sure that the code runs smoothly. But seriously, who has time for that when Netflix is within our hand’s reach? Well, I have got you, pal! By changing it to a dictionary, we will be hitting roughly the same time and memory complexity but with a way more maintainable code snippet:

Congrats, you have survived it. I cannot tell you how many times I have spent on counting the closing parentheses, but hey, this gets the job done! We have slashed another 10ms from pandas .loc[]. However, this snippet is simply not maintainable, which means, it is not acceptable.

If possible, go for numba.njit; otherwise, np.select with a dict will help you sail afar, my friends. Remember, every little bit of improvement helps! Let me know if you have learnt something new from this! And please also let me know if there are other neat tricks that I have missed!

The application would rely on the existence of aggregates and extensive metadata tags for every dataset available within the company and its external sources. These tags will be stored in a dedicated metadata search tool that the application will use to answer users’ queries by matching keywords in the query with the corresponding metadata tags (via an NLP module). It will then run a dynamic notebook with the corresponding parameters to generate a fully-functioning dashboard, assigning for each chosen dataset a corresponding chart type.

Just a few years ago, notebooks were merely used for ad-hoc data exploration and analysis, while dashboards were the norm when it came to business-oriented analytics, KPIs, and visualizations. But then slowly the notebook-based use cases started to multiply thanks to the shift towards data-centric approaches for all kinds of businesses and services. This shift necessitates tools that are flexible, extensible, and easy to put in place and evolve — characteristics that BI-era dashboards lack.

So you may ask, how can we implement the logic we state above with a bisection function like np.where()? The answer is simple, yet disturbing. Nesting np.where()… (Spoiler alert: this snippet might be triggering)It uses a simular syntax as np.where(), except that the first argument is now a list of conditions, which should have the same length as the choices. One thing to bear in mind when using np.select() is that a choice will be selected as soon as the first condition has been met. This means that if a superset rule comes before a subset rule in the list, the subset choice will never be chosen. To put it in context:The application runs search queries on the metadata engine (using the aforementioned tags and keywords) and retrieves lists of all the datasets (tables, columns, APIs, etc.) that may interest the user

Related posts:

Im Zeitalter der Transformation bewegt sich die Erde in die Auflösung der Anbindung all ihre Diener; jeder Ruf gen Himmel ist untersagt, wenn das letzte Tor am Filament zu singen beginnt; jeder Geist…

Get Resident Evil 4 steam key free. Activate Resident Evil 4 CD key and start downloading your game on Steam. Activation code is delivered instantly! If you’re a fan of survival horror games, then…

Nairobi National Park is a unique and special wildlife reserve located just outside the bustling city of Nairobi, Kenya. It is the only national parks in the world situated within a capital city’s…