One of our customers said: Our small mining operation needed to go from paper based process to digital forms. At first, Google forms allowed us to use this Web-based platform that lets individuals and businesses of all sizes build customizable forms to conduct surveys and generate real-time response charts.
We saw that a small sample of our field workers quickly adopted the new way of working.
Step 1: accomplished.
Now unto step 2.
How do we deploy this unto our whole team? We needed email notifications, offline response collection when without wifi on the field. Our CIO and his director of operations needed deep data and trends analysis as well. Our inspectors, when doing their audits, needed to capture approx. 25 high definition pictures, some audio notes and a video which wasn't really possible with google forms.
So, we can 100% credit the use of google forms to our transition towards a paperless process, but as we navigated saashub.com a little more, we were able to discover a world of alternatives. We strongly suggest to start using google forms before undergoing a big implementation plan towards such enterprise level inspection tools like nspek or even cheaper solutions like prontoforms.
I am not sure if we would start with google's solution first if we would to do this digital transformation all over, but it did allow us to discover it's limits pretty quickly.
At some point, we needed custom fields and functions, and none of us was able to code, so the nSpek training that comes with the application definitely sets it's self apart, giving us full autonomy.
Based on our record, Dask seems to be more popular. It has been mentiond 16 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk. Source: over 2 years ago
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec. Source: over 2 years ago
Dask: Distributed data frames, machine learning and more. - Source: dev.to / over 2 years ago
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:. - Source: dev.to / over 2 years ago
I’m quite sure dask helps and has a pandas like api though will use disk and not just RAM. Source: over 2 years ago
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Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
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NumPy - NumPy is the fundamental package for scientific computing with Python
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