Swifter | macOS app to manage carthage | DevOps library

I usually don't do matrix manipulation with DataFrame but with numpy.array. So I will first convert them

This flow seems to have not been officially supported since the retirement of TwitterKit on May 1, 2018. Any applications that still offer this functionality to their end users likely have some sort of partnership with Twitter to allow for this which is otherwise undocumented publicly.

The reasoning for this was detailed sparsely in a related Twitter Developer Forums thread:

Unfortunately at this time we have had to make some incremental adjustments that are not so smooth for everyone; especially as you’ve found, for mobile app developers. We’re in the middle of a transition to a new API platform (via Twitter Developer Labs) and there will be some changes as we go along - we are asking for your feedback to help us, and if you check the ideas, authentication and authorization is something we’ve heard a lot about.

I cannot tell you today exactly when we’ll get to an enhanced solution, but this is something we are actively working on.

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The official guidance is to use the traditional WebView method until Twitter officially re-releases this capability:

I don’t believe that this is possible any longer, so I would encourage you to use the full OAuth flow. I apologise that this may not be what your users prefer, but this is the documented way to gain authorisation (we do not document or support URL schemes for the native iOS app).

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We do not support or document any means of users signing in to Twitter outside of the OAuth flow; any use of undocumented features is subject to change without notice.

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As linked above Twitter appears to be soliciting feedback on this via their UserVoice page, should you feel so inclined to suggest the re-implementation of such a feature.

Convert this method in async, passing closure with [TweetSentimentClassifierInput] as closure argument, and an error as secondary closure argument,

Use numpy.where with Series.str.contains:

To fix the slow code, I just initialized the import and translate client outside the function once.

In the case of the 403 POST error, I had to create another GCP account. When I saw the quotas in the old account (trial), nothing was exceeded or close to, but the trial period apparently ended and I didn't have the free credits ($400) anymore. I tried enabling billing for the API (and checked my card wasn't defunct) but that didn't change much. Translate by batch worked in my newer account.

So, it was just an account issue rather than an API issue.

Use codable and a jsonEncoder to convert the users array to data and then convert them back to a jsonObject and pass it in:

First idea is use vectorized solution with pass column to to_datetime and generate ouput column by numpy.where:

Because I want to speed up calculating "norm" , I was used "swifter". It works on PC but it doesn't work on server! After removing that, It works!

This error is happening because of sending large objects to redis. merged_df is a large dataframe and since you are calling get_meal_category 10 times, Ray will attempt to serialize merged_df 10 times. Instead if you put merged_df into the Ray object store just once, and then pass along a reference to the object, this should work.

EDIT: Since the classifier is also large, do something similar for that as well.

Can you try something like this: