wholly | jQuery plugin used to select the entire table row | Grid library

Is there a way to convert the following into code that takes advantage of pyspark parallelization in the for loop?

I'm trying to get some insight in this room for optimization for a SQL query (BigQuery). I have this segment of a WHERE clause that needs to include all instances where h.isEntrance is TRUE or where h.hitNumber = 1. I've tested it back and forth with CASE statements, and with OR statements for them, and the results aren't wholly conclusive.

It seems like the CASE is faster for shorter data pulls, and the OR is faster for longer data pulls, but that doesn't make sense to me. Is there a difference between these or is it likely something else driving this difference? Is one faster/is there another better option for incorporating this logical requirement into my query? Below the statement is my full query for context in case that's helpful.

Also open to any other optimizations I may have overlooked within this query as lowering the runtime for this query is paramount to its usefulness.

Thanks!

During update Visual Studio 2019 to latest version (16.9), setup failed. Stupidly I closed installer window without read the error. When I retryed the update, the Installer crashes few seconds after start. I have uninstalled wholly Visual Studio, but Installer still crash. I think something is left dirty after first update fail, I tried to search in log files recoverd by collect.exe tools, but I can't found usefull information in that bilions of messages. Reinstalling Windows must be the last choice, there is a way to take crash error for undertand the problem and search for a solution?

EDIT

Also InstallCleanup.exe don't solve the problem

I have recently sourced and curated a lot of reddit data from Google Bigquery.

The dataset looks like this:

Before passing this data to word2vec to create a vocabulary and be trained, it is required that I properly tokenize the 'body_cleaned' column.

I have attempted the tokenization with both manually created functions and NLTK's word_tokenize, but for now I'll keep it focused on using word_tokenize.

Because my dataset is rather large, close to 12 million rows, it is impossible for me to open and perform functions on the dataset in one go. Pandas tries to load everything to RAM and as you can understand it crashes, even on a system with 24GB of ram.

I am facing the following issue:

• When I tokenize the dataset (using NTLK word_tokenize), if I perform the function on the dataset as a whole, it correctly tokenizes and word2vec accepts that input and learns/outputs words correctly in its vocabulary.
• When I tokenize the dataset by first batching the dataframe and iterating through it, the resulting token column is not what word2vec prefers; although word2vec trains its model on the data gathered for over 4 hours, the resulting vocabulary it has learnt consists of single characters in several encodings, as well as emojis - not words.

To troubleshoot this, I created a tiny subset of my data and tried to perform the tokenization on that data in two different ways:

• Knowing that my computer can handle performing the action on the dataset, I simply did:

I know how to do this for UIWebView, but it is deprecated. I have figured out how to hide both the vertical and horizontal scroll indicators, disable scrollview bounces and disable the pinch gesture recognizer but still haven't found a way to wholly disable horizontal scrolling in the webview. Any help would be appreciated, below is my WebView.Swift.

I am trying get the definitions of certain words using this code:

It could be my code is wrongly implemented, but I'm finding that while I can serve up GET requests from literal data, I cannot update that data and have it shown as updated in subsequent GET requests. I also cannot have POST requests update the data.

So it behaves as though somewhere in Python's HTTPServer or BaseHTTPRequestHandler there's caching or forking happening.

Thanks in advance for looking it over, but, gently, no, I do not want to use a non-core 3.8 module or re-write with a wholly different framework or some Flask. I think this should work, but it's misbehaving in a way I can't spot why. If I were using C or Go's built in libraries it'd expect it would not be as much of a head scratcher (for me).

To demonstrate, you'd run the following python implementation, and load http://127.0.0.1:8081/ two or three times:

1. Summarize the problem

• The goal is for the styling of the header/navbar to change as the viewer scrolls down the page. The header is already sticky/fixed in place, but I want the background/text color to change once the user scrolls down. All other components are adhering to the styling that I want so far, but trying to figure out this header has really been confusing for me.

1. Describe what you've tried

• I tried to use a hook that I found online here https://www.ibrahima-ndaw.com/blog/build-a-sticky-nav-with-react/ but am unsure how to translate from this react app to a Gatsby app (for instance, I have a Layout component and I import this into gatsby-browser.
• Another thing I tried was using this https://github.com/tailwindlabs/headlessui/blob/develop/packages/%40headlessui-react/README.md#component-api particularly the Transition component. It seems to suit my needs but I am wholly unable to understand how to get the Gatsby App to recognize any scroll events. I was able to at least test the component with the button example, and it worked, but the goal is to use some kind of scrolling event.

1. Show some code -Here is my Header component (utilizing tailwindcss):

I'm using python 3.8, plotly 4.14.1, pandas1.2.0

I can't work out how to separate my data in pandas and assign data to counters so I can update a heatmap.

I want to create a risk matrix of impact x likelihood and have those numbers shown on a Plotly heat map.

Hard coding the data into the dataframe and it works as expected

below with figure factory