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I am trying to check if the value in 'diff' column is greater than 0 if it is, then the value in 'worth' should be False else it should be True

I am using the below code to compute and check but it always gives me True. Can anyone point here what is the mistake. I am attaching pic of output as well

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I was working on my project and was using pm2-runtime command for the runtime environment but the problem coming in my terminal while running the command npm i gives 2 level warnings that are

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Hello dear stackoverflow users. I have an accordion. When this accordion is open, I want to make the invisible eye icon next to it visible. But which accordion is clicked, only its eye icon will open. Please help me :)

My code :

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I have a data frame like this,

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I have bitcoin historical data. I split the "DATE" Colum as "year month days and hour" because I wanted to sort data based on hours["AS it is hourly base data"]. the data goes up to 2021-12 ie Decmber["the dates goes from 1 to 30 every month"]. I want to sort this data further as:- "2019-Jan, 2020-Jan 20201-Jan" then "2019-Feb,2020-Feb, 2021-Feb" and soon on

Year Month DAy Hour open high low close 2019 1 1 0 3700.05 3725.58 3698.83 3715.09 2019 2 1 0 3700.05 3725.58 3698.83 3715.09 2019 3 1 0 3700.05 3725.58 3698.83 3715.09 2019 4 1 0 3700.05 3725.58 3698.83 3715.09 2019 5 1 0 3700.05 3725.58 3698.83 3715.09

can this be done by saying I have split the "DATE" column? if YES please any suggestion on how this can be achieved

The original "DATE" colum was as follows :- 2019-01-01T00:00:00Z

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I have a dataframe output from the python script which gives following output

Datetime High Low Time 546 2021-06-15 14:30:00 15891.049805 15868.049805 14:30:00 547 2021-06-15 14:45:00 15883.000000 15869.900391 14:45:00 548 2021-06-15 15:00:00 15881.500000 15866.500000 15:00:00 549 2021-06-15 15:15:00 15877.750000 15854.549805 15:15:00 550 2021-06-15 15:30:00 15869.250000 15869.250000 15:30:00

i Want to remove all rows where time is equal to 15:30:00. tried different things but unable to do. Help please.

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I have a dataset with the name of Danish ministers and their position from 1990 to 2020 (data comes from dataset called WhoGovern; https://politicscentre.nuffield.ox.ac.uk/whogov-dataset/). The dataset consists of the ministers name, the ministers position, the prestige of that position, and the year in which the minister had that given position.

My problem is that some ministers are counted twice in the same year (i.e., the rows aren't unique in terms of name and year). See the example in the picture below, where "Bertel Haarder" was both Minister of Health and Minister of Interior Affairs in 2010 and 2021.

I want to create a dataset, where all the rows are unique combinations of name and year. However, I do not want to remove any information from the dataset. Instead, I want to use the information in the prestige column to combine the duplicated rows into one. The observations with the highest prestige should be the main observations, where the other information should be added in a new column, e.g., position2 and prestige2. In the example with Bertel Haarder the data should look like this:

(PS: Sorry for bad presenting of the tables, but didn't know how to create a nice looking table...)

Here's the dataset for creating a reproducible example with observations from 2010-2020:

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The Question

How do I best execute memory-intensive pipelines in Apache Beam?

Background

I've written a pipeline that takes the Naemura Bird dataset and converts the images and annotations to TF Records with TF Examples of the required format for the TF object detection API.

I tested the pipeline using DirectRunner with a small subset of images (4 or 5) and it worked fine.

The Problem

When running the pipeline with a bigger data set (day 1 of 3, ~21GB) it crashes after a while with a non-descriptive SIGKILL. I do see a memory peak before the crash and assume that the process is killed because of a too high memory load.

I ran the pipeline through strace. These are the last lines in the trace:

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I have a question about how rebasing works in git, in part because whenever I ask other devs questions about it I get vague, abstract, high level "architect-y speak" that doesn't make a whole lot of sense to me.

It sounds as if rebasing "replays" commits, one after another (so sequentially) from the source branch over the changes in my working branch, is this the case? So if I have a feature branch, say, feature/xyz-123 that was cut from develop originally, and then I rebase from origin/develop, then it replays all the commits made to develop since I branched off of it. Furthermore, it does so, one develop commit at a time, until all the changes have been "replayed" into my feature branch, yes?

If anything I have said above is incorrect or misled, please begin by correcting me! But assuming I'm more or less correct, I'm not seeing how this is any different than merging in changes from develop by doing a git merge develop. Don't both methods result with all the latest changes from develop making their way into feature/xyz-123?

I'm sure this is not the case but I'm just not seeing the forest through the trees here. If someone could give a concrete example (with perhaps some mock commits and git command line invocations) I might be able to understand the difference in how rebase works versus a merge. Thanks in advance!

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