A2 | Similar to Lua and Javascript | Infrastructure Automation library

I am relatively new in dealing with txt and json datasets. I have a dialogue dataset in a txt file and i want to convert it into a csv file with each new line converted into a column. and when the next dialog starts (next paragraph), it starts with a new row. so i get data in format of

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I have been trying to sort the Column values from A to Z which are populated in the List Box.

I have tried with the following but it does not adjust it. Any help will be appreciated.

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I'm trying to parallelize a merge-sort algorithm. What I'm doing is dividing the input array for each thread, then merging the threads results. The way I'm trying to merge the results is something like this:

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I'd like to return the value of the start and end of a data frame based on the data it contains. Any suggestion is welcomed.

Data structure:

Output:

Sample data:

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Dataset looks like this : This is a sample dataset for number of employee login activity named - activity

I need to calculate few metrics, was able to do in python data frames, but new in mySQL.

1. what is the average number of employee active per day for month of jan 2018 by dept ( was able to do somewhat half of it, but results coming are not correct.

2. number of unique active employee (login >0) per month for jan 2018 for each dept_id (was able to do it)

3. month over month growth for all dept_id from dec-2017 to jan 2018 where at least one employee was active (login >0) - no idea how to do this in sql

4. fraction of users who were active in each dept_id for dec 2017 and were also active in the same dept_id for jan 2018

5. how many employee login in on 3 or more consecutive days in jan 2018

Any help would be appreciated.

Query written for case 1:

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So I was really ripping my hair out why two different sessions of R with the same data were producing wildly different times to complete the same task. After a lot of restarting R, cleaning out all my variables, and really running a clean R, I found the issue: the new data structure provided by vroom and readr is, for some reason, super sluggish on my script. Of course the easiest thing to solve this is to convert your data into a tibble as soon as you load it in. Or is there some other explanation, like poor coding praxis in my functions that can explain the sluggish behavior? Or, is this a bug with recent updates of these packages? If so and if someone is more experienced with reporting bugs to tidyverse, then here is a repex showing the behavior cause I feel that this is out of my ballpark.

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Overview

I am trying to tabulate time over days under Google Sheets and see each person's availability based on their start and end times which changes almost every week.

File Information I have this Sample Availability Timesheet with two Sheet-Tabs.

Master Sheet-Tab: This Sheet-Tab contains the list of employees with their respective start-time & end-time.

Availability Sheet-Tab: This Sheet-Tab contains the list of employees and a timescale with one hour hop. The resource availability is marked with Y, and by N if the resource is not available using the following formula:

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I'm working with some data where I have hourly observations for patients. In some cases, some of the features for a specific patient are completely empty. I'm trying to find a way to impute the data by using constant average that's based off a population subset of 50 other patients who have the same gender and a similar age. I've given a simplified look at the data below:

HR O2Sat Temp Platelets Age Gender PatientID 80 98 36.5 NaN 52 1 A0 82 96 37.0 NaN 52 1 A0 82 100 36.3 160 53 1 A1 90 93 36.6 165 53 1 A1 83 95 35.9 140 23 0 A2 79 98 36.2 155 23 0 A2 88 92 36.6 163 60 0 A3 90 91 36.3 165 60 0 A3 81 95 37.1 NaN 20 0 A4 81 92 36.9 NaN 20 0 A4

I've reordered the dataframe by age and have this code so far

data = data.sort_values(['Age']).groupby(['PatientID','Gender']).apply(lambda x: x.fillna(x.mean()))

But I know that that's going to use all of the available data to find the mean but I'm not sure how to limit it to 50 patients of a similar age.

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I need to generate a list of dates in a dataframe by days and that each day is a row in the new dataframe, taking into account the start date and the end date of each record.

Input Dataframe:

A B Start End A1 B1 2021-05-15 00:00:00 2021-05-17 00:00:00 A1 B2 2021-05-30 00:00:00 2021-06-02 00:00:00 A2 B3 2021-05-10 00:00:00 2021-05-12 00:00:00 A2 B4 2021-06-02 00:00:00 2021-06-04 00:00:00

Expected Output:

A B Start End A1 B1 2021-05-15 00:00:00 2021-05-16 00:00:00 A1 B1 2021-05-16 00:00:00 2021-05-17 00:00:00 A1 B2 2021-05-30 00:00:00 2021-05-31 00:00:00 A1 B2 2021-05-31 00:00:00 2021-06-01 00:00:00 A1 B2 2021-06-01 00:00:00 2021-06-02 00:00:00 A2 B3 2021-05-10 00:00:00 2021-05-11 00:00:00 A2 B3 2021-05-11 00:00:00 2021-05-12 00:00:00 A2 B4 2021-06-02 00:00:00 2021-06-03 00:00:00 A2 B4 2021-06-03 00:00:00 2021-06-04 00:00:00 ...