accounting | Double-entry bookkeeping library

This math is not platform specific and I'll take any language as an answer. This is difficult to explain why I'm doing this, but I'll try to include images.

I have a view (View A) that overlays a map as a container. The purpose is to contain our content while remaining fixed to the map as the user drags the map. That view has a coordinate system where it's origin is in the top left of the screen. It will be our absolute coordinate system, where we are trying to convert the positions to and from.

Next, we have a Rectangle that is formed in the intersection between View A and what is visible on the screen. I achieved that with the following property in my UIView:

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If I have the following saved Excel document with 26007.930562 in every cell, where the column names represent the Excel formatting I am using for the given cell:

and I run the following Python code:

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I want to create two new columns in job_transitions_sample.csv and add the wage data from wage_data_sample.csv for both Title 1 and Title 2:

job_transitions_sample.csv:

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I am needing help returning the current values in my SQL view in SQL Server.

I have a whole lot of columns that are joined by a UNION ALL in a SQL view. One of those values is the average pay of that individual over a year.

The current view looks something like:

Person Location Average A X 30,000 B Y 40,000 C X 50,000 D Z 30,000 E Y 60,000

(Please excuse the dummy variables and data)

This average value was calculated from two values in a different table, and has been joined onto the view with a LEFT OUTER JOIN. This means any future calculations based on [Average] will be outside the cte.

I am now wanting to add another column that obtains the 'Package Average'. This is the average of the average, but only accounting for each value once. In this example, that would be 45,000 (as opposed to 42,000 if we counted for the second 30,000).

I am also wanting this Package Average placed on every line of the view.

I know of AVG(DISTINCT [Average]) however that requires an aggregate. I also know of the analytical function avg([Average]) however that doesn't working with DISTINCT.

Any advice would be greatly appreciated.

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Why is rendering the parent component and the child trying to enter the child component

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This question is part of a previous one that I had (How to get the average temperature for N days prior to a specific date in R?), however things have complicated a bit (at least for me).

I have two datasets: one dataset (A) has the temperatures for each day, and on the other dataset (B) I have the individuals id and the date of birth (dob). I need to get the average temperature for the last 3 days prior to the dob of each individual. For example: if individual 1 was born in 02/20/2021, I need the average temperature from 02/17/2021 to 02/19/2021. However, I also have data coming from different weather station id (in df A), that needs to correspond with the right farm (in df B). Is there a way I could do that in R, so my output would be ind | dob | avg_temp, accounting for the right weather station. Here is one example data (in my real case, my data has a very large number of days, individuals, farms and station):

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I have a dataframe accounting different LEGO pieces contained in each of my LEGO set boxes. For each set box, there are always many different regular pieces, but somemtimes the box contains also some additional spare pieces. So the dataframe has a boolean column to distinguish that condition.

Now I want to summarize the dataset so I get just one row per LEGO set (groupby set_id) with a new column for the total amount of pieces in that set box (aggregated sum of "quantity").

My problem is that I also want two additional columns for accounting how many of those pieces are "regular" and how many are "spare", based on the True/False column.

Is there any way of calculating those three sum columns by creating just one additional dataframe and just one .agg() call?

Instead of creating 3 dataframes and merging columns, which is my current approach:

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I have some data in Excel that's already laid out in groups (it's a Statement of Cash Flows from some accounting software) that I'm reading into Pandas with the read_excel() method. I want to create an index on the first 4 columns, but I can't figure out how to maintain the hierarchy of accounts. Here's what the hierarchy looks like:

When I try to either create a multi-index from the first 4 columns or if I do a ffill() on them, Pandas (effectively) does this...

The highlighted cells are incorrect for what I need and they should be blank/NaN to maintain the hierarchy. When the subcategory B2 on Level 2 starts, any categories from levels 3 and 4 should not be filled.

Here's what I'm trying to achieve:

Ultimately, these are Statements of Cash Flows from multiple years that have slightly different charts of accounts, so my hope was to import them into Pandas DataFrames and merge them so the accounts all line up across the whole time period... this is just the first step in the process. I could code this into a dictionary manually, but am wondering if it's possible more simply within Pandas?

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Frequently I need to do something in SQL Server that I just don't know how to do properly.

Basically, if I have a table #sample that is like this:

DEPARTMENT DATE ACCOUNTING A FINANCE A PAYROLL A ACCOUNTING B PAYROLL A FINANCE A PAYROLL B PAYROLL C

I need to query that table to get an output that is this:

DEPARTMENT DATE DUPECHECK ACCOUNTING A 1 ACCOUNTING B 2 FINANCE A 1 FINANCE A 1 PAYROLL A 1 PAYROLL A 1 PAYROLL B 2 PAYROLL C 3

In Oracle Hyperion Brio at my old job I used to create a column that value 1 for every row and writing a function Cume on that column with a break value of the column I needed to count.

In Excel, I could create a concatenated column that has my break values, and then do a countif(concat:concat,concat1) copy that formula down.

I feel like it should be something similar to this, but that's not it at all:

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