voter | Мастер-класс для студентов МАИ

I want to keep track a vote on each time it's submitted. All corresponding data in the submittedVoteId is what I want to use to count the vote for the aspirants

Here are the data I got when voters submitted their votes

I'm simulating US governmental structure and I have a list of n voters (random length between 10,000 and 100,000).

I'd like to create constituencies, which means subdividing that into states, and thence into districts.

All of these are randomly sized.

How can I take a list of n elements and create m lists of random size, which together constitute the original n?

I'm creating my first Flask project. The first part of the project is obviusly the login part. I made with html tho page for sign up and login. Then I wrote the flask part of the project. The code is the following, divided in the main.py file

So there is CM_satisfaction column that contains different values like Fully Dissatisfied, Somewhat Satisfied, and Fully_Satisfied in pandas. I need how many voters where Fully_Satisfied. I have found out that using

I'm new to programming and to Swift; apologies if I'm asking an obvious question. I'm trying to simulate a ranked-choice voting algorithm. Several parts of it work, but I'm stuck on a basic idea: I need to generate an arbitrary number of arrays that contain each voter's imaginary votes. I want software to produce many arrays that look more or less like this:

I am trying to normalize an array of data of the following structure Array. using the createEntityAdapter.

After fetching the data with a createAsyncThunk and returning them, I did set them in the extraReducers like this:

/*

I am trying to program a Tideman alternative method algorithm.
I am taking i/p through command-line-argument.
When I try to print the populated candidate array.
It is printing single characters when format specifier is char type. The result of other specifier is attached.
*/

/*

Same logic when executed on CS50 IDE, it prints the strings as expected.
I mean if the CLA is Alice Bob Charlie, then at 0th index Alice and so on...
This step is crucial as later the names from the voters would be compared.
I don't how to proceed from here. I came to know only about this when I reached the comparison stage. */

I am working with some election data using pandas. I would like to know how votes from party A would transfer to parties B and C in each of the 650 seats if party A did not exist.

We assume that we know that nationally:

• to_B: 48% transfer to party B
• to_C: 32% would transfer to party C
• to_dnv: 20% would not vote

I am looking to generate a normal distribution of numbers between 0 and 1 for each seat, where:

1. The sum of each row sums to 1
2. The average of column to_B is 0.48
3. The average of column to_C is 0.32
4. The average of column to_dnvis 0.2

As an example with completely separate numbers:

Here in this manual example:

• All rows sum to 1
• The average of to_B is 0.3, which we would have defined before generation.
• The average of to_C is 0.4, which we would have defined before generation.
• The average of to_dnv is 0.3, which we would have defined before generation.

The motivation is such that later I zip together this table with another separate table I have already built which contains the election results of each seat. Then I will use these normally distributed numbers to redistribute party A's votes into party B, C and DNV.

What is the best way to go about generating such a matrix? Preferably in Pandas.

Code so far:

I am implementing a form in which the user can dynamically add or remove dropdowns. Through the dropdown the user may manage the participants/voters.

After selecting a user, the selected user should not re-appear in the dropdown list, in the other available dropdowns.

The part with the dynamic dropdowns is exported to a new function component VotingGroups. In this component I am passing callbacks and two states (groups, unselectedUsers).