elections | A platform to collect candidates profiles for all elections | Dataset library

I have a login page. There, the user enters data and submits to the API. The API gives an answer that there is such a user and gives him an id from the database. I write this id in storage. Next, the user is taken to the home page. There is a component that is responsible for getting the username (and indeed all other data) the essence of the component: 1 The parameter receives an id and forms it into a json request. 2 The parameter sends this request to the API and receives the user's data in the response (if the id matches) 3) return which draws the interface and gives the user data from the API response

Problem: When changing an account (or re-logging in), it gives a json request error (in fact, the API does not accept an empty request, so it rejects it) The point of getting an ID is 100%. When the application is updated again, the id turns out to be generated in json and after that I already get data about the user.

How to fix it? In fact, it must first receive the id, and only then the id is sent and the data is received, however, at the first entry into the application, he does not want to receive the ID immediately, but only after a reboot (ctlr + s in VS code)

//LOGIN.js

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The first line gives the number of entries. Further, each entry contains the name of the candidate and the number of votes cast for him in one of the states. Summarize the results of the elections: for each candidate, determine the number of votes cast for him. Use dictionaries to complete the tasks.

Input: Number of voting records (integer number), then pairs <> - <>

Output: Print the solution of the problem.

Example:

Input:

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I'm currently learning Elastic, I've created this dataset on french presidentials elections from 1965 to 2017 and I want to query the sum of all the documents matching "tour" = 1 and "election" = 1974.

I've done this, but it's not working, what I did wrong ?

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I have a dataset with the following structure

index candidato Page Name Post Created Date Total Interactions Likes Shares Comments Love Angry 0 António Costa Observador 2022-01-03 4500 340 400 433 545 565

There are 9 different candidato (candidates) and 27 different Page Name

Full dataset can be found here

I need to find a way to calculate, for each Page Name, the totals and the percentage of Total Interactions, Likes, Shares, Comments, Love, and Angry that will result in a DataFrame with the following structure

candidato Page Name Total Interactions Total Interactions Percentage Total Likes Total Likes Percentage Other Columns Other Columns Percentage António Costa Observador 6500 34 23 1% 540 23% Rui Rio Observador 4500 23 value percentage value percentage

The reason why I need to calculate this is in order to produce a percent stacked bar chart such as this one:

What is the best way to achieve this with Pandas? Thank you in advance for your help.

Disclosure This question is to help in a non-for-profit project that analyzes media behaviour, and bias, towards Portuguese candidates to the 2022 general elections. The prior report was made using Google Sheets but analyzing the datasets with Python is the best way, since I plan on doing this every 3 months.

The GitHub repo can be found here, where you can access all datasets and code used.

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I am trying to understand various functions provided by etcd election api and what they mean semantically.

In their official documentation very briefly mentioned about what each function does, and no examples are provided. For example we have methods:

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Consider that we are running Raft on 3 machines: A, B, C and let A be the leader. There is a network partition that splits C, from A, B. Call the current term t. A and B remain on term 2, with no additional messages besides periodic heartbeats. At this time, C enters candidate state and increments term to 3, votes for itself, times out, and repeats. After say 10 cycles, the network partition is resolved. Now the state is A[2], B[2], C[12]; C will reject AppendEntries RPC from A as the term 2 is less than its current term, 10; C cannot assemble a quorum and will continue to run the leader election protocol as a candidate, and become increasingly more divergent from the current term value of A and B.

The question is then, how does Raft (or Raft-derived implementations) handle this issue? Some thoughts I had included:

1. Such a situation is an availability issue, rather than a safety violation. Ignore and let human operators handle by killing or resetting C
2. Exponential backoff to decrease the divergence of C per elections
3. Have C use lastApplied instead of currentTerm as the basis for rejecting or accepting the AppendEntries RPC. That is, we trust the log as the source of truth for terms, rather than currentTerm value. This is already used to ensure that C would not win as per the Election Restriction, however the paper seems to indicate that this "up-to-date" property is a grounds for not voting for C, but is not grounds for C to acquiesce and reset to a follower.

Note: terminology as per In Search of an Understandable Consensus Algorithm (Extended Version)

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I apologise if this has been asked before. I am trying to add median values to the peak of a grouped density plot (example below).

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I have election data where two of the elections fail to differentiate between the contest for Treasurer at the state level and the contest for County Treasurer. Data is presented like so:

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