Tools and data for measuring the popularity & growth of various programming languages.

Overview

growth-data

Tools and data for measuring the popularity & growth of various programming languages.

Install the dependencies

$ pip install -r requirements.txt

Example queries

Number of (non-fork) repositories

sqlite> .mode column
sqlite> SELECT
    ds,
    github_search_q AS q,
    MAX(github_search_total_count) AS num_repos
  FROM github_search
  GROUP BY 1, 2
  ORDER BY 3;
ds          q                                  num_repos
----------  ---------------------------------  ---------
2021-12-22  language:tla and fork:false        64       
2021-12-22  language:lean and fork:false       75       
2021-12-22  language:idris and fork:false      140      
2021-12-22  language:agda and fork:false       192      
2021-12-22  language:ada and fork:false        438      
2021-12-22  language:coq and fork:false        509      
2021-12-22  language:erlang and fork:false     2260     
2021-12-22  language:ocaml and fork:false      2278     
2021-12-22  language:fortran and fork:false    3196     
2021-12-22  language:verilog and fork:false    3882     
2021-12-22  language:assembly and fork:false   8654     
2021-12-22  language:haskell and fork:false    10052    
2021-12-22  language:terraform and fork:false  10254    
2021-12-22  language:rust and fork:false       21906    
2021-12-22  language:go and fork:false         67601    
2021-12-22  language:r and fork:false          114942   
2021-12-22  language:c and fork:false          174439   
2021-12-22  language:c++ and fork:false        270351   
2021-12-22  language:python and fork:false     762729   
2021-12-22  language:java and fork:false       943381   
sqlite> 

Stats about the average (non-fork) repository

sqlite> .mode column
sqlite> SELECT
    github_search.ds AS ds,
    github_search_q AS q,
    COUNT(*) AS repos,
    SUM(github_repo_has_issues) AS repos_with_issues,
    SUM(github_repo_has_wiki) AS repos_with_wiki,
    SUM(github_repo_has_pages) AS repos_with_pages,
    SUM(github_repo_license_name != '') AS repos_with_license,
    SUM(github_repo_size) AS sum_repo_size,
    SUM(github_repo_stargazers_count) AS sum_stars,
    AVG(github_repo_stargazers_count) AS avg_stars,
    AVG(github_repo_forks_count) AS avg_forks,
    AVG(github_repo_size) AS avg_size,
    AVG(github_repo_open_issues_count) AS avg_open_issues
  FROM github_search INNER JOIN github_search_repo
  ON github_search.obj_id = github_search_obj_id
  GROUP BY 1, 2
  ORDER BY 3;
ds          q                              repos  repos_with_issues  repos_with_wiki  repos_with_pages  repos_with_license  sum_repo_size  sum_stars  avg_stars         avg_forks         avg_size          avg_open_issues  
----------  -----------------------------  -----  -----------------  ---------------  ----------------  ------------------  -------------  ---------  ----------------  ----------------  ----------------  -----------------
2021-12-22  language:tla and fork:false    64     63                 61               1                 23                  1393879        1937       30.265625         2.34375           21779.359375      0.359375         
2021-12-22  language:lean and fork:false   75     73                 72               5                 22                  1119783        1475       19.6666666666667  1.85333333333333  14930.44          1.61333333333333 
2021-12-22  language:idris and fork:false  140    139                136              4                 63                  108818         1242       8.87142857142857  0.85              777.271428571429  0.728571428571429
2021-12-22  language:agda and fork:false   192    188                187              9                 51                  394233         1725       8.984375          0.90625           2053.296875       0.291666666666667
2021-12-22  language:ada and fork:false    438    421                406              12                155                 2387761        2210       5.04566210045662  1.13926940639269  5451.50913242009  1.09360730593607 
2021-12-22  language:coq and fork:false    509    502                493              42                204                 2894476        4304       8.45579567779961  1.50098231827112  5686.59332023576  0.846758349705305
sqlite>

Stats about the average recently-updated (non-fork) repository

sqlite> .mode column
sqlite> SELECT
    github_search.ds AS ds,
    github_search_q AS q,
    COUNT(*) AS repos,
    SUM(github_repo_has_issues) AS repos_with_issues,
    SUM(github_repo_has_wiki) AS repos_with_wiki,
    SUM(github_repo_has_pages) AS repos_with_pages,
    SUM(github_repo_license_name != '') AS repos_with_license,
    SUM(github_repo_size) AS sum_repo_size,
    SUM(github_repo_stargazers_count) AS sum_stars,
    AVG(github_repo_stargazers_count) AS avg_stars,
    AVG(github_repo_forks_count) AS avg_forks,
    AVG(github_repo_size) AS avg_size,
    AVG(github_repo_open_issues_count) AS avg_open_issues
  FROM github_search INNER JOIN github_search_repo
  ON github_search.obj_id = github_search_obj_id
  WHERE github_repo_updated_at >= '2021-01-01T00:00:00Z'
  GROUP BY 1, 2
  ORDER BY 3;
ds          q                              repos  repos_with_issues  repos_with_wiki  repos_with_pages  repos_with_license  sum_repo_size  sum_stars  avg_stars         avg_forks         avg_size          avg_open_issues  
----------  -----------------------------  -----  -----------------  ---------------  ----------------  ------------------  -------------  ---------  ----------------  ----------------  ----------------  -----------------
2021-12-22  language:tla and fork:false    33     32                 30               1                 18                  1322462        1921       58.2121212121212  4.39393939393939  40074.6060606061  0.636363636363636
2021-12-22  language:idris and fork:false  44     44                 43               3                 23                  33576          1052       23.9090909090909  2.22727272727273  763.090909090909  1.61363636363636 
2021-12-22  language:lean and fork:false   46     44                 43               3                 14                  1116533        1442       31.3478260869565  2.93478260869565  24272.4565217391  2.58695652173913 
2021-12-22  language:agda and fork:false   77     74                 75               8                 24                  310115         1520       19.7402597402597  1.93506493506494  4027.46753246753  0.376623376623377
2021-12-22  language:ada and fork:false    168    165                148              10                82                  1615474        2065       12.2916666666667  2.67261904761905  9615.91666666667  2.80357142857143 
2021-12-22  language:coq and fork:false    211    206                201              32                113                 1962100        4018       19.042654028436   3.22748815165877  9299.05213270142  1.89099526066351 
sqlite> 
Code for the paper: Sequence-to-Sequence Learning with Latent Neural Grammars

Code for the paper: Sequence-to-Sequence Learning with Latent Neural Grammars

Yoon Kim 43 Dec 23, 2022
New Modeling The Background CodeBase

Modeling the Background for Incremental Learning in Semantic Segmentation This is the updated official PyTorch implementation of our work: "Modeling t

Fabio Cermelli 9 Dec 28, 2022
This github repo is for Neurips 2021 paper, NORESQA A Framework for Speech Quality Assessment using Non-Matching References.

NORESQA: Speech Quality Assessment using Non-Matching References This is a Pytorch implementation for using NORESQA. It contains minimal code to predi

Meta Research 36 Dec 08, 2022
File-based TF-IDF: Calculates keywords in a document, using a word corpus.

File-based TF-IDF Calculates keywords in a document, using a word corpus. Why? Because I found myself with hundreds of plain text files, with no way t

Jakob Lindskog 1 Feb 11, 2022
This repository has a implementations of data augmentation for NLP for Japanese.

daaja This repository has a implementations of data augmentation for NLP for Japanese: EDA: Easy Data Augmentation Techniques for Boosting Performance

Koga Kobayashi 60 Nov 11, 2022
IndoBERTweet is the first large-scale pretrained model for Indonesian Twitter. Published at EMNLP 2021 (main conference)

IndoBERTweet 🐦 🇮🇩 1. Paper Fajri Koto, Jey Han Lau, and Timothy Baldwin. IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effe

IndoLEM 40 Nov 30, 2022
中文問句產生器;使用台達電閱讀理解資料集(DRCD)

Transformer QG on DRCD The inputs of the model refers to we integrate C and A into a new C' in the following form. C' = [c1, c2, ..., [HL], a1, ..., a

Philip 1 Oct 22, 2021
🌐 Translation microservice powered by AI

Dot Translate 🌐 A microservice for quick and local translation using A.I. This service starts a local webserver used for neural machine translation.

Dot HQ 48 Nov 22, 2022
Long text token classification using LongFormer

Long text token classification using LongFormer

abhishek thakur 161 Aug 07, 2022
This is the code for the EMNLP 2021 paper AEDA: An Easier Data Augmentation Technique for Text Classification

The baseline code is for EDA: Easy Data Augmentation techniques for boosting performance on text classification tasks

Akbar Karimi 81 Dec 09, 2022
Seonghwan Kim 24 Sep 11, 2022
Minimal GUI for accessing the Watson Text to Speech service.

Description Minimal graphical application for accessing the Watson Text to Speech service. Requirements Python 3 plus all dependencies listed in requi

Moritz Maxeiner 1 Oct 22, 2021
This repository implements a brute-force spellchecker utilizing the Damerau-Levenshtein edit distance.

About spellchecker.py Implementing a highly-accurate, brute-force, and dynamically programmed spellchecking program that utilizes the Damerau-Levensht

Raihan Ahmed 1 Dec 11, 2021
simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.

Quickly train T5 models in just 3 lines of code + ONNX support simpleT5 is built on top of PyTorch-lightning ⚡️ and Transformers 🤗 that lets you quic

Shivanand Roy 220 Dec 30, 2022
Knowledge Oriented Programming Language

KoPL: 面向知识的推理问答编程语言 安装 | 快速开始 | 文档 KoPL全称 Knowledge oriented Programing Language, 是一个为复杂推理问答而设计的编程语言。我们可以将自然语言问题表示为由基本函数组合而成的KoPL程序,程序运行的结果就是问题的答案。目前,

THU-KEG 62 Dec 12, 2022
An extension for asreview implements a version of the tf-idf feature extractor that saves the matrix and the vocabulary.

Extension - matrix and vocabulary extractor for TF-IDF and Doc2Vec An extension for ASReview that adds a tf-idf extractor that saves the matrix and th

ASReview 4 Jun 17, 2022
The model is designed to train a single and large neural network in order to predict correct translation by reading the given sentence.

Neural Machine Translation communication system The model is basically direct to convert one source language to another targeted language using encode

Nishant Banjade 7 Sep 22, 2022
Binary LSTM model for text classification

Text Classification The purpose of this repository is to create a neural network model of NLP with deep learning for binary classification of texts re

Nikita Elenberger 1 Mar 11, 2022
Multilingual finetuning of Machine Translation model on low-resource languages. Project for Deep Natural Language Processing course.

Low-resource-Machine-Translation This repository contains the code for the project relative to the course Deep Natural Language Processing. The goal o

Andrea Cavallo 3 Jun 22, 2022