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> 
Exploring dimension-reduced embeddings

sleepwalk Exploring dimension-reduced embeddings This is the code repository. See here for the Sleepwalk web page. License and disclaimer This program

S. Anders's research group at ZMBH 91 Nov 29, 2022
DensePhrases provides answers to your natural language questions from the entire Wikipedia in real-time

DensePhrases provides answers to your natural language questions from the entire Wikipedia in real-time. While it efficiently searches the answers out of 60 billion phrases in Wikipedia, it is also v

Jinhyuk Lee 543 Jan 08, 2023
Speech Recognition Database Management with python

Speech Recognition Database Management The main aim of this project is to recogn

Abhishek Kumar Jha 2 Feb 02, 2022
BERN2: an advanced neural biomedical namedentity recognition and normalization tool

BERN2 We present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by

DMIS Laboratory - Korea University 99 Jan 06, 2023
Two-stage text summarization with BERT and BART

Two-Stage Text Summarization Description We experiment with a 2-stage summarization model on CNN/DailyMail dataset that combines the ability to filter

Yukai Yang (Alexis) 6 Oct 22, 2022
Pretrain CPM - 大规模预训练语言模型的预训练代码

CPM-Pretrain 版本更新记录 为了促进中文自然语言处理研究的发展,本项目提供了大规模预训练语言模型的预训练代码。项目主要基于DeepSpeed、Megatron实现,可以支持数据并行、模型加速、流水并行的代码。 安装 1、首先安装pytorch等基础依赖,再安装APEX以支持fp16。 p

Tsinghua AI 37 Dec 06, 2022
An easy-to-use framework for BERT models, with trainers, various NLP tasks and detailed annonations

FantasyBert English | 中文 Introduction An easy-to-use framework for BERT models, with trainers, various NLP tasks and detailed annonations. You can imp

Fan 137 Oct 26, 2022
Course project of [email protected]

NaiveMT Prepare Clone this repository git clone [email protected]:Poeroz/NaiveMT.git

Poeroz 2 Apr 24, 2022
Search-Engine - 📖 AI based search engine

Search Engine AI based search engine that was trained on 25000 samples, feel free to train on up to 1.2M sample from kaggle dataset, link below StackS

Vladislav Kruglikov 2 Nov 29, 2022
Edge-Augmented Graph Transformer

Edge-augmented Graph Transformer Introduction This is the official implementation of the Edge-augmented Graph Transformer (EGT) as described in https:

Md Shamim Hussain 21 Dec 14, 2022
Count the frequency of letters or words in a text file and show a graph.

Word Counter By EBUS Coding Club Count the frequency of letters or words in a text file and show a graph. Requirements Python 3.9 or higher matplotlib

EBUS Coding Club 0 Apr 09, 2022
PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Deepvoice3_pytorch PyTorch implementation of convolutional networks-based text-to-speech synthesis models: arXiv:1710.07654: Deep Voice 3: Scaling Tex

Ryuichi Yamamoto 1.8k Dec 30, 2022
Python bot created with Selenium that can guess the daily Wordle word correct 96.8% of the time.

Wordle_Bot Python bot created with Selenium that can guess the daily Wordle word correct 96.8% of the time. It will log onto the wordle website and en

Lucas Polidori 15 Dec 11, 2022
Estimation of the CEFR complexity score of a given word, sentence or text.

NLP-Swedish … allows to estimate CEFR (Common European Framework of References) complexity score of a given word, sentence or text. CEFR scores come f

3 Apr 30, 2022
official ( API ) for the zAmericanEnglish app in [ Google play ] and [ App store ]

official ( API ) for the zAmericanEnglish app in [ Google play ] and [ App store ]

Plugin 3 Jan 12, 2022
SentAugment is a data augmentation technique for semi-supervised learning in NLP.

SentAugment SentAugment is a data augmentation technique for semi-supervised learning in NLP. It uses state-of-the-art sentence embeddings to structur

Meta Research 363 Dec 30, 2022
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers

beyond masking Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers The code is coming Figure 1: Pipeline of token-based pre-

Yunjie Tian 23 Sep 27, 2022
BERN2: an advanced neural biomedical namedentity recognition and normalization tool

BERN2 We present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by

DMIS Laboratory - Korea University 99 Jan 06, 2023
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.09

Keon Lee 142 Jan 06, 2023
Code for producing Japanese GPT-2 provided by rinna Co., Ltd.

japanese-gpt2 This repository provides the code for training Japanese GPT-2 models. This code has been used for producing japanese-gpt2-medium release

rinna Co.,Ltd. 491 Jan 07, 2023