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> 
Scene Text Retrieval via Joint Text Detection and Similarity Learning

This is the code of "Scene Text Retrieval via Joint Text Detection and Similarity Learning". For more details, please refer to our CVPR2021 paper.

79 Nov 29, 2022
Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 models for speech recognition

Wav2Vec2 STT Python Beta Software Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 mode

David Zurow 22 Dec 29, 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
RecipeReduce: Simplified Recipe Processing for Lazy Programmers

RecipeReduce This repo will help you figure out the amount of ingredients to buy for a certain number of meals with selected recipes. RecipeReduce Get

Qibin Chen 9 Apr 22, 2022
TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech

TFPNER TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech Named entity recognition (NER), which aims at identifyin

1 Feb 07, 2022
Product-Review-Summarizer - Created a product review summarizer which clustered thousands of product reviews and summarized them into a maximum of 500 characters, saving precious time of customers and helping them make a wise buying decision.

Product-Review-Summarizer - Created a product review summarizer which clustered thousands of product reviews and summarized them into a maximum of 500 characters, saving precious time of customers an

Parv Bhatt 1 Jan 01, 2022
构建一个多源(公众号、RSS)、干净、个性化的阅读环境

2C 构建一个多源(公众号、RSS)、干净、个性化的阅读环境 作为一名微信公众号的重度用户,公众号一直被我设为汲取知识的地方。随着使用程度的增加,相信大家或多或少会有一个比较头疼的问题——广告问题。 假设你关注的公众号有十来个,若一个公众号两周接一次广告,理论上你会面临二十多次广告,实际上会更多,运

howie.hu 678 Dec 28, 2022
Source code for the paper "TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations"

TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations Created by Jiahao Pang, Duanshun Li, and Dong Tian from InterDigital In

InterDigital 21 Dec 29, 2022
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering

Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2

Google Research Datasets 52 Jun 21, 2022
DeBERTa: Decoding-enhanced BERT with Disentangled Attention

DeBERTa: Decoding-enhanced BERT with Disentangled Attention This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Dis

Microsoft 1.2k Jan 03, 2023
Multilingual Emotion classification using BERT (fine-tuning). Published at the WASSA workshop (ACL2022).

XLM-EMO: Multilingual Emotion Prediction in Social Media Text Abstract Detecting emotion in text allows social and computational scientists to study h

MilaNLP 35 Sep 17, 2022
Creating a python chatbot that Starbucks users can text to place an order + help cut wait time of a normal coffee.

Creating a python chatbot that Starbucks users can text to place an order + help cut wait time of a normal coffee.

2 Jan 20, 2022
Tools for curating biomedical training data for large-scale language modeling

Tools for curating biomedical training data for large-scale language modeling

BigScience Workshop 242 Dec 25, 2022
Natural Language Processing library built with AllenNLP 🌲🌱

Custom Natural Language Processing with big and small models 🌲🌱

Recognai 65 Sep 13, 2022
Shellcode antivirus evasion framework

Schrodinger's Cat Schrodinger'sCat is a Shellcode antivirus evasion framework Technical principle Please visit my blog https://idiotc4t.com/ How to us

idiotc4t 27 Jul 09, 2022
GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model

GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub.

Nathan Cooper 2.3k Jan 01, 2023
Weakly-supervised Text Classification Based on Keyword Graph

Weakly-supervised Text Classification Based on Keyword Graph How to run? Download data Our dataset follows previous works. For long texts, we follow C

Hello_World 20 Dec 29, 2022
Searching keywords in PDF file folders

keyword_searching Steps to use this Python scripts: (1)Paste this script into the file folder containing the PDF files you need to search from; (2)Thi

1 Nov 08, 2021
Exploration of BERT-based models on twitter sentiment classifications

twitter-sentiment-analysis Explore the relationship between twitter sentiment of Tesla and its stock price/return. Explore the effect of different BER

Sammy Cui 2 Oct 02, 2022
Header-only C++ HNSW implementation with python bindings

Hnswlib - fast approximate nearest neighbor search Header-only C++ HNSW implementation with python bindings. NEWS: version 0.6 Thanks to (@dyashuni) h

2.3k Jan 05, 2023