Track the daily growth of GitHub stars over a time period to gauge the open-source popularity of various repositories.
To interface with the GitHub REST API using Python, the PyGitHub
library is required. Utilize this library to interact with GitHub resources such as repositories, user profiles, and organizations within your Python applications.
Run the following command to install dependencies listed in requirements.txt
:
pip install -r requirements.txt # Install required libraries
First, update the TOKEN
in count_stars.py
on line 15 with your valid GitHub access token. Then execute the script:
# Before running, ensure TOKEN is set to your GitHub access token.
python count_stars.py
The output below shows the star count for various repositories on April 10th, 2022, over the last 30 days since May 2nd, 2022:
Counting stars for last 30.0 days from 02 May 2022
ultralytics/yolov5 1572 stars (52.4/day) : 6%|▌ | 1572/25683 [00:16<04:15, 94.53it/s]
facebookresearch/detectron2 391 stars (13.0/day) : 2%|▏ | 391/20723 [00:04<03:56, 85.86it/s]
deepmind/deepmind-research 165 stars (5.5/day) : 2%|▏ | 165/10079 [00:01<01:50, 89.52it/s]
aws/amazon-sagemaker-examples 120 stars (4.0/day) : 2%|▏ | 120/6830 [00:02<02:16, 49.17it/s]
awslabs/autogluon 127 stars (4.2/day) : 3%|▎ | 127/4436 [00:01<01:00, 71.45it/s]
microsoft/LightGBM 122 stars (4.1/day) : 1%| | 122/13730 [00:01<03:10, 71.54it/s]
openai/gpt-3 95 stars (3.2/day) : 1%| | 95/11225 [00:01<03:34, 52.00it/s]
apple/turicreate 40 stars (1.3/day) : 0%| | 40/10676 [00:00<02:24, 73.59it/s]
apple/coremltools 41 stars (1.4/day) : 2%|▏ | 41/2641 [00:00<00:46, 56.00it/s]
google/automl 55 stars (1.8/day) : 1%| | 55/4991 [00:00<01:25, 57.53it/s]
google-research/google-research 548 stars (18.3/day) : 2%|▏ | 548/23087 [00:07<05:11, 72.37it/s]
google-research/vision_transformer 279 stars (9.3/day) : 6%|▌ | 279/5043 [00:02<00:49, 95.93it/s]
google-research/bert 283 stars (9.4/day) : 1%| | 283/31066 [00:03<07:01, 73.11it/s]
NVlabs/stylegan3 158 stars (5.3/day) : 4%|▍ | 158/4045 [00:01<00:44, 86.41it/s]
Tencent/ncnn 278 stars (9.3/day) : 2%|▏ | 278/14440 [00:03<02:41, 87.55it/s]
Megvii-BaseDetection/YOLOX 273 stars (9.1/day) : 4%|▍ | 273/6286 [00:02<01:04, 92.53it/s]
PaddlePaddle/Paddle 239 stars (8.0/day) : 1%|▏ | 239/18086 [00:02<03:33, 83.73it/s]
rwightman/pytorch-image-models 772 stars (25.7/day) : 4%|▍ | 772/18169 [00:08<03:21, 86.24it/s]
streamlit/streamlit 375 stars (12.5/day) : 2%|▏ | 375/18834 [00:03<03:07, 98.67it/s]
explosion/spaCy 234 stars (7.8/day) : 1%| | 234/23249 [00:02<03:47, 101.24it/s]
PyTorchLightning/pytorch-lightning 407 stars (13.6/day) : 2%|▏ | 407/18246 [00:04<03:02, 97.83it/s]
ray-project/ray 545 stars (18.2/day) : 3%|▎ | 545/20228 [00:05<03:03, 107.33it/s]
fastai/fastai 136 stars (4.5/day) : 1%| | 136/22202 [00:01<04:28, 82.22it/s]
AlexeyAB/darknet 248 stars (8.3/day) : 1%|▏ | 248/18993 [00:02<03:40, 84.84it/s]
pjreddie/darknet 201 stars (6.7/day) : 1%| | 201/22651 [00:02<05:13, 71.62it/s]
WongKinYiu/yolor 92 stars (3.1/day) : 6%|▌ | 92/1559 [00:01<00:16, 87.69it/s]
wandb/client 66 stars (2.2/day) : 2%|▏ | 66/3853 [00:00<00:46, 82.16it/s]
Deci-AI/super-gradients 74 stars (2.5/day) : 19%|█▉ | 74/380 [00:00<00:03, 96.71it/s]
neuralmagic/sparseml 105 stars (3.5/day) : 11%|█ | 105/947 [00:01<00:08, 101.97it/s]
mosaicml/composer 247 stars (8.2/day) : 19%|█▉ | 247/1306 [00:02<00:10, 104.76it/s]
nebuly-ai/nebullvm 205 stars (6.8/day) : 20%|█▉ | 205/1045 [00:02<00:08, 97.46it/s]
Done in 125.7s
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