---
dataset_info:
- config_name: pypi-downloads
  features:
  - name: day
    dtype: date32
  - name: num_downloads
    dtype: int64
  splits:
  - name: train
    num_bytes: 18804
    num_examples: 1567
  download_size: 14524
  dataset_size: 18804
- config_name: stargazers
  features:
  - name: starred_at
    dtype: timestamp[s, tz=UTC]
  - name: user
    dtype: string
  splits:
  - name: train
    num_bytes: 192240
    num_examples: 9005
  download_size: 188639
  dataset_size: 192240
configs:
- config_name: pypi-downloads
  data_files:
  - split: train
    path: pypi-downloads/train-*
- config_name: stargazers
  data_files:
  - split: train
    path: stargazers/train-*
---

## Stars

```python
import requests
from datetime import datetime
from datasets import Dataset
import pyarrow as pa
import os

def get_stargazers(owner, repo, token):
    # Initialize the count and the page number
    page = 1
    stargazers = []
    while True:
        # Construct the URL for the stargazers with pagination
        stargazers_url = f"https://api.github.com/repos/{owner}/{repo}/stargazers?page={page}&per_page=100"

        # Send the request to GitHub API with appropriate headers
        headers = {"Accept": "application/vnd.github.v3.star+json", "Authorization": "token " + token}
        response = requests.get(stargazers_url, headers=headers)

        if response.status_code != 200:
            raise Exception(f"Failed to fetch stargazers with status code {response.status_code}: {response.text}")

        stargazers_page = response.json()

        if not stargazers_page:  # Exit the loop if there are no more stargazers to process
            break

        stargazers.extend(stargazers_page)
        page += 1  # Move to the next page

    return stargazers

token = os.environ.get("GITHUB_PAT")
stargazers = get_stargazers("huggingface", "trl", token)
stargazers = {key: [stargazer[key] for stargazer in stargazers] for key in stargazers[0].keys()}
dataset = Dataset.from_dict(stargazers)

def clean(example):
    starred_at = datetime.strptime(example["starred_at"], "%Y-%m-%dT%H:%M:%SZ")
    starred_at = pa.scalar(starred_at, type=pa.timestamp("s", tz="UTC"))
    return {"starred_at": starred_at, "user": example["user"]["login"]}

dataset = dataset.map(clean, remove_columns=dataset.column_names)
dataset.push_to_hub("qgallouedec/trl-metrics", config_name="stargazers")
```


## Pypi downloads

```python
from datasets import Dataset
from google.cloud import bigquery
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "propane-tree-432413-4c3e2b5e6b3c.json"

# Initialize a BigQuery client
client = bigquery.Client()

# Define your query
query = """
#standardSQL
WITH daily_downloads AS (
  SELECT
    DATE(timestamp) AS day,
    COUNT(*) AS num_downloads
  FROM
    `bigquery-public-data.pypi.file_downloads`
  WHERE
    file.project = 'trl'
    -- Filter for the last 12 months
    AND DATE(timestamp) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 54 MONTH) AND CURRENT_DATE()
  GROUP BY
    day
)
SELECT
  day,
  num_downloads
FROM
  daily_downloads
ORDER BY
  day DESC
"""

# Execute the query
query_job = client.query(query)

# Fetch the results
results = query_job.result()

# Convert the results to a pandas DataFrame and then to a Dataset
df = results.to_dataframe()
dataset = Dataset.from_pandas(df)

dataset.push_to_hub("qgallouedec/trl-metrics", config_name="pypi-downloads")
```