Have you ever wondered how to identify undervalued projects with high growth potential using PyPI download stats? As a value investor or data scientist, you're constantly searching for ways to analyze large amounts of data and make informed investment decisions. However, analyzing PyPI download stats manually can be time-consuming and prone to errors. This post addresses the need for a scalable and automated approach to analyzing PyPI download stats for value investing insights. By the end of this post, you'll have a clear understanding of how to build a value investing research framework using PyPI download stats and Python.
Key Takeaways
- A value investing research framework can be built using PyPI download stats and Python.
- The framework can identify undervalued projects with high potential for growth.
- The approach leverages the `requests` library to fetch PyPI download stats, the `pandas` library to calculate key metrics, and the `matplotlib` library for data visualization.
The Problem
As a value investor, you're constantly searching for undervalued projects with high growth potential. However, analyzing PyPI download stats manually can be time-consuming and prone to errors. You need a scalable and automated approach to analyzing PyPI download stats for value investing insights. This approach should be able to handle large amounts of data and provide accurate results.
Data and Sources
The data source used in this post is the PyPI Download Stats API, specifically the endpoint `https://pypistats.org/api/packages/requests/overall`. This API provides daily download counts for the `requests` package. You can access the API directly using the link above. Data accessed on 2026-07-12.
Loading the Data
To load the data, we'll use the `requests` library to fetch the PyPI download stats from the API endpoint.
import requests
response = requests.get("https://pypistats.org/api/packages/requests/overall")
data = response.json()
The Core Logic
The core logic of the framework involves calculating key metrics such as the daily download count, weekly download count, and monthly download count. We'll use the `pandas` library to calculate these metrics.
import pandas as pd
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
daily_download_count = df['downloads'].resample('D').sum()
weekly_download_count = df['downloads'].resample('W').sum()
monthly_download_count = df['downloads'].resample('M').sum()
Identifying Undervalued Projects
To identify undervalued projects, we'll use the calculated metrics to determine which projects have high growth potential. We'll use the `matplotlib` library to visualize the data.
import matplotlib.pyplot as plt
plt.figure(figsize=(10,6))
plt.plot(daily_download_count)
plt.title('Daily Download Count')
plt.xlabel('Date')
plt.ylabel('Download Count')
plt.show()
Putting It Together
Now that we have the core logic and the data, let's put it all together. We'll create a function that fetches the data, calculates the metrics, and visualizes the data.
def analyze_pypi_download_stats():
response = requests.get("https://pypistats.org/api/packages/requests/overall")
data = response.json()
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
daily_download_count = df['downloads'].resample('D').sum()
weekly_download_count = df['downloads'].resample('W').sum()
monthly_download_count = df['downloads'].resample('M').sum()
plt.figure(figsize=(10,6))
plt.plot(daily_download_count)
plt.title('Daily Download Count')
plt.xlabel('Date')
plt.ylabel('Download Count')
plt.show()
Complete Script
The full runnable script combining all steps:
#!/usr/bin/env python3
import requests
import pandas as pd
import matplotlib.pyplot as plt
def analyze_pypi_download_stats():
response = requests.get("https://pypistats.org/api/packages/requests/overall")
data = response.json()
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
daily_download_count = df['downloads'].resample('D').sum()
weekly_download_count = df['downloads'].resample('W').sum()
monthly_download_count = df['downloads'].resample('M').sum()
plt.figure(figsize=(10,6))
plt.plot(daily_download_count)
plt.title('Daily Download Count')
plt.xlabel('Date')
plt.ylabel('Download Count')
plt.show()
if __name__ == "__main__":
analyze_pypi_download_stats()
Expected Output
When you run the script, you should see a plot of the daily download count over time. This plot can help you identify trends and patterns in the data.
Limitations and Tradeoffs
This approach has several limitations and tradeoffs. First, the data is limited to the `requests` package, and may not be representative of all PyPI packages. Second, the approach relies on the accuracy of the PyPI download stats API, which may be subject to errors or biases. Finally, the approach is simplified and may not capture all the complexities of the data.
Frequently Asked Questions
What is the PyPI Download Stats API?
The PyPI Download Stats API is a API that provides daily download counts for PyPI packages.
How do I access the API?
You can access the API directly using the link `https://pypistats.org/api/packages/requests/overall`.
What libraries do I need to install to run the script?
You need to install the `requests`, `pandas`, and `matplotlib` libraries to run the script.
What I'd Change
In conclusion, building a value investing research framework using PyPI download stats and Python is a powerful approach to identifying undervalued projects with high growth potential. However, this approach has several limitations and tradeoffs. To improve this approach, I would consider using more advanced machine learning techniques, such as natural language processing or deep learning, to analyze the data. I would also consider using more comprehensive data sources, such as GitHub or Stack Overflow, to get a more complete picture of the projects. Additionally, I would consider using more advanced data visualization techniques, such as interactive dashboards or animations, to help communicate the insights to stakeholders. By leveraging these advancements, you can create a more robust and effective value investing research framework that helps you make informed investment decisions.
