> For the complete documentation index, see [llms.txt](https://docs.tickertrends.io/tickertrends/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tickertrends.io/tickertrends/documentation-v2/2.-data-coverage-and-data-types/2.2-data-types/pypack-downloads.md).

# PyPack Downloads

#### Overview

PyPack Downloads tracks download activity for Python packages distributed through the Python package ecosystem (PyPI). This dataset helps measure adoption trends for Python libraries, frameworks, and developer tools by monitoring package download volume over time.

#### Historical Length

Historical coverage varies depending on the package, with multi-month to multi-year history available for supported libraries.

#### Granularity

Data is available at daily granularity, allowing users to monitor adoption trends, ecosystem momentum, and shifts in developer usage over time.

#### Update Frequency

Updated regularly as new package download data becomes available.

#### Methodology

PyPack Downloads measures the number of downloads for Python packages distributed through the Python Package Index (PyPI), the primary package repository for Python software.

This includes libraries, machine learning frameworks, developer tooling, web frameworks, infrastructure packages, and utilities such as pandas, numpy, requests, FastAPI, PyTorch, or OpenAI-related SDKs.

Because Python package downloads often reflect real developer usage, experimentation, and ecosystem adoption, this dataset can serve as a leading indicator for framework momentum, AI tooling adoption, infrastructure growth, and shifts in software development trends.

Download activity can be especially useful for identifying breakout open-source technologies, accelerating developer interest, or competitive changes across technical ecosystems.

#### Use Cases

* Tracking adoption trends for Python frameworks and libraries
* Monitoring AI, machine learning, and developer tooling momentum
* Comparing growth between competing Python packages
* Identifying emerging open-source technology adoption
* Measuring ecosystem shifts in developer behavior
* Evaluating technical product traction over time

#### Example Visualization

<figure><img src="/files/7jF8maPSBjtk7zNZjTyG" alt=""><figcaption></figcaption></figure>


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