Add landscape analysis

2024-landscape.md

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+# 2024 Landscape Analysis
+
+Python is widely adopted in data science, and its use for statistics is expanding rapidly, particularly in education and applied research.
+The statistical ecosystem in Python is currently anchored by four major libraries:
+
+- [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html), which provides a comprehensive suite of probability distributions, summary statistics, and basic statistical tests;
+- [statsmodels](https://www.statsmodels.org/), which offers tools for econometrics, classical statistics, and statistical modeling—including linear and generalized linear models, time series analysis, and hypothesis testing;
+- [scikit-learn](https://scikit-learn.org/), which is best known for machine learning but also supports some statistical modeling, offering a consistent API for predictive analytics and data preprocessing; and
+- [seaborn](https://seaborn.pydata.org/), a library built on top of matplotlib that excels at creating informative and attractive statistical graphics, making it easier to visualize distributions, relationships, and trends in data.
+
+These core libraries are generally well-tested, reliable, and uphold high software engineering standards, making them trusted foundations for research and application.
+Libraries like scikit-learn are especially valued for their clean, consistent interfaces and their integration with the broader Python data stack, which streamlines workflows and enhances usability for both new and experienced users.
+
+While there are many smaller, specialized packages available, the ecosystem remains dominated by these large, general-purpose libraries. This concentration of resources ensures stability and quality but can also limit the visibility and adoption of innovative or niche statistical tools.
+As Python’s role in statistics continues to grow, fostering a more diverse and accessible ecosystem will be key to meeting the evolving needs of educators, researchers, and practitioners.
+
+# Relationship to Other Languages
+
+R remains the gold standard for statistics, with better branding, a more cohesive ecosystem, and more teaching resources.
+R's [tidyverse](https://www.tidyverse.org/) and [RStudio](https://posit.co/products/open-source/rstudio/) provide a smoother user experience for statistics, and CRAN offers a vast repository of statistical packages.
+
+:::{table} Python vs. R for Statistics
+:label: table
+:align: center
+
+| Aspect              | Python (Scientific Python)                                                                                                                                   | R (CRAN, tidyverse)                                                                               |
+| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- |
+| Core Libraries      | [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html), [statsmodels](https://www.statsmodels.org/), [scikit-learn](https://scikit-learn.org/) | [base R](https://www.r-project.org/), [tidyverse](https://www.tidyverse.org/), many CRAN packages |
+| User Experience     | Fragmented, less cohesive                                                                                                                                    | Cohesive, tidyverse pipelines, RStudio                                                            |
+| Teaching Resources  | Improving, but less abundant                                                                                                                                 | Extensive, beginner-friendly                                                                      |
+| Community           | Large, less connected in statistics                                                                                                                          | Strong, statistics-focused, welcoming                                                             |
+| Package Development | High barriers, less modularity                                                                                                                               | Easy, many small packages, dev tools                                                              |
+| Interoperability    | Needs improvement (data structures, APIs)                                                                                                                    | Strong within tidyverse, RStudio                                                                  |
+| Branding            | Data science/machine learning focus                                                                                                                          | Statistics-focused                                                                                |
+
+:::
+
+**Interoperability**: While some users switch between Python and R in their workflows, true interoperability is limited.
+Most projects use one language at a time, often leveraging R for data manipulation and Python for modeling or vice versa.
+
+**Other Platforms**: Tools like GraphPad Prism remain popular among practicing scientists for basic statistical analyses, indicating that neither Python nor R fully dominates all applied domains.
+
+# Weaknesses and Needs
+
+Despite Python's strengths, several challenges remain.
+
+- **Fragmentation**: The ecosystem is fragmented, with major libraries (e.g., statsmodels vs. scikit-learn) adopting incompatible APIs and workflows, leading to confusion for users and students.
+- **User Experience**: There is no central landing place or unified entry point for statistics in Python, unlike R's [tidyverse](https://www.tidyverse.org/) or RStudio, making it harder for newcomers to get started.
+- **Interoperability**: Data structures (such as those from [pandas](https://pandas.pydata.org/) and [NumPy](https://numpy.org/)) do not always work seamlessly across libraries, requiring conversions and leading to unpredictable function outputs compared to R's tidyverse pipelines.
+- **Teaching Resources**: Python lacks the abundance of user-friendly, statistics-focused tutorials and case studies found in the R community.
+- **Contributor Barriers**: Contributing to core libraries can be difficult due to high standards and lack of modularity.
+  Small, specialized packages exist but are less visible and less widely used than in R.
+- **Statistical Methods Coverage**: Some advanced or niche statistical methods are missing or hard to find, especially compared to R's vast [CRAN](https://cran.r-project.org/) repository.
+- **Community and Culture**: The Python statistics community is less cohesive and connected than R's, which benefits from a strong identity and established events.
+
+# Conclusion
+
+Python's statistics ecosystem is powerful but fragmented, with significant opportunities for improvement in usability, interoperability, teaching resources, and community cohesion.
+While R remains the default for statistics, Python is gaining ground, especially as data science and machine learning continue to grow in influence.
+Stronger integration, better documentation, and a more unified vision could help Python become a true peer to R in the statistics domain.
+In particular, Python needs:
+
+- A unified, user-friendly interface for statistics.
+- Improved interoperability between core data structures and libraries.
+- More accessible teaching resources and case studies focused on statistics.
+- Lower barriers for contributors and greater visibility for specialized statistical packages.
+- Stronger community identity and central organization for statistics in Python.
+
+The Statistical Python project seeks to address these needs by fostering collaboration, sharing best practices, and building a sustainable, inclusive community.
+As a domain stack within the [Scientific Python project](https://scientific-python.org/), and with support from the NSF POSE Phase I grant, we are committed to making Python a premier platform for statistical computing, education, and research.

about.md

@@ -10,6 +10,4 @@ The Statistical Python project was launched with support from a [grant from the
 We are now completing Phase I, which has centered on scoping activities to inform the transition into a sustainable open-source ecosystem.
 During this phase, we conducted interviews with stakeholders across the statistical and scientific Python communities, engaged with related domain-stack OSEs to learn from their experiences, and organized a workshop to gather input on community needs and technical directions.
 
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 Based on our [2024 Landscape Analysis](2024-landscape), we ...
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