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| 1 | +--- |
| 2 | +title: 2024 Landscape Analysis |
| 3 | +site: |
| 4 | + hide_toc: true |
| 5 | + hide_footer_links: true |
| 6 | +--- |
| 7 | + |
| 8 | +Python is widely adopted in data science, and its use for statistics is expanding rapidly, particularly in education and applied research. |
| 9 | +The statistical ecosystem in Python is currently anchored by four major libraries: |
| 10 | + |
| 11 | +- [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; |
| 12 | +- [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; |
| 13 | +- [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 |
| 14 | +- [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. |
| 15 | + |
| 16 | +These core libraries are generally well-tested, reliable, and uphold high software engineering standards, making them trusted foundations for research and application. |
| 17 | +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. |
| 18 | + |
| 19 | +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. |
| 20 | +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. |
| 21 | + |
| 22 | +# Relationship to Other Languages |
| 23 | + |
| 24 | +R remains the gold standard for statistics, with better branding, a more cohesive ecosystem, and more teaching resources. |
| 25 | +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. |
| 26 | + |
| 27 | +:::{table} Python vs. R for Statistics |
| 28 | +:label: table |
| 29 | +:align: center |
| 30 | + |
| 31 | +| Aspect | Python (Scientific Python) | R (CRAN, tidyverse) | |
| 32 | +| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- | |
| 33 | +| 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 | |
| 34 | +| User Experience | Fragmented, less cohesive | Cohesive, tidyverse pipelines, RStudio | |
| 35 | +| Teaching Resources | Improving, but less abundant | Extensive, beginner-friendly | |
| 36 | +| Community | Large, less connected in statistics | Strong, statistics-focused, welcoming | |
| 37 | +| Package Development | High barriers, less modularity | Easy, many small packages, dev tools | |
| 38 | +| Interoperability | Needs improvement (data structures, APIs) | Strong within tidyverse, RStudio | |
| 39 | +| Branding | Data science/machine learning focus | Statistics-focused | |
| 40 | + |
| 41 | +::: |
| 42 | + |
| 43 | +**Interoperability**: While some users switch between Python and R in their workflows, true interoperability is limited. |
| 44 | +Most projects use one language at a time, often leveraging R for data manipulation and Python for modeling or vice versa. |
| 45 | + |
| 46 | +**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. |
| 47 | + |
| 48 | +# Weaknesses and Needs |
| 49 | + |
| 50 | +Despite Python's strengths, several challenges remain. |
| 51 | + |
| 52 | +- **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. |
| 53 | +- **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. |
| 54 | +- **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. |
| 55 | +- **Teaching Resources**: Python lacks the abundance of user-friendly, statistics-focused tutorials and case studies found in the R community. |
| 56 | +- **Contributor Barriers**: Contributing to core libraries can be difficult due to high standards and lack of modularity. |
| 57 | + Small, specialized packages exist but are less visible and less widely used than in R. |
| 58 | +- **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. |
| 59 | +- **Community and Culture**: The Python statistics community is less cohesive and connected than R's, which benefits from a strong identity and established events. |
| 60 | + |
| 61 | +# Conclusion |
| 62 | + |
| 63 | +Python's statistics ecosystem is powerful but fragmented, with significant opportunities for improvement in usability, interoperability, teaching resources, and community cohesion. |
| 64 | +While R remains the default for statistics, Python is gaining ground, especially as data science and machine learning continue to grow in influence. |
| 65 | +Stronger integration, better documentation, and a more unified vision could help Python become a true peer to R in the statistics domain. |
| 66 | +In particular, Python needs: |
| 67 | + |
| 68 | +- A unified, user-friendly interface for statistics, possibly modeled after R's tidyverse. |
| 69 | +- Improved interoperability between core data structures and libraries. |
| 70 | +- More accessible teaching resources and case studies focused on statistics. |
| 71 | +- Lower barriers for contributors and greater visibility for specialized statistical packages. |
| 72 | +- Stronger community identity and central organization for statistics in Python. |
| 73 | + |
| 74 | +The Statistical Python project seeks to address these needs by fostering collaboration, sharing best practices, and building a sustainable, inclusive community. |
| 75 | +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. |
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