The Tool Desk
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The evidence below comes from survey and report cycles spanning 2023–2025. These sources measure different things—Python developers’ self-reported use, workplace analytics expectations, and activity inside Snowflake’s own platform—so their percentages should not be combined into a single leaderboard.
Which data science tools looked likely to gain ground in 2025?
On the evidence available around the turn of 2024–25, the strongest candidates were Polars for dataframe processing, PyTorch and Hugging Face Transformers in Python machine learning, and Python-based AI work within cloud data platforms. These are directional signals, not proof of who won the whole market. Established tools still had substantial use: pandas and NumPy in data processing, scikit-learn in machine learning, and SQL in workplace analytics.
Tool choice is also task-specific. A library used by Python developers is not directly comparable with a database query language, a notebook, or a managed cloud platform. The most useful comparison is by stage of work.
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What the surveys say—and what they measure
The Python Developers Survey 2024 is a self-report survey of Python developers, not a census of all data professionals. It found that 51% of surveyed developers were involved in data exploration and processing. In that task group, respondents reported pandas use at 80%, NumPy at 75%, Spark at 16%, Polars at 15%, and Airflow at 15%. Respondents could report multiple tools, so the figures are not mutually exclusive market shares. Python Developers Survey 2024 results
For context, JetBrains’ analysis of the preceding survey cycle says data exploration and processing involved 48% of Python developers; among respondents doing that work, 77% reported pandas. The survey collection ran from November 2023 through February 2024. JetBrains described Polars as attracting attention and reported that 10% of respondents in the 2023 survey used it as their processing tool. Polars 1.0 was released in July 2024. JetBrains anticipated higher Polars use in the newer survey, but that was an expectation, not a measured result; the later survey’s reported figure is 15%. JetBrains’ analysis of the survey cycle
These figures suggest a mature Python data stack with room for alternatives—not a wholesale replacement of pandas. The survey samples and task bases differ, so the 77% and 80% pandas results should be read as separate survey findings, not as a trend line precise enough to establish growth.
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Data access and querying: SQL remains central to workplace analytics
Python is prominent in developer surveys, but data work often starts with querying business data. A Spring 2025 article in the Journal of Information Systems Education reported 2024 analytics-tool expectations from a pool spanning multiple information systems and IT job roles. On that study’s rating scale, SQL scored 3.30, Excel 3.23, Azure Synapse 3.20, Python 3.18, SAS 3.13, Snowflake 3.10, Power BI 3.08, Apache Spark 3.08, Tableau 3.03, and R/RStudio 2.98. The sample is not representative of every data-science practitioner, and these are expectation ratings rather than tool adoption percentages. Journal of Information Systems Education, 36(2), Spring 2025
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The comparison explains why SQL belongs in a data-science tool discussion even when Python libraries dominate a Python-specific survey: query languages, spreadsheets, business-intelligence products, and programming libraries serve different parts of an organization’s workflow.
Tabular exploration: pandas is established; Polars is a plausible gainer
pandas and NumPy
In the 2024 Python survey, pandas and NumPy were the most commonly reported processing tools among respondents doing exploration and processing. JetBrains analyst Cheuk Ting Ho, a PSF Board Member and JetBrains Developer Advocate, said pandas, then a 15-year-old project, was still at the top of the most commonly used data-processing tools in the survey analysis. That is an observation about the surveyed Python community, not a universal measure of data-industry use. JetBrains’ analysis
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Polars
Polars is the clearest dataframe challenger in the cited Python evidence: the 2024 survey lists it at 15% among Python developers doing data exploration and processing, compared with 10% of respondents reporting it as their processing tool in the 2023 survey. JetBrains highlights its speed and parallel-processing positioning, and its 1.0 release in July 2024 provides a milestone for the project. The available numbers do not establish that Polars will displace pandas, or that its adoption is accelerating across all data teams.
For a reader choosing a tool, the evidence supports a measured view: pandas remains the established default in the survey population, while Polars is worth watching when dataframe performance or parallel processing matters. The survey figures alone do not tell which option is faster for a particular dataset, workload, or machine.
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In the Python Developers Survey 2024, 38% of surveyed Python developers said they trained or generated predictions using machine-learning models, six percentage points higher than the prior year. Among that group, tool responses were scikit-learn 68%, PyTorch 66%, TensorFlow 49%, SciPy 42%, Keras 30%, Hugging Face Transformers 28%, and XGBoost 23%. Respondents could select more than one tool, and these percentages describe the surveyed Python ML group, not global framework share. Python Developers Survey 2024 results
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Compared with the prior-year figures on the same survey page, PyTorch rose from 60% to 66% and Hugging Face Transformers from 22% to 28%; scikit-learn moved from 67% to 68%, TensorFlow from 48% to 49%, and XGBoost from 22% to 23%. These are year-to-year self-reports within the survey, not proof that one framework is winning every kind of machine-learning work. The results support a picture of several significant tools rather than a single framework replacing the rest.
Training and experimentation: notebooks remain visible
Jupyter Notebook was selected by 50% of respondents in the Python survey’s training-platform results. Managed services also appeared: Amazon SageMaker at 11%, AzureML at 9%, Databricks at 6%, and Vertex AI at 6%. These figures are responses from the surveyed Python ML community; they are not global platform shares. They show that notebook-based experimentation remains a substantial workflow alongside hosted environments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.AI adoption and governance are shaping demand, not crowning a winner
Anaconda’s 2024 State of Data Science report describes more than 3,000 practitioners across 136 countries. It reports that 87% of practitioners were increasing AI adoption, 49% of companies were adding AI Data Analysts, 46% of companies were creating AI Engineering roles, and 42% of organizations cited security as their main AI challenge. These are figures framed by Anaconda’s report, not universal workforce or organizational counts. Anaconda State of Data Science 2024
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AI demand can create openings for tools used to build, evaluate, and operationalize models, but the report does not identify one tool as the beneficiary. Security concerns also make deployment, access controls, and governance relevant selection criteria—not just model-building speed.
Snowflake’s 2024 report offers a platform-specific view. Using aggregated, anonymized activity across more than 9,000 global Snowflake accounts, and generally comparing January 2024 monthly averages with January 2023, it reports Python usage growth of more than 500% year over year; its companion blog specifies 571%. It also reports that enterprises doubled use of key governance features and increased use of that data by nearly 150%. These metrics describe Snowflake’s ecosystem and should not be interpreted as general-market adoption rates. Snowflake Data Trends 2024 · Snowflake report methodology and details
The Snowflake blog further reports that more than 20,000 developers worked on 33,000-plus LLM applications in the Streamlit community between April 2023 and January 2024, while chatbots’ share of those applications rose from 18% in April to 46% by January. That is a signal of activity in a specific platform community, not a count of all LLM development. Snowflake EVP of Product Christian Kleinerman characterized much of the activity as likely experimentation and pilot projects; that is a vendor executive’s interpretation of the company’s telemetry, not independent confirmation of a market-wide transformation. Snowflake Data Trends 2024 blog
How to choose tools for a real workflow
Use the tool that fits the stage of work and the environment your team must support. The evidence points to these practical distinctions:
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- Querying business data: Include SQL and the organization’s warehouse or analytics platform in the decision. The workplace-analytics study gives SQL a strong rating, while Python survey results answer a different question.
- Exploring and transforming tables: pandas and NumPy are established in the surveyed Python community; Polars is a credible alternative to evaluate for workloads where its performance and parallel-processing design may fit.
- Classical machine learning: scikit-learn was the most commonly reported framework in the survey’s Python ML group, narrowly ahead of PyTorch in reported use. That is a useful prevalence signal, not a recommendation independent of the task.
- Deep learning and model experimentation: PyTorch and TensorFlow both had substantial reported use; Hugging Face Transformers gained visibility in the survey. Choose according to model, ecosystem, team skills, and deployment constraints rather than treating the survey as a universal ranking.
- Training environments: Jupyter Notebook remains common in the surveyed group, while managed platforms appear in the results. Organizational security, governance, and deployment requirements can matter as much as experimentation convenience.
What the evidence does not establish
The cited sources do not provide a single independent, representative 2025 market-share ranking across data-science tools. Developer self-reports, role-specific expectations, and vendor-platform telemetry measure different populations and behaviors. Taken together, they point to Python’s continued importance, established use of pandas and NumPy, plausible gains for Polars and selected ML tools, and ongoing relevance for SQL and enterprise platforms—but they cannot identify one overall winner.
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