For statistical analysis in Python, use pandas to prepare and inspect data, SciPy for many classical statistical tests and distributions, and statsmodels for interpretable models and statistical inference. Choose a procedure based on your outcome, study design, assumptions, and goal—not simply on which function is easiest to call.
Which Python library should you use?
The libraries serve different roles and often work together. pandas organizes and transforms data; SciPy supplies many direct statistical procedures; statsmodels fits statistical models and exposes results suited to inference. A Jupyter notebook can keep code, output, equations, and interpretation together.
| Library or tool | Best suited to | Examples |
|---|---|---|
| pandas | Data structures and preparation | DataFrames, missing-data operations, grouping, reshaping, date and time-series handling, import/export, and plotting. |
| SciPy | Classical statistics and distributions | Summary and frequency statistics, correlations, hypothesis tests, confidence intervals, probability distributions, kernel-density estimation, and quasi-Monte Carlo tools. |
| statsmodels | Statistical models, tests, and exploration | Linear and generalized linear models, regression, ANOVA, time-series methods, nonparametric methods, treatment effects, contingency tables, and multivariate statistics. |
| Jupyter | Recording and communicating an analysis | A notebook combines executable code, results, equations, and explanatory prose. |
For example, a typical analysis begins with a pandas DataFrame, uses SciPy for a targeted test or statsmodels for a model, and records the analysis in a notebook. The Python and Jupyter basics teaching resource describes this broader scientific Python stack.
How to carry out an analysis
- Define the question and study design. Identify the outcome, groups or predictors, whether observations are independent or repeated, and whether the aim is inference, prediction, or forecasting. These choices narrow the valid methods.
- Load and inspect the data with pandas. Check column types, group sizes, missingness, duplicates, and implausible values. Use grouping, reshaping, and date handling as needed; keep track of any exclusions or transformations.
- Explore and visualize. Summaries and plots can reveal distributions, outliers, group differences, and possible relationships. Matplotlib and Seaborn are commonly discussed alongside the scientific Python stack in the SciPy statistics lecture notes.
- Choose a procedure that matches the question. Use a direct test in SciPy for many standard comparisons or correlations; use statsmodels when you need a fitted model, formula-based specification, or model-oriented inference. Verify assumptions rather than treating the package function as a method recommendation.
- Check diagnostics and interpret the result. Inspect model fit and relevant assumptions, then report the estimate or effect, uncertainty such as a confidence interval, and the test result where relevant. A p-value alone does not convey the magnitude or precision of an effect.
- Make the work reproducible. Keep the data-cleaning decisions, analysis code, outputs, and interpretation together in a notebook or otherwise documented workflow. Note package versions and the specific API documentation used, since APIs and documentation change.
How to choose a statistical method
There is no single test for a label such as “compare groups.” Decide what the outcome represents, how many groups or measurements are involved, and how observations are related. Then check distributional assumptions, missing-data implications, and whether the purpose is explanation, prediction, or forecasting.
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- Outcome type: A continuous measurement, category, count, or time-ordered value may call for different model families.
- Groups and measurements: The number of groups matters, as does whether measurements come from independent subjects or are paired or repeated on the same subjects.
- Assumptions: Distributional and design assumptions differ among procedures. SciPy explicitly cautions that tests listed in different sections are not interchangeable.
- Missing data: Decide how missing observations affect the analysis before choosing a test or model; data handling can change the sample being analyzed.
- Interpretation and diagnostics: Prefer an approach whose estimates, uncertainty, and diagnostic checks answer the actual question.
- Goal: Inference about effects, prediction of outcomes, and forecasting future values are distinct goals, even when the same dataset is involved.
Common analysis tasks and where to start
Hypothesis tests and confidence intervals
For many standard tests—including one-sample and paired procedures, t-tests, and one-way ANOVA—SciPy offers functions in scipy.stats. Its reference also covers correlations, distributions, and confidence intervals. Confirm that the test’s assumptions and independence structure fit the study before interpreting its result.
Regression and interpretable models
Use statsmodels when the question calls for a model and interpretable statistical output. Its documentation covers linear and generalized linear models, hypothesis testing, and R-style formulas that can work with pandas DataFrames. Model choice depends on the outcome and design; regression is not one universal procedure.
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ANOVA
ANOVA methods are available in both SciPy and statsmodels, but the existence of similarly named procedures does not make their assumptions or intended use identical. Match the method to the group structure and model needed, and inspect the relevant diagnostics.
Time-series analysis
pandas includes date and time-series functionality useful for preparing time-indexed data. statsmodels documents time-series methods for statistical modeling. Distinguish a time-series model used for inference from a forecasting task, and account for the ordering and dependence of observations.
What to report
Make the conclusion understandable without requiring readers to reverse-engineer a p-value. State the question and method, describe the analyzed sample and important exclusions, and report the effect estimate with an uncertainty measure where appropriate. Include the test statistic and p-value when they answer the inferential question, alongside—not instead of—the effect and its uncertainty. Explain relevant assumptions, diagnostics, and limitations in the context of the study design.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Documentation and version checks
Package capabilities and APIs evolve. Before relying on a code example, check the documentation for the version installed in your environment; the official references for pandas, SciPy statistics, and statsmodels are appropriate starting points. The statsmodels documentation search result dated August 27, 2026 identified version 0.15.0; confirm the installed version rather than assuming it applies to every environment.
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