STATISTICAL SOFTWARE
Python Statistical Analysis Service
Reproducible Pipelines, Sound Models
Our Python statistical analysis service carries out regression, mixed-model, time series and resampling analysis in Python, working from your data, scripts and notebooks, with scripts, package versions and a data preparation pipeline provided as agreed so that each result can be rebuilt from the raw files.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What Is Python Used For in Research?
Python is a general-purpose programming language that researchers use for statistical analysis through libraries such as pandas for data handling, statsmodels and SciPy for statistical models and tests, and scikit-learn for predictive modeling. Researchers choose it to prepare large or messy data, to combine statistics with other computation, and to document an analysis in scripts or notebooks that mix code, output and explanation. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides a Python statistical analysis service in which an in-house PhD-level statistician carries out the agreed analysis in Python, working from your data and code and recording the environment so that the analysis can be rerun.
WHEN TO ENGAGE
Four Points at Which Researchers Bring Us a Python Project
Each of these requests turns on the same question, whether the inputs and the code can be trusted, and differs in how much of the analysis already exists.
Model to Fit and Interpret The question is set, but the aim, inference about an effect or prediction for new cases, decides which library is used and how the model is judged, and that choice is made before the code is written.
Inputs in Doubt Data types, category encodings or missing-value codes may be wrong, and the data preparation has to be traced before any model built on it is trusted.
Notebook or Code to Review A notebook from a collaborator or drafted with an AI tool needs correcting and extending. Python data analysis consulting here first separates the cells that prepare data from those that model them.
Analysis to Make Reproducible Results must be rebuilt from the raw files on another machine, so the code, the environment and the data preparation are organized to run from the very start.
ANALYSES IN PYTHON
Analyses Carried Out in Python for Your Research Data
The library follows the model and whether the aim is inference or prediction, and each card links to the method page behind the choice of approach.
Multiple Linear Regression
Python statsmodels analysis fits least squares from a model formula, with coefficient tables, robust standard errors where warranted, and diagnostics for residuals and influential points.
Logistic Regression
Binary outcomes are modeled for inference with statsmodels or for prediction with scikit-learn, and the aim decides whether the report gives odds ratios with intervals or out-of-sample performance.
Linear Mixed Models (Multilevel Modeling)
Clustered or repeated data are fitted with random effects that the library supports, matched to the design, and where it cannot represent the design, another platform is recommended.
Time Series Analysis
Series are indexed by true dates, checked for stationarity and modeled with ARIMA-type models, and forecasts are evaluated on later periods, not on the data used to fit them.
Principal Component Analysis (PCA)
Variables are standardized, components are retained by a stated criterion and interpreted through their loadings, and the decomposition is fitted on training data when it feeds a predictive model.
Bootstrapping
Intervals for statistics without a simple formula come from resampling at the correct level of the data, with a fixed seed, a stated number of resamples and a documented interval method.
COMMON PYTHON ISSUES
Where Python Analyses Go Wrong Before the Model
A Python model will fit almost any table of numbers, so the errors that matter occur in how the inputs were built, and each check below is made before an estimate is reported.
Data-Type Coercion Columns read from files are guessed as numbers, text, dates or generic objects, so a stray character can turn a numeric column into text. Data types are declared and verified on import.
Category Encoding Dummy coding, the reference category and the dummy-variable trap decide what coefficients mean and whether a model can be estimated, so the encoding is explicit and the reference level is documented.
Train and Test Leakage Scaling, imputation, feature selection or tuning done before the split lets test information into the model and flatters its performance. These steps are fitted within the training data only.
Related Rows Across Splits When one participant, site or patient contributes several rows, a random split puts related rows on both sides. Splits and cross-validation folds are grouped by that unit instead.
Hidden Notebook State Execution counts out of sequence, or a name defined in a cell that was later edited or deleted, mean the saved outputs depend on a session that no longer exists. Such outputs are discarded and regenerated from the code as it now stands.
Missing Values in Model Inputs Library defaults differ: some functions drop incomplete rows silently, others stop with an error, and others return a missing result. The handling is chosen explicitly and the rows used are counted.
DELIVERABLES
What You Receive From a Python Analysis
The files follow a reproducible Python project, agreed in the scope and provided where applicable, so that every result can be rebuilt from the raw files.
Scripts and Notebooks Commented scripts that prepare and model the data, and notebooks that only present their outputs, each running from top to bottom in a new session.
Environment and Package Versions The Python version and the package versions, recorded in a requirements or environment file, so that the same setup can be rebuilt by a coauthor or reviewer.
Data Preparation Pipeline A deterministic pipeline that rebuilds the analysis dataset from the raw files, with fixed seeds wherever randomness is involved.
Cleaned Data and Dictionary The analysis dataset saved in an open format such as CSV or Parquet, with a dictionary of each column's declared type, the category encoding and reference level, and every missing-value rule.
Tables and Figures Tables and figures written out by the scripts in Word, LaTeX or image formats as agreed, each showing the estimate, its interval and the rows used.
Statistical Interpretation For inferential models, each estimate is interpreted with its uncertainty; for predictive models, performance on held-out data is explained, with the limits of each stated plainly.
OTHER PLATFORMS
When Another Platform May Be More Appropriate
Python handles data well, but where a method has a fuller implementation elsewhere, or the team works in another program, the analysis is better placed there.
ILLUSTRATIVE EXAMPLE
How a Python Analysis May Look in Practice
An illustration of how leakage and an out-of-order notebook are found and removed from a baseline model.
ILLUSTRATIVE EXAMPLE · BUILDING ENERGY EFFICIENCY RESEARCH · UNITED STATES
After a lighting retrofit, a university building-science group needed to compare observed daily electricity use with what a baseline model, trained on earlier months and weather data, predicted for the same days.
The notebook already existed, and its coefficients changed between runs because cells had been executed out of order and the random split had no fixed seed. Two further problems were found. Sensor readings from one file had been read as text, and the notebook standardized and filled missing values across the whole dataset before the split, so information from later months entered the baseline model. The data types were declared, the preprocessing was refitted on the pre-retrofit training period only, validation followed time order rather than a random split, and the notebook was restarted and run from top to bottom. The environment and the random seeds were recorded.
Outcome: The group received the estimated difference between observed and predicted use with its confidence interval, the error of the baseline model on held-out months, the scripts and notebook, and an environment file that rebuilds the analysis.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
RESEARCH DISCIPLINES
Research Fields Where Python Is Common
Python is common in data science, bioinformatics, engineering, computational social science and business analytics, and the same questions about data types, leakage and run order arise in any field that analyzes data with code.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCHER VOICE
What Researchers Say
Feedback from a data scientist on keeping the work in Python while data-type coercion, category encoding and leakage were corrected.
“I needed the work kept in Python because that is what our team uses internally. The main problem was data-type coercion, category encoding, and leakage between training and test data. OnlineSPSS corrected the setup and returned the project files with clear reporting, which made later changes much easier.”
Statistical Support Within Your Research OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, including the Python scripts and notebooks described in the scope. The researcher or research team retains the scientific question, substantive interpretation, conclusions and overall responsibility for the research.
FREQUENTLY ASKED QUESTIONS
Questions About Python Analysis for Research
Short answers to what researchers ask before sharing a Python project, from the models we build and existing notebooks to reproducibility, library differences and confidentiality.
Which statistical models do you build in Python, and with which libraries?
Python statistical modeling at OnlineSPSS uses statsmodels for regression, mixed models and time series, SciPy for classical tests, and scikit-learn for predictive models and dimension reduction. Resampling is written explicitly where no library routine fits, and the library behind each result is named in the methods text.
Can you review a notebook or script that I already have?
Yes. The notebook is executed from a new kernel in a fresh environment before anything is changed, which shows whether its saved outputs can be reproduced at all, and the data preparation is then read step by step. A finished analysis that needs only an independent check can go through Independent Statistical Review, with a written account of what holds.
Can you turn my notebook into a reproducible pipeline?
Yes, within the agreed scope. For a reproducible Python analysis, data preparation and models move from notebook cells into scripts that run in a fixed order, and the notebook keeps the narrative and the figures drawn from their outputs. Package versions are pinned in an environment file, seeds are fixed wherever resampling or a random split is used, and each notebook has to run from top to bottom in a new session.
Why do scikit-learn and statsmodels give different logistic regression coefficients?
Because scikit-learn’s LogisticRegression is penalized by default and statsmodels’ Logit is not. scikit-learn applies L2 regularization unless told otherwise, which shrinks coefficients toward zero and reports no standard errors, while statsmodels fits the unpenalized maximum likelihood model with standard errors and confidence intervals. For odds ratios in a paper, the model and any penalty are chosen deliberately and stated; for prediction, the penalty is tuned within the training data.
How do you protect my research data and confidentiality?
Your data, scripts and notebooks are handled confidentially, and project information is not published. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as the dataset and any notebooks, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
Python Analysis With Us
Share your research question, data and any existing scripts or notebooks, and say whether the aim is inference or prediction. We review the code and notebooks and confirm the Python setup before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014