Principle:Explodinggradients Ragas Results Analysis
| Knowledge Sources | Domains | Last Updated |
|---|---|---|
| explodinggradients/ragas | Data Analysis, Experiment Management | 2026-02-10 |
Overview
Results Analysis is the principle of converting structured experiment results into tabular DataFrame format to bridge evaluation outputs with the pandas ecosystem for filtering, aggregation, visualization, and statistical comparison.
Description
Evaluation experiments produce structured results stored in DataTable containers (either Dataset or Experiment instances). While these containers provide a clean list-like interface for data management, many analysis tasks -- such as computing summary statistics, filtering by criteria, grouping by categories, creating visualizations, or exporting to spreadsheets -- are far more naturally expressed using the pandas DataFrame API. Results Analysis bridges this gap through a conversion mechanism that transforms structured evaluation data into pandas DataFrames.
Uniform Conversion: The conversion handles both validated (Pydantic model) and unvalidated (dictionary) data transparently. For Pydantic models, each entry is converted via model_dump() to extract a flat dictionary. For dictionary entries, they are used directly. This uniform approach means the conversion works regardless of whether the dataset was created with a typed schema or in flexible dictionary mode.
Preservation of Structure: Each field in the evaluation data becomes a column in the resulting DataFrame, and each entry becomes a row. Nested structures within Pydantic models are flattened through model_dump(), ensuring compatibility with the tabular DataFrame format. Column names are derived from the dictionary keys or model field names, preserving the semantic meaning of each field.
Ecosystem Integration: Once converted to a DataFrame, the full power of the pandas ecosystem becomes available: describe() for summary statistics, groupby() for segmented analysis, plot() for visualization, merge() for joining datasets, to_csv() for export, and hundreds of other operations. This integration is particularly valuable for comparing experiment results across runs or analyzing score distributions.
Bidirectional Conversion: The system supports both directions: to_pandas() for converting DataTable to DataFrame, and from_pandas() for creating a DataTable from a DataFrame. This round-trip capability enables workflows where data is loaded into a DataFrame for processing and then converted back for use in the Ragas evaluation pipeline.
Usage
Use the Results Analysis principle when:
- Analyzing experiment results with pandas operations (filtering, grouping, statistics)
- Comparing scores across multiple experiments by merging DataFrames
- Visualizing evaluation results using matplotlib, seaborn, or other plotting libraries
- Exporting results to CSV or Excel for reporting
- Computing aggregate metrics like mean scores, pass rates, or score distributions
- Performing statistical tests on evaluation results
Theoretical Basis
The theoretical foundation applies the Bridge Pattern between structured domain objects and tabular data representations:
PROCEDURE to_pandas(datatable):
1. Initialize an empty list of dictionaries
2. FOR each entry in the datatable:
IF entry is a Pydantic BaseModel:
Convert to dictionary via model_dump()
Append to the list
ELIF entry is a dictionary:
Append directly to the list
ELSE:
Raise TypeError (unexpected data type)
3. Create a pandas DataFrame from the list of dictionaries
Each dictionary key becomes a column
Each dictionary becomes a row
4. Return the DataFrame
PROCEDURE from_pandas(dataframe, name, backend, data_model):
1. Convert DataFrame to list of dictionaries via to_dict(orient="records")
2. IF data_model is provided:
Validate each dictionary against the Pydantic model
Store validated model instances
3. Create a new DataTable with the name, backend, and data
4. Return the DataTable
This bidirectional conversion ensures that evaluation data can flow freely between the structured Ragas pipeline and the flexible pandas analysis environment without loss of information.