row-by-row iteration
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
row-by-row iteration has 11 facts recorded in Dontopedia across 4 references, with 2 live disagreements.
Mostly:rdf:type(4), extracts(3), used in(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
comparedToCompared to(2)
- Boolean Indexing Efficiency
ex:boolean-indexing-efficiency - Efficiency Claim
ex:efficiency-claim
impliesImplies(1)
- Code Snippet
ex:code-snippet
superiorToSuperior to(1)
- Efficiency Comparison
ex:efficiency-comparison
Other facts (10)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Iteration Pattern | [1] |
| Rdf:type | Loop Structure | [2] |
| Rdf:type | Technique | [3] |
| Rdf:type | Data Processing Method | [3] |
| Extracts | query | [4] |
| Extracts | context | [4] |
| Extracts | ground_truth_documents | [4] |
| Used in | Compare Cleaning | [1] |
| Iterates Over | Openrefine | [1] |
| Inferior to | Boolean Indexing | [3] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (4)
ctx:claims/beam/4bf72c19-e147-4c83-b922-030035464495ctx:claims/beam/1803a023-7e2b-437b-86c1-6e6daf7524e3- full textbeam-chunktext/plain1 KB
doc:beam/1803a023-7e2b-437b-86c1-6e6daf7524e3Show excerpt
remaining_duration -= row['duration'] # Display completed tasks print("\nCompleted tasks:") print(completed_tasks) # Display remaining tasks remaining_tasks = df[~df['task'].isin(completed_tasks)][['task', 'priority', 'duration']]…
ctx:claims/beam/8bf9ec46-2c0a-4990-b74d-e0b079d65b51- full textbeam-chunktext/plain1 KB
doc:beam/8bf9ec46-2c0a-4990-b74d-e0b079d65b51Show excerpt
- Use `pd.read_csv` to load the documents into a `DataFrame`. 2. **Debugging Logic**: - Use boolean indexing to update the `'error'` column. This method is more efficient and works in place. 3. **Returning the Updated DataFrame**: …
ctx:claims/beam/4cc521bd-2791-4334-88dc-f5e3519e2d92- full textbeam-chunktext/plain1 KB
doc:beam/4cc521bd-2791-4334-88dc-f5e3519e2d92Show excerpt
2. **Split the Dataset**: Divide the dataset into training and testing sets. 3. **Evaluate Precision and Recall**: Use precision and recall to evaluate the relevance of the retrieved documents. 4. **User Feedback**: Optionally, collect user…
See also
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