iterrows
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
iterrows has 14 facts recorded in Dontopedia across 5 references, with 3 live disagreements.
Mostly:rdf:type(5), used in(2), belongs to list(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (6)
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.
usesMethodUses Method(3)
- Compare Cleaning
ex:compare_cleaning - Original Code
ex:original-code - Parallel Execution
ex:parallel_execution
hasMethodHas Method(1)
- Datasets
ex:datasets
iteratedOverIterated Over(1)
- Datasets
ex:datasets
usesDataFrameMethodUses Data Frame Method(1)
- Compare Cleaning
ex:compare_cleaning
Other facts (9)
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 | Method | [1] |
| Rdf:type | Method | [2] |
| Rdf:type | Method | [3] |
| Rdf:type | Method | [4] |
| Rdf:type | Data Frame Method | [5] |
| Used in | Compare Cleaning | [1] |
| Used in | Process Batch | [3] |
| Belongs to List | Pandas Library | [2] |
| Called in | Process Batch | [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 (5)
ctx:claims/beam/4bf72c19-e147-4c83-b922-030035464495ctx:claims/beam/abbe86bc-57a3-4347-aab0-645abb0507b7- full textbeam-chunktext/plain1 KB
doc:beam/abbe86bc-57a3-4347-aab0-645abb0507b7Show excerpt
# Define a function to compare the two datasets def compare_cleaning(openrefine, manual): # Calculate the number of matching entries matches = 0 for index, row in openrefine.iterrows(): if row.equals(manual.loc[index]): …
ctx:claims/beam/890d9056-b31d-4cb1-86b8-e5c106107150ctx:claims/beam/8cf0486b-7a52-401d-a035-133c1cdeb419- full textbeam-chunktext/plain1 KB
doc:beam/8cf0486b-7a52-401d-a035-133c1cdeb419Show excerpt
# Apply debugging logic row['error'] = 0 return df # Test the function documents = "path/to/documents.csv" result = reduce_training_errors(documents) print(result) ``` Can you help me identify what's going …
ctx:claims/beam/53b6e60a-57f4-4a01-b2a5-ba77515229e4- full textbeam-chunktext/plain1 KB
doc:beam/53b6e60a-57f4-4a01-b2a5-ba77515229e4Show excerpt
num_cores = 4 # Adjust based on your system's capabilities tuned_datasets = Parallel(n_jobs=num_cores)(delayed(secure_tuning)(row) for _, row in datasets.iterrows()) # Convert the list of results back to a DataFrame tuned_datasets = pd.Da…
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