data.iloc[start:end]

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

data.iloc[start:end] has 33 facts recorded in Dontopedia across 14 references, with 3 live disagreements.

33 facts·16 predicates·14 sources·3 in dispute

Mostly:rdf:type(11), uses(2), extracts(1)

Rdf:typein dispute`rdf:type`

List Operation^[1]all time · 15d7388e 43fd 4058 8b3c 713df105541b
Operation^[2]all time · 7fb0fddf 6dd9 471f A36a 857a26f28141
Python Operation^[3]sourceall time · 541131ce B263 49a7 9215 60ee694bc819
Python Operation^[4]all time · De383db7 Ff0a 4d39 85dd 02ba575a322e
Slicing Operation^[5]all time · E3b4edc5 6ce9 47ff B092 3eb3e280084b
Operation^[7]all time · 74437243 4507 4df1 B2dc C949aea841d6
List Slicing^[9]sourceall time · B28296e8 D424 4c69 B112 9bdbaeddc220
List Slicing Operation^[10]all time · 02a78e85 75b8 44ad 845e 833d1a39bae2
List Operation^[11]sourceall time · Dad116a3 2105 43a3 93d8 198911a2b349
List Operation^[12]all time · 598ca712 19ba 4363 B6ed 843a3ccf4768

Inbound 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.

`slicesInput`Slices Input(1)

Process Queries in Batchesex:process_queries_in_batches

`usedIn`Used in(1)

Batch Size Variableex:batch-size-variable

`uses`Uses(1)

Batch Processing Loopex:batch-processing-loop

`usesOperation`Uses Operation(1)

Process Queries Batchex:process-queries-batch

Other facts (16)

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.

16 facts

Predicate	Value	Ref
Uses	Range Parameters	[2]
Uses	Slice Notation	[8]
Extracts	Document Batch	[1]
Syntax	Slice Notation	[4]
Pattern	i:i+1	[6]
Uses Range	Start End Range	[7]
Creates	Batch Texts Variable	[8]
Start Index	I Variable	[9]
End Index	I Plus Batch Size	[9]
Slices From Index	I	[10]
Slices to Index	I Plus Batch Size	[10]
Uses Slice Syntax	true	[11]
Slices	Queries Variable	[13]
Uses Start Index	I	[13]
Uses End Index	End Index Calculation	[13]
Slicing Expression	segments[i:i + batch_size]	[14]

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.

33 undated

—

typebeam/15d7388e-43fd-4058-8b3c-713df105541b

ex:ListOperation

—

extractsbeam/15d7388e-43fd-4058-8b3c-713df105541b

ex:document-batch

—

usesbeam/7fb0fddf-6dd9-471f-a36a-857a26f28141

ex:range-parameters

—

typebeam/7fb0fddf-6dd9-471f-a36a-857a26f28141

ex:Operation

—

labelbeam/7fb0fddf-6dd9-471f-a36a-857a26f28141

List slicing with batch_size

—

typebeam/541131ce-b263-49a7-9215-60ee694bc819

ex:PythonOperation

—

typebeam/de383db7-ff0a-4d39-85dd-02ba575a322e

ex:PythonOperation

—

labelbeam/de383db7-ff0a-4d39-85dd-02ba575a322e

list slicing operation

—

syntaxbeam/de383db7-ff0a-4d39-85dd-02ba575a322e

ex:slice-notation

—

typebeam/e3b4edc5-6ce9-47ff-b092-3eb3e280084b

ex:Slicing-operation

—

patternbeam/827c1c76-62d2-479f-970a-d589dd9c297f

i:i+1

—

typebeam/74437243-4507-4df1-b2dc-c949aea841d6

ex:Operation

—

labelbeam/74437243-4507-4df1-b2dc-c949aea841d6

data.iloc[start:end]

—

usesRangebeam/74437243-4507-4df1-b2dc-c949aea841d6

ex:start-end-range

—

usesbeam/a25d423f-87ea-4766-ab98-7d69c454663b

ex:slice-notation

—

createsbeam/a25d423f-87ea-4766-ab98-7d69c454663b

ex:batch-texts-variable

—

typebeam/b28296e8-d424-4c69-b112-9bdbaeddc220

ex:List-Slicing

—

startIndexbeam/b28296e8-d424-4c69-b112-9bdbaeddc220

ex:i-variable

—

endIndexbeam/b28296e8-d424-4c69-b112-9bdbaeddc220

ex:i-plus-batch-size

—

typebeam/02a78e85-75b8-44ad-845e-833d1a39bae2

ex:list-slicing-operation

—

labelbeam/02a78e85-75b8-44ad-845e-833d1a39bae2

queries[i:i+batch_size]

—

slicesFromIndexbeam/02a78e85-75b8-44ad-845e-833d1a39bae2

ex:i

—

slicesToIndexbeam/02a78e85-75b8-44ad-845e-833d1a39bae2

ex:i-plus-batch-size

—

typebeam/dad116a3-2105-43a3-93d8-198911a2b349

ex:ListOperation

—

usesSliceSyntaxbeam/dad116a3-2105-43a3-93d8-198911a2b349

true

—

typebeam/598ca712-19ba-4363-b6ed-843a3ccf4768

ex:ListOperation

—

typebeam/d3dd63ff-b7e5-4717-8f41-9969d9f06a45

ex:CodeOperation

—

labelbeam/d3dd63ff-b7e5-4717-8f41-9969d9f06a45

queries[i:i + batch_size]

—

slicesbeam/d3dd63ff-b7e5-4717-8f41-9969d9f06a45

ex:queries-variable

—

usesStartIndexbeam/d3dd63ff-b7e5-4717-8f41-9969d9f06a45

ex:i

—

usesEndIndexbeam/d3dd63ff-b7e5-4717-8f41-9969d9f06a45

ex:end-index-calculation

—

labelbeam/80755d41-e377-4779-92c9-b54cb0b21c0f

Segment Batch Slicing

—

slicingExpressionbeam/80755d41-e377-4779-92c9-b54cb0b21c0f

segments[i:i + batch_size]

References (14)

14 references

[1]15d7388e 43fd 4058 8b3c 713df105541b2 facts
ctx:claims/beam/15d7388e-43fd-4058-8b3c-713df105541b
[2]7fb0fddf 6dd9 471f A36a 857a26f281413 facts
ctx:claims/beam/7fb0fddf-6dd9-471f-a36a-857a26f28141
[3]541131ce B263 49a7 9215 60ee694bc8191 fact
ctx:claims/beam/541131ce-b263-49a7-9215-60ee694bc819
- full textbeam-chunk
  text/plain1 KBdoc:beam/541131ce-b263-49a7-9215-60ee694bc819
  Show excerpt
  1. **Monitor Memory Usage**: Use tools like `psutil` in Python to monitor the memory usage of your script. This can help you identify if your script is running out of memory. 2. **Optimize Data Structures**: Ensure that you are using effic…
[4]De383db7 Ff0a 4d39 85dd 02ba575a322e3 facts
ctx:claims/beam/de383db7-ff0a-4d39-85dd-02ba575a322e
[5]E3b4edc5 6ce9 47ff B092 3eb3e280084b1 fact
ctx:claims/beam/e3b4edc5-6ce9-47ff-b092-3eb3e280084b
- full textbeam-chunk
  text/plain1 KBdoc:beam/e3b4edc5-6ce9-47ff-b092-3eb3e280084b
  Show excerpt
  return lang # Fallback to polyglot for rare languages detector = Detector(text) return detector.language.code except langdetect.LangDetectException: logging.error(f"Unable to detect l…
[6]827c1c76 62d2 479f 970a D589dd9c297f1 fact
ctx:claims/beam/827c1c76-62d2-479f-970a-d589dd9c297f
- full textbeam-chunk
  text/plain1 KBdoc:beam/827c1c76-62d2-479f-970a-d589dd9c297f
  Show excerpt
  x = torch.relu(self.fc1(x)) x = self.fc2(x) return x # Initialize the modules and move them to the GPU device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") complexity_scoring_module = ComplexityS…
[7]74437243 4507 4df1 B2dc C949aea841d63 facts
ctx:claims/beam/74437243-4507-4df1-b2dc-c949aea841d6
[8]A25d423f 87ea 4766 Ab98 7d69c454663b2 facts
ctx:claims/beam/a25d423f-87ea-4766-ab98-7d69c454663b
[9]B28296e8 D424 4c69 B112 9bdbaeddc2203 facts
ctx:claims/beam/b28296e8-d424-4c69-b112-9bdbaeddc220
- full textbeam-chunk
  text/plain1 KBdoc:beam/b28296e8-d424-4c69-b112-9bdbaeddc220
  Show excerpt
  futures = {executor.submit(self.rewrite_query, query): query for query in queries} for future in as_completed(futures): rewritten_queries.append(future.result()) return rewritten_queries …
[10]02a78e85 75b8 44ad 845e 833d1a39bae24 facts
ctx:claims/beam/02a78e85-75b8-44ad-845e-833d1a39bae2
- full textbeam-chunk
  text/plain1 KBdoc:beam/02a78e85-75b8-44ad-845e-833d1a39bae2
  Show excerpt
  outputs = self.model.generate(**inputs) reformulated_query = self.tokenizer.decode(outputs[0], skip_special_tokens=True) self.redis_client.set(query, reformulated_query, ex=3600) # Cache for 1 hour return re…
[11]Dad116a3 2105 43a3 93d8 198911a2b3492 facts
ctx:claims/beam/dad116a3-2105-43a3-93d8-198911a2b349
- full textbeam-chunk
  text/plain1 KBdoc:beam/dad116a3-2105-43a3-93d8-198911a2b349
  Show excerpt
  futures = [executor.submit(reformulate_query, query) for query in queries] for future in as_completed(futures): results.append(future.result()) return results ``` #### 5. Batch Processing Process queries in…
[12]598ca712 19ba 4363 B6ed 843a3ccf47681 fact
ctx:claims/beam/598ca712-19ba-4363-b6ed-843a3ccf4768
- full textbeam-chunk
  text/plain1 KBdoc:beam/598ca712-19ba-4363-b6ed-843a3ccf4768
  Show excerpt
  return reformulated_query, end_time - start_time # Define a function to process queries in batches def process_queries_in_batches(queries, batch_size=100): results = [] for i in range(0, len(queries), batch_size): batch…
[13]D3dd63ff B7e5 4717 8f41 9969d9f06a455 facts
ctx:claims/beam/d3dd63ff-b7e5-4717-8f41-9969d9f06a45
[14]80755d41 E377 4779 92c9 B54cb0b21c0f2 facts
ctx:claims/beam/80755d41-e377-4779-92c9-b54cb0b21c0f
- full textbeam-chunk
  text/plain1 KBdoc:beam/80755d41-e377-4779-92c9-b54cb0b21c0f
  Show excerpt
  Here's an improved version of your code that leverages LangChain for context chaining and optimizes processing speed: ```python import langchain from concurrent.futures import ProcessPoolExecutor from typing import List # Configure loggin…

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.

data.iloc[start:end]

Rdf:typein disputerdf:type

Inbound mentions (4)

slicesInputSlices Input(1)

usedInUsed in(1)

usesUses(1)

usesOperationUses Operation(1)

Other facts (16)

Timeline

References (14)

See also

Keep researching

Rdf:typein dispute`rdf:type`

`slicesInput`Slices Input(1)

`usedIn`Used in(1)

`uses`Uses(1)

`usesOperation`Uses Operation(1)