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|---|---|---|---|---|---|---|---|---|---|
64732561_c0 | 64732561 | python | 0 | Title: sum rows in 3d numpy
Problem title: sum rows in 3d numpy
Tags: python, numpy
Problem: sum rows in 3d numpy
Code signals: np.array | sum rows in 3d numpy sum rows in 3d numpy python numpy np.array sum rows in 3d numpy Assume the following numpy is given: One has to find the weight of each point in each row in relation to the sum of the row. in reference to the example above the expected result need to be: | [
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53425250_c0 | 53425250 | python | 0 | Title: Pandas Dataframe
Problem title: Pandas Dataframe
Tags: dataframe, python
Problem: Pandas Dataframe
Code signals: Pandas, Dataframe | Pandas Dataframe Pandas Dataframe dataframe python Pandas Dataframe Pandas Dataframe For example: I am wondering how to output the following dataframe: Thanks | [
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54369667_c0 | 54369667 | python | 0 | Title: Inconsistent Date
Problem title: Inconsistent Date
Tags: python, pandas
Problem: Inconsistent Date
Code signals: Inconsistent, Date | Inconsistent Date Inconsistent Date python pandas Inconsistent Date Inconsistent Date The dates in my dataset are inconsistent. Is there any way to make them in a particular format like YY/DD/MM? All these dates are of the month of January and are continuous but date and month flipped from the 7th row. CSV Data looks s... | [
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42763060_c0 | 42763060 | python | 0 | "Title: check on pandas dataframe\nProblem title: check on pandas dataframe\nTags: dataframe, python(...TRUNCATED) | "check on pandas dataframe check on pandas dataframe dataframe python pandas check on pandas datafra(...TRUNCATED) | [0.0303955078125,0.0027313232421875,-0.010498046875,0.0113525390625,0.0010528564453125,-0.0230712890(...TRUNCATED) | [12765,98,2652,1124,2053,160328,17198,50828,150350,390,138,3365,316,1779,1884,29479,60212,17721,4034(...TRUNCATED) | [0.2364501953125,0.1544189453125,0.1468505859375,0.1751708984375,0.15673828125,0.254150390625,0.0654(...TRUNCATED) | embed |
42360433_c0 | 42360433 | python | 0 | "Title: Tracing Code in Python\nProblem title: Tracing Code in Python\nTags: python\nProblem: Tracin(...TRUNCATED) | "Tracing Code in Python Tracing Code in Python python Tracing Python Tracing Code in Python Hey guys(...TRUNCATED) | [0.00726318359375,0.021728515625,-0.00106048583984375,0.00860595703125,-0.0038909912109375,0.0010833(...TRUNCATED) | [4937,21896,28864,23,145581,17198,50828,28240,51484,180663,3525,47,765,42276,33662,22929,117914,1445(...TRUNCATED) | [0.2181396484375,0.1824951171875,0.251220703125,0.0858154296875,0.2734375,0.1092529296875,0.11877441(...TRUNCATED) | embed |
5957341_c0 | 5957341 | python | 0 | "Title: Python list confusion\nProblem title: Python list confusion\nTags: list, python\nProblem: Py(...TRUNCATED) | "Python list confusion Python list confusion list python Python a_list Python list confusion Let's s(...TRUNCATED) | [0.00445556640625,-0.002044677734375,-0.000019550323486328125,0.0037689208984375,0.0028076171875,0.0(...TRUNCATED) | [145581,5303,169463,17198,50828,10,454,6562,25632,18151,139392,3444,2811,40383,5117,12830,84751,7319(...TRUNCATED) | [0.244873046875,0.2197265625,0.213134765625,0.08447265625,0.0877685546875,0.0933837890625,0.00863647(...TRUNCATED) | embed |
67099244_c1 | 67099244 | python | 1 | "06:00.874 - 00:01:00.767 - 00:07:04.123 - 00:00:05.678 - 00:03:05.429 - 00:07:45.346 - 00:04:2.345 (...TRUNCATED) | "06:00.874 - 00:01:00.767 - 00:07:04.123 - 00:00:05.678 - 00:03:05.429 - 00:07:45.346 - 00:04:2.345 (...TRUNCATED) | [-0.006195068359375,-0.000545501708984375,-0.00830078125,0.011474609375,0.0027313232421875,-0.013488(...TRUNCATED) | [158520,5,19308,617,20,7227,21032,6632,161412,32682,33296,48636,110095,25673,169842,28724,4235,15989(...TRUNCATED) | [0.31982421875,0.139404296875,0.26318359375,0.26123046875,0.1778564453125,0.152099609375,0.166625976(...TRUNCATED) | embed |
65907915_c3 | 65907915 | python | 3 | "699,260 1701,260 1704,258 1706,258 1708,255 1708,217 1704,217 1701,214 1701,198 1699,196 1699,194 1(...TRUNCATED) | "699,260 1701,260 1704,258 1706,258 1708,255 1708,217 1704,217 1701,214 1701,198 1699,196 1699,194 1(...TRUNCATED) | [0.0107421875,-0.0017852783203125,-0.005950927734375,0.0194091796875,-0.00433349609375,0.00848388671(...TRUNCATED) | [305,5046,4,97544,20390,133063,4598,729,7709,304,10057,9016,8318,135455,2489,2592,6746,2947,1019,611(...TRUNCATED) | [0.1890869140625,0.3203125,0.1270751953125,0.312255859375,0.2371826171875,0.110107421875,0.208984375(...TRUNCATED) | embed |
22550302_c0 | 22550302 | python | 0 | "Title: Find neighbors in a matrix?\nProblem title: Find neighbors in a matrix?\nTags: python\nProbl(...TRUNCATED) | "Find neighbors in a matrix? Find neighbors in a matrix? python Find Find neighbors in a matrix? Her(...TRUNCATED) | [0.007659912109375,0.00982666015625,-0.00171661376953125,-0.003387451171875,0.002166748046875,0.0021(...TRUNCATED) | [26040,208244,7,23,50944,425,32,10,17198,50828,11853,361,966,101935,534,21416,3912,142424,19069,4567(...TRUNCATED) | [0.1697998046875,0.2958984375,0.2017822265625,0.1065673828125,0.2352294921875,0.1541748046875,0.0087(...TRUNCATED) | embed |
12675938_c0 | 12675938 | python | 0 | "Title: finding diagonal numbers\nProblem title: finding diagonal numbers\nTags: list, multidimensio(...TRUNCATED) | "finding diagonal numbers finding diagonal numbers list multidimensional-array python finding diagon(...TRUNCATED) | [0.00537109375,0.00775146484375,-0.018310546875,-0.000598907470703125,-0.0040283203125,0.00228881835(...TRUNCATED) | [90791,207997,101935,5303,6024,157955,19305,53,17198,50828,765,903,10298,3688,1660,116,397,3871,54,7(...TRUNCATED) | [0.1551513671875,0.28515625,0.1986083984375,0.1798095703125,0.060028076171875,0.121337890625,0.09460(...TRUNCATED) | embed |
End of preview. Expand in Data Studio
Python StackOverflow Vector Dataset Datasheet
1. What This Dataset Is
This dataset is the Python-specific vector shard of the Stack2Graph StackOverflow retrieval corpus. Each Hugging Face dataset repository contains exactly one language dataset.
It is optimized for dense+sparse retrieval, Qdrant restoration, and embedding-based RAG experiments.
It is used in the Stack2Graph project as the vector counterpart to the language-scoped RDF knowledge graph shards.
See the Stack2Graph repository for more details: https://github.com/tha-atlas/Stack2Graph
2. Repository Layout
dataset_manifest.json
question_metadata_*.parquet
chunk_records_*.parquet
question_records_*.parquet
dataset_manifest.json: language-scoped manifest for this dataset shard.question_metadata_*.parquet: per-question metadata and retrieval bookkeeping.chunk_records_*.parquet: chunk-level vector rows when parent-child indexing is enabled.question_records_*.parquet: question-level vector rows when chunking is disabled or exported alongside chunk data.
3. Data Model And Coverage
The dataset is derived from Stack Overflow questions selected for the Python programming language. It contains the structured records needed to rebuild the Stack2Graph Qdrant collection for that language.
Coverage scope:
- records are retained when they match the Stack2Graph supported language-tag set
- this repository contains only the Python shard
- the archive may contain both metadata-only and retrieval-ready vector rows depending on the export mode
4. Recommended Preprocessing
- Read
dataset_manifest.jsonfirst and use it as the source of truth for included Parquet files. - Load all Parquet shards for this repository into your vector indexing pipeline.
- Rebuild or restore the Qdrant collection
stackoverflow_python_vector. - Preserve attribution and license metadata during downstream export.
5. Automatic Download And Vector DB Setup
You do not need to regenerate embeddings from GraphDB to use this dataset.
In the Stack2Graph repository, you can use the automation script
python -m experiment.load_hf_datasets_into_services --skip-kg to download dataset artifacts
and prepare the vector database service state automatically.
Typical workflow:
- Clone and configure Stack2Graph (
.envwith HF token and service paths). - Clone and configure Stack2Graph (
.envwith HF token and service paths). - Start required local services:
docker compose up -d
- Run the loader script:
python -m experiment.load_hf_datasets_into_services --skip-kg
For manual usage without automation, directly ingest the listed Parquet files into your vector database.
6. Quality Notes And Caveats
- A Stack Overflow question may belong to multiple language shards when tagged with multiple languages.
- Embeddings and sparse representations depend on the configured export pipeline and model versions.
- As with community-generated data, content may include noise, bias, and temporal drift.
7. Intended Use
- semantic retrieval and reranking
- RAG and hybrid retriever experiments
- vector database benchmarking and diagnostics
- language-scoped developer tooling research
8. Limitations
- Not a complete mirror of all Stack Overflow content.
- Not all export modes include the same row types or chunk layouts.
- Best used together with the Stack2Graph retrieval pipeline and Qdrant-compatible tooling.
9. Licensing And Attribution
This dataset inherits Stack Overflow source licensing and attribution requirements. Ensure compliant attribution and redistribution practices in all derived artifacts.
10. Suggested Citation
If you use this dataset, cite the Stack2Graph work:
- Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering
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