Dataset research report
Github Jupyter Code To Text research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
Dataset description This dataset consists of sequences of Python code followed by a a docstring explaining its function. It was constructed by concatenating code and text pairs from this dataset that were originally code and markdown cells in Jupyter Notebooks. The content of each example the following: [CODE] """ Explanation: [TEXT] End of explanation """ [CODE] """ Explanation: [TEXT] End of explanation """ ... How to use it from datasets import load_dataset ds =… See the full description on the dataset page: https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text.
Research Context
Github Jupyter Code To Text: 500 rows by 4 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.
Data Profile
Chart Evidence
These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.
license by record count
Most common license values across records.
Open and export this chartFollow-Up Queries
Preview Rows
| # | repo_nametext | pathtext | licensetext | contenttext |
|---|---|---|---|---|
| 1 | keras-team/keras-io | examples/vision/ipynb/mnist_convnet.ipynb | apache-2.0 | import numpy as np from tensorflow import keras from tensorflow.keras import layers """ Explanation: Simple MNIST convnet Author: fchollet… |
| 2 | tensorflow/docs-l10n | site/ja/tfx/tutorials/tfx/components_keras.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. … |
| 3 | ganguli-lab/twpca | notebooks/warp_unit_tests.ipynb | mit | _, _, data = twpca.datasets.jittered_neuron() model = TWPCA(data, n_components=1, warpinit='identity') np.all(np.isclose(model.params['war… |
| 4 | oddt/notebooks | DUD-E.ipynb | bsd-3-clause | from __future__ import print_function, division, unicode_literals import oddt from oddt.datasets import dude print(oddt.__version__) """ … |
| 5 | iAInNet/tensorflow_in_action | _pratice_cifar10.ipynb | gpl-3.0 | max_steps = 3000 batch_size = 128 data_dir = 'data/cifar10/cifar-10-batches-bin/' model_dir = 'model/_cifar10_v2/' """ Explanation: 全局参数 E… |
| 6 | mitdbg/modeldb | demos/webinar-2020-5-6/02-mdb_versioned/01-train/01 Basic NLP.ipynb | mit | !python -m spacy download en_core_web_sm """ Explanation: Versioning Example (Part 1/3) In this example, we'll train an NLP model for sent… |
Data Dictionary
- repo_name text
- path text
- license text
- content text
Method And Limits
- Load the catalog entry and preview rows from the processed dataset file.
- Infer numeric, categorical, time, and location fields from real columns.
- Generate a small set of defensive Plotly chart specifications from that profile.
- Expose each chart idea as a query link so the report can be rerun or exported in Helix.
This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.