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Dataset research report

Codeparrot Clean research report

A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.

storageHf descriptioncodeparrot--codeparrot-clean.parquet view_list500 rows

Executive Summary

CodeParrot 🦜 Dataset Cleaned What is it? A dataset of Python files from Github. This is the deduplicated version of the codeparrot. Processing The original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned with the following steps: Deduplication Remove exact matches Filtering Average line length < 100 Maximum line length < 1000 Alpha numeric characters fraction > 0.25 Remove auto-generated files (keyword search) For… See the full description on the dataset page: https://huggingface.co/datasets/codeparrot/codeparrot-clean.

Finding 1The dataset has 500 rows available in the catalog.
Finding 2The catalog exposes 11 documented or inferred columns.
Finding 3Helix has 5 ready query prompts for this dataset.
Finding 4This report includes 4 generated chart views.

Research Context

Codeparrot Clean: 500 rows by 11 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.

Data Profile

Rows500
Columns11
Numeric cols3
Categorical cols2

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.

Follow-Up Queries

Preview Rows

# repo_nametext pathtext copiestext sizetext contenttext licensetext hashinteger line_meanfloat
1 ahmedbodi/AutobahnPythonexamples/asyncio/websocket/echo/client_coroutines.py132044############################################################################### ## ## Copyright (C) 2013-2014 Tavendo GmbH ## ## Licensed…apache-2.0782206174409495080131.44
2 ifduyue/djangodjango/core/checks/registry.py133108from itertools import chain from django.utils.itercompat import is_iterable class Tags: """ Built-in tags for internal checks. …bsd-3-clause-203568689637296769730.71
3 kmike/scikit-learnsklearn/utils/__init__.py310094""" The :mod:`sklearn.utils` module includes various utilites. """ from collections import Sequence import numpy as np from scipy.sparse …bsd-3-clause233470957761116065126.88
4 houlixin/BBB-TISDKlinux-devkit/sysroots/i686-arago-linux/usr/lib/python2.7/encodings/cp1250.py59313942""" Python Character Mapping Codec cp1250 generated from 'MAPPINGS/VENDORS/MICSFT/WINDOWS/CP1250.TXT' with gencodec.py. """#" import code…gpl-2.0-635683201851518218144.41
5 dataxu/ansiblelib/ansible/modules/system/kernel_blacklist.py1254009#!/usr/bin/python # encoding: utf-8 -*- # Copyright: (c) 2013, Matthias Vogelgesang <[email protected]> # GNU General Public …gpl-3.0849877108444572676124.86
6 163gal/Time-Linelibs_arm/wx/_controls.py2332374# This file was created automatically by SWIG 1.3.29. # Don't modify this file, modify the SWIG interface instead. import _controls_ impor…gpl-3.0-383935324736331595041.47

Data Dictionary

  • repo_name text
  • path text
  • copies text
  • size text
  • content text
  • license categorical
  • hash numeric
  • line_mean numeric
  • line_max numeric
  • alpha_frac numeric
  • autogenerated bool

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.

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