Skip to main content

Thorreznou Library 🖥

Bootcamp Data Science | Full Time Class - November 2021

Library for machine learning projects with Python. The aim is to speed up the work process to the repetitive tasks that the data scientist usually carries out. The library has three parts: EDA, ML and VIZ. Each part focuses on a field of work, exploratory data analysis, machine learning, visualisation.

Visualization

  • visualizeME_palettes_or_colors
  • visualizeME_and_describe_violinbox
  • visualizeME_and_describe_barplot
  • visualizeME_FigureWords
  • visualizeME_bagel_look_top
  • visualizeME_and_describe_Spidey
  • visualizeME_c_matrix
  • visualizeME_scores_models

Exploratory Data Analysis (EDA)

  • resize_image
  • standarize_numbers
  • coordinates
  • reduce_img
  • process_color
  • reduce_col_palette
  • math_expect
  • overview
  • outlier_removal
  • datetime_into_columns
  • missing_data

Machine Learning

  • divide
  • prepare_data
  • model_scoring_classification
  • model_scoring_regression
  • random_forest_classif
  • SVC1
  • LogistRegress
  • corr_target
  • knn
  • poly_reg
  • lin_reg

Launch library

pip install thorreznou

imagen

Authors 🕶

  • Group VIS: Marta, Natalia, Óscar
  • Group EDA: David, Isabella, Jorge
  • Group ML: Dani, Fer, Ana
  • Group LAUNCH: Javi, Fede, Erik
  • Project Manager: Miguel

Metadata

Release files for Thorreznou 0.1.14

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for Thorreznou 0.1.14
File Size Uploaded Provenance Actions
Thorreznou-0.1.14.tar.gz 143.2 kB Without provenance Details for Thorreznou-0.1.14.tar.gz

Release files / Thorreznou-0.1.14.tar.gz

Download URL Thorreznou-0.1.14.tar.gz
Size 143.2 kB
Tags Source
SHA-256 checksum
How to use checksums
4c3d4bca9bfeb7f722584ad777f882158e5304080730e3b0ab09c5d40c143100
BLAKE2b-256 checksum
How to use checksums
5d7c1cea9839ef80edf93ea5a4fa9945cad5cb69d4d4482070f37c1a71f27fef
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.21.0 requests-toolbelt/0.9.1 urllib3/1.24.3 tqdm/4.62.3 importlib-metadata/4.8.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.12

Security for Thorreznou 0.1.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page