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plotly

Skill2 starsChanged 4 months agoNo licenceView on GitHub

Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).

  • Installs packages

15 other projects use a version of this, 10 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use scikit-learn, statsmodels and 3 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • Hongyu-yuSkill

    scikit-learn

    Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

    Also in 4 projects that use this

  • tondevrelSkill

    statsmodels

    Advanced statistical modeling and hypothesis testing. Complementary to SciPy's stats module, it provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. Use for linear regression, GLM, time series analysis, ANOVA, survival analysis, causal inference, and statistical hypothesis testing. Load when working with OLS, WLS, logistic regression, Poisson regression, ARIMA, SARIMAX, statistical diagnostics, p-values, confidence intervals, or R-style statistical analysis.

    Also in 4 projects that use this

  • silvainfmSkill

    polars

    Lightning-fast DataFrame library written in Rust for high-performance data manipulation and analysis. Use when user wants blazing fast data transformations, working with large datasets, lazy evaluation pipelines, or needs better performance than pandas. Ideal for ETL, data wrangling, aggregations, joins, and reading/writing CSV, Parquet, JSON files.

    Also in 4 projects that use this

  • tondevrelSkill

    dask

    A flexible library for parallel computing in Python. It scales Python libraries like NumPy, pandas, and scikit-learn to multi-core systems or distributed clusters. Features lazy evaluation and task scheduling for data that exceeds RAM capacity. Use for out-of-core computing, parallel processing, distributed computing, large-scale data analysis, dask.array, dask.dataframe, dask.delayed, dask.bag, task scheduling, lazy evaluation, and scaling beyond memory limits.

    Also in 3 projects that use this

  • +1 more from tondevrel

The file

We split this skill into the rules it gives an agent

1 rule. 1 is common, 0 are distinctive, and 0 are very specific to this project.

  • 1

    Common

    Most of the 13 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 13 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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