# Coding Cheatsheets ```{tip} When you're working in Python it can be *super* helpful to regularly refer to these resources. Remember that you can always use any `API reference` link below to get a comprehensive list of *all* the functions and methods in a library - a bit nicer than only relying on `?` in your notebook. ``` ## Course Packages Throughout the course we've been making use of the following Python libraries in case you want to create a reproducible environment for yourself: - [`numpy`](https://numpy.org/doc/stable/index.html) - [`scipy`](https://scipy.org/) - [`matplotlib`](https://matplotlib.org/) - [`seaborn`](https://seaborn.pydata.org/) - [`polars`](https://docs.pola.rs/user-guide/getting-started/) - [`statsmodels`](https://www.statsmodels.org/stable/user-guide.html) - [`marginaleffects`](https://marginaleffects.com/) - [`pymer4`](https://eshinjolly.com/pymer4/) - [`ipywidgets`](https://ipywidgets.readthedocs.io/en/stable/) - *optional just for interactive demos* ## Computing Basics - [Terminal commands cheatsheet](https://www.git-tower.com/blog/media/pages/posts/command-line-cheat-sheet/64337e6bae-1733170994/command-line-cheat-sheet-large01.avif) - [Git & Github cheatsheet](/pages/git) - [More Git commands](https://education.github.com/git-cheat-sheet-education.pdf) ## Coding Notebooks - [Python Notebooks in VSCode](https://code.visualstudio.com/docs/datascience/jupyter-notebooks) - [Python Notebooks in JupyterLab](https://jupyterlab.readthedocs.io/en/latest/user/notebook.html) - To show multiple outputs from a single cell add this to the top of your notebook: - `%config InteractiveShell.ast_node_interactivity = "all" ` - Other options include `last`, `last_expr` (default), `last_expr_or_assign`, `none` - To produce higher-resolution plots add this to the top of your notebook: - `%config InlineBackend.figure_formats = 'retina'` ## Conda Environments - [Conda overview and basic commands](https://nbis-reproducible-research.readthedocs.io/en/course_2104/conda/) - [Conda and sharing Python environments for reproducible research](https://rse.shef.ac.uk/conda-environments-for-effective-and-reproducible-research/04-sharing-environments/index.html) - [Conda command cheatsheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf) ## Python Basics - [Basics cheatsheet](https://media.datacamp.com/legacy/image/upload/v1694526244/Marketing/Blog/Python_Basics_Cheat_Sheet-updated.pdf) - [Interactive Reference](https://www.pythoncheatsheet.org/cheatsheet/basics) - [Python for R users](https://cran.r-project.org/web/packages/reticulate/vignettes/python_primer.html) ## Numpy - numerical array library - [Numpy tutorials](https://numpy.org/numpy-tutorials/) - [Numpy API reference](https://numpy.org/doc/stable/reference/index.html) - [Numpy Cheatsheet](https://media.datacamp.com/legacy/image/upload/v1676302459/Marketing/Blog/Numpy_Cheat_Sheet.pdf) - [Numpy for MATLAB users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) ## Matplotlib - basic plotting library - [Matplotlib user guide](https://matplotlib.org/stable/users/index.html) - [Matplotlib API reference](https://matplotlib.org/stable/api/index.html) - [Matplotlib tutorials](https://matplotlib.org/stable/tutorials/index.html) - [Matplotlib cheatsheets](https://matplotlib.org/cheatsheets/) ## Scipy - scientific functions and statistics library - [SciPy user guide](https://docs.scipy.org/doc/scipy/tutorial/index.html) - [SciPy API reference](https://docs.scipy.org/doc/scipy/reference/index.html) - [Summary statistics](https://docs.scipy.org/doc/scipy/reference/stats.html#summary-statistics) - [Resampling, i.e. montecarlo, bootstrap, permutation](https://docs.scipy.org/doc/scipy/reference/stats.html#resampling-and-monte-carlo-methods) - [Hypothesis testing](https://docs.scipy.org/doc/scipy/reference/stats.html#hypothesis-tests-and-related-functions) ## Polars - DataFrame and tidy-data analysis library - [Polars user guide](https://docs.pola.rs/) - [Polars API reference](https://docs.pola.rs/api/python/dev/reference/index.html) - [Tidyverse and Polars side-by-side](https://robertmitchellv.com/blog/2022-07-r-python-side-by-side/r-python-side-by-side.html) - [Polars Rgonomic patterns](https://www.emilyriederer.com/post/py-rgo-polars/) - [Pandas - alternative DataFrame library we're NOT using](https://pandas.pydata.org/docs/index.html) ## Seaborn - statistical visualization library - [Seaborn user guide](https://seaborn.pydata.org/tutorial.html) - [Seaborn API](https://seaborn.pydata.org/api.html) - [Seaborn cheatsheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf) ## Statsmodels - regression library - [Using formulas in statsmodels](https://www.statsmodels.org/stable/example_formulas.html) - [OLS model reference](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.OLS.html#statsmodels.regression.linear_model.OLS) - [Regression results reference](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults) - [Post-hoc pairwise tests](https://www.statsmodels.org/stable/dev/generated/statsmodels.base.model.GenericLikelihoodModelResults.t_test_pairwise.html#statsmodels.base.model.GenericLikelihoodModelResults.t_test_pairwise) - [`anova_lm` for model comparison](https://www.statsmodels.org/stable/generated/statsmodels.stats.anova.anova_lm.html#statsmodels.stats.anova.anova_lm) ## MarginalEffects - like `emmeans` in R - [Predictions & Average Predictions](https://marginaleffects.com/man/python/predictions.html) ## Pymer4 - multi-level/linear-mixed-models - [Pymer4 Usage Guide](https://eshinjolly.com/pymer4/auto_examples/index.html) - [Repeated Measures Overview](https://eshinjolly.com/2019/02/18/rep_measures/) - [`.fit()` method reference](https://eshinjolly.com/pymer4/api.html#pymer4.models.Lmer.fit) - [`.post_hoc()` method reference](https://eshinjolly.com/pymer4/api.html#pymer4.models.Lmer.post_hoc)