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The installer includes packages such as:\n\n* Jupyter (for interactive notebooks);\n* Numpy (for arrays);\n* Scipy (extended scientific computing);\n* Matplotlib (plotting);\n* Pandas (data science);\n* Statsmodels (statistics);\n* Scikit-Image (image processing);\n* Scikit-Learn (machine learning and statistics);\n\nas well as many others.  See the `./recipes/scientific-python/construct.yaml`\nfile for details.\n\nWe imagine these installers will be particularly useful for beginner students\ntaking courses in science, statistics or data science, to get them up and\nrunning with a single download and install step.\n\n## Where can I get the installers?\n\nWe will make a release soon.  When we have done so, please visit [the release\npage](https://github.com/scientific-python/installer/releases/latest) to\ndownload an installer for your OS. \u003c!-- TODO: replace with link to tutorial\nif/when written. --\u003e\n\n## Development\n\nLocally, installers can be built using `tools/build_local.sh`. Steps:\n\n1. Set up and activate a `conda` env with a forked version of `constructor`:\n   ```console\n   $ conda env create -f environment.yml\n   $ conda activate constructor-env\n   ```\n2. Run `./tools/build_local.sh`.\n3. Install the environment for your platform.\n4. Test it using the `tests/`.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscientific-python%2Finstaller","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscientific-python%2Finstaller","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscientific-python%2Finstaller/lists"}