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We use real data, acquired in a physics experiment\ndesigned to study the flow of superfluid helium in a low-dimensional setup.\nPandas loads the (tabular) data into a DataFrame (2-dimensional labelled data\nstructure). Pandas lets you handle web-hosted data, heterogeneous data (e.g.,\nsome columns are numeric and others are character strings), missing data,\ncomments (e.g., lab notes). Pandas also offers native timeseries support. We\ncover some best practices for testing data and statistics (distributions). We\nintroduce the concept of tidy data, which results from wrangling the raw data\nso that they become easy to work with (i.e., transform, visualize, model).\n\n## Local setup\n\nWe use cross-platform package manager [conda](https://conda.io/).\nWe recommend using the [Miniconda](https://conda.io/miniconda.html)\ndistribution. Once you have downloaded your Miniconda installer, run\nthe following command (adapt if necessary):\n\n    $ bash ~/Downloads/Miniconda3-latest-Linux-x86_64.sh\n\nand follow the installation steps. Now create a sandboxed environment\nfor this project:\n\n    $ conda env create -f environment.yml\n    $ source activate advanced-pandas\n    $ jupyter notebook\n\nIf you edit file `environment.yml` (to add or update a dependency), then\nrun:\n\n    $ conda env update -f environment.yml\n\n## Remote setup\n\nCheck out this\n[draft](https://www.authorea.com/users/153798/articles/213273-deploying-computing-environments).\n\n## References\n\n* [PyData 101 slides](https://speakerdeck.com/jakevdp/pydata-101)\nby Jake VanderPlas\n* [Tidy Data article](https://www.jstatsoft.org/article/view/v059i10/)\nby Hadley Wickham\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkcor%2Fexp-testing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmkcor%2Fexp-testing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkcor%2Fexp-testing/lists"}