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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
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            "Programming Language :: Python :: 3.5",
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        "description": "# seipy\n\nHelper functions for the python data science stack as well as spark, AWS, jupyter.\n\n## What is it\n\nThis library contains helpers and wrappers for common data science libraries in the python stack:\n- pandas\n- numpy\n- scipy\n- sklearn\n- matplotlib\n- pyspark\n\nThere are also functions that simplify common manipulations for machine learning and data science\nin general, as well as interfacing with the following tools:\n- s3\n- jupyter\n- aws\n- spark SQL\n\n## Installation\n```\n# PyPI\npip install seipy\n```\n\n## Here are some examples\n\n### pandas\n\n#### Apply function to unique DataFrame entries only (for speedup)\n```\nfrom seipy import apply_uniq\ndf2 = apply_uniq(df, orig_col, new_col, _func)\n```\nThis will return the same DataFrame as performing:\n`df[new_col] = df[orig_col].apply(_func)`\nbut is much more performant when there are many duplicate entries in `orig_col`.\n\nIt works by performing the function `_func` only on the unique entries and then merging with the original DataFrame.\nOriginally answered on stack overflow:\nhttps://stackoverflow.com/questions/46798532/how-do-you-effectively-use-pd-dataframe-apply-on-rows-with-duplicate-values/\n\n#### Filtering DataFrame with multiple conditions\n```\nfrom seipy import filt\n# example with keyword arguments\nfilt(df,\n     season=\"summer\",\n     age=(\">\", 18),\n     sport=(\"isin\", [\"Basketball\", \"Soccer\"]),\n     name=(\"contains\", \"Armstrong\")\n    )\n\n# example with dict notation\na = {'season': \"summer\", 'age': (\">\", 18)}\nfilt(df, **a)\n```\n\n### linear algebra\n\n```\nfrom seipy import distmat\ndistmat()\n```\nThis will prints possible distance metrics such as \"euclidean\" \"chebyshev\", \"hamming\".\n\n```\ndistmat(fframe, metric)\n```\nThis generates a distance matrix using `metric`.\nNote, this function is a wrapper of scipy.spatial.distance.cdist\n\n\n### jupyter\n\n```\nfrom seipy import notebook_contains\nnotebook_contains(search_str,\n                  on_docker=False,\n                  git_dir='~/git/experiments/',\n                  start_date='2015-01-01', end_date='2018-12-31')\n```\nPrints a list of notebooks that contain the str `search_str`.\nVery useful for these situations: \"Where's that notebook where I was trying that one thing that one time?\"\n\n### s3\n```\nfrom seipy import s3zip_func\ns3zip_func(s3zip_path, _func, cred_fpath=cred_fpath, **kwargs)\n```\nThis one's kinda nice. It allows one to apply a function `_func` to each subfile in a zip file sitting on s3.\nI use it to filter and enrich some csv files that periodically get zipped to s3, for example.\n\n\n### spark and s3 on jupyter\n\n```\nfrom seipy import s3spark_init\nspark = s3spark_init(cred_fpath)\n```\nReturns `spark`, a `SparkSession` that makes it possible to interact with s3 from jupyter notebooks.\n`cred_fpath` is the file path to the aws credentials file containing your keys.\n\n\n### Miscellaneous\n\n```\nfrom seiji import merge_two_dicts\nmerge_two_dicts(dict_1, dict_2)\n```\nReturns the merged dict `{**dict_1, **dict_2}`.\nAn extension for mulitple dicts is `reduce(lambda d1,d2: {**d1,**d2}, dict_args[0])`\n\n### Getting help\n\nPlease either post an issue on this github repo, or email the author `seiji dot armstrong at gmail` with feedback,\nfeature requests, or to complain that something doesn't work as expected.\n\n\n\n\n",
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