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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# `yo_fluq_ds`\n\nThis package is an data science-specific update for [`yo_fluq`](https://pypi.org/project/yo-fluq/) that introduces:\n* querying and output for `pandas` data structures and files in `Queryable`\n* handy `feed`-based extension methods.\n\nThe main reason for separating `yo_fluq_ds` from `yo_fluq` is that data science functionality requires huge packages like `pandas` and `matplotlib`, which I didn't want to include in a basic package.\n\n## Small useful classes\n\n* `Obj` is an ordered dict with a member-like access: `obj.a=12` works exactly as `obj['a']=12`\n* `OrderedEnum` is `Enum` with ordering, it's useful when using enums in `pandas`, because the basic enumeration cannot be used as keys for `group_by`\n\n## Pull-queries updates\n\n### Combinatorics\n\n`Query.combinatorics` has some useful method to create lazy combinatorics enumerations:\n* `cartesian(en1,en2,...)` will create a cartesian product of enumerations in `en1`, `en2`, etc.\n* `grid(field1=en1,field2=en2)` will create an enumeration of `Obj` with fields `field1`, `field2` that runs over cartesian product of `en1`, `en2`, etc.\n* `triangle` is query-like replacement for loops `i=0..N`, `j=0..i`\n* `powerset` produces all the subsets of a given set\n\n### File system\n\nAdds several aggregators/query sources to work with files.\n\n* `to_text_file`/`Query.file.text`: text file, its lines are interpreted as enumeration's objects\n* `to_zip_file`/`Query.file.zipped_text`: zipped text file\n* `to_pickle_file`/`Query.file.pickle`: a internal format, lazily writes a sequence of objects in pickle format in one file.\n* `to_zip_folder`/`Query.to_zipped_folder`: representation for `KeyValuePair`: filenames are keys, its concent is values\n\nAdds `FileIO` class with one-line instruction to read text, json, pickle, jsonpickle, yaml files.\n\nAdds `Query.folder` method to create enumeration of `Path` objects from folder\n\n### `pandas`\n\n* Adds `to_series`, `to_dataframe` and `to_ndarray` aggregators\n* Adds `Query.series` to convert series in `KeyValuePair` enumeration\n* Adds `Query.df` to convert dataframe in `Obj` (or `dict`) enumeration\n\nAdds `feed` method to `DataFrame`, `Series`, `DataFrameGroupBy` and `SeriesGroupBy` by monkey-patching. It is now possible to write something like:\n```python\n(df\n    .loc[df.status=='shipped']\n    .feed(lambda z: groupby(z.date.dt.to_period('M')))\n    .size()\n)\n```\nWhen calling lambda inside `feed`, `z` will be assigned to the dataframe after filtering out.\n\nThis technique allows longer fluent instructions for `pandas`, which is otherwise impossible due to filtering.\n\n## `feed`-extension methods.\n\nSome methods from `yo_fluq_ds` are not incorporated into `Queryables`, because they are used not that often and I want to avoid overloading `Queryable` with such methods. So, they are accessible only via `feed` method.\n\nAll of them are inside `fluq` module.\n\n### For `Queryable`\n\n* `fluq.with_progress_bar` is a Queryable-friendly wrapping over `tqdm`. It automatically detects notebooks/console environments. The `total` (length of enumerable) in most cases is known from `Queryable.length` field, but sometimes needs to be provided.\n* `fluq.with_plots(columns,figsize)` will create plots for each of elements in enumerable and return the enumerable of `ItemWithAx`. Very handy to draw several plots at once, e.g. for different columns in dataframe\n* `fluq.pairwise` converts enumerable to the enumerable of pair of neighbouring elements\n\n### For `pandas`\n\n* `fluq.fractions` can be used where size is normally used to determine the relative size of the groups\n* `fluq.trimmer` can be used to trim too high/too low values from the series, thus facilitating histograms' creation.\n\n\n\n\n\n\n",
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