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    "info": {
        "author": "Richard Bann",
        "author_email": "richardbann@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6"
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        "description": "# rel2tree\n\nConvert your list of data into `JSON` serializable structure.\n\n## Motivation\n\nLet's suppose you have a set of data given as a list of dicts:\n\n```py\nimport json\n\n[\n  {\"name\": \"Jane\", \"city\": \"New York\", \"sales\": 23},\n  {\"name\": \"Joe\", \"city\": \"New York\", \"sales\": 11},\n  {\"name\": \"Jane\", \"city\": \"Chicago\", \"sales\": 21},\n  {\"name\": \"Jane\", \"city\": \"New York\", \"sales\": 4},\n  {\"name\": \"Joe\", \"city\": \"New York\", \"sales\": 13},\n  {\"name\": \"Joe\", \"city\": \"Chicago\", \"sales\": 31},\n  {\"name\": \"Jane\", \"city\": \"New York\", \"sales\": 7},\n]\n```\n\nYou may want a nice summary, something like this:\n\n```json\n[\n  {\n    \"name\": \"Jane\",\n    \"cities\": [\n      {\n        \"city\": \"New York\",\n        \"sales\": 34\n      },\n      {\n        \"city\": \"Chicago\",\n        \"sales\": 21\n      }\n    ],\n    \"sum\": 55\n  },\n  {\n    \"name\": \"Joe\",\n    \"cities\": [\n      {\n        \"city\": \"New York\",\n        \"sales\": 24\n      },\n      {\n        \"city\": \"Chicago\",\n        \"sales\": 31\n      }\n    ],\n    \"sum\": 55\n  }\n]\n```\n\nThis can be done relatively easily by iterating over the data\nset and building the final structure.\n\n```py\nsummary = {}\nfor record in data:\n    this_person = summary.setdefault(record[\"name\"], {\n        \"name\": record[\"name\"],\n        \"cities\": {},\n        \"sum\": 0,\n    })\n    this_person_cities = this_person[\"cities\"].setdefault(record[\"city\"], {\n        \"city\": record[\"city\"],\n        \"sum\": 0,\n    })\n    this_person_cities[\"sum\"] += record[\"sales\"]\n    this_person[\"sum\"] += record[\"sum\"]\nsummary = list(summary.values())\nfor person in summary:\n    person[\"cities\"] = list(person[\"cities\"].values())\n\nprint(json.dumps(summary))\n```\n\nAlthough the above code works well, but it has some problems.\n\n- Not declarative: by looking at the code it is not trivial to tell the final data\n  structure.\n- Error-prone.\n- The complexity grows with more complex business logic\n  or by adding an additional level.\n- Not reusable.\n\nLet's see how you do it with `rel2tree`:\n\n```py\nfrom rel2tree import f  # NOQA\n\nsummary = f.groupby(lambda x: x[\"name\"], f.dict({\n    \"name\": f.groupkey(),\n    \"cities\": f.groupby(lambda x: x[\"city\"], f.dict({\n        \"city\": f.groupkey(),\n        \"sum\": f.map(lambda x: x[\"sales\"]).t(sum)\n    })),\n    \"sum\": f.map(lambda x: x[\"sales\"]).t(sum)\n}))\n\nprint(json.dumps(summary(data)))\n```\n\n## Tutorial\n\n### `map`, `sort`, `filter`, `distinct`\n\nThe only object one can import from `rel2tree` is `f`, which is of type `F`\nso we will call it an `F` object.\n`f` is callable, but - on it's own does nothing:\n\n```py\nprint(f(2))\n# 2\n```\n\nLet's say we have a list of numbers (`numbers`) and we want\nto duplicate all of it's elements. This can be done in many ways:\n\n- using a list comprehension:\n  ```py\n  out = [2 * x for x in numbers]\n  ```\n- using map:\n  ```py\n  out = map(lambda x: 2 * x, numbers)\n  ```\n- defining a function (for reusability)\n  ```py\n  import functools\n  dup = functools.partial(map, lambda x: 2 * x)\n  out = dup(numbers)\n  ```\n\nUsing an `f` it looks like this:\n\n```py\nnumbers = range(15)\ndup = f.map(lambda x: 2 * x)\nout = dup(numbers)\n```\n\nThis simply made our third approach a little more terse.\n\nNow what if our task is to add 1 to each element after\nduplication? Can we reuse our `dup` function? As\nthe result of `f.map` has the same type as `f`, we can\nuse map again:\n\n```py\ndupplus1 = dup.map(lambda x: x + 1)\n```\n\n`f.sort(fnc)` sorts our list based on the value of `fnc`\napplied to the items (just as the `key` argument of python's)\n`sorted`. `f.filter(fnc)` keeps only those `i` items, where\n`fnc(i)` is ture(ish). These methods also return `F`s\n(internally the type of `f` is `F`) so they are chainable.\nThe `F` below first duplicates, then filters out big\nnumbers and finally sorts them. (`f.sort`, without a function sorts the elements.)\n\n```py\nf.map(lambda x: 2 * x).filter(lambda x: x < 10).sort()\n```\n\n### `dict`\n\nBack to our `numbers`, but with the desired output of\n\n```json\n{\n  \"even\": [0, 2, 4, 6, 8, 10, 12, 14],\n  \"odd\": [1, 3, 5, 7, 9, 11, 13]\n}\n```\n\nWe can combine the dict method to achive this:\n\n```py\nsummary = f.dict({\n    \"even\": f.filter(lambda x: (x % 2 == 0)),\n    \"odd\": f.filter(lambda x: (x % 2 == 1)),\n})\n```\n\nIf the dictionary values are `F` objects, those objects will be called with\nthe input list to form the final values, otherwise the values will be left as is.\n\n### `groupby`\n\nTo generalize the above example, we can group our numbers based on the remainder\ndevided by, say, 3:\n\n```py\nsummary = f.groupby(lambda x: x % 3)\n# [[0, 3, 6, 9, 12], [1, 4, 7, 10, 13], [2, 5, 8, 11, 14]]\n```\n\nTo make it more informative, the desired output should be:\n\n```json\n[\n  { \"remainder\": 0, \"numbers\": [0, 3, 6, 9, 12] },\n  { \"remainder\": 1, \"numbers\": [1, 4, 7, 10, 13] },\n  { \"remainder\": 2, \"numbers\": [2, 5, 8, 11, 14] }\n]\n```\n\nThis can be done by using `groupkey`:\n\n```py\nsummary = f.groupby(lambda x: x % 3, f.dict({\n  \"remainder\": f.groupkey(),\n  \"numbers\": f\n}))\n```\n\n`f.groupkey(level=0)` gives the deepest level group key, while `f.groupkey(1)`\nis the one level above group key in case of nested `groupby`'s.",
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