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        "author": "Michael Ross",
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            "License :: OSI Approved :: MIT License",
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        "description": "##### For example usage, see:\nhttps://github.com/michael-ross-ven/vengeance_example/blob/main/vengeance_example/flux_example.py\n<br/>\n<br/>\nhttps://github.com/michael-ross-ven/vengeance_example/blob/main/vengeance_example/excel_example.py\n<br/>\n<br/>\n\n## Managing data stored as rows and columns shouldn't be complicated.\n\nWhen given a list of lists in Python, your first instinct is to loop over rows and modify column values, in-place. It's the most \nnatural way to think about the data, because **conceptually, each row is some entity, and each column is a property of that row**, \nmuch like a list of objects.\n\nA headache when dealing with list of lists however, is having to keep track of columns by integer elements; it would be nice to \nreplace the indices on each row with named attributes, and have these applied even when the columns are not known ahead of time, \nsuch as when pulling data from a sql table or csv file.\n\n    for row in matrix:\n        row[17]            # what's in that 18th column again?\n\n    for row in matrix:\n        row.customer_id    # oh, duh\n\n\n### Doesn't the pandas DataFrame already already solve this?\nIn a DataFrame, data is taken out of its native nested list format and is organized in column-major order, which comes with some \nadvantages as well as drawbacks.\n\n##### Row-major order:\n        \n        [['attribute_a', 'attribute_b', 'attribute_c'],\n         ['a',           'b',           3.0],\n         ['a',           'b',           3.0],\n         ['a',           'b',           3.0]]\n\n##### Column-major order:\n        \n        {'attribute_a': array(['a', 'a', 'a'], dtype='<U1'),\n         'attribute_b': array(['b', 'b', 'b'], dtype='<U1'),\n         'attribute_c': array([3.,   3.,  3.], dtype=float64)}\n\n\nIn column-major order, values in a single column are usually all of the same datatype, and can be packed into consecutive \naddresses in memory as an actual array, which can be iterated extremely quickly. But in a DataFrame, the ability to organize \ndata more intuitively, *where each row is some entity, and each column is a property of that row*, is mind-numbingly slow. \n(DataFrame.iterrows() and DataFrame.apply() incur a huge performance penalty, and can be 1,000x times slower than a built-in list)\n\nDataFrames are intended to make heavy use of vectorization, where operations can be applied to an entire set of values at once, \nperformed as instructions at the microprocessor level. But removal of explicit loops requires specialized methods for almost \nevery operation and modification, which often makes the syntax convoluted. The restricted ability to iterate a DataFrame makes \nits transformations counter-inituitive to write and more effortful to read, especially when method-chaining is overused.\n\n    # wait, what exactly does this do again?\n    df['column'] = np.sign(df.column.diff().fillna(0)).shift(-1).fillna(0) \\\n                   .apply(lambda x: (x['column'].head(1),\n                                     x.shape[0],\n                                     x['start'].iloc[-1] - x['start'].iloc[0]))\n\n\n##### DataFrame Advantages:\n* vectorized operations on contiguous arrays are very fast\n\n##### DataFrame Disadvantages:\n* syntax doesnt always drive intuition\n* iteration by rows is almost completely out of the question \\\n  (& working with json files is notoriously difficult)\n* harder to debug / inspect when vectorized operations return an error\n\n##### But I mean, why are we working in Python to begin with?\n* emphasis on code readability\n* less concerned about hyper-optimized execution times\n* datatypes are abstracted away\n\n##### [So does the DataFrame really reinforce what makes Python so great?](https://en.wikipedia.org/wiki/Zen_of_Python)\n>\"Explicit is better than implicit\" \\\n\"Sparse is better than dense\" \\\n\"Readability counts\" \\\n\"There should be one\u2013 and preferably only one \u2013obvious way to do it\"\n>\n\n<br/>\n\n### vengeance.flux_cls\n* similar idea behind a pandas DataFrame, but is more closely aligned with Python's design philosophy\n* when you're willing to trade for a little bit of speed for a lot simplicity\n* a lightweight, pure-python wrapper class around list of lists\n* applies named attributes to rows; attribute values are mutable during iteration\n* provides convenience aggregate operations (sort, filter, groupby, etc)\n* excellent for prototyping and data-wrangling\n\n###### Row-Major Iteration\n    \n    # organized like csv data, attribute names are provided in first row\n    matrix = [['attribute_a', 'attribute_b', 'attribute_c'],\n              ['a',           'b',           3.0],\n              ['a',           'b',           3.0],\n              ['a',           'b',           3.0]]\n    flux = vengeance.flux_cls(matrix)\n\n    # row attributes can be accessed by name or by sequential index\n    for row in flux:\n        a = row.attribute_a\n        a = row['attribute_a']\n        a = row[-1]\n        a = row.values[:-2]\n\n        row.attribute_a    = None\n        row['attribute_a'] = None\n        row[-1]            = None\n        row.values[:2]     = [None, None]\n\n    # transformations are compositional and self-documenting\n    for row in flux:\n        row.hypotenuse = math.sqrt(row.side_a**2 +,\n                                   row.side_b**2)\n\n    matrix = list(flux.values())\n\n\n###### Columns\n    column = flux['attribute_a']\n\n    flux.rename_columns({'attribute_a': 'renamed_a',\n                         'attribute_b': 'renamed_b'})\n    flux.insert_columns((0, 'inserted_a'),\n                        (2, 'inserted_b'))\n    flux.delete_columns('inserted_a',\n                        'inserted_b')\n\n\n###### Rows\n    rows = [['c', 'd', 4.0],\n            ['c', 'd', 4.0],\n            ['c', 'd', 4.0]]\n\n    flux.append_rows(rows)\n    flux.insert_rows(5, rows)\n\n    flux_c = flux_a + flux_b\n\n\n###### Sort / Filter / Apply\n    flux.sort('attribute_c')\n    flux.filter(lambda row: row.attribute_b != 'c')\n    u = flux.unique('attribute_a', 'attribute_b')\n\n    # apply functions like you'd normally do in Python: with comprehensions\n    flux['attribute_new'] = [some_function(v) for v in flux['attribute_a']]\n\n\n###### Groupby\n    matrix = [['year', 'month', 'random_float'],\n              ['2000', '01',     random.uniform(0, 9)],\n              ['2000', '02',     random.uniform(0, 9)],\n              ['2001', '01',     random.uniform(0, 9)],\n              ['2001', '01',     random.uniform(0, 9)],\n              ['2001', '01',     random.uniform(0, 9)],\n              ['2002', '01',     random.uniform(0, 9)]]\n    flux = flux_cls(matrix)\n\n    dict_1 = flux.map_rows_append('year', 'month')\n    countifs = {k: len(rows) for k, rows in dict_1.items()}\n    sumifs   = {k: sum(row.random_float for row in rows)\n                                        for k, rows in dict_1.items()}\n\n    dict_2 = flux.map_rows_nested('year', 'month')\n    rows_1 = dict_1[('2001', '01')]\n    rows_2 = dict_2['2001']['01']\n\n\n###### Read / Write Files\n    flux.to_csv('file.csv')\n    flux = flux_cls.from_csv('file.csv')\n\n    flux.to_json('file.json')\n    flux = flux_cls.from_json('file.json')\n\n\n\n",
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