vengeance 1.0.14

Data library focusing on pure python data structures and Excel interaction

## Managing tabular data shouldn’t be complicated.

If values are stored in a matrix, it should’t be any harder to iterate or modify than a normal list. One enhancement, however, would be to have row values accessible by column names instead of by integer indeces,eg

for row in m:

row.header

for row in m:

row[17] (did i count those columns correctly?)

#### Two naive solutions for this are:

1. Convert rows to dictionaries

   Using duplicate dictionary instances for every row has a high memory footprint, and makes accessing values by index more complicated, eg

   [{‘col_a’: 1.0, ‘col_b’: ‘b’, ‘col_c’: ‘c’},

   {‘col_a’: 1.0, ‘col_b’: ‘b’, ‘col_c’: ‘c’}]

2. Convert rows to namedtuples

   Named tuples do not have per-instance dictionaries, so they are lightweight and require no more memory than regular tuples, but their values are read-only (which makes this kinda a dealbreaker)

Another possibility would be to store the values in column-major order, like in a database. This has a further advantage in that all values in the same column are usually of the same data type, allowing them to be stored more efficiently

row-major order:

[[‘coi_a’, ‘col_b’, ‘col_c’],

[1.0, ‘b’, ‘c’], [1.0, ‘b’, ‘c’], [1.0, ‘b’, ‘c’]]

column-major order:

{‘col_a’: [1.0, 1.0, 1.0],

‘col_a’: [‘b’, ‘b’, ‘b’], ‘col_a’: [‘c’, ‘c’, ‘c’]}

This is essentially what a pandas DataFrame is. The drawback to this is a major conceptual overhead. - Intuitively, each row is some entity, each column is a property of that row

• DataFrames have some great features, but also require specialized syntax that can get very awkward and requires a lot of memorization

The flux_cls attempts to balance ease-of-use and performance. It has the following attributes: - row-major iteration - named attributes on rows - value mutability on rows - light memory footprint - efficient updates and modifications