pandasbox 0.2.0

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PandasBox

Containers for finding Python objects by attribute. Backed by Pandas.

pip install pandasbox

⚠️ This project is not being maintained. It's unclear if it's actually got a use case.

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Usage

from pandasbox import PandasBox
tb = PandasBox(
    [{'item': 1, 'size': 1000, 'shape': 'square'}],  # provide a list of objects or dicts 
    {'size': int, 'shape': str})                     # specify attributes to store
tb.find('size >= 1000 and shape == "square"')        # find by attribute value

The objects can be any container of class, dataclass, namedtuple, or dict objects.

There are two classes available.

• PandasBox: SQLite-backed container. Faster when finding a few of your objects (< 10%).
• PandasBox: Pandas-backed container. Faster when finding many of your objects (>= 10%).

You can add(), add_many(), update(), and remove() items from a PandasBox or PandasBox.

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How it works

When you do: PandasBox(list_of_objects, on={'size': int, 'shape': string}) or PandasBox(...)

A table or dataframe is created with 3 columns:

• size
• shape
• Python object reference

On find(), a query will run to find the matching objects.

Only the relevant attributes of the object are copied into the table. The rest of the object remains in memory.

An ideal use case is when you have "heavy" objects containing images / audio / large texts, plus some small metadata fields that you want to find by. Just make a PandasBox or PandasBox on the metadata, and use it to find the object without needing to serialize / deserialize the heavy stuff.

The pandasbox containers are especially good when finding by < and >. If you only need ==, consider filtered -- it is based on dict lookups which are faster in that case.

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API

The API is largely the same across PandasBox and PandasBox. The only difference is initialization.

PandasBox Init

PandasBox(
        objs: Optional[Iterable[Any]] = None,
        on: Optional[Dict[str, Any]] = None,
        index: Optional[List[ Union[Tuple[str], str]]] = None
)

Creates a PandasBox.

• objs is optional. It can be any container of class, dataclass, dict, or namedtuple objects.
• on is required. It specifies the attributes and types to index. The allowed types are float, int, bool, and str.
• index specifies the indices to create on the SQLite table. If unspecified, a single-column index is made on each attribute.

The index parameter is the key to getting good performance. A multi-column index can often speed up find() operations. index=[('a', 'b', 'c'), 'd'] will create a multi-column index on (a, b, c) and a single-column index on d. Conversely, some columns such as those containing string data may perform better without an index.

See SQLite index documentation for more insights.

PandasBox Init

Creates a PandasBox.

PandasBox(
        objs: Optional[Iterable[Any]] = None,
        on: Optional[Dict[str, Any]] = None,
        : Optional[List[ Union[Tuple[str], str]]] = None
)

Other API functions

The remaining functions are the same for both PandasBox and PandasBox; exceptions as noted.

find()

find(where: Optional[str]) -> List finds objects matching the query string in where.

Examples:

• tb.find('b == True and string == "okay"')
• tb.find('(x == 0 and y >= 1000.0) or x == 9')

If where is unspecified, all objects in the container are returned.

The syntax of where is nearly identical between pandas and sqlite. Exceptions:

• In sqlite, use find('x is null') / find('x is not null').
• In pandas, use find('x != x') to match nulls, or find('x == x') for non-nulls.
• Sqlite accepts either = or == for equality; pandas accepts only ==.

Consult the syntax for SQLite queries or pandas queries as needed.

add(), add_many()

add(obj:Any)
add_many(objs:Iterable[Any])

The add() method adds a single object. If you have many objects, it is much faster to add_many() than it is to call add() on each one.

If an added object is missing an attribute, the object will still be added. The missing attribute will be given a None value.

update()

update(self, obj: Any, updates: Dict[str, Any]) updates attributes of a single object in the index.

updates is a dict containing the new values for each changed attribute, e.g. {'x': 5.5, 'b': True}.

If you change an indexed object's attributes without calling update(), the PandasBox will be out of sync and return inaccurate results.

update() will changes both the value in the PandasBox table and the object's value.

Update is fast (less than 1 ms), it's O(log n) in both sqlite and pandas.

remove()

remove(self, obj: Any) removes an object.

Remove is fast (less than 1ms) in SQLite but slower (tens of ms) in Pandas. This is because removing an item requires rebuilding arrays there.

Container methods

You can do the usual container things:

• Length: len(tb)
• Contains: obj in tb
• Iteration: for obj in tb: ...

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Performance

	Baseline	SqliteBox	PandasBox
Find 1 item	0.9s	0.2ms	43.1ms
Find 10 items	0.9s	0.7ms	44.9ms
Find 100 items	1.0s	1.9ms	43.8ms
Find 1K items	1.0s	6.7ms	43.9ms
Find 10K items	1.1s	27.2ms	47.6ms
Find 100K items	1.2s	0.18s	88.3ms
Find 1M items	1.7s	1.37s	0.24s
Find 10M items	2.9s	10.6s	0.45s

This is a benchmark on random range queries against a dataset of 10 million (10^7) objects indexed on two numeric fields. Baseline is a Python list comprehension.

This is the same data as a graph, showing relative speedup. Each line is divided by baseline. Note that both axis labels are powers of 10. So 10^3 on the Y-axis indicates a 1000X speedup.

See examples for more performance tests.