monthpack 0.1.0

Python library for storing and working with monthly-period data.

monthpack

monthpack is a Python library for organizing period-based data sources, such as bank statements, income statements, and similar records.

The project is centered around local source.config.json files that define:

• base metadata without period
• persistent changes starting at a given period
• temporary changes for one specific period
• placeholders such as {period}, {period.year}, and {period.month}

Current Layout

monthpack/
  data/
  src/
    monthpack/
  pyproject.toml
  README.md

Example

from monthpack import Source

source = Source.from_path("data/source/source.config.json")
metadata = source.resolve_metadata(202401, storage=0)

print(metadata.period)
print(metadata.year)
print(metadata.month)
print(metadata.inpath)
print(metadata["reader"])

data = source.read((202401, 202406), storage=0, skip_error=True)

Config Templates

monthpack also exposes a helper function for generating a starter source.config.json file:

from monthpack import write_sample_config

write_sample_config("data/sample/source.config.json")

This helper generates one example file with three storages already configured:

• dataframe: pandas with pandas_type = "dataframe"
• series: pandas with pandas_type = "series"
• pickle: pickle

source.config.json

In general terms, a source.config.json file is structured like this:

{
    "input": {
        "relative": true,
        "directory": "input"
    },
    "output": {
        "relative": true,
        "directory": "output"
    },
    "storage": [
        {
            "name": "main",
            "writer": "pandas",
            "collection": "concat",
            "concat_axis": 0,
            "period_label": "period",
            "persistence": true,
            "metadata": [
                {
                    "outpath": "{period.year}/{period}_{name}.bin"
                }
            ]
        }
    ],
    "metadata": [
        {
            "inpath": "**/{period}_*.csv",
            "reader": "csv"
        },
        {
            "period": 202507,
            "inpath": "**/{period}_*.xlsx",
            "reader": "excel"
        }
    ]
}

Field overview:

• metadata: temporal metadata definitions. Entries without period are base values; entries with period override from that month onward; entries with temporary: true apply only for that exact month.
• storage: processed-data storage definitions. Each item defines writer and collection behavior, and can also contain its own metadata list.
• input: optional input directory configuration. If relative is true, directory is resolved relative to the JSON file.
• output: optional output directory configuration. If relative is true, directory is resolved relative to the JSON file.

At runtime, Source.from_path(...) reads this file, resolves relative directory references from input and output, and builds a Source instance from it.

Source.resolve_metadata(...) returns a Metadata object. Resolved keys are available both as attributes and as dictionary-style accessors, so user transforms can use either metadata.inpath or metadata["inpath"]. The period itself is exposed as metadata.period, not as metadata["period"].

When period=None, resolve_metadata(...) returns only the base metadata, without applying any periodic or temporary entries.

Storage references can be passed either as:

• an index, for example storage=0
• a storage name, for example storage="main"

When name is defined inside storage, it must be unique across the configuration.

Read Behavior

• source.read(period, ...) reads one period.
• source.read(None, ...) reads the atemporal/base case.
• source.read([period1, period2, ...], ...) respects the exact order of the list.
• source.read((start, end), ...) expands a continuous monthly range, ascending or descending according to the tuple order.
• source.read_one(period, ...) is the single-period helper used internally.

skip_error=True returns None for missing-read cases such as a missing processed file or a missing persistence anchor. With skip_error=False, those cases raise FileNotFoundError. Programming errors inside transforms are not swallowed.

Storage Options

Within each storage item:

• name: optional unique identifier that lets the storage be referenced by name instead of only by index.
• writer: currently supports pandas and pickle.
• pandas_type: required when writer = "pandas". Use dataframe or series.
• collection: one of list, dict, or concat.
• concat_axis: axis used when collection = "concat".
• period_label: when defined, adds the requested period to pandas outputs during collection reads. For DataFrame, it is used as a column name; for Series, it is used as the outer index level name.
• persistence: when true, only metadata entries of type periodic act as anchors; later periods reuse the latest valid anchor.
• metadata: storage-specific metadata. This is also where outpath should be declared.

Within storage metadata:

• outpath: output path template for the stored artifact.

User Mode

Source can run in read-only user mode:

source.set_user()
data = source.read(202401)

In user mode:

• read(...) only returns already processed data.
• missing processed files are not regenerated from raw inputs.
• save(...) is not available.