cached-historical-data-fetcher 0.2.31
Python utility for fetching any historical data using caching.
License: MIT
Requires Python: <3.13,>=3.9

# Cached Historical Data Fetcher

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Python utility for fetching any historical data using caching.
Suitable for acquiring data that is added frequently and incrementally, e.g. news, posts, weather, etc.

## Installation

Install this via pip (or your favourite package manager):

```shell
pip install cached-historical-data-fetcher
```

## Features

- Uses cache built on top of [`joblib`](https://github.com/joblib/joblib), [`lz4`](https://github.com/lz4/lz4) and [`aiofiles`](https://github.com/Tinche/aiofiles).
- Ready to use with [`asyncio`](https://docs.python.org/3/library/asyncio.html), [`aiohttp`](https://github.com/aio-libs/aiohttp), [`aiohttp-client-cache`](https://github.com/requests-cache/aiohttp-client-cache). Uses `asyncio.gather` for fetching chunks in parallel. (For performance reasons, only using `aiohttp-client-cache` is probably not a good idea when fetching large number of chunks (web requests).)
- Based on [`pandas`](https://github.com/pandas-dev/pandas) and supports `MultiIndex`.

## Usage

### `HistoricalDataCache`, `HistoricalDataCacheWithChunk` and `HistoricalDataCacheWithFixedChunk`

Override `get_one()` method to fetch data for one chunk. `update()` method will call `get_one()` for each unfetched chunk and concatenate results, then save to cache.

```python
from cached_historical_data_fetcher import HistoricalDataCacheWithFixedChunk
from pandas import DataFrame, Timedelta, Timestamp
from typing import Any

# define cache class
class MyCacheWithFixedChunk(HistoricalDataCacheWithFixedChunk[Timestamp, Timedelta, Any]):
    delay_seconds = 0.0 # delay between chunks (requests) in seconds
    interval = Timedelta(days=1) # interval between chunks, can be any type
    start_index = Timestamp.utcnow().floor("10D") # start index, can be any type

    async def get_one(self, start: Timestamp, *args: Any, **kwargs: Any) -> DataFrame:
        """Fetch data for one chunk."""
        return DataFrame({"day": [start.day]}, index=[start])

# get complete data
print(await MyCacheWithFixedChunk().update())
```

```shell
                           day
2023-09-30 00:00:00+00:00   30
2023-10-01 00:00:00+00:00    1
2023-10-02 00:00:00+00:00    2
```

**See [example.ipynb](example.ipynb) for real-world example.**

### `IdCacheWithFixedChunk`

Override `get_one` method to fetch data for one chunk in the same way as in `HistoricalDataCacheWithFixedChunk`.
After updating `ids` by calling `set_ids()`, `update()` method will call `get_one()` for every unfetched id and concatenate results, then save to cache.

```python
from cached_historical_data_fetcher import IdCacheWithFixedChunk
from pandas import DataFrame
from typing import Any

class MyIdCache(IdCacheWithFixedChunk[str, Any]):
    delay_seconds = 0.0 # delay between chunks (requests) in seconds

    async def get_one(self, start: str, *args: Any, **kwargs: Any) -> DataFrame:
        """Fetch data for one chunk."""
        return DataFrame({"id+hello": [start + "+hello"]}, index=[start])

cache = MyIdCache() # create cache
cache.set_ids(["a"]) # set ids
cache.set_ids(["b"]) # set ids again, now `cache.ids` is ["a", "b"]
print(await cache.update(reload=True)) # discard previous cache and fetch again
cache.set_ids(["b", "c"]) # set ids again, now `cache.ids` is ["a", "b", "c"]
print(await cache.update()) # fetch only new data
```

```shell
       id+hello
    a   a+hello
    b   b+hello
       id+hello
    a   a+hello
    b   b+hello
    c   c+hello
```

## Contributors ✨

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

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This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!