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antarctic-0.5.0
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...
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | antarctic-0.5.0 |
| نام | antarctic |
| نسخه کتابخانه | 0.5.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Thomas Schmelzer |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/tschm/antarctic |
| آدرس اینترنتی | https://pypi.org/project/antarctic/ |
| مجوز | - |
# Antarctic
[](https://github.com/tschm/antarctic/actions/)
[](https://deepsource.io/gh/tschm/antarctic/?ref=repository-badge)
Project to persist Pandas data structures in a MongoDB database.
## Installation
```python
pip install antarctic
```
## Usage
This project (unless the popular arctic project which I admire) is based on top of [MongoEngine](https://pypi.org/project/mongoengine/).
MongoEngine is an ORM for MongoDB. MongoDB stores documents. We introduce a new field and extend the Document class
to make Antarctic a convenient choice for storing Pandas (time series) data.
### Fields
We introduce first a new field --- the PandasField.
```python
from mongoengine import Document, connect
from antarctic.pandas_fields import PandasField
# connect with your existing MongoDB (here I am using a popular interface mocking a MongoDB)
client = connect(db="test", host="mongomock://localhost")
# Define the blueprint for a portfolio document
class Portfolio(Document):
nav = PandasField()
weights = PandasField()
prices = PandasField()
```
The portfolio objects works exactly the way you think it works
```python
p = Portfolio()
p.nav = pd.Series(...)
p.prices = pd.DataFrame(...)
p.save()
print(p.nav)
print(p.prices)
```
Behind the scenes we convert the both Series and Frame objects into parquet bytestreams and
store them in a MongoDB database.
The format should also be readable by R.
#### Documents
In most cases we have copies of very similar documents, e.g. we store Portfolios and Symbols rather than just a Portfolio or a Symbol.
For this purpose we have developed the abstract `XDocument` class relying on the Document class of MongoEngine.
It provides some convenient tools to simplify looping over all or a subset of Documents of the same type, e.g.
```python
from antarctic.document import XDocument
from antarctic.pandas_fields import PandasField
client = connect(db="test", host="mongodb://localhost")
class Symbol(XDocument):
price = PandasField()
```
We define a bunch of symbols and assign a price for each (or some of it):
```python
s1 = Symbol(name="A", price=pd.Series(...)).save()
s2 = Symbol(name="B", price=pd.Series(...)).save()
# We can access subsets like
for symbol in Symbol.subset(names=["B"]):
print(symbol)
# often we need a dictionary of Symbols:
Symbol.to_dict(objects=[s1, s2])
# Each XDocument also provides a field for reference data:
s1.reference["MyProp1"] = "ABC"
s2.reference["MyProp2"] = "BCD"
# You can loop over (subsets) of Symbols and extract reference and/or series data
print(Symbol.reference_frame(objects=[s1, s2]))
print(Symbol.series(series="price"))
print(Symbol.apply(func=lambda x: x.price.mean(), default=np.nan))
```
The XDocument class is exposing DataFrames both for reference and time series data.
There is an `apply` method for using a function on (subset) of documents.
### Database vs. Datastore
Storing json or bytestream representations of Pandas objects is not exactly a database. Appending is rather expensive as one would have
to extract the original Pandas object, append to it and convert the new object back into a json or bytestream representation.
Clever sharding can mitigate such effects but at the end of the day you shouldn't update such objects too often. Often practitioners
use a small database for recording (e.g. over the last 24h) and update the MongoDB database once a day. It's extremely fast to read the Pandas objects
out of such a construction.
Often such concepts are called DataStores.
نیازمندی
| مقدار | نام |
|---|---|
| - | setuptools |
| - | mongoengine |
| - | pandas |
| - | pyarrow |
| - | fastparquet |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9.0 | Python |
نحوه نصب
نصب پکیج whl antarctic-0.5.0:
pip install antarctic-0.5.0.whl
نصب پکیج tar.gz antarctic-0.5.0:
pip install antarctic-0.5.0.tar.gz