Project to persist Pandas data structures in a MongoDB database.
pip install antarcticThis project (unless the popular arctic project which I admire) is based on top of 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.
We introduce first a new field --- the PandasField.
from mongoengine import Document, connect
from antarctic.pandas_field 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
p = Portfolio()
p.nav = pd.Series(...).to_frame(name="nav")
p.prices = pd.DataFrame(...)
p.save()
print(p.nav["nav"])
print(p.prices)Behind the scenes we convert the Frame objects into parquet bytestreams and store them in a MongoDB database.
The format should also be readable by R.
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.
from antarctic.document import XDocument
from antarctic.pandas_field 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):
s1 = Symbol(name="A", price=pd.Series(...).to_frame(name="price")).save()
s2 = Symbol(name="B", price=pd.Series(...).to_frame(name="price")).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.frame(series="price", key="price"))
print(Symbol.apply(func=lambda x: x.price["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.
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.