A mongoDB to pandas DataFrame converter with a pandas filter style.
pip install mongodf
import mongodf
import pymongo
mongo = pymongo.MongoClient("mongodb://mongo:27017")
# create a dataframe from a mongoDB collection
df = mongodf.from_mongo(mongo, "DB", "Collection")
# filter values
df = df[(df["colA"] == "Test") & (df.ColB.isin([1, 2]))]
# filter columns
df = df[["colA", "colC"]]
# compute a pandas.DataFrame
df.compute()| colA | colC | |
|---|---|---|
| 0 | Test | NaN |
| 1 | Test | 12 |
import plotly.express as px
df = px.data.gapminder()
cache = MongoDFCache(
host="mongodb://mongo:27017",
database="mongodfcache",
expire_after_seconds=20,
)
# put the dataframe into the mongo cache
# the name can be auto generated, array_group can be a list of cols
id = cache.cache_dataframe(df, "test_df", array_group=True)
# get a mongodf without reading all the data
cdf = cache.get_dataframe(id)
# get the metadata and the content of the dataframe
gcdf.get_meta()
gcdf.compute()
