Schema Examples

On this page

Nested Data with Schema

Nested Data with Projections

This guide shows examples of how to use PyMongoArrow schemas in common situations.

Nested Data with Schema

When performing aggregate or find operations, you can provide a schema for nested data by using the struct object. There can be conflicting names in sub-documents compared to their parent documents.

>>> from pymongo import MongoClient
... from pymongoarrow.api import Schema, find_arrow_all
... from pyarrow import struct, field, int32
... coll = MongoClient().db.coll
... coll.insert_many(
...     [
...         {"start": "string", "prop": {"name": "foo", "start": 0}},
...         {"start": "string", "prop": {"name": "bar", "start": 10}},
...     ]
... )
... arrow_table = find_arrow_all(
...     coll, {}, schema=Schema({"start": str, "prop": struct([field("start", int32())])})
... )
... print(arrow_table)
pyarrow.Table
start: string
prop: struct<start: int32>
  child 0, start: int32
----
start: [["string","string"]]
prop: [
  -- is_valid: all not null
  -- child 0 type: int32
[0,10]]

You can do the same thing when using Pandas and NumPy:

>>> df = find_pandas_all(
...     coll, {}, schema=Schema({"start": str, "prop": struct([field("start", int32())])})
... )
... print(df)
    start           prop
0  string   {'start': 0}
1  string  {'start': 10}

Nested Data with Projections

You can also use projections to flatten the data before passing it to PyMongoArrow. The following example illustrates how to do this by using a very simple nested document structure:

>>> df = find_pandas_all(
...     coll,
...     {
...         "prop.start": {
...             "$gte": 0,
...             "$lte": 10,
...         }
...     },
...     projection={"propName": "$prop.name", "propStart": "$prop.start"},
...     schema=Schema({"_id": ObjectIdType(), "propStart": int, "propName": str}),
... )
... print(df)
                                 _id  propStart propName
0  b'c\xec2\x98R(\xc9\x1e@#\xcc\xbb'          0      foo
1  b'c\xec2\x98R(\xc9\x1e@#\xcc\xbc'         10      bar

When performing an aggregate operation, you can flatten the fields by using the $project stage, as shown in the following example:

>>> df = aggregate_pandas_all(
...     coll,
...     pipeline=[
...         {"$match": {"prop.start": {"$gte": 0, "$lte": 10}}},
...         {
...             "$project": {
...                 "propStart": "$prop.start",
...                 "propName": "$prop.name",
...             }
...         },
...     ],
... )

Back

Data Types

FAQ