ARROW-116 Add Documentation for Auto-Discovery of Schemas (#93)

Author: juliusgeo

bindings/python/README.rst

@@ -28,6 +28,8 @@ DataFrames or NumPy arrays.
   1    2  16.9
   2    3   2.3
   >>> arrow_table = client.db.test.find_arrow_all({}, schema=schema)
+  # The schema may also be omitted
+  >>> arrow_table = client.db.test.find_arrow_all({})
   >>> arrow_table
   pyarrow.Table
   _id: int64

bindings/python/docs/source/quickstart.rst

@@ -58,8 +58,9 @@ query. We can do so using **PyMongo**::
 
 Defining the schema
 -------------------
-**PyMongoArrow** relies upon a **user-specified** data schema to marshall
-query result sets into tabular form. Users can define the schema by
+**PyMongoArrow** relies upon a data schema to marshall
+query result sets into tabular form. This schema can either be automatically inferred from the data,
+or provided by the user. Users can define the schema by
 instantiating :class:`pymongoarrow.api.Schema` using a mapping of field names
 to type-specifiers, e.g.::
 
@@ -70,6 +71,15 @@ There are multiple permissible type-identifiers for each supported BSON type.
 For a full-list of supported types and associated type-identifiers see
 :doc:`supported_types`.
 
+.. note::
+
+   For all of the examples below, the schema can be omitted like so::
+
+    arrow_table = client.db.data.find_arrow_all({'amount': {'$gt': 0}})
+
+   In this case, PyMongoArrow will try to automatically apply a schema based on
+   the data contained in the first batch.
+
 Find operations
 ---------------
 We are now ready to query our data. Let's start by running a ``find``