Add documentation on how to deal with nulls in data (#138)

* Added hint how to flatten result efficiently

* null conversion added

* Update schemas.rst

* Update schemas.rst

revert

* Added review comments of @juliusgeo

* Update supported_types.rst

* lint

---------

Co-authored-by: Steven Silvester <[email protected]>

Author: sibbiii

bindings/python/docs/source/supported_types.rst

@@ -53,3 +53,34 @@ respectively, and '_id' that is an `ObjectId`, your schema can be defined as::
 
 Unsupported data types in a schema cause a ``ValueError`` identifying the
 field and its data type.
+
+Null Values and Conversion to Pandas DataFrames
+-----------------------------------------------
+
+In Arrow, all Arrays are always nullable.
+Pandas has experimental nullable data types as, e.g., "Int64" (note the capital "I").
+You can instruct Arrow to create a pandas DataFrame using nullable dtypes
+with the code below (taken from `here <https://arrow.apache.org/docs/python/pandas.html>`_)
+
+.. code-block:: pycon
+
+   >>> dtype_mapping = {
+   ...     pa.int8(): pd.Int8Dtype(),
+   ...     pa.int16(): pd.Int16Dtype(),
+   ...     pa.int32(): pd.Int32Dtype(),
+   ...     pa.int64(): pd.Int64Dtype(),
+   ...     pa.uint8(): pd.UInt8Dtype(),
+   ...     pa.uint16(): pd.UInt16Dtype(),
+   ...     pa.uint32(): pd.UInt32Dtype(),
+   ...     pa.uint64(): pd.UInt64Dtype(),
+   ...     pa.bool_(): pd.BooleanDtype(),
+   ...     pa.float32(): pd.Float32Dtype(),
+   ...     pa.float64(): pd.Float64Dtype(),
+   ...     pa.string(): pd.StringDtype(),
+   ... }
+   ... df = arrow_table.to_pandas(
+   ...     types_mapper=dtype_mapping.get, split_blocks=True, self_destruct=True
+   ... )
+   ... del arrow_table
+
+Defining a conversion for `pa.string()` in addition converts Arrow strings to NumPy strings, and not objects.