jorisvandenbossche commented on a change in pull request #75:
URL: https://github.com/apache/arrow-cookbook/pull/75#discussion_r709318491
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File path: python/source/schema.rst
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@@ -108,4 +108,87 @@ as far as they are compatible
pyarrow.Table
col1: int32
col2: string
- col3: double
\ No newline at end of file
+ col3: double
+
+Merging multiple schemas
+========================
+
+When you have multiple separate groups of data that you want to combine
+it might be necessary to unify their schemas to create a superset of them
+that applies to all data sources.
+
+.. testcode::
+
+ import pyarrow as pa
+
+ first_schema = pa.schema([
+ ("country", pa.string()),
+ ("population", pa.int32())
+ ])
+
+ second_schema = pa.schema([
+ ("country_code", pa.string()),
+ ("language", pa.string())
+ ])
+
+:func:`unify_schemas` can be used to combine multiple schemas into
+a single one:
+
+.. testcode::
+
+ union_schema = pa.unify_schemas([first_schema, second_schema])
+
+ print(union_schema)
+
+.. testoutput::
+
+ country: string
+ population: int32
+ country_code: string
+ language: string
+
+If the combined schemas have overlapping columns, they can still be combined
+as far as the colliding columns retain the same type (``country_code``):
Review comment:
Small suggestion (but take it or leave it :)): I would personally
directly show an example where there is a column in common, instead of
splitting it in two examples first without common column and then with.
Having no columns in common at all doesn't seem like the typical use case
for this, so directly showing the second example can simplify the cookbook
entry a bit (one example less to show), while you can still explicitly mention
how the common column is handled.
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