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fix snowflake formatting issue #346

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merged 3 commits into from
Oct 30, 2024
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Merged

fix snowflake formatting issue #346

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52b3533
fix snowflake formatting issue
fdosani Oct 30, 2024
2c37176
bump version
fdosani Oct 30, 2024
f7e2940
PR typo fix
fdosani Oct 30, 2024
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2 changes: 1 addition & 1 deletion datacompy/__init__.py
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Expand Up @@ -18,7 +18,7 @@
Then extended to carry that functionality over to Spark Dataframes.
"""

__version__ = "0.14.2"
__version__ = "0.14.3"

import platform
from warnings import warn
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26 changes: 15 additions & 11 deletions docs/source/snowflake_usage.rst
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Expand Up @@ -6,11 +6,12 @@ For ``SnowflakeCompare``
- ``on_index`` is not supported.
- Joining is done using ``EQUAL_NULL`` which is the equality test that is safe for null values.
- Compares ``snowflake.snowpark.DataFrame``, which can be provided as either raw Snowflake dataframes
or the as the names of full names of valid snowflake tables, which we will process into Snowpark dataframes.
or the as the names of full names of valid snowflake tables, which we will process into Snowpark dataframes.
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SnowflakeCompare Object Setup
---------------------------------------------------
SnowflakeCompare setup
----------------------

There are two ways to specify input dataframes for ``SnowflakeCompare``

Provide Snowpark dataframes
Expand Down Expand Up @@ -66,11 +67,12 @@ Provide Snowpark dataframes
print(compare.report())


Provide the full name (``{db}.{schema}.{table_name}``) of valid Snowflake tables
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Provide the full name (``db.schema.table_name``) of valid Snowflake tables
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Given the dataframes from the prior examples...

.. code-block:: python

df_1.write.mode("overwrite").save_as_table("toy_table_1")
df_2.write.mode("overwrite").save_as_table("toy_table_2")

Expand Down Expand Up @@ -210,6 +212,7 @@ There are a few convenience methods and attributes available after the compariso
print(compare.df2_unq_columns())
# OrderedSet()


Duplicate rows
--------------

Expand Down Expand Up @@ -260,9 +263,10 @@ as uniquely in the second.

Additional considerations
-------------------------
- It is strongly recommended against joining on float columns (or any column with floating point precision).
Columns joining tables are compared on the basis of an exact comparison, therefore if the values comparing
your float columns are not exact, you will likely get unexpected results.
- Case-sensitive columns are only partially supported. We essentially treat case-sensitive
columns as if they are case-insensitive. Therefore you may use case-sensitive columns as long as
you don't have several columns with the same name differentiated only be case sensitivity.

- It is strongly recommended against joining on float columns or any column with floating point precision.
Columns joining tables are compared on the basis of an exact comparison, therefore if the values
comparing your float columns are not exact, you will likely get unexpected results.
- Case-sensitive columns are only partially supported. We essentially treat case-sensitive columns as
if they are case-insensitive. Therefore you may use case-sensitive columns as long as you don't have several
columns with the same name differentiated only be case sensitivity.
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