+
+

Snowpark/Snowflake Usage

+

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.

+
+

SnowflakeCompare Object Setup

+

There are two ways to specify input dataframes for SnowflakeCompare

+
+

Provide Snowpark dataframes

+
from snowflake.snowpark import Session
+from snowflake.snowpark import Row
+import datetime
+import datacompy.snowflake as sp
+
+connection_parameters = {
+    ...
+}
+session = Session.builder.configs(connection_parameters).create()
+
+data1 = [
+Row(acct_id=10000001234, dollar_amt=123.45, name='George Maharis', float_fld=14530.1555,
+    date_fld=datetime.date(2017, 1, 1)),
+Row(acct_id=10000001235, dollar_amt=0.45, name='Michael Bluth', float_fld=1.0,
+    date_fld=datetime.date(2017, 1, 1)),
+Row(acct_id=10000001236, dollar_amt=1345.0, name='George Bluth', float_fld=None,
+    date_fld=datetime.date(2017, 1, 1)),
+Row(acct_id=10000001237, dollar_amt=123456.0, name='Bob Loblaw', float_fld=345.12,
+    date_fld=datetime.date(2017, 1, 1)),
+Row(acct_id=10000001239, dollar_amt=1.05, name='Lucille Bluth', float_fld=None,
+    date_fld=datetime.date(2017, 1, 1)),
+]
+
+data2 = [
+    Row(acct_id=10000001234, dollar_amt=123.4, name='George Michael Bluth', float_fld=14530.155),
+    Row(acct_id=10000001235, dollar_amt=0.45, name='Michael Bluth', float_fld=None),
+    Row(acct_id=None, dollar_amt=1345.0, name='George Bluth', float_fld=1.0),
+    Row(acct_id=10000001237, dollar_amt=123456.0, name='Robert Loblaw', float_fld=345.12),
+    Row(acct_id=10000001238, dollar_amt=1.05, name='Loose Seal Bluth', float_fld=111.0),
+]
+
+df_1 = session.createDataFrame(data1)
+df_2 = session.createDataFrame(data2)
+
+compare = sp.SnowflakeCompare(
+    session,
+    df_1,
+    df_2,
+    join_columns=['acct_id'],
+    rel_tol=1e-03,
+    abs_tol=1e-04,
+)
+compare.matches(ignore_extra_columns=False)
+
+# This method prints out a human-readable report summarizing and sampling differences
+print(compare.report())
+
+
+
+
+

Provide the full name ({db}.{schema}.{table_name}) of valid Snowflake tables

+

Given the dataframes from the prior examples…

+
+
+
+

Reports

+

A report is generated by calling report(), which returns a string. +Here is a sample report generated by datacompy for the two tables above, +joined on acct_id (Note: the names for your dataframes are extracted from +the name of the provided Snowflake table. If you chose to directly use Snowpark +dataframes, then the names will default to DF1 and DF2.):

+
DataComPy Comparison
+--------------------
+
+DataFrame Summary
+-----------------
+
+DataFrame  Columns  Rows
+0       DF1        5     5
+1       DF2        4     5
+
+Column Summary
+--------------
+
+Number of columns in common: 4
+Number of columns in DF1 but not in DF2: 1
+Number of columns in DF2 but not in DF1: 0
+
+Row Summary
+-----------
+
+Matched on: ACCT_ID
+Any duplicates on match values: No
+Absolute Tolerance: 0
+Relative Tolerance: 0
+Number of rows in common: 4
+Number of rows in DF1 but not in DF2: 1
+Number of rows in DF2 but not in DF1: 1
+
+Number of rows with some compared columns unequal: 4
+Number of rows with all compared columns equal: 0
+
+Column Comparison
+-----------------
+
+Number of columns compared with some values unequal: 3
+Number of columns compared with all values equal: 1
+Total number of values which compare unequal: 6
+
+Columns with Unequal Values or Types
+------------------------------------
+
+    Column         DF1 dtype         DF2 dtype  # Unequal  Max Diff  # Null Diff
+0  DOLLAR_AMT            double            double          1    0.0500            0
+2   FLOAT_FLD            double            double          3    0.0005            2
+1        NAME  string(16777216)  string(16777216)          2       NaN            0
+
+Sample Rows with Unequal Values
+-------------------------------
+
+    ACCT_ID  DOLLAR_AMT (DF1)  DOLLAR_AMT (DF2)
+0  10000001234            123.45             123.4
+
+    ACCT_ID      NAME (DF1)            NAME (DF2)
+0  10000001234  George Maharis  George Michael Bluth
+1  10000001237      Bob Loblaw         Robert Loblaw
+
+    ACCT_ID  FLOAT_FLD (DF1)  FLOAT_FLD (DF2)
+0  10000001234       14530.1555        14530.155
+1  10000001235           1.0000              NaN
+2  10000001236              NaN            1.000
+
+Sample Rows Only in DF1 (First 10 Columns)
+------------------------------------------
+
+ACCT_ID_DF1  DOLLAR_AMT_DF1       NAME_DF1  FLOAT_FLD_DF1 DATE_FLD_DF1
+0  10000001239            1.05  Lucille Bluth            NaN   2017-01-01
+
+Sample Rows Only in DF2 (First 10 Columns)
+------------------------------------------
+
+ACCT_ID_DF2  DOLLAR_AMT_DF2          NAME_DF2  FLOAT_FLD_DF2
+0  10000001238            1.05  Loose Seal Bluth          111.0
+
+
+
+
+

Convenience Methods

+

There are a few convenience methods and attributes available after the comparison has been run:

+
compare.intersect_rows[['name_df1', 'name_df2', 'name_match']].show()
+# --------------------------------------------------------
+# |"NAME_DF1"      |"NAME_DF2"            |"NAME_MATCH"  |
+# --------------------------------------------------------
+# |George Maharis  |George Michael Bluth  |False         |
+# |Michael Bluth   |Michael Bluth         |True          |
+# |George Bluth    |George Bluth          |True          |
+# |Bob Loblaw      |Robert Loblaw         |False         |
+# --------------------------------------------------------
+
+compare.df1_unq_rows.show()
+# ---------------------------------------------------------------------------------------
+# |"ACCT_ID_DF1"  |"DOLLAR_AMT_DF1"  |"NAME_DF1"     |"FLOAT_FLD_DF1"  |"DATE_FLD_DF1"  |
+# ---------------------------------------------------------------------------------------
+# |10000001239    |1.05              |Lucille Bluth  |NULL             |2017-01-01      |
+# ---------------------------------------------------------------------------------------
+
+compare.df2_unq_rows.show()
+# -------------------------------------------------------------------------
+# |"ACCT_ID_DF2"  |"DOLLAR_AMT_DF2"  |"NAME_DF2"        |"FLOAT_FLD_DF2"  |
+# -------------------------------------------------------------------------
+# |10000001238    |1.05              |Loose Seal Bluth  |111.0            |
+# -------------------------------------------------------------------------
+
+print(compare.intersect_columns())
+# OrderedSet(['acct_id', 'dollar_amt', 'name', 'float_fld'])
+
+print(compare.df1_unq_columns())
+# OrderedSet(['date_fld'])
+
+print(compare.df2_unq_columns())
+# OrderedSet()
+
+
+
+
+

Duplicate rows

+

Datacompy will try to handle rows that are duplicate in the join columns. It does this behind the +scenes by generating a unique ID within each unique group of the join columns. For example, if you +have two dataframes you’re trying to join on acct_id:

+
+ + + + + + + + + + + + + + + + + +

acct_id

name

1

George Maharis

1

Michael Bluth

2

George Bluth

+
+
+ + + + + + + + + + + + + + + + + + + + +

acct_id

name

1

George Maharis

1

Michael Bluth

1

Tony Wonder

2

George Bluth

+
+

Datacompy will generate a unique temporary ID for joining:

+
+ + + + + + + + + + + + + + + + + + + + + +

acct_id

name

temp_id

1

George Maharis

0

1

Michael Bluth

1

2

George Bluth

0

+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + +

acct_id

name

temp_id

1

George Maharis

0

1

Michael Bluth

1

1

Tony Wonder

2

2

George Bluth

0

+
+

And then merge the two dataframes on a combination of the join_columns you specified and the temporary +ID, before dropping the temp_id again. So the first two rows in the first dataframe will match the +first two rows in the second dataframe, and the third row in the second dataframe will be recognized +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.

+
+
+ +