snowflake.snowpark.functions.approx_percentile_combine¶

snowflake.snowpark.functions.approx_percentile_combine(state: Union[Column, str]) → Column[source]¶

Combines (merges) percentile input states into a single output state. This allows scenarios where APPROX_PERCENTILE_ACCUMULATE is run over horizontal partitions of the same table, producing an algorithm state for each table partition. These states can later be combined using APPROX_PERCENTILE_COMBINE, producing the same output state as a single run of APPROX_PERCENTILE_ACCUMULATE over the entire table.

Example::

>>> df1 = session.create_dataframe([1,2,3,4,5], schema=["a"])
>>> df2 = session.create_dataframe([6,7,8,9,10], schema=["b"])
>>> df_accu1 = df1.select(approx_percentile_accumulate("a").alias("app_percentile_accu"))
>>> df_accu2 = df2.select(approx_percentile_accumulate("b").alias("app_percentile_accu"))
>>> df_accu1.union(df_accu2).select(approx_percentile_combine("app_percentile_accu").alias("result")).show()
------------------------------
|"RESULT"                    |
------------------------------
|{                           |
|  "state": [                |
|    1.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    2.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    3.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    4.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    5.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    6.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    7.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    8.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    9.000000000000000e+00,  |
|    1.000000000000000e+00,  |
|    1.000000000000000e+01,  |
|    1.000000000000000e+00   |
|  ],                        |
|  "type": "tdigest",        |
|  "version": 1              |
|}                           |
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