[jira] [Created] (SPARK-21537) toPandas() should handle nested columns (as a Pandas MultiIndex)

["Eric O. LEBIGOT (EOL) (JIRA)" <ji...@apache.org>]

Eric O. LEBIGOT (EOL) created SPARK-21537:
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             Summary: toPandas() should handle nested columns (as a Pandas MultiIndex)
                 Key: SPARK-21537
                 URL: https://issues.apache.org/jira/browse/SPARK-21537
             Project: Spark
          Issue Type: Improvement
          Components: PySpark
    Affects Versions: 2.2.0
            Reporter: Eric O. LEBIGOT (EOL)


The conversion of a *PySpark dataframe with nested columns* to Pandas (with `toPandas()`) does not convert nested columns into their Pandas equivalent, i.e. *columns indexed by a [MultiIndex|https://pandas.pydata.org/pandas-docs/stable/advanced.html]*.

For example, a dataframe with the following structure:
{code:java}
>>> df.printSchema()
root
 |-- device_ID: string (nullable = true)
 |-- time_origin_UTC: timestamp (nullable = true)
 |-- duration_s: integer (nullable = true)
 |-- session_time_UTC: timestamp (nullable = true)
 |-- probes_by_AP: struct (nullable = true)
 |    |-- aa:bb:cc:dd:ee:ff: array (nullable = true)
 |    |    |-- element: struct (containsNull = true)
 |    |    |    |-- delay_s: float (nullable = true)
 |    |    |    |-- RSSI: short (nullable = true)
 |-- max_RSSI_info_by_AP: struct (nullable = true)
 |    |-- aa:bb:cc:dd:ee:ff: struct (nullable = true)
 |    |    |-- delay_s: float (nullable = true)
 |    |    |-- RSSI: short (nullable = true)
{code}
yields a Pandas dataframe where the `max_RSSI_info_by_AP` column is _not_ nested inside Pandas (through a MultiIndex):
{code}
>>> df_pandas_version["max_RSSI_info_by_AP", "aa:bb:cc:dd:ee:ff", "RSSI"]. # Should work!
(…)
KeyError: ('max_RSSI_info_by_AP', 'aa:bb:cc:dd:ee:ff', 'RSSI')

>>> sessions_in_period["max_RSSI_info_by_AP"].iloc[0]
Row(aa:bb:cc:dd:ee:ff=Row(delay_s=0.0, RSSI=6))

>>> type(_)  # PySpark type, instead of Pandas!
pyspark.sql.types.Row
{code}

It would be much more convenient if the Spark dataframe did the conversion to Pandas more thoroughly.



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