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https://issues.apache.org/jira/browse/SPARK-21537?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Eric O. LEBIGOT (EOL) updated SPARK-21537:
------------------------------------------
Description:
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 = df.toPandas()
>>> 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')
>>> df_pandas_version["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.
was:
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.
> 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)
> Labels: pandas
>
> 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 = df.toPandas()
> >>> 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')
> >>> df_pandas_version["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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