spark-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Abdeali Kothari <>
Subject PySpark syntax vs Pandas syntax
Date Tue, 26 Mar 2019 05:56:50 GMT
I was doing some spark to pandas (and vice versa) conversion because some
of the pandas codes we have don't work on huge data. And some spark codes
work very slow on small data.

It was nice to see that pyspark had some similar syntax for the common
pandas operations that the python community is used to.

GroupBy aggs: df.groupby(['col2']).agg({'col2': 'count'}).show()
Column selects: df[['col1', 'col2']]
Row Filters: df[df['col1'] < 3.0]

I was wondering about a bunch of other functions in pandas which seemed
common. And thought there must've been a discussion about it in the
community - hence started this thread.

I was wondering whether there has been discussion on adding the following

*Column setters*:
In Pandas:
df['col3'] = df['col1'] * 3.0
While I do the following in PySpark:
df = df.withColumn('col3', df['col1'] * 3.0)

*Column apply()*:
In Pandas:
df['col3'] = df['col1'].apply(lambda x: x * 3.0)
While I do the following in PySpark:
df = df.withColumn('col3', F.udf(lambda x: x * 3.0, 'float')(df['col1']))

I understand that this one cannot be as simple as in pandas due to the
output-type that's needed here. But could be done like:
df['col3'] = df['col1'].apply((lambda x: x * 3.0), 'float')

Multi column in pandas is:
df['col3'] = df[['col1', 'col2']].apply(lambda x: x.col1 * 3.0)
Maybe this can be done in pyspark as or if we can send a pyspark.sql.Row
directly it would be similar (?):
df['col3'] = df[['col1', 'col2']].apply((lambda col1, col2: col1 * 3.0),

In Pandas:
While I do the following in PySpark:
df.toDF(*[{'col2': 'col3'}.get(i, i) for i in df.columns])

*To Dictionary*:
In Pandas:
While I do the following in PySpark:
{ [row[i] for row in df.collect()] for i, f in

I thought I'd start the discussion with these and come back to some of the
others I see that could be helpful.

*Note*: (with the column functions in mind) I understand the concept of the
DataFrame cannot be modified. And I am not suggesting we change that nor
any underlying principle. Just trying to add syntactic sugar here.

View raw message