How to use Pyspark equivalent for reset_index() in python

Question

I'd like to know equivalence in PySpark to the use of reset_index() command used in pandas. When using the default command (reset_index), as follows:

data.reset_index()

I get an error:

"DataFrame' object has no attribute 'reset_index' error"

Can you provide more to your question - what you are trying to achieve ? what is the expected outcome in a tabular format ? — dsk
– dsk, Commented Nov 6, 2020 at 5:57
You cannot use reset_index because Spark has not concept of index. The dataframe is distributed and is fundamentally different from pandas. — mck
– mck, Commented Nov 6, 2020 at 6:53
If you just want to provide a numerical id to the rows then you can use monotonically_increasing_id — user238607
– user238607, Commented Nov 6, 2020 at 8:23
If your problem is as simple as mine this can help https://stackoverflow.com/questions/52318016/pyspark-add-sequential-and-deterministic-index-to-dataframe — CAV
– CAV, Commented Jul 16, 2021 at 22:30

Ben Kaan · Accepted Answer · 2023-01-30 21:10:09Z

2

Like the other comments mentioned, if you do need to add an index to your DF, you can use:

from pyspark.sql.functions import monotonically_increasing_id

df = df.withColumn("index_column",monotonically_increasing_id())

answered Jan 30, 2023 at 21:10

Ben Kaan

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