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Reading partitions one at a time from Python

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Reading partitions one at a time from Python

Hi, I am trying to read a partitioned dataset using Python. I got a list of partitions using the following code. But I do not know how to read those partitions one by one as dataframe.

mydataset = dataiku.Dataset("Data")
mydataset_df = mydataset.list_partitions(raise_if_empty=True)


Kindly suggest


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2 Replies


in a notebook, you'd go like:

import dataiku
ds = dataiku.Dataset('Data')
for p in ds.list_partitions():
    ds.read_partitions = [p]
    df = ds.get_dataframe()

Note that in a recipe,  the read_partitions field will be filled by DSS automatically when you create the Dataset object.



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First, are you trying to achieve that from a python recipe (in the flow), or from a notebook ? If you are in a python recipe, you usually cannot handle yourself partitions, as they are managed in the recipe parameters. However, you can override that by setting the "ignore_flow" field to "True" in every occurence of "Dataset". It would look like

import dataiku
my_dataset = dataiku.Dataset("my_dataset_name", ignore_flow=True)


Then, the solution to read only some partitions of a dataset is to leverage the "add_read_partitions" function from the "Dataset" class. However, this functions only "adds" the partition, meaning, it does not remove the previously added ones. Therefore, each time you call it, you need to reset th read partition by using a new instance of Dataset. This would look like:

import dataiku
partitions = dataiku.Dataset("Data").list_partitions(raise_if_empty=True)
dataset_partitions_df = {}
for partition in partitions:
    dataset = dataiku.Dataset("Data")  # reinitializing the read partition
    dataset_partition_df = dataset.get_dataframe()
    dataset_partitions_df[partition] = dataset_partition_df
# => outputs {"part1": .. df of part1 .., "part2": .. df of part2 .., ...}


Hope this helps

Best regards

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