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https://issues.apache.org/jira/browse/ARROW-9455?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joris Van den Bossche updated ARROW-9455:
-----------------------------------------
Summary: [Python] add option for taking all columns from all files in
pa.dataset (was: Request: add option for taking all columns from all files in
pa.dataset)
> [Python] add option for taking all columns from all files in pa.dataset
> -----------------------------------------------------------------------
>
> Key: ARROW-9455
> URL: https://issues.apache.org/jira/browse/ARROW-9455
> Project: Apache Arrow
> Issue Type: Wish
> Components: Python
> Reporter: David Cortes
> Priority: Minor
>
> In PyArrow's dataset class, if I give it multiple parquet files in a list and
> these parquet files have potentially different columns, it will always take
> the schema from the first parquet file in the list, thus ignoring columns
> that the first file doesn't have. Getting all columns within the files into
> the same dataset implies passing a manual schema or constructing one by
> iterating over the files and checking for their columns.
>
> Would be nicer if PyArrow's dataset class could have an option to
> automatically take all columns within the files from which it is constructed.
> {code:java}
> import numpy as np, pandas as pd
> df1 = pd.DataFrame({
> "col1" : np.arange(10),
> "col2" : np.random.choice(["a", "b"], size=10)
> })
> df2 = pd.DataFrame({
> "col1" : np.arange(10, 20),
> "col3" : np.random.random(size=10)
> })
> df1.to_parquet("df1.parquet")
> df2.to_parquet("df2.parquet"){code}
> {code:java}
> import pyarrow.dataset as pds
> ff = ["df1.parquet", "df2.parquet"]{code}
> {code:java}
> ### Code below will generate a DF with col1 and col2, but no col3{code}
> {code:java}
> pds.dataset(ff, format="parquet").to_table().to_pandas()
> {code}
>
>
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