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https://issues.apache.org/jira/browse/SPARK-30421?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17022900#comment-17022900
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Tobias Hermann commented on SPARK-30421:
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[~dongjoon] I'm glad we are aligned now. :)
For future reference:
The original Pandas example
{quote}df.drop(columns=["col1"]).loc[df["col1"] == 1]
{quote}
accesses the (unnamed) dataframe resulting from the drop call by row index
(loc). This would even work (but not be very meaningful) by using a totally
independent dataframe for this filtering.
{quote}df_foo = pd.DataFrame(data=\{'foo': [0, 1]})
df_bar = pd.DataFrame(data=\{'bar': ["a", "b"]})
df_bar.loc[df_foo["foo"] == 1]
{quote}
> Dropped columns still available for filtering
> ---------------------------------------------
>
> Key: SPARK-30421
> URL: https://issues.apache.org/jira/browse/SPARK-30421
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.4.4
> Reporter: Tobias Hermann
> Priority: Minor
>
> The following minimal example:
> {quote}val df = Seq((0, "a"), (1, "b")).toDF("foo", "bar")
> df.select("foo").where($"bar" === "a").show
> df.drop("bar").where($"bar" === "a").show
> {quote}
> should result in an error like the following:
> {quote}org.apache.spark.sql.AnalysisException: cannot resolve '`bar`' given
> input columns: [foo];
> {quote}
> However, it does not but instead works without error, as if the column "bar"
> would exist.
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