alamb commented on code in PR #13939:
URL: https://github.com/apache/datafusion/pull/13939#discussion_r1899450386
##########
datafusion-examples/examples/dataframe.rs:
##########
@@ -206,3 +215,38 @@ async fn write_out(ctx: &SessionContext) ->
std::result::Result<(), DataFusionEr
Ok(())
}
+
+/// This example demonstrates how to use the to_date series
+/// of functions in the DataFrame API as well as via sql.
+async fn query_to_date() -> Result<()> {
+ // define a schema.
+ let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Utf8,
false)]));
+
+ // define data.
+ let batch = RecordBatch::try_new(
+ schema,
+ vec![Arc::new(StringArray::from(vec![
+ "2020-09-08T13:42:29Z",
+ "2020-09-08T13:42:29.190855-05:00",
+ "2020-08-09 12:13:29",
+ "2020-01-02",
+ ]))],
+ )?;
+
+ // declare a new context. In spark API, this corresponds to a new spark
SQLsession
+ let ctx = SessionContext::new();
+
+ // declare a table in memory. In spark API, this corresponds to
createDataFrame(...).
+ ctx.register_batch("t", batch)?;
+ let df = ctx.table("t").await?;
+
+ // use to_date function to convert col 'a' to timestamp type using the
default parsing
+ let df = df.with_column("a", to_date(vec![col("a")]))?;
+
+ let df = df.select_columns(&["a"])?;
+
+ // print the results
+ df.show().await?;
Review Comment:
Good idea -- done in
[e06d83f](https://github.com/apache/datafusion/pull/13939/commits/e06d83ff4729d111484911238eadbf06a551f7e8)
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