Remainder. Alias for mod.

And. Alias for and.

Multiply. Alias for multiply.

Plus. Alias for plus.

Minus. Alias for minus.

Divide. Alias for divide.

Less than. Alias for lt.

Less than or equal to. Alias for leq.

Equal to. You can use this for comparisons against a null value. Alias for equal_null.

Not equal to. Alias for not_equal.

Equal to. Alias for equal_to. Use this instead of == to perform an equality check in an expression. For example:

lhs.filter(col("a") === 10).join(rhs, rhs("id") === lhs("id"))

Greater than. Alias for gt.

Greater than or equal to. Alias for geq.

Returns a new renamed Column. Alias for name.

And.

Returns the element (field) at the specified index in a column that contains semi-structured data.

The method applies case-sensitive matching to the names of the specified elements.

This is equivalent to using bracket notation in SQL (column[index]).

• If the column is an ARRAY value, this function extracts the VARIANT value of the array element at the specified index.
• If the index points outside of the array boundaries or if an element does not exist at the specified index (e.g. if the array is sparsely populated), the method returns NULL.
• If the column is a VARIANT value, this function first checks if the VARIANT value contains an ARRAY value.
• If the VARIANT value does not contain an ARRAY value, the method returns NULL.
• Otherwise, the method works as described above.

For example:

import com.snowflake.snowpark.functions.col
df.select(col("src")(1)(0)("name")(0))

Returns the specified element (field) in a column that contains semi-structured data.

The method applies case-sensitive matching to the names of the specified elements.

This is equivalent to using bracket notation in SQL (column['element']).

• If the column is an OBJECT value, this function extracts the VARIANT value of the element with the specified name from the OBJECT value.
• If the element is not found, the method returns NULL.
• You must not specify an empty string for the element name.
• If the column is a VARIANT value, this function first checks if the VARIANT value contains an OBJECT value.
• If the VARIANT value does not contain an OBJECT value, the method returns NULL.
• Otherwise, the method works as described above.

For example:

import com.snowflake.snowpark.functions.col
df.select(col("src")("salesperson")("emails")(0))

Returns a new renamed Column. Alias for name.

Returns a Column expression with values sorted in ascending order.

Returns a Column expression with values sorted in ascending order (null values sorted before non-null values).

Returns a Column expression with values sorted in ascending order (null values sorted after non-null values).

Between lower bound and upper bound.

Bitwise and.

Bitwise or.

Bitwise xor.

Casts the values in the Column to the specified data type.

Examples

val df = Seq(123, 456, 789).toDF("a")
df.select(col("a").cast("string").as("casted")).schema.toString
// res: String = "StructType[StructField(CASTED, String, Nullable = false)]"

Casts the values in the Column to the specified data type.

Returns a copy of the original Column with the specified collationSpec property, rather than the original collation specification property.

For details, see the Snowflake documentation on collation specifications.

Returns a Column expression with values sorted in descending order.

Returns a Column expression with values sorted in descending order (null values sorted before non-null values).

Returns a Column expression with values sorted in descending order (null values sorted after non-null values).

Divide.

Is NaN.

Equal to. You can use this for comparisons against a null value.

Equal to. Same as ===.

Greater than or equal to.

Returns the column name (if the column has a name).

Greater than.

Returns a conditional expression that you can pass to the filter or where method to perform a WHERE ... IN query with a specified subquery.

The expression evaluates to true if the value in the column is one of the values in the column of the same name in a specified DataFrame.

For example, the following code returns a DataFrame that contains the rows where the column "a" of df2 contains one of the values from column "a" in df1. This is equivalent to SELECT * FROM table2 WHERE a IN (SELECT a FROM table1).

val df1 = session.table(table1)
val df2 = session.table(table2)
df2.filter(col("a").in(df1))

Returns a conditional expression that you can pass to the filter or where method to perform the equivalent of a WHERE ... IN query with a specified list of values.

The expression evaluates to true if the value in the column is one of the values in a specified sequence.

For example, the following code returns a DataFrame that contains the rows where the column "a" contains the value 1, 2, or 3. This is equivalent to SELECT * FROM table WHERE a IN (1, 2, 3).

df.filter(df("a").in(Seq(1, 2, 3)))

Wrapper for is_null function.

Is not null.

Is null.

Less than or equal to.

Allows case-sensitive matching of strings based on comparison with a pattern.

For details, see the Snowflake documentation on LIKE.

Less than.

Minus.

Remainder.

Multiply.

Returns a new renamed Column.

Not equal to.

Or.

Returns a windows frame, based on an empty WindowSpec expression.

Returns a windows frame, based on the specified WindowSpec.

Plus.

Returns true if this Column matches the specified regular expression.

For details, see the Snowflake documentation on regular expressions.

Returns a string representation of the expression corresponding to this Column instance.

Unary not.

Unary minus.

Returns a Column expression that adds a WITHIN GROUP clause to sort the rows by the specified sequence of columns.

This method is supported on Column expressions returned by some of the aggregate functions, including functions.array_agg, LISTAGG(), PERCENTILE_CONT(), and PERCENTILE_DISC().

For example:

import com.snowflake.snowpark.functions._
import session.implicits._
// Create a DataFrame from a sequence.
val df = Seq((3, "v1"), (1, "v3"), (2, "v2")).toDF("a", "b")
// Create a DataFrame containing the values in "a" sorted by "b".
df.select(array_agg(col("a")).withinGroup(Seq(col("b"))))
// Create a DataFrame containing the values in "a" grouped by "b"
// and sorted by "a" in descending order.
df.select(
  array_agg(Seq(col("a")))
  .withinGroup(col("a").desc)
  .over(Window.partitionBy(col("b")))
)

For details, see the Snowflake documentation for the aggregate function that you are using (e.g. ARRAY_AGG).

Returns a Column expression that adds a WITHIN GROUP clause to sort the rows by the specified columns.

This method is supported on Column expressions returned by some of the aggregate functions, including functions.array_agg, LISTAGG(), PERCENTILE_CONT(), and PERCENTILE_DISC().

For example:

import com.snowflake.snowpark.functions._
import session.implicits._
// Create a DataFrame from a sequence.
val df = Seq((3, "v1"), (1, "v3"), (2, "v2")).toDF("a", "b")
// Create a DataFrame containing the values in "a" sorted by "b".
val dfArrayAgg = df.select(array_agg(col("a")).withinGroup(col("b")))
// Create a DataFrame containing the values in "a" grouped by "b"
// and sorted by "a" in descending order.
var dfArrayAggWindow = df.select(
  array_agg(col("a"))
  .withinGroup(col("a").desc)
  .over(Window.partitionBy(col("b")))
)

For details, see the Snowflake documentation for the aggregate function that you are using (e.g. ARRAY_AGG).

Or. Alias for or.