snowflake.snowpark.Column.in_¶

Column.in_(*vals: Union[None, bool, int, float, str, bytearray, Decimal, date, datetime, time, bytes, NaTType, float64, list, tuple, dict, Iterable[Union[None, bool, int, float, str, bytearray, Decimal, date, datetime, time, bytes, NaTType, float64, list, tuple, dict]], Column, Iterable[Column], DataFrame]) → Column[source]¶

Returns a conditional expression that you can pass to the DataFrame.filter() or where DataFrame.where() to perform the equivalent of a WHERE … IN query with a specified list of values. You can also pass this to a DataFrame.select() call.

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).

isin() is an alias for in_().

Examples:

>>> from snowflake.snowpark.functions import lit
>>> df = session.create_dataframe([[1, "x"], [2, "y"] ,[4, "z"]], schema=["a", "b"])
>>> # Basic example
>>> df.filter(df["a"].in_(lit(1), lit(2), lit(3))).collect()
[Row(A=1, B='x'), Row(A=2, B='y')]

>>> # Check in membership for a DataFrame that has a single column
>>> df_for_in = session.create_dataframe([[1], [2] ,[3]], schema=["col1"])
>>> df.filter(df["a"].in_(df_for_in)).sort(df["a"].asc()).collect()
[Row(A=1, B='x'), Row(A=2, B='y')]

>>> # Use in with a select method call
>>> df.select(df["a"].in_(lit(1), lit(2), lit(3)).alias("is_in_list")).collect()
[Row(IS_IN_LIST=True), Row(IS_IN_LIST=True), Row(IS_IN_LIST=False)]

>>> # Use in with column object
>>> df2 = session.create_dataframe([[1, 1], [2, 4] ,[3, 0]], schema=["a", "b"])
>>> df2.select(df2["a"].in_(df2["b"]).alias("is_a_in_b")).collect()
[Row(IS_A_IN_B=True), Row(IS_A_IN_B=False), Row(IS_A_IN_B=False)]

Parameters:: vals – The literal values, the columns in the same DataFrame, or a DataFrame instance to use to check for membership against this column.