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The schema of a Polars DataFrame or LazyFrame sets out the names of the columns and their datatypes. You can see the schema with the .collect_schema method on a DataFrame or LazyFrame

lf = pl.LazyFrame({"foo": ["a", "b", "c"], "bar": [0, 1, 2]})

Schema({'foo': String, 'bar': Int64})

The schema plays an important role in the lazy API.

Type checking in the lazy API

One advantage of the lazy API is that Polars will check the schema before any data is processed. This check happens when you execute your lazy query.

We see how this works in the following simple example where we call the .round expression on the integer bar column.


lf = pl.LazyFrame({"foo": ["a", "b", "c"], "bar": [0, 1, 2]}).with_columns(

The .round expression is only valid for columns with a floating point dtype. Calling .round on an integer column means the operation will raise an InvalidOperationError when we evaluate the query with collect. This schema check happens before the data is processed when we call collect.

except Exception as e:
`round` operation not supported for dtype `i64`

If we executed this query in eager mode the error would only be found once the data had been processed in all earlier steps.

When we execute a lazy query Polars checks for any potential InvalidOperationError before the time-consuming step of actually processing the data in the pipeline.

The lazy API must know the schema

In the lazy API the Polars query optimizer must be able to infer the schema at every step of a query plan. This means that operations where the schema is not knowable in advance cannot be used with the lazy API.

The classic example of an operation where the schema is not knowable in advance is a .pivot operation. In a .pivot the new column names come from data in one of the columns. As these column names cannot be known in advance a .pivot is not available in the lazy API.

Dealing with operations not available in the lazy API

If your pipeline includes an operation that is not available in the lazy API it is normally best to:

  • run the pipeline in lazy mode up until that point
  • execute the pipeline with .collect to materialize a DataFrame
  • do the non-lazy operation on the DataFrame
  • convert the output back to a LazyFrame with .lazy and continue in lazy mode

We show how to deal with a non-lazy operation in this example where we:

  • create a simple DataFrame
  • convert it to a LazyFrame with .lazy
  • do a transformation using .with_columns
  • execute the query before the pivot with .collect to get a DataFrame
  • do the .pivot on the DataFrame
  • convert back in lazy mode
  • do a .filter
  • finish by executing the query with .collect to get a DataFrame

collect · lazy · pivot · filter

lazy_eager_query = (
            "id": ["a", "b", "c"],
            "month": ["jan", "feb", "mar"],
            "values": [0, 1, 2],
    .with_columns((2 * pl.col("values")).alias("double_values"))
        index="id", columns="month", values="double_values", aggregate_function="first"

shape: (2, 4)
│ id  ┆ jan  ┆ feb  ┆ mar  │
│ --- ┆ ---  ┆ ---  ┆ ---  │
│ str ┆ i64  ┆ i64  ┆ i64  │
│ a   ┆ 0    ┆ null ┆ null │
│ b   ┆ null ┆ 2    ┆ null │