Last modified: May 10, 2026 By Alexander Williams

Polars Data Type Casting & Schema Management

Data type casting and schema management are core skills in Polars. They help you control how data is stored and processed. This guide shows you how to change column types and manage your DataFrame schema.

Polars is a fast DataFrame library for Python. It focuses on performance and memory efficiency. Understanding its type system is key to writing clean code.

We will cover casting with cast, schema inspection, and schema modification. You will learn practical examples and see clear output.

What is Data Type Casting?

Data type casting means converting a column from one type to another. For example, you might change a string column to an integer. This is common when reading messy data.

Polars uses strict types. Each column has one data type. You must cast explicitly when types don't match.

The main method for casting is cast. You apply it to a column using pl.col("name").cast(pl.Int32). This returns a new column with the desired type.

You can also cast multiple columns at once. Use with_columns and a list of cast expressions.

Why Manage Schema?

Schema management ensures data consistency. A well-defined schema prevents errors during analysis. It also improves performance because Polars can optimize queries.

When you read files, Polars infers a schema. Sometimes it guesses wrong. You can override the schema to fix issues.

For example, a CSV might have a date column read as a string. You can cast it to a date type. This makes time-based operations faster.

Schema management also helps when you combine DataFrames. Matching schemas avoid type conflicts.

Basic Casting Example

Let's start with a simple example. We create a DataFrame with mixed types.

import polars as pl

# Create a DataFrame with a string column
df = pl.DataFrame({
    "id": ["1", "2", "3"],
    "value": ["10.5", "20.3", "30.1"]
})

# Cast id to integer and value to float
df_casted = df.with_columns([
    pl.col("id").cast(pl.Int32),
    pl.col("value").cast(pl.Float64)
])

print(df_casted)

shape: (3, 2)
┌─────┬───────┐
│ id  ┆ value │
│ --- ┆ ---   │
│ i32 ┆ f64   │
╞═════╪═══════╡
│ 1   ┆ 10.5  │
│ 2   ┆ 20.3  │
│ 3   ┆ 30.1  │
└─────┴───────┘

Notice the types changed. The id column is now Int32. The value column is Float64. This makes calculations safe.

Schema Inspection

You can inspect a DataFrame's schema with the schema property. It returns a dictionary of column names and types.

# Check schema of original DataFrame
print(df.schema)

# Check schema after casting
print(df_casted.schema)

{'id': String, 'value': String}
{'id': Int32, 'value': Float64}

You can also use dtypes to get a list of types. This is useful for quick checks.

print(df.dtypes)
# Output: [String, String]

Casting with Error Handling

Sometimes casting fails. For example, casting "abc" to integer raises an error. Use strict=False to set invalid values to null.

# DataFrame with invalid data
df_bad = pl.DataFrame({
    "id": ["1", "x", "3"]
})

# Cast with strict=False to handle errors
df_clean = df_bad.with_columns(
    pl.col("id").cast(pl.Int32, strict=False)
)

print(df_clean)

shape: (3, 1)
┌──────┐
│ id   │
│ ---  │
│ i32  │
╞══════╡
│ 1    │
│ null │
│ 3    │
└──────┘

The invalid value "x" becomes null. This keeps your DataFrame valid.

Schema Management When Reading Files

When reading a CSV, you can define the schema explicitly. This avoids type inference errors.

# Read CSV with custom schema
schema = {
    "date": pl.Date,
    "sales": pl.Float64,
    "region": pl.String
}

# Assume we have a file 'data.csv'
# df_read = pl.read_csv("data.csv", schema=schema)
# print(df_read.schema)

This forces the columns to the correct types. It is faster than reading and casting later.

For Parquet files, schema is preserved. You rarely need to cast. But you can still inspect it.

Modifying Schema with with_columns

The with_columns method is powerful. It lets you add, replace, or cast columns in one step.

# Add a new column and cast another
df_mod = df.with_columns([
    pl.col("value").cast(pl.Float64).alias("value_float"),
    pl.lit(True).alias("flag")
])

print(df_mod)

shape: (3, 3)
┌─────┬───────┬────────────┬───────┐
│ id  ┆ value ┆ value_float┆ flag  │
│ --- ┆ ---   ┆ ---        ┆ ---   │
│ str ┆ str   ┆ f64        ┆ bool  │
╞═════╪═══════╪════════════╪═══════╡
│ 1   ┆ 10.5  ┆ 10.5       ┆ true  │
│ 2   ┆ 20.3  ┆ 20.3       ┆ true  │
│ 3   ┆ 30.1  ┆ 30.1       ┆ true  │
└─────┴───────┴────────────┴───────┘

You can also drop columns to simplify the schema. Use drop method.

Common Data Types in Polars

Polars supports many types. Here are the most common ones:

  • pl.Int32, pl.Int64 for integers
  • pl.Float32, pl.Float64 for floats
  • pl.String for text
  • pl.Date, pl.Datetime for dates
  • pl.Boolean for true/false

Choose the smallest type that fits your data. For example, use Int32 instead of Int64 for small numbers. This saves memory.

Practical Example: Cleaning a Dataset

Let's combine everything. We read a messy CSV, inspect schema, cast types, and handle errors.

# Simulate messy data
import io

csv_data = """name,age,salary
Alice,30,50000
Bob,25,abc
Charlie,35,70000"""

df_raw = pl.read_csv(io.StringIO(csv_data))
print("Original schema:", df_raw.schema)

# Cast age to integer and salary to float, handle errors
df_clean = df_raw.with_columns([
    pl.col("age").cast(pl.Int32),
    pl.col("salary").cast(pl.Float64, strict=False)
])

print("Cleaned schema:", df_clean.schema)
print(df_clean)

Original schema: {'name': String, 'age': String, 'salary': String}
Cleaned schema: {'name': String, 'age': Int32, 'salary': Float64}
shape: (3, 3)
┌───────┬─────┬────────┐
│ name  ┆ age ┆ salary │
│ ---   ┆ --- ┆ ---    │
│ str   ┆ i32 ┆ f64    │
╞═══════╪═════╪════════╡
│ Alice ┆ 30  ┆ 50000.0│
│ Bob   ┆ 25  ┆ null   │
│ Charlie┆ 35 ┆ 70000.0│
└───────┴─────┴────────┘

The invalid salary becomes null. The schema is now clean for analysis.

Working with Null Values

After casting, you may have nulls. Use fill_null to replace them. For example, fill missing salaries with the mean.

# Fill null values with 0
df_filled = df_clean.with_columns(
    pl.col("salary").fill_null(0)
)

print(df_filled)

shape: (3, 3)
┌───────┬─────┬────────┐
│ name  ┆ age ┆ salary │
│ ---   ┆ --- ┆ ---    │
│ str   ┆ i32 ┆ f64    │
╞═══════╪═════╪════════╡
│ Alice ┆ 30  ┆ 50000.0│
│ Bob   ┆ 25  ┆ 0.0    │
│ Charlie┆ 35 ┆ 70000.0│
└───────┴─────┴────────┘

This keeps your data complete.

Advanced: Casting Multiple Columns at Once

You can cast all columns of a certain type. For example, cast all string columns to categorical.

# Cast all string columns to categorical
df_cat = df_clean.with_columns(
    pl.col(pl.String).cast(pl.Categorical)
)

print(df_cat.schema)

{'name': Categorical, 'age': Int32, 'salary': Float64}

This is useful for memory optimization.

Conclusion

Data type casting and schema management are essential in Polars. They ensure data integrity and improve performance. Use cast to change types, schema to inspect, and with_columns to modify.

Always handle errors with strict=False. This prevents crashes from bad data. Practice these techniques to write robust data pipelines.

For more on Polars basics, check our Polars DataFrames and Series Guide. Learn about Select Columns & Filter Rows in Polars for data manipulation. Also see Polars Expression System Core Concept for deeper insights.

Recent Tutorials: