How to Replace Values > Threshold in Polars DataFrame

To replace values greater than a threshold with zero in specific columns of a Polars DataFrame, you need to use column selection with conditional logic. The key is to apply the transformation only to numeric columns while preserving non-numeric columns like dates.

Here’s the correct approach using multiple methods:

import polars as pl

# Method 1: Using clip() - most efficient for simple thresholding
df_capped = df.with_columns(
    pl.col(pl.Float32 | pl.Float64).clip(upper_bound=20)
)

# Method 2: Using when().then() with column selection
df_conditioned = df.with_columns(
    pl.when(pl.col(pl.Float32 | pl.Float64) > 20)
    .then(0)
    .otherwise(pl.col(pl.Float32 | pl.Float64))
    .keep_name()
)

# Method 3: Using set() with boolean mask (from SparkByExamples)
df_masked = df.with_columns(
    pl.col(pl.Float32 | pl.Float64).set(pl.col(pl.Float32 | pl.Float64) > 20, 0)
)

Contents

• Understanding Column Selection
• Efficient Threshold Replacement Methods
• Handling Multiple Numeric Types
• Preserving Non-Numeric Columns
• Performance Comparison
• Practical Examples

Understanding Column Selection

Polars uses powerful column selection patterns. The pl.col(pl.Float32 | pl.Float64) expression selects only float columns, which is perfect for your use case. You can also use pl.col(pl.NUMERIC_TYPES) to include all numeric types.

# Select all numeric columns
numeric_cols = pl.col(pl.INTEGER_DTYPES | pl.FLOAT_DTYPES)

# Exclude specific columns
numeric_cols_only = pl.col(pl.NUMERIC_TYPES).exclude("date")

Efficient Threshold Replacement Methods

1. Using clip() - Most Performant

The clip() function is the most efficient way to replace values above/below thresholds:

df_capped = df.with_columns(
    pl.col(pl.NUMERIC_TYPES)
    .clip(lower_bound=0, upper_bound=20)  # Replace values > 20 with 20
)

2. Using when().then() - Most Flexible

For complex conditional logic:

df_conditioned = df.with_columns(
    pl.when(pl.col(pl.NUMERIC_TYPES) > 20)
    .then(0)
    .otherwise(pl.col(pl.NUMERIC_TYPES))
    .name.keep()
)

3. Using set() with Boolean Mask

From the SparkByExamples documentation:

df_masked = df.with_columns(
    pl.col(pl.NUMERIC_TYPES).set(
        pl.col(pl.NUMERIC_TYPES) > 20, 
        0
    )
)

Handling Multiple Numeric Types

To ensure all numeric columns are processed, use the comprehensive numeric type selector:

# Include all numeric types
df_processed = df.with_columns(
    pl.when(pl.col(pl.NUMERIC_TYPES) > 20)
    .then(0)
    .otherwise(pl.col(pl.NUMERIC_TYPES))
    .name.keep()
)

If you need to exclude specific columns like dates:

df_processed = df.with_columns(
    pl.when(pl.col(pl.NUMERIC_TYPES).exclude("date") > 20)
    .then(0)
    .otherwise(pl.col(pl.NUMERIC_TYPES).exclude("date"))
    .name.keep()
)

Preserving Non-Numeric Columns

The with_columns() method automatically preserves all columns not explicitly modified. This is why your previous attempt with select() failed - it created a new DataFrame with only the specified columns.

Here’s the complete solution that preserves all columns:

# Replace values > 20 with 0 in all numeric columns, keep others unchanged
df_final = df.with_columns(
    pl.when(pl.col(pl.NUMERIC_TYPES) > 20)
    .then(0)
    .otherwise(pl.col(pl.NUMERIC_TYPES))
    .name.keep()
)

Performance Comparison

Based on the research findings:

1. clip() is the fastest for simple threshold operations
2. when().then() is more flexible but slightly slower
3. set() with mask is intermediate performance

For most use cases, clip() is recommended when you simply need to cap values at a threshold.

Practical Examples

Example 1: Basic Threshold Replacement

import polars as pl

# Create sample DataFrame
df = pl.DataFrame({
    "date": ["2023-01-01", "2023-01-02", "2023-01-03"],
    "sales": [15, 25, 30],
    "inventory": [100, 150, 200],
    "temperature": [18.5, 22.3, 25.1]
})

# Replace values > 20 with 0
df_result = df.with_columns(
    pl.when(pl.col(pl.NUMERIC_TYPES) > 20)
    .then(0)
    .otherwise(pl.col(pl.NUMERIC_TYPES))
    .name.keep()
)

print(df_result)

Example 2: Using Different Thresholds

# Replace sales > 25 with 0, inventory > 120 with 0, temperature > 22 with 0
df_result = df.with_columns([
    pl.when(pl.col("sales") > 25).then(0).otherwise(pl.col("sales")).alias("sales"),
    pl.when(pl.col("inventory") > 120).then(0).otherwise(pl.col("inventory")).alias("inventory"),
    pl.when(pl.col("temperature") > 22).then(0).otherwise(pl.col("temperature")).alias("temperature")
])

The key insight is that Polars’ column selection expressions combined with with_columns() allow you to apply transformations selectively while preserving the original DataFrame structure. This approach is both efficient and readable for your use case.

Sources

1. Polars Expressions Documentation - Column Selection
2. SparkByExamples - Replace Values in Polars Series
3. Polars Expr.replace Documentation
4. StackOverflow - Polars Replacing Values Greater than Max
5. Polars GitHub Issue - Add replace functionality

Conclusion

To replace values greater than a threshold with zero in specific Polars DataFrame columns:

1. Use with_columns() to preserve all columns while applying transformations
2. Select numeric columns with pl.col(pl.NUMERIC_TYPES) or specific types
3. Apply threshold logic using clip(), when().then(), or set() with boolean masks
4. The clip() method provides the best performance for simple threshold operations
5. Always test with your specific data types and column names

The most efficient solution for your use case would be:

df_final = df.with_columns(
    pl.col(pl.NUMERIC_TYPES).clip(upper_bound=20)
)

This approach will automatically replace all values greater than 20 with 20 in all numeric columns while preserving non-numeric columns like dates.