Expressions

Column expressions for data transformations. Flowfile follows the Polars expressions API, with additional features for Flowfile-formula syntax.

Flowfile formula syntax

Flowfile supports a simplified formula syntax for expressions, allowing you to use bracket notation for column references. This will render nicely in the Flowfile UI and was implemented to decrease the learning curve for users coming from Excel, PowerBI, Alteryx and Tableau. You can try it out here: Flowfile Formula Playground.

For example, instead of using ff.col("price"), you can use [price] in supported operations. Operations that support this syntax are documented below.

• ff.with_columns You can use flowfile_formulas: Optional[List[str]] = None, output_column_names: Optional[List[str]] = None, to create new columns based on Flowfile-formula syntax. The number of columns in output_column_names must match the number of formulas in flowfile_formulas.
• ff.filter You can use the flowfile_formula parameter to filter rows based on Flowfile Formula syntax.

Column References

import flowfile as ff

# Polars style
ff.col("price")

# Flowfile formula syntax (in supported operations)
"[price]"  # Equivalent to ff.col("price")

Example Flowfile formula syntax

import flowfile as ff

df = ff.FlowFrame({
    "product": ["Widget", "Gadget", "Tool"],
    "price": [10.50, 25.00, 8.75],
    "quantity": [100, 50, 200],
    "discount": [0.1, 0.15, 0.05]
})

# Using Flowfile formula syntax (Excel-like)
result = df.with_columns(
    flowfile_formulas=[
        "[price] * [quantity]",                    # Simple multiplication
        "[price] * (1 - [discount])",              # With parentheses
        "if [quantity] > 75 then 'High' else 'Low' endif",      # Conditional
        "round([price] * [discount], 2)"
    ],
    output_column_names=["revenue", "discounted_price", "volume_category", "discount_amount"],
    description="Calculate derived metrics"
)

# Mix formulas with regular Polars expressions
output = df.with_columns([
    ff.col("price").round(0).alias("price_rounded")  # Polars style
]).with_columns(
    flowfile_formulas=["[price_rounded] * [quantity]"],  # Formula style
    output_column_names=["estimated_revenue"]
)

Arithmetic Operations

# Standard expressions
df = df.with_columns([
    (ff.col("price") * ff.col("quantity")).alias("revenue"),
    (ff.col("price") * 1.1).alias("price_with_tax"),
    (ff.col("total") / ff.col("count")).alias("average")
])

# Formula syntax
df = df.with_columns(
    flowfile_formulas=[
        "[price] * [quantity]",
        "[price] * 1.1",
        "[total] / [count]"
    ],
    output_column_names=["revenue", "price_with_tax", "average"]
)

Formula Syntax

Use [column_name] in formula strings for simpler syntax when supported by the operation.

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Want to see more about the Flowfile python api? Check out the reference documentation.