Expressions

FlowFrame methods accept standard Polars expressions — ff.col, operators, ff.when, and the rest of the expression API you already know from Polars. Expressions are the default and most powerful way to express transformations, covering the entire Polars API.

Column references and arithmetic

import flowfile as ff

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"),
])

Conditional logic

df = df.with_columns(
    ff.when(ff.col("quantity") > 75)
    .then(ff.lit("High"))
    .otherwise(ff.lit("Low"))
    .alias("volume_category")
)

Filtering

df = df.filter(ff.col("price") > 100)
df = df.filter(ff.col("status") != "cancelled", description="Drop cancelled orders")

Namespaces

Polars expression namespaces work as expected:

df = df.with_columns([
    ff.col("name").str.to_uppercase().alias("name_upper"),
    ff.col("order_date").dt.year().alias("order_year"),
])

Because expressions stay lazy, they compose into a single optimized query plan — see FlowFrame and FlowGraph.

Looking for the Excel-like [column] syntax?

That is the Flowfile formula language — a separate, simpler syntax shared with the visual editor. See Formulas in Python for the FlowFrame methods that accept it, and the Formula Language guide for the language itself.

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Want to see more of the Flowfile Python API? Check out the reference documentation.