Data Types

Flowfile supports all Polars data types. This page covers the most commonly used types and conversions.

Supported Types

Type	Description	Example
Int8, Int16, Int32, Int64	Signed integers	123
UInt8, UInt16, UInt32, UInt64	Unsigned integers	456
Float32, Float64	Floating point	12.34
Boolean	True/False values	True
Utf8 / String	Text data	"hello"
Date	Date without time	2024-01-15
Datetime	Date with time	2024-01-15 14:30:00
Time	Time without date	14:30:00
Duration	Time delta	2 days
List	Nested arrays	[1, 2, 3]
Struct	Nested objects	{"a": 1, "b": 2}

Type Casting

import flowfile as ff

df = ff.FlowFrame({
    "int_col": [1, 2, 3],
    "str_col": ["10", "20", "30"],
    "date_str": ["2024-01-01", "2024-01-02", "2024-01-03"]
})

# Cast types
df = df.with_columns([
    ff.col("int_col").cast(ff.Float64).alias("float_col"),
    ff.col("str_col").cast(ff.Int32).alias("parsed_int"),
    ff.col("date_str").str.strptime(ff.Date, "%Y-%m-%d").alias("date_col")
])

Schema Inspection

df.schema returns a Polars Schema — a dict-like mapping of column name to dtype, resolved without reading any data. Access dtypes by column name, not by position.

# Get the schema without processing data
print(df.schema)
# Schema([('int_col', Int64), ('str_col', String)])

# Look up a specific column's type by name
print(df.schema["int_col"])
# Int64

# Enumerate columns and types
for name, dtype in df.schema.items():
    print(name, dtype)

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