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Python API Tutorials

Learn to build powerful data pipelines with code through practical, hands-on examples.

Available Tutorials

Building Flows with Code

The complete guide to creating data pipelines programmatically while maintaining visual compatibility.

You'll learn: - Creating pipelines with the FlowFrame API - Using Polars-compatible operations - Automatically generating visual graphs - Switching between code and visual editing

Perfect for: - Python developers new to Flowfile - Data scientists wanting reproducible pipelines - Anyone preferring code over drag-and-drop

Coming Soon

Data Pipeline Patterns

Common patterns for ETL, data cleaning, and analysis.

Performance Optimization

Advanced techniques for handling large datasets efficiently.

Integration Examples

Connecting Flowfile with pandas, scikit-learn, and other tools.

Tutorial Style

Our Python tutorials focus on: - Real-world examples - Practical use cases you'll actually encounter - Code-first approach - Everything done programmatically - Visual integration - How to leverage the UI when helpful - Best practices - Production-ready patterns

Quick Examples

ETL Pipeline

import flowfile as ff

# Extract
raw_data = ff.read_csv("sales.csv")

# Transform
transformed = (
    raw_data
    .filter(ff.col("amount") > 0)
    .with_columns([
        ff.col("date").str.strptime(ff.Date, "%Y-%m-%d")
    ])
    .group_by("region")
    .agg(ff.col("amount").sum())
)

# Load
transformed.write_parquet("output.parquet")

Data Validation

# Check for data quality issues
df = ff.read_csv("input.csv")

# Find duplicates
duplicates = df.group_by("id").agg(
    ff.count().alias("count")
).filter(ff.col("count") > 1)

# Find nulls
null_counts = df.select([
    ff.col(c).is_null().sum().alias(f"{c}_nulls")
    for c in df.columns
])

Resources

Want more tutorials? Let us know what you'd like to see in our GitHub Discussions!