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import unispark as us
# Create from dictionary
df = us.DataFrame({
"name": ["Alice", "Bob", "Charlie"],
"age": [25, 30, 35],
"city": ["NYC", "SF", "LA"]
})
# Or from file
df = us.read_parquet("data.parquet")
df = us.read_csv("data.csv")# Filter, group, aggregate - familiar API
result = df.filter("age > 25") \
.groupby("city") \
.agg(us.count("name"), us.mean("age")) \
.sort("city")
# View results
print(result.show())# Run SQL queries on your data
result = us.sql("""
SELECT city, COUNT(*) as count, AVG(age) as avg_age
FROM users
WHERE age > 25
GROUP BY city
ORDER BY count DESC
""")Everything you need for any data transformation.
Select, filter, sort, group, aggregate. All the operations you use daily, running faster.
Write SQL queries on your data. Mix DataFrame API and SQL in the same workflow.
Inner, left, right, full outer joins. Union, intersect, except. Combine data from anywhere.
Rank, row number, lag, lead, running totals. Complex analytics made simple.
Extract, transform, and load data from multiple sources.
# Load from multiple sources
orders = us.read_parquet("orders.parquet")
customers = us.read_csv("customers.csv")
# Join and transform
result = orders.join(customers, "customer_id") \
.filter("order_date >= '2024-01-01'") \
.groupby("customer_segment") \
.agg(us.sum("amount"))
# Save results
result.to_parquet("output.parquet")Calculate running totals and rankings.
# Running total by category
result = df.with_column(
"running_total",
us.sum("sales").over(
partition_by="category",
order_by="date"
)
)
# Rank within groups
result = df.with_column(
"rank",
us.row_number().over(
partition_by="department",
order_by="salary"
)
)Learn how to create interactive dashboards from your data in minutes.
Create automated data pipelines that run reliably, every time.
Tips and techniques for handling datasets that don't fit in memory.
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