Core Features¶
Essential DataFrame operations, filtering, grouping, and data manipulation powered by pandas and Dask with optional Rust acceleration.
Overview¶
- 📊 DataFrame Operations - Full pandas/Dask API compatibility
- 🔍 Advanced Filtering - SQL-like query expressions
- 📈 Aggregations - groupby, pivot, statistics
- 🔄 Joins & Merges - Efficient data combining
- ⚡ Rust Fast-Paths - 8x faster metadata operations
Quick Start¶
import parquetframe as pf
# Read data
df = pf.read_parquet("data.parquet")
# Filter
active = df[df['status'] == 'active']
# Group and aggregate
summary = df.groupby('category').agg({'value': ['sum', 'mean', 'count']})
# Join
result = pf.merge(df1, df2, on='id', how='left')