The Performance Advantage of No-Copy DataFrame Operations

• static-frame

  8 403 9.9 Python

  Immutable and statically-typeable DataFrames with runtime type and data validation

  

A NumPy array is a Python object that stores data in a contiguous C-array buffer. The excellent performance of these arrays comes not only from this compact representation, but also from the ability of arrays to share "views" of that buffer among many arrays. NumPy makes frequent use of "no-copy" array operations, producing derived arrays without copying underling data buffers. By taking full advantage of NumPy's efficiency, the StaticFrame DataFrame library offers orders-of-magnitude better performance than Pandas for many common operations.

• frame-fixtures

  1 6 0.0 Python

  Use compact expressions to create diverse, deterministic DataFrame fixtures with StaticFrame

  

To compare performance, we will use the FrameFixtures library to create two DataFrames of 10,000 rows by 1,000 columns of heterogeneous types. For both we can convert the StaticFrame Frame into a Pandas DataFrame.

• InfluxDB

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  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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