1. About StaticFrame¶
The StaticFrame library consists of the Series and Frame, immutable data structures for one- and two-dimensional calculations with self-aligning, labelled axes. StaticFrame offers an alternative to Pandas. While many interfaces for data extraction and manipulation are similar to Pandas, StaticFrame deviates from Pandas in many ways: all data is immutable, and all indices must be unique; all vector processing uses NumPy, and the full range of NumPy data types is preserved; the implementation is concise and lightweight; consistent naming and interfaces are used throughout; and flexible approaches to iteration and function application, with built-in options for parallelization, are provided.
The current release of StaticFrame is an alpha release, meaning that interfaces may change, functionality may be incomplete, and there may be significant flaws. Please assist in development by reporting any bugs or request any missing features.
StaticFrame is not a drop-in replacement for Pandas. While some conventions and API components are directly borrowed from Pandas, some are completely different, either by necessity (due to the immutable data model) or by choice (offering more uniform, less redundant, and more explicit interfaces). As StaticFrame does not support in-place mutation, architectures that made significant use of mutability in Pandas will require refactoring.
StaticFrame is lightweight. It has few dependencies (Pandas is not a dependency). The core library is less than 10,000 lines of code, less than 5% the size of the Pandas code base 1.
StaticFrame does not aspire to be an all-in-one framework for all aspects of data processing and visualization. StaticFrame focuses on providing efficient and powerful data structures with consistent, clear, and stable interfaces.
StaticFrame aspires to have comparable or better performance than Pandas. While this is already the case for some core operations (See Performance), some important functions are far more performant in Pandas (such as reading delimited text files via
pd.read_csv). StaticFrame provides easy conversion to and from Pandas to bridge needed functionality or performance.
StaticFrame does not implement its own types or numeric computation routines, relying entirely on NumPy. NumPy offers desirable stability in performance and interface. For working with SciPy and related tools, StaticFrame exposes easy access to NumPy arrays.
static_frame.Frame store data in immutable NumPy arrays. Once created, array values cannot be changed. StaticFrame manages NumPy arrays, setting the
ndarray.flags.writeable attribute to False on all managed and returned NumPy arrays.
>>> import static_frame as sf >>> import numpy as np >>> s = sf.Series((67, 62, 27, 14), index=('Jupiter', 'Saturn', 'Uranus', 'Neptune'), dtype=np.int64) >>> s #doctest: +NORMALIZE_WHITESPACE <Series> <Index> Jupiter 67 Saturn 62 Uranus 27 Neptune 14 <<U7> <int64> >>> s['Jupiter'] = 68 Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'Series' object does not support item assignment >>> s.iloc = 68 Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'InterfaceGetItem' object does not support item assignment >>> s.values = 68 Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: assignment destination is read-only
To mutate values in a
Frame, a copy must be made. Convenient functional interfaces to assign to a copy are provided, using conventions familiar to NumPy and Pandas users.
>>> s.assign['Jupiter'](69) <Series> <Index> Jupiter 69 Saturn 62 Uranus 27 Neptune 14 <<U7> <int64> >>> s.assign['Uranus':](s['Uranus':] - 2) <Series> <Index> Jupiter 67 Saturn 62 Uranus 25 Neptune 12 <<U7> <int64> >>> s.assign.iloc[[0, 3]]((68, 11)) <Series> <Index> Jupiter 68 Saturn 62 Uranus 27 Neptune 11 <<U7> <int64>
Immutable data has the overwhelming benefit of providing the confidence that a client of a
Frame cannot mutate its data. This removes the need for many unnecessary copies, and forces clients to only make copies when absolutely necessary.
There is no guarantee that using immutable data will produce correct code or more resilient and robust libraries. It is true, however, that using immutable data removes countless opportunities for introducing flaws in data processing routines and libraries.
The ideas behind StaticFrame developed out of years of work with Pandas and related tabular data structures by the Investment Systems team at Research Affiliates, LLC. In May of 2017 Christopher Ariza proposed the basic model to the Investment Systems team and began implementation. The first public release was in May 2018.
The following presentations and interviews describe StaticFrame in greater depth.
PyCon US 2019, lightning talk (starting at 53:00): https://pyvideo.org/pycon-us-2019/friday-lightning-talksbreak-pycon-2019.html
Talk Python to Me, interview: https://talkpython.fm/episodes/show/204/staticframe-like-pandas-but-safer
These members of the Investment Systems team have contributed greatly to the design of StaticFrame:
Thanks also for additional contributions from GitHub users.