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Context manager for handling temporary variables in Jupyter Notebook, IPython, etc.

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Most Recent Stable Release:


Use Jupyter Notebook?

Constantly run into problems from obsolete variables hanging around in the namespace?

tempvars can help.

Developing in Jupyter notebooks can sometimes be frustrating. For example, it’s aggravating to debug a worksheet for half an hour, only to discover that a carried-over variable name was hanging around in the notebook namespace and causing problems. Or, to open a notebook that “worked fine” the last time it was used, but only because of random, obsolete variables that happened to be lingering in the namespace. Wrapping notebook code in functions/classes is an effective way of avoiding these sorts of problems, but it’s rarely effective or efficient to do this in the initial exploratory phase of in-notebook development.

TempVars is a context manager that helps to avoid these pitfalls by clearing selected identifiers from the namespace for the duration of its scope, then restoring them afterwards (or not, if desired). Further, any variables created within the managed context that match the TempVars filtering criteria are removed from the namespace upon exiting, ensuring these values do not spuriously contribute to following code. For convenience, all variables removed from the namespace at entry and exit are stored for later reference (see example code below).

Due to the way Python handles non-global scopes, TempVars can only be used at the global scope. Any attempt to use TempVars in non-global contexts will result in a RuntimeError. Viable use-cases include Jupyter notebooks, the IPython and basic Python REPLs, and the outermost scope of executed and imported modules. Preliminary testing indicates it also works with cauldron-notebook, though it may be less helpful there due to its step-local scoping paradigm (shared values must be passed around via cauldron.shared).

After installing with pip install tempvars, import as:

>>> from tempvars import TempVars

For typical use in a Jupyter notebook cell, the recommended approach is to pick a marker to use on all variables that are to be temporary, and enclose the entire cell in a TempVars context. For example, one could prefix all temporary variables with t_ and make use of the starts argument:

>>> foo = 5
>>> with TempVars(starts=['t_']):
...     print(foo)
...     t_bar = 8
...     print(foo + t_bar)
>>> print('t_bar' in dir())

A similar effect can be achieved with a suffix such as _t and the ends argument.

Temporary variable masking can also be introduced to existing code in a more selective fashion via the names argument:

>>> foo = 5
>>> bar = 7
>>> with TempVars(names=['bar']):
...     print(foo)
...     print('bar' in dir())
>>> print(foo * bar)

Setting the restore argument to False instructs TempVars not to restore any masked variables to the namespace after its context exits. This is potentially useful to avoid carryover of common helper variables (arr, df, i, etc.) to downstream cells that may have been created earlier in a notebook:

>>> for k in ['foo', 'bar']:
...     pass
>>> print(k)
>>> with TempVars(names=['k'], restore=False):
...     print('k' in dir())
>>> print('k' in dir())

TempVars stores the values of variables it removes from the namespace, should they need to be accessed. A bound with/as statement must be used in order to enable this:

>>> foo = 5
>>> with TempVars(names=['foo']) as tv:
...     print('foo' in dir())
...     print(tv.stored_nsvars['foo'])
...     foo = 8
...     print(foo)
>>> print(foo)
>>> print(tv.retained_tempvars['foo'])

Available on PyPI: pip install tempvars.

Full documentation at Read the Docs.

Source on GitHub. Bug reports and feature requests are welcomed at the Issues page there. If you like the idea of an enhancement already in the Issues list, please comment to say so; it’ll help with prioritization.

Copyright (c) Brian Skinn 2017-2018

License: The MIT License. See LICENSE.txt for full license terms.

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