Popular recipes tagged "expressions" but not "evaluation"

Classifying characters using nested conditional expressions (Python)

2017-04-27T21:26:00-07:00

Python recipe 580792 by Vasudev Ram (characters, classification, conditional_expressions, expressions, join, lambda, map).

Python has a feature called conditional expressions, similar to C's ternary operator. For example:

print n, 'is odd' if n % 2 == 1 else 'is even'

Here, the conditional expression is this part of the print statement above:

'is odd' if n % 2 == 1 else 'is even'

This expression evaluates to 'is odd' if the condition after the if keyword is True, and evaluates to 'is even' otherwise.

The Python Language Reference section for conditional expressions shows that they can be nested. This recipe shows that we can use nested conditional expressions (within a return statement in a user-defined function) to classify characters into lowercase letters, uppercase letters, or neither.

It also shows how to do the same task using map, lambda and string.join, again with a nested conditional expression, but without using return or a user-defined function.

Evaluator 2.0 (Python)

2010-11-25T18:24:09-08:00

Python recipe 577469 by Stephen Chappell (evaluator, expressions, math, parser).

This is a complete rewrite of recipe 576790. While aiming to maintain similar functionality and continuing its implementation for self-academic purposes, a much cleaner parser / tokenizer and operator execution engine were developed. A slightly different math syntax is supported in this version, but it is arguably better and more capable than it previously was. Base prefixes are a feature now supported, and the single downgrade is calculating with integers instead of floats.

Calculations with error propagation, and semi-formal expressions (Python)

2010-01-15T06:08:56-08:00

Python recipe 576721 by Eric-Olivier LE BIGOT (calculus, error_propagation, expressions, formal_calculations, lazy_evaluation, uncertainties). Revision 22.

Do not use this module, but use instead the more powerful uncertainties.py module.

Module for performing calculations with error propagation, such as (1 +- 0.1) * 2 = 2 +- 0.2. Mathematical operations (addition, etc.), operations defined in the math module (sin, atan,...) and logical operations (<, >, etc.) can be used.

Correlations between parts of an expression are correctly taken into account (for instance, the error on "x-x" is strictly zero).

Code written for floats should directly work with the numbers with uncertainty defined here, without much need for modifications.

The module also contains a class that represents non-evaluated mathematical expressions. This class is used for performing the differentiation required by the error propagation calculation, but can be used on its own, for manipulating "semi-formal" expressions whose variables can be accessed.