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Scientific Computing with Python - Second Edition

You're reading from  Scientific Computing with Python - Second Edition

Product type Book
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Pages 392 pages
Edition 2nd Edition
Languages
Authors (3):
Claus Führer Claus Führer
Profile icon Claus Führer
Jan Erik Solem Jan Erik Solem
Olivier Verdier Olivier Verdier
View More author details

Table of Contents (23) Chapters

Preface Getting Started Variables and Basic Types Container Types Linear Algebra - Arrays Advanced Array Concepts Plotting Functions Classes Iterating Series and Dataframes - Working with Pandas Communication by a Graphical User Interface Error and Exception Handling Namespaces, Scopes, and Modules Input and Output Testing Symbolic Computations - SymPy Interacting with the Operating System Python for Parallel Computing Comprehensive Examples About Packt Other Books You May Enjoy References

16.2.5 Lambda functions

In Section 7.7: Anonymous functions, we saw how to define so-called anonymous functions in Python. The SymPy counterpart is the command Lambda. Note the difference; lambda is a keyword while Lambda is a constructor.

The command Lambda takes two arguments, the symbol of the function's independent variable, and a SymPy expression to evaluate the function.

Here is an example that defines air resistance (also called drag) as a function of speed:

C,rho,A,v=symbols('C rho A v')
# C drag coefficient, A coss-sectional area, rho density
# v speed
f_drag = Lambda(v,-Rational(1,2)*C*rho*A*v**2)

f_drag is displayed as a graphical expression:

This function can be evaluated in the usual way by providing it with an argument:

x = symbols('x')
f_drag(2)
f_drag(x/3)

Which results in the expressions:

It is also possible to create functions in several variables by just providing the first parameter...

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