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Summary Python Data Science Toolbox

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User-defined functions Multiple Parameters and Return Values Scope and user-defined functions Nested functions Default and flexible arguments Lambda functions Introduction to error handling Introduction to iterators Playing with iterators Using iterators to load large files into memory List comprehensions Advanced comprehensions Introduction to generator expressions Wrapping up comprehensions and generators Using Python generators for streaming data Using pandas' read_csv iterator for streaming data Basics Cheat Sheet

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User-defined
functions
P Y T H O N D ATA S C I E N C E T O O L B O X ( PA R




Hugo Bowne-Anderson
Instructor

,You’ll learn:
De ne functions without parameters

De ne functions with one parameter

De ne functions that return a value

Later: multiple arguments, multiple return values

,Built-in functions
str()

x = str(5)


print(x)


'5'


print(type(x))


<class 'str'>

, Defining a function
def square(): # <- Function header
new_value = 4 ** 2 # <- Function body
print(new_value)
square()


16

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Written in
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SUMMARY

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