{"id":40530,"date":"2020-04-03T08:40:12","date_gmt":"2020-04-03T12:40:12","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=40530"},"modified":"2022-11-21T09:45:17","modified_gmt":"2022-11-21T14:45:17","slug":"python-function-tutorial-part-v","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/python-function-tutorial-part-v\/","title":{"rendered":"Python Function Tutorial \u2013 Part V"},"content":{"rendered":"\n

See Part I<\/a>, Part II<\/a> <\/em>, Part III<\/a> and Part IV<\/a> of this installment to learn more about Python functions.<\/em><\/p>\n\n\n\n

Python functions with multiple arguments and a return<\/code> statement<\/strong><\/p>\n\n\n\n

Both versions of the greet<\/code> Python functions defined above were actually straightforward in terms of functionality that they perform. One more functionality that functions are capable of performing is to return a value to the calling statement using the keyword return<\/code>. Consider a function that takes several parameters, performs some mathematical calculation on it and returns the output. For example:<\/p>\n\n\n\n

\n# Function with two parameters ‘a’ and ‘b’
\ndef add(a, b):
\n “””Computes the addition and returns the result.
\n It does not implement the print statement.”””
\n result = a + b # Computes addition
\n return result # Returns the result variable\n<\/p>\n\n\n\n

This user defined function add<\/code> takes two parameters a<\/code> and b<\/code>, sums them together and assigns its output to a variable result<\/code> and ultimately returns the variable to calling statement as shown below:<\/p>\n\n\n\n

\n# Calling the add function
\nx = 5
\ny =
\nprint(f’The addition of {x} and {y} is {add(x, y)}.’)\n<\/p>\n\n\n\n

We call the function add<\/code> with two arguments x<\/code> and y<\/code> (as the function definition has two parameters) initialized with 5<\/code> and 6<\/code> respectively, and the addition returned by the function gets printed via the print<\/code> statement as shown below:<\/p>\n\n\n\n

\nThe addition of 5 and 6 is 11.\n<\/p>\n\n\n\n

Similarly, Python functions can also return multiple values based on the implementation. The following function demonstrates the same.<\/p>\n\n\n\n

\n# Function definition
\ndef upper_lower(x):
\n “””Returns the upper and lower version of the string.
\n The value must be a string, else it will result in an error.
\n This function does not implement any error handling mechanism.”””
\n upper = x.upper() # Convert x to upper string
\n lower = x.lower() # Convert x to lower string
\n return upper, lower # Return both variables upper and lower\n<\/p>\n\n\n\n

The above upper_lower<\/code> function takes one argument x<\/code> (a string) and converts it to their upper and lower versions. Let us call it and see the output.<\/p>\n\n\n\n

Note: The function upper_lower implicitly assumes to have a string as a parameter. Providing an integer or float value as an argument while calling will result in an error.<\/em><\/p>\n\n\n\n

\n# Calling the function
\nupper, lower = upper_lower(‘Python’)
\n# Printing output
\nprint(upper)
\nPYTHON
\nprint(lower)
\npython\n<\/p>\n\n\n\n

Here, the call to upper_lower<\/code> function has been assigned to two variables upper<\/code> and lower<\/code> as the function returns two values which will be unpacked to each variable respectively and the same can be verified in the output shown above.<\/p>\n\n\n\n

In the next installment, the author will discuss<\/em> Python functions with default arguments.<\/em><\/p>\n\n\n\n

Visit https:\/\/www.quantinsti.com\/<\/a> for ready-to-use Python functions as applied in trading and data analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"

QuantInsti demonstrates how to write Python functions with multiple arguments and a return statement. Get ready-to-use Python scripts and code along!<\/p>\n","protected":false},"author":328,"featured_media":40540,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,349,338,350,341,344],"tags":[851,5615,806,5614,852,595],"contributors-categories":[13654],"class_list":["post-40530","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-python-development","category-ibkr-quant-news","category-quant-asia-pacific","category-quant-development","category-quant-regions","tag-algo-trading","tag-argument","tag-data-science","tag-lambda","tag-machine-learning","tag-python","contributors-categories-quantinsti"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nPython Function Tutorial \u2013 Part V | IBKR Quant<\/title>\n<meta name=\"description\" content=\"QuantInsti demonstrates how to write Python functions with multiple arguments and a return statement. 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