{"id":46108,"date":"2020-05-22T09:09:00","date_gmt":"2020-05-22T13:09:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=46108"},"modified":"2022-11-21T09:45:34","modified_gmt":"2022-11-21T14:45:34","slug":"python-function-tutorial-part-viii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/python-function-tutorial-part-viii\/","title":{"rendered":"Python Function Tutorial \u2013 Part VIII"},"content":{"rendered":"\n

See<\/em> <\/strong>Python functions with variable length arguments<\/a><\/em> to get up-to-date in this tutorial.<\/em><\/p>\n\n\n\n

DocStrings<\/strong><\/p>\n\n\n\n

Python has a nifty feature called documentation string<\/em>, usually referred to by its shorter name docstrings<\/em>. This is an important but not required tool that should be used every time we write a program since it helps to document the program better and makes it easier to understand.<\/p>\n\n\n\n

Docstrings are written within triple single\/double quotes just after definition header. They are written on the first logical line of a python function. Docstrings are not limited to functions only; they also apply to modules and classes. The convention followed for a docstring is a multi-line string where the first line starts with a capital letter and ends with a dot. The second line is blank followed by any detailed explanation starting from the third line. It is strongly advised to follow this convention for all docstrings. Let’s see this in practice with the help of an example:<\/p>\n\n\n\n

\ndef power(x, y):
\n “””Equivalent to x**y or built-in pow() with two arguments.
\n x and y should be numerical values else an appropriate error will be thrown for incompatible types.
\n Parameters:
\n x (int or float): Base value for the power operation.
\n y (int or float): Power to which base value should be raised.
\n Returns:
\n int or float: It returns x raised to the power of y.
\n “””
\n try:
\n return x ** y
\n except Exception as e:
\n print(e)\n\n<\/p>\n\n\n\n

Preview(opens in a new tab)<\/p>\n\n\n\n

The Python function power<\/code> defined above returns the raised value of the argument x<\/code> powered to y<\/code>. The thing of our interest is the docstring written within '''<\/code> which documents the function. We can access a docstring of any function using the __doc__<\/code> attribute (notice the double underscores<\/em>) of that function. The docstring for the power<\/code> function can be accessed with the following code: <\/em><\/p>\n\n\n\n

\nprint(power.__doc__)
\n\n<\/p>\n\n\n\n

And the output is shown below:<\/p>\n\n\n\n

\nEquivalent to x**y or built-in pow() with two arguments.
\nx and y should be numerical values else an appropriate error will be thrown for incompatible types.
\nParameters:
\nx (int or float): Base value for the power operation.
\ny (int or float): Power to which base value should be raised.
\nReturns:
\nint or float: It returns x raised to the power of y.\n\n<\/p>\n\n\n\n

We have already seen the indirect usage of docstrings in previous sections. When we use a python function help<\/code> in Python, it will show up the docstring. What it does is fetch the __doc__<\/code> attribute of that function and displays it in a neat manner. If we ask for the help on the user defined power<\/code> using the print(help(power))<\/code>, Python will return the same output as shown above that we got using the print(power.__doc__)<\/code>.<\/p>\n\n\n\n

In the next installment, the author will provide code for Nested Python functions and non-local variable<\/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 goes over an important concept, DocStrings, which helps to document Python code better and makes it easier to understand.<\/p>\n","protected":false},"author":328,"featured_media":40540,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341,344],"tags":[851,5615,806,5614,852,595],"contributors-categories":[13654],"class_list":{"0":"post-46108","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-asia-pacific","12":"category-quant-development","13":"category-quant-regions","14":"tag-algo-trading","15":"tag-argument","16":"tag-data-science","17":"tag-lambda","18":"tag-machine-learning","19":"tag-python","20":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nPython Function 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