Dictionaries are great data-types for storing <code>values </code>with an attribute field known as the <code>key </code>, in so-called <code>key-value pairs </code>. When extracting key-value pairs from dictionaries, avoid running into <code>KeyError </code>exceptions with the <code>.get </code>method instead of the more traditional <code>[key] </code>method. The <code>.get </code>method provides a default value if the key is not present.In <code>line 6-10 </code>our program stops running because of the <code>KeyError </code>exception, but in <code>line 11-14 </code>our program continues, using the default <code>'undefined' </code>string that is set in <code>line 12 </code>as the second argument of the <code>.get </code>method.When you have the possibility of assigning a <code>default </code>value for accessing key-value pairs in a dictionary, always try to use the <code>.get </code>method to avoid your program from stopping prematurely, especially when in production.

.get  instead of  [key]  for dictionary iterations

I love the <code>zip </code>function and it has saved me countless (nested) loops. I use it mostly for iterating over two data types at the same time, where I need the indexes to be equal.You can do this with any data type or generator. For instance, you could create dictionaries without looping over the separate lists, as such:Later on, we’ll introduce the <code>*args </code>operator, which in combination with the <code>zip function </code>is very powerful!

Loop two iterators with the zip function

the <code>* </code>prefix operator was added to the multiple assignment syntax, allowing us to unpack the remaining items in an iterable.The <code>** </code>operator does something similar, but with keyword arguments. The <code>** </code>operator allows us to take a dictionary of key-value pairs and unpack it into keyword arguments in a function call.

Multiple assignment with *  and  **

List, tuple, and dictionary comprehensions are ways to code more efficiently: do the same in fewer lines of code.Both <code>lines 2-4 </code>and <code>lines 13-15 </code>are compressed in single-line expressions in <code>line 6 </code>and <code>line 17 </code>. This saves up unnecessary loops and creates a cleaner codebase.

List comprehensions

Hallelujah! That is what I thought when I learned about the Python 3.6+ update that includes a new way of formatting strings: the Python formatted string literal. String formatting in Python has come a long way.F-strings consider everything within { curly brackets } as an expression, and with these expressions, we can do simple arithmetic but also functions and method calls!

String formatting with f-strings

Making your Python code as re-usable as possible should be one of your main concerns. But what if you’re working on a Unix platform and your colleague is working on Windows?The path delimiter on Windows is <code>\ </code>, but on my Linux or Mac system, it is <code>/ </code>. Avoid dealing with these nuances by using the built-in <code>os </code>library:

Platform-independent directory delimiters

Unpacking variables are probably most used for functions that return multiple variables, such as in the example below.But it is also useful for data types that contain multiple items. The only important notion here is that, if not otherwise defined, variable unpacking results in <code>tuples </code>.The <code>_ </code>operator is an unnamed variable , essentially a variable that you’re not interested in and won’t be doing anything with, for instance in the following case:

Variable unpacking

Here are the seven tips and code bites that I use every day in my work as a data scientist.

Platform-independent directory delimiters

Similar ideas to Platform-independent directory delimiters

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