You clearly see the result of each list and the operations that were described in them! ![]() Remember in maths, the common ways to describe lists (or sets, or tuples, or vectors) are: S = Luckily, Python has the solution for you: it offers you a way to implement a mathematical notation to do this: list comprehension. List Comprehension in Python: The Mathematics ![]() List comprehensions in Python are constructed as follows: list_variable = īut how do you get to this formula-like way of building and using these constructs in Python? Let's dig a little bit deeper. With the recap of the Python lists fresh in mind, you can easily see that defining and creating lists in Python can be a tiresome job: typing in all the values separately can take quite some time and you can easily make mistakes. Tip: build your own list in the IPython shell that is contained within the above DataCamp Light chunk! Take a look at the following example of a list:ĮyJsYW5ndWFnZSI6InB5dGhvbiIsInNhbXBsZSI6IiMgQXNzaWduIGludGVnZXIgdmFsdWVzIHRvIGBhYCBhbmQgYGJgXG5hID0gNFxuYiA9IDlcblxuIyBDcmVhdGUgYSBsaXN0IHdpdGggdGhlIHZhcmlhYmxlcyBgYWAgYW5kIGBiYCBcbmNvdW50X2xpc3QgPSBbMSwyLDMsYSw1LDYsNyw4LGIsMTBdIn0= The values that you put in a Python list can be of any data type, even lists! You can then assign your list to a variable. Now, on a practical note: you build up a list with two square brackets Inside these brackets, you'll use commas to separate your values. Tip: if you'd like to know more, test or practice your knowledge of Python lists, you can do so by going through the most common questions on Python lists here. This means that they can be iterated Other examples of sequences are Strings, tuples, or sets. This makes lists in Python "sequence types", as they behave like a sequence. Important to note here is that lists are ordered collections of items or objects. In fact, these values don't need to be of the same type: they can be a combination of boolean, String, integer. A list in Python is different from, for example, int or bool, in the sense that it's a compound data type: you can group values together in lists. Other data structures that you might know are tuples, dictionaries and sets. Lists are one of the four built-in data structures in Python. Instead, you store all of these values in a Python list. However, in data science, you'll often work with many data points, which will make it hard to keep on storing every value in a separate variable. You have saved each and every value in a separate variable: each variable represents a single value. If you're also interested in tackling list comprehensions together with iterators and generators? Check out DataCamp's Python Data Science Toolbox course!īy now, you will have probably played around with values that had several data types. Lastly, you'll dive into nested list comprehensions to iterate multiple times over lists.When you've got the basics down, it's also time to fine-tune your list comprehensions by adding conditionals to them: you'll learn how you can include conditionals in list comprehensions and how you can handle multiple if conditions and if-else statements. ![]() ![]() You'll not only read about this, but you'll also make some exercises! Next, you'll dive into Python lists comprehensions: you'll learn more about the mathematics behind Python lists, how you can construct list comprehensions, how you can rewrite them as for loops or lambda functions.You'll first get a short recap of what Python lists are and how they compare to other Python data structures.This tutorial will go over this last topic: One other way to do this is by using list comprehensions. You can easily use a lambda function or a for loop As you well know, there are multiple ways to go about this. When doing data science, you might find yourself wanting to read lists of lists, filtering column names, removing vowels from a list or flattening a matrix.
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