Python dictionary comprehension if else

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Python dictionary comprehension if else

NEWBEDEVPythonJavascriptLinuxCheat sheet List/Dictionary Comprehension is an elegant one-liner to iterate over a List/Dictionary. They allow us to create new objects based on the already existing ones. The syntax for both List and Dictionary Comprehension are pretty similar and although I¡¯ll be

discussing both, I¡¯ll use List comprehension to talk about nest comprehension and conditional comprehension. The ¡®Normal¡¯ way of iterating over a list using For Loops Let¡¯s assume we have a list of numbers and we want to create a new list that stores the squares of the numbers. This is how we would do

it using for loops List Comprehension Using List Comprehension we could write lines 2-4 in a single line. As you can see, it looks much cleaner and elegant. Dictionary Comprehension Let¡¯s assume we want to create a dictionary that stores the index as a key and the value at that index as the value. List

Comprehension with an If Condition Let¡¯s say we want to store the square of the number only if it¡¯s an even number. We can simply add an if statement after the iterable List Comprehension with If/Else Condition. Now, suppose we want to store the squares of even numbers and the cubes of odd

numbers. The syntax is slightly different as compared to list comprehension with if statement. Nested List Comprehension We can have an outer for loop with an inner for loop. The first for loop would be the outer for loop and the second for loop would be inner for loop. The expression (i,j) would evaluate

(0,0) , (0,1) , (0,2)¡­. since j is in the inner for loop. Some tips Don¡¯t overuse List/Dictionary Comprehension. It is a one-liner for a reason. Although it makes your code look clean, ensure it is readable and can be understood by other developersList comprehension doesn¡¯t call append during every iteration

and in general, they are faster than for loops. However, if you are using a complex function inside your list comprehension, it will affect performance. Dictionary Comprehension is a concise and memory-efficient way to create and initialize dictionaries in one line of Python code. It consists of two parts:

expression and context. The expression defines how to map keys to values. The context loops over an iterable using a single-line for loop and defines which (key,value) pairs to include in the new dictionary.The following example shows how to use dictionary comprehension to create a mapping from

women to man:men = ['Bob', 'Frank', 'Pete'] women = ['Alice', 'Ann', 'Liz'] # One-Liner Dictionary Comprehension pairs = {w:m for w, m in zip(women, men)} # Print the result to the shell print(pairs) # {'Bob': 'Alice', 'Frank': 'Ann', 'Pete': 'Liz'}Next, you¡¯ll dive into a short Python exercise to open and close your

knowledge gaps and strengthen your intuitive understanding.Interactive Python ShellExecute the following one-liner dictionary comprehension in the interactive code shell: Exercise: Change the code so that each value x maps to its cube x**3 for the first eleven values from 0 to 10 (inclusive)! Related

Article: Every Python master has also mastered the dictionary data structure. Check out our full tutorial on the Finxter blog here.Next, you¡¯ll dive even deeper into the powerful dictionary comprehension operator in a step-by-step manner!Python Dictionary Comprehension ZipDictionary comprehension lets

you create dictionaries in a clean, easy to understand and Pythonic manner. However, if you have two lists, you can create a dictionary from them using dict(zip()). names = ['Adam', 'Beth', 'Charlie', 'Dani', 'Ethan'] countries = ['Argentina', 'Bulgaria', 'Colombia', 'Denmark', 'Estonia'] dict_zip =

dict(zip(names, countries)) >>> dict_zip {'Adam': 'Argentina', 'Beth': 'Bulgaria', 'Charlie': 'Colombia', 'Dani': 'Denmark', 'Ethan': 'Estonia'} You can also do this using a for loop>>> new_dict = {} >>> for name, country in zip(names, countries): new_dict[name] = country >>> new_dict {'Adam': 'Argentina',

'Beth': 'Bulgaria', 'Charlie': 'Colombia', 'Dani': 'Denmark', 'Ethan': 'Estonia'}You initialize your dict and iterator variables with descriptive names. To iterate over both lists at the same time, you zip them together. You add key-value pairs as desired. This takes 3 lines.Using dictionary comprehension turns this

into one line!

dict_comp = {name: country for name, country in zip(names, countries)} >>> dict_comp {'Adam': 'Argentina', 'Beth': 'Bulgaria', 'Charlie': 'Colombia', 'Dani': 'Denmark', 'Ethan': 'Estonia'}Dictionary comprehensions are a bit like for loops in reverse. First, we state what we want our key-value

pairs to be. Then we use the same for loop and wrap everything in curly braces.Note that every comprehension can be written as a for loop. If you ever get results you don¡¯t expect, try it as a for loop to see what is happening.Here¡¯s a common mistakedict_comp_bad = {name: country for name in names

for country in countries} >>> dict_comp_bad {'Adam': 'Estonia', 'Beth': 'Estonia', 'Charlie': 'Estonia', 'Dani': 'Estonia', 'Ethan': 'Estonia'} What¡¯s going on? Let¡¯s write it as a for loop to see. First, we¡¯ll write it out to make sure we are getting the same, undesired, result.bad_dict = {} for name in names: for

country in countries: bad_dict[name] = country >>> bad_dict {'Adam': 'Estonia', 'Beth': 'Estonia', 'Charlie': 'Estonia', 'Dani': 'Estonia', 'Ethan': 'Estonia'} Now we¡¯ll use the bug-finder¡¯s best friend: the print statement!# Don't initialise dict to just check for loop logic for name in names: for country in countries:

print(name, country) Adam Argentina Adam Bulgaria Adam Colombia Adam Denmark Adam Estonia Beth Argentina Beth Bulgaria Beth Colombia ... Ethan Colombia Ethan Denmark Ethan EstoniaHere we remove the dictionary to check what is actually happening in the loop. Now we see the problem!

The issue is we have nested for loops. The loop says: for each name pair it with every country. Since dictionary keys can only appear, the value gets overwritten on each iteration. So each key¡¯s value is the final one that appears in the loop ¨C 'Estonia'.The solution is to remove the nested for loops and use

zip() instead.Python Nested Dictionaries with Dictionary Comprehensionsnums = [0, 1, 2, 3, 4, 5] dict_nums = {n: {'even': n % 2 == 0, 'square': n**2, 'cube': n**3, 'square_root': n**0.5} for n in nums} # Pretty print for ease of reading >>> pprint(dict_nums) {0: {'cube': 0, 'even': True, 'square': 0, 'square_root':

0.0}, 1: {'cube': 1, 'even': False, 'square': 1, 'square_root': 1.0}, 2: {'cube': 8, 'even': True, 'square': 4, 'square_root': 1.4142135623730951}, 3: {'cube': 27, 'even': False, 'square': 9, 'square_root': 1.7320508075688772}, 4: {'cube': 64, 'even': True, 'square': 16, 'square_root': 2.0}, 5: {'cube': 125, 'even': False,

'square': 25, 'square_root': 2.23606797749979}}This is where comprehensions become powerful. We define a dictionary within a dictionary to create lots of information in a few lines of code. The syntax is exactly the same as above but our value is more complex than the first example.Remember that our

key value pairs must be unique and so we cannot create a dictionary like the following>>> nums = [0, 1, 2, 3, 4, 5] >>> wrong_dict = {'number': num, 'square': num ** 2 for num in nums} File "", line 1 wrong_dict = {'number': num, 'square': num ** 2 for num in nums} ^ SyntaxError: invalid syntaxWe can only

define one pattern for key-value pairs in a comprehension. But if you could define more, it wouldn¡¯t be very helpful. We would overwrite our key-value pairs on each iteration as keys must be unique.If-Elif-Else Statements nums = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # Just the even numbers even_squares = {n: n

** 2 for n in nums if n % 2 == 0} # Just the odd numbers odd_squares = {n: n ** 2 for n in nums if n % 2 == 1} >>> even_dict {0: 0, 2: 4, 4: 16, 6: 36, 8: 64, 10: 100} >>> odd_dict {1: 1, 3: 9, 5: 25, 7: 49, 9: 81}We can apply if conditions after the for statement. This affects all the values you are iterating

over.You can also apply them to your key and value definitions. We¡¯ll now create different key-value pairs based on whether a number is odd or even.# Use parenthesis to aid readability different_vals = {n: ('even' if n % 2 == 0 else 'odd') for n in range(5)} >>> different_vals {0: 'even', 1: 'odd', 2: 'even', 3:

'odd', 4: 'even'} We can get really complex and use if/else statements in both the key-value definitions and after the for loop!# Change each key using an f-string {(f'{n}_cubed' if n % 2 == 1 else f'{n}_squared'): # Cube odd numbers, square even numbers (n ** 3 if n % 2 == 1 else n ** 2) # The numbers 010 inclusive for n in range(11) # If they are not multiples of 3 if n % 3 != 0} {'1_cubed': 1, '2_squared': 4, '4_squared': 16, '5_cubed': 125, '7_cubed': 343, '8_squared': 64, '10_squared': 100}It is relatively simple to do this using comprehensions. Trying to do so with a for loop or dict() constructor would be

much harder.Alternative FormulationsThe two statements are actually semantically identical:dict([(i, chr(65+i)) for i in range(4)])Is identical to:{i : chr(65+i) for i in range(4)}ExamplesLet¡¯s consider five examples of dictionary comprehensions to strengthen your understanding! The examples are improved

and simplified versions of the code given here.Dict Comprehension Example 1Problem: create a dict comprehension from a list of integers.# Example 1: # (key, value) --> (string, int) print({str(i):i for i in range(5)}) # {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4}Each integer i is first converted to a string using the str()

conversion function. The resulting mapping from str(i) to i is then stored in the dictionary for all integer values i in 0, 1, 2, 3, and 4.Dict Comprehension Example 2Problem: Given a list of fruit names as strings. Use dict comprehension to create a dictionary with the list elements as keys and their length as

values.# Example 2: fruits = ['apple', 'mango', 'banana','cherry'] d = {fruit:len(fruit) for fruit in fruits} print(d) # {'apple': 5, 'mango': 5, 'banana': 6, 'cherry': 6}You iterate over each fruit in the list. Then, you map each fruit string to its length using Python¡¯s built-in len() function that counts the number of

characters in the string.Dict Comprehension Example 3Problem: Create a dictionary with dict comprehension with list elements as keys and their capitalized variants as values.# Example 3: d = {fruit:fruit.capitalize() for fruit in fruits} print(d) # {'apple': 'Apple', 'mango': 'Mango', 'banana': 'Banana', 'cherry':

'Cherry'}The string.capitalize() function capitalizes only the first letter of the given string.Dict Comprehension Example 4Problem: Use the enumerate() function on a list to create tuples (i, x) for the position i of the element x. Use dict comprehension with list elements as keys and their indices as values.#

Example 4: d = {f:i for i,f in enumerate(fruits)} print(d) # {'apple': 0, 'mango': 1, 'banana': 2, 'cherry': 3}The enumerate(fruits) function returns the (index, element) pairs of the iterable fruits. You catch the former in variable i and the latter in variable f. Now, you inverse the mapping via f:i.Dict Comprehension

Example 5Problem: Reverse the (key, value) mappings of a given dictionary.Roughly speaking, you want to obtain (value, key) mappings¡ªalthough the old values must be seen as the new keys!# Example 5: # Original dictionary: d = {str(i): i for i in range(5)} print(d) # {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4} #

Reversed dictionary: d_r = {v:k for k,v in d.items()} print(d_r) # {0: '0', 1: '1', 2: '2', 3: '3', 4: '4'}You use the dict.items() function to return the (key, value) pairs of the original dictionary d.Let¡¯s wrap up with an interactive code shell to try it yourself: Exercise: Modify each dictionary comprehension statement by

changing at least one thing!External ResourcesThis tutorial is based on various resources and online sources.Where to Go From Here?Enough theory, let¡¯s get some practice!To become successful in coding, you need to get out there and solve real problems for real people. That¡¯s how you can become a

six-figure earner easily. And that¡¯s how you polish the skills you really need in practice. After all, what¡¯s the use of learning theory that nobody ever needs?Practice projects is how you sharpen your saw in coding!Do you want to become a code master by focusing on practical code projects that actually

earn you money and solve problems for people?Then become a Python freelance developer! It¡¯s the best way of approaching the task of improving your Python skills¡ªeven if you are a complete beginner.Join my free webinar ¡°How to Build Your High-Income Skill Python¡± and watch how I grew my coding

business online and how you can, too¡ªfrom the comfort of your own home.Join the free webinar now! While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.To help students reach higher levels of Python success, he founded the

programming education website . He¡¯s author of the popular programming book Python One-Liners (NoStarch 2020), coauthor of the Coffee Break Python series of self-published books, computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.His

passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.

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