Dictionary comprehension python

Dictionaries are data types in Python which allows us to store data in key/value pair. For example: my_dict = Here, we can see that we retrieved the item prices in dollars and converted them to pounds. Using dictionary comprehension makes this task much simpler and shorter. Conditionals in Dictionary Comprehension We can further customize dictionary comprehension by adding conditions to it. Let's look at an example. Example 4: If Conditional Dictionary Comprehension original_dict = print(dictionary) It can further be unfolded: dictionary = dict() for k1 in range(11, 16): dictionary[k1] = dict() for k2 in range(1, 6): dictionary[k1][k2] = k1*k2 print(dictionary) All these three programs give us the same output. Advantages of Using Dictionary Comprehension As we can see, dictionary comprehension shortens the process of dictionary initialization by a lot. It makes the code more pythonic. Using dictionary comprehension in our code can shorten the lines of code while keeping the logic intact. Warnings on Using Dictionary Comprehension Even though dictionary comprehensions are great for writing elegant code that is easy to read, they are not always the right choice. We must be careful while using them as : • They can sometimes make the code run slower and consume more memory. • They can also decrease the readability of the code. We must not try to fit a difficult logic or a large number of dictionary comprehension inside them just for the sake of making the code single lined. ...

Learn all about Python dictionary comprehensions, including how to create dictionaries, using conditionals (if-else statements), and how to nest comprehensions with easy to follow steps and examples! Dictionary Comprehensions are similar to Python List Comprehensions. If you want to learn about those as well, Table of Contents • • • • • • • • • • What are Dictionaries (dicts) in Python? Dictionaries (or, dicts) in Python are unordered collections of items. Other compound data types (such as lists or tuples) have only a value as an element, a dictionary has a key:value pair as its element. Dictionaries allow you to easily retrieve values when you know the key. If you want to learn everything you need to know about dictionaries in Python, check out How Do You Create a Python Dictionary? All you need to do to create a dictionary in Python is to place items into curly braces, separated by a comma. Let’s create a dictionary for the book Harry Potter and the Philosopher’s Stone: hp = Warnings with Python Dictionary Comprehensions Dictionary comprehensions are very Pythonic in how they written, but can also get far more complicated to read than for-loops. It may be better to write a longer for-loop that makes the code easier to follow, rather than fitting it all on a single line. Remember, future-readability is important! Conclusion Congratulations! You now know everything you need to know about Python dictionary comprehensions! In this post, you learned what dictionaries are, h...

Dictionaries are powerful built-in data structures in Python that store data as key-value pairs. Dictionary Comprehension can be super helpful in creating new dictionaries from existing dictionaries and iterables. In this tutorial, we'll learn how dictionary comprehensions work in Python by coding some simple examples. What is a Dictionary in Python? Dictionaries in Python allow us to store a series of mappings between two sets of values, namely, the keys and the values. • All items in the dictionary are enclosed within a pair of curly braces The above code does the following: • For each item in our discount_dict, it taps into the value of the discount. • If the discount is fewer than $30, it adds the corresponding customer:discount pair to our new dictionary customer_10. Notice how Alex, Bob, and Dave who got fewer than $30 discount have been added to the new dictionary. Conclusion Let's summarize what we've learned in this tutorial. We've seen how to use Dictionary Comprehension to create Python dictionaries from: • only one iterable, • two iterables, and • an existing dictionary using conditions to filter through the items. Thank you for reading. Happy learning!🎉 Related Posts Here's a post explaining the working of

Use a In fact, you don't even need to iterate over the iterable if it already comprehends some kind of mapping, the dict constructor doing it graciously for you: >>> ts = [(1, 2), (3, 4), (5, 6)] >>> dict(ts) Create a dictionary with list comprehension in Python I like the Python list comprehension syntax. Can it be used to create dictionaries too? For example, by iterating over pairs of keys and values: mydict = Here we are just testing for if the last character is divisible by 2 to filter out data before mapping the keys and values. Here is another example of dictionary creation using dict comprehension: What i am tring to do here is to create a alphabet dictionary where each pair; is the english letter and its corresponding position in english alphabet >>> import string >>> dict1 = >>> Notice the use of enumerate here to get a list of alphabets and their indexes in the list and swapping the alphabets and indices to generate the key value pair for dictionary Hope it gives a good idea of dictionary comp to you and encourages you to use it more often to make your code compact This code will create dictionary using list comprehension for multiple lists with different values that can be used for pd.DataFrame() #Multiple lists model=['A', 'B', 'C', 'D'] launched=[1983,1984,1984,1984] discontinued=[1986, 1985, 1984, 1986] #Dictionary with list comprehension keys=['model','launched','discontinued'] vals=[model, launched,discontinued] data = #Convert dict to dataframe df=pd...

Comprehensions in Python provide us with a short and concise way to construct new sequences (such as lists, set, dictionary etc.) using sequences which have been already defined. Python supports the following 4 types of comprehensions: • List Comprehensions • Dictionary Comprehensions • Set Comprehensions • Generator Comprehensions List Comprehensions: List Comprehensions provide an elegant way to create new lists. The following is the basic structure of a list comprehension: Output: Output List using list comprehension: [1, 4, 9, 16, 25, 36, 49, 64, 81] Dictionary Comprehensions: Extending the idea of list comprehensions, we can also create a dictionary using dictionary comprehensions. The basic structure of a dictionary comprehension looks like below. output_dict = Output: Output Dictionary using dictionary comprehensions: Example #2: Given two lists containing the names of states and their corresponding capitals, construct a dictionary which maps the states with their respective capitals. Let’s see how to do this using for loops and dictionary comprehension. Output: Output Dictionary using dictionary comprehensions: . Let’s look at the following example to understand set comprehensions. Example #1 : Suppose we want to create an output set which contains only the even numbers that are present in the input list. Note that set will discard all the duplicate values. Let’s see how we can do this using for loops and set comprehension. Output: Output Set using set comprehens...

Dictionaries (or dict in Python) are a way of storing elements just like you would in a Python list. But, rather than accessing elements using its index, you assign a fixed key to it and access the element using the key. What you now deal with is a "key-value" pair, which is sometimes a more appropriate data structure for many problems instead of a simple list. You will often have to deal with dictionaries when doing data science, which makes dictionary comprehension a skill that you will want to master. In this tutorial: • First, you'll see what a Python dictionary really is and how you can use it effectively. • Next, you'll learn about Python dictionary comprehensions: you will see what it is, why it is important, and how it can serve as an alternative to for loops and lambda functions. • You will learn how to add conditionals into dictionary comprehensions: you will work with if conditions, multiple if conditions, and also if-else statements. • Lastly, you will see what nested dictionary comprehension is, how you can use it, and how you can potentially rewrite it with for loops. Let's get started... Python Dictionary A dictionary in Python is a collection of items accessed by a specific key rather than by an index. What does this mean? Imagine a dictionary in the real world... when you need to look up the meaning of a word, you try to find the meaning using the word itself and not the possible index of the word. Python dictionaries work with the same concept, the word w...

As of Python version 3.7, dictionaries are ordered. In Python 3.6 and earlier, dictionaries are unordered. When we say that dictionaries are ordered, it means that the items have a defined order, and that order will not change. Unordered means that the items does not have a defined order, you cannot refer to an item by using an index. Changeable Dictionaries are changeable, meaning that we can change, add or remove items after the dictionary has been created. Duplicates Not Allowed Dictionaries cannot have two items with the same key:

Output : Using conditional statements in dictionary comprehension Example 1: We can use Dictionary comprehensions with if and else statements and with other expressions too. This example below maps the numbers to their cubes that are not divisible by 4.