Skip to main content

Python - Dictionary - Comprehension, Iteration, Items, Keys, Values

🐍 Advance Dictionaries

There is very little one can do with dictionaries. Dictionaries are very useful, but because of their relatively complex state, very few modifications can be done in simple ways. Here are a couple of things you do with dictionaries.

Dictionary Comprehension

Just like List Comprehensions, Dictionary Data Types also support their own version of comprehension for quick creation. It is not as commonly used as List Comprehensions. Here is an example of Dictionary Comprehension.
Input
d = {x:x**2 for x in range(10)}
d
Output
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
Another example where we pick keys from one place and values from another.
Input
d = {k:v**2 for k,v in zip(['a','b','c','d','e','f','g','h','i','j'],range(10))}
d
Output
{'a': 0, 'b': 1, 'c': 4, 'd': 9, 'e': 16, 'f': 25, 'g': 36, 'h': 49, 'i': 64, 'j': 81}
One of the reasons it is not as common is the difficulty in structuring key names that are not based off the values.

Iteration

Keys

Keys are the left half of the paired elements in a dictionary. It can be simply accessed in the following way:
Input
list_of_keys = d.keys()
list_of_keys
Output
dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Or they can be iterated through in the following way:
Input
for k in d.keys():
    print(k)
Output
0
1
2
3
4
5
6
7
8
9

Values

Values are the right half of the paired elements in a dictionary. It can be simply accessed in the following way:
Input
list_of_values = d.values()
list_of_values
Output
dict_values([0, 1, 4, 9, 16, 25, 36, 49, 64, 81])
Or they can be iterated through in the following way:
Input
for k in d.values():
    print(k)
Output
0
1
4
9
16
25
36
49
64
81

Items

An item is a pair of elements in a dictionary. It can be simply accessed in the following way:
Input
list_of_items = d.items()
list_of_items
Output
dict_items([(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25), (6, 36), (7, 49), (8, 64), (9, 81)])
Or they can be iterated through in the following way:
Input
for k in d.values():
    print(k)
Output
(0, 0)
(1, 1)
(2, 4)
(3, 9)
(4, 16)
(5, 25)
(6, 36)
(7, 49)
(8, 64)
(9, 81)

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

Difference between .exec() and .execPopulate() in Mongoose?

Here I answer what is the difference between .exec() and .execPopulate() in Mongoose? .exec() is used with a query while .execPopulate() is used with a document Syntax for .exec() is as follows: Model.query() . populate ( 'field' ) . exec () // returns promise . then ( function ( document ) { console . log ( document ); }); Syntax for .execPopulate() is as follows: fetchedDocument . populate ( 'field' ) . execPopulate () // returns promise . then ( function ( document ) { console . log ( document ); }); When working with individual document use .execPopulate(), for model query use .exec(). Both returns a promise. One can do without .exec() or .execPopulate() but then has to pass a callback in populate.
1 comment
Read more

Python - List - Append, Count, Extend, Index, Insert, Pop, Remove, Reverse, Sort

🐍 Advance List List is widely used and it's functionalities are heavily useful. Append Adds one element at the end of the list. Syntax list1.append(value) Input l1 = [1, 2, 3] l1.append(4) l1 Output [1, 2, 3, 4] append can be used to add any datatype in a list. It can even add list inside list. Caution: Append does not return anything. It just appends the list. Count .count(value) counts the number of occurrences of an element in the list. Syntax list1.count(value) Input l1 = [1, 2, 3, 4, 3] l1.count(3) Output 2 It returns 0 if the value is not found in the list. Extend .count(value) counts the number of occurrences of an element in the list. Syntax list1.extend(list) Input l1 = [1, 2, 3] l1.extend([4, 5]) Output [1, 2, 3, 4, 5] If we use append, entire list will be added to the first list like one element. Extend, i nstead of considering a list as one element, it joins the two lists one after other. Append works in the following way. Input l1 = [1, 2, 3] l1.append([4, 5]) Output...
Post a Comment
Read more

Machine Learning — Supervised, Unsupervised, and Reinforcement — Explanation with Example

🤖 Let's take an example of machine learning and see how it can be performed in three different ways — Supervised, Unsupervised, and Reinforcement. We want a program to be able to identify apple in pictures Supervised Learning You will create or use a model that takes a set of pictures of apple and it analyses the commonality in those pictures. Now when you show a new picture to the program, it will identify whether it has an apple or not. It can also provide details on how confident is the program about it. Unsupervised Learning In this method, you create or use a model that goes through some images and tries to group them as per the commonalities it observes such as color, shape, size, partern, etc. And now you can go through the groups and inform the program what to call them. So, you can inform the program about the group that is apple mostly. Next time you show a picture, it can tell if an apple is there or not. Reinforcement Learning Here the model you create or...
Post a Comment
Read more