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Searching for an item in a list involves a linear search and in the worst case scenario is O(n). So, adding/removing items at the end of a list and accessing items by index are fast and efficient operations with O(1). You can use an index to get an item in a list and no matter how big the list is, the cost of accessing an item by index remains relatively constant, hence O(1). Again, this is another example of O(n), where the cost of finding a match is linear and in direct proportion with the number of elements in the list. In the worst case scenario, if this item is at the end of the list, all items in the list need to be scanned before finding the match. This means, it iterates over all items in its internal array and if it finds a match, it returns it. IndexOf, Contains and Find), List performs a linear search. When using methods that involve searching for an item(e.g. We represent the execution cost of this operation with Big O notation: O(1). This is why the cost of this operation is relatively constant and is not dependent on the number of items in the list. The existing items do not have to be shifted. Adding/Removing Items at the EndĪdding/removing an item at the end of a list is a relatively fast operation and does not depend on the size of the list. So, as n grows, the execution time of the algorithm increases in direct proportion to n. We specify the cost of this operation using Big O notation: O(n), which simply means the cost increases linearly in direct proportion to the size of the input. The larger the list, the more costly this operation is going to be. In the worst case scenario, if you add/remove an item at the very beginning of a list, it needs to shift all existing items. If you add/remove an item at the beginning or middle of a list, it needs to shift one or more items in its internal array. Adding/Removing Items at the Beginning or Middle Now, let’s see where a list performs well and where it doesn’t. Return the number of items in the list
Check to see if the list contains an item Here are some useful operations with lists: If you plan to store large number of objects in a list, you can reduce the cost of reallocations of the internal array by setting an initial size: These days, it’s common to use lists instead of arrays, even if you’re working with a fixed set of items. If it becomes full, it’ll create a new larger array, and will copy items from the existing array into the new one. Internally, a list uses an array for storage.
That’s why they’re also called dynamic arrays or vectors. Unlike arrays that are fixed in size, lists can grow in size dynamically. If you’re not familiar with generics, check out my YouTube video. Represents a list of objects that can be accessed by an index.
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For each type, I’ll explain what it is, when to use and how to use it. So, in this post, I’m going to explore the following collection types. In the future posts in this series, I’ll be covering other collection types that are used in special cases, where performance and concurrency are critical. NET collections, I’m going to cover 5 essential collection types that every C# developer must know. These are the collections that you’ll use 80 – 90% of the time, if not more.
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In this post, which is the first in the series on.
NET has left you confused, you’re not alone. NET is like finding the right camera in a camera shop! There are so many options to choose from, and each is strong in certain scenarios and weak in others. It’s the number one language employers are looking for and gives you 4x more job opportunities than C#.įinding the right collection in.
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UPDATE (Nov 5 2018): While you’re here to learn C# better, I strongly recommend you to watch my Python tutorial on YouTube. October 21st, 2015 Comments 5 C# Collections that Every C# Developer Must Know
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