A list of lists
Let’s start with a simple example of converting [[0,1], [2,3]] into [0,1,2,3]. This type of flattening is called shallow flattening as it will only flatten lists of one level depth.
Using list comprehension
List comprehension is a way to create lists in one line of code. Let’s see how we can use list comprehension for flattening the lists.
flatten_list = [item for subl in l for item in subl]
Let’s break this line of code.
The first loop is “for subl in l” and the second nested loop is “ for item in subl ”.
Deep flattening
When we will try to flatten a list of varying depth like this [ [ 0, 1 ], [ [ 2 ] ][ 3, 4 ] ] list with shallow flattening, the output will be as follows:
But our goal is to convert [ [ 0, 1 ], [ [ 2 ] ], [ 3, 4 ] ] this list to this [ 0, 1, 2, 3, 4 ] list. This problem can be solved with deep flattening. In deep flattening, the process undergoes multiple levels of depths to create a flattened list.
There is a built-in function named deepflatten in the iteration_utilities library. You need to install this library using.
We have successfully achieved our target. Let’s take another example by changing the depth of the list.
Using recursion
To flatten a list recursively, we will call the function inside itself to run until the end:
Check whether the list length is equal to 1. If true, then check whether the type of the first index of the list is a “list”.if true, then call the function that flattens the list else, store the number in the result.
The function will be like this:
When we run this code against this [[0,1], [2], [3,4]] list, the results will be:
Flatten without recursion
To flatten a list without recursion, we will use a while loop until we pop all the elements from it. Take a look at the code, you will have a better understanding:
Flatten nested lists
To flatten a nested list, you can use deep flattening. For deep flattening lists within lists, use the given below code:
Also, you can use the recursive function as we did above.
A list of tuples
Flattening a list of tuples of a single depth is the same as flattening lists within lists. For shallow flattening of tuples, use the following code:
For deep flattening, a list of tuples with varying depth (nested), you can use the code given below:
Flatten 2d array
Let’s take a 2d array of 5×5 dimensions and convert it to a flattened list. Check the following code:
A list of NumPy arrays
There are three built-in functions defined in NumPy library that can convert the NumPy array into flattened lists.
numpy.ravel()
numpy.flatten()
numpy.reshape(-1)
numpy.ravel()
numpy.flatten()
numpy.reshape(-1)
The difference between these three functions is speed. The flatten function returns a copy every time it flattens the array. So, if you have a large data set, don’t use the flatten function; it’s the slower one.
Flatten JSON objects
For flattening JSON objects, there is a built-in function in the flatten_json library named flatten().
You first need to install it using pip:
pip install flatten_json
Then you can use this function in our code:
Flatten a list of objects
You can flatten a list of objects using a built-in function available in the itertools library with function name itertools.chain.from_iterable() Let’s see how to use this function:
The same operation can be achieved using list comprehension too:
Flatten a list of DataFrames
For flattening a list of DataFrames, the pandas library has a built-in function for flattening called df.concat() Let’s take a look at code:
Flatten & remove duplicates
First, we will flatten our list, then we will remove the duplicates.
For flattening the list, we will use our own flatten_without_rec() function, and then we will remove the duplicates.
Let us have a look at the code:
Flatten a dictionary into a list
You can flatten a dictionary to a list using a simple for loop:
Let us have a look at the code:
Using reduce
The reduce() function is defined in the functools library. You first need to import reduce from the functools.
Let’s take a look at the code:
We flattened lists with different shapes & types in different ways. I hope you find the tutorial useful. Keep coming back.
Thank you.
Apache web sunucusuna alternatif olarak yaygın biçimde kullanılan Nginx’in 1.18.0 sürümü duyuruldu. 2002 yılında bir rus mail sunucu sitesi için Rus yazılım mühendisi Igor Sysoev tarafından geliştirilmeye başlanan hafif, stabil, hızlı bir mail istemcisi olarak kodlanan ve 2005 senesinde geliştirilmesi için açık kaynak olarak dağıtımına başlanan yazılım; GNU/Linux, FreeBSD, Windows, MacOS ve Solaris işletim sistemlerinde çalışabilmektedir. Apacheden % 400 daha performanslı olduğu söylenen Nginx; özellikle yüksek trafikli ve yoğun web siteleri için oldukça iyi bir seçenektir. Ayrıca belirtmek gerekirse, Nginx, LiteSpeed ile karşılaştırıldığında çok daha az cpu kullanır. Nginx özellikle static content serve etmek konusunda oldukça başarılıdır ve reverse proxy olarak da yaygın olarak kullanılmaktadır. Nginx 1.18.0 hakkında ayrıntılı bilgi edinmek için değişiklikler sayfasını inceleyebilirsiniz.
Eskiden GNU dmd olarak bilinen ve sistem servisleri ile ilgilenen bir servis yöneticisi olan 
Linux Çekirdeğinin tanınmış geliştiricisi Greg Kroah-Hartman, geçen hafta 
Sabit 
20+ examples for flattening lists in 
Ubuntu tarafından yayımlanan haftalık haber bülteni 