Python: Flatten Lists of Lists (4 Ways) • datagy (2023)

FAQs

What is the most efficient way to flatten a list of lists in Python? ›

You can flatten a Python list using a list comprehension, a nested for loop, and the itertools. chain() method. The list comprehension is the most “Pythonic” method and is therefore favoured in most cases. While nested for loops are effective, they consume more lines of code than a list comprehension.

In Python, you can flatten a list of lists (nested list, 2D list) using itertools. chain. from_iterable() , sum() , and list comprehensions. Use ravel() or flatten() to flatten a NumPy array ndarray .

To flatten a list of lists, use the list comprehension statement [x for l in lst for x in l] . To modify all elements in a list of lists (e.g., increment them by one), use a list comprehension of list comprehensions [[x+1 for x in l] for l in lst] .

flatten() method in Python is used to return a copy of a given array in such a way that it is collapsed into one dimension.

The easiest way to reduce the time complexity is to avoid a for loop whenever possible. Ideally, you may want to check the time complexity of time Python functions you use, and refactor your code by selecting the less complex functions.

One way to flatten an iterable-of-iterables is with a for loop. We can loop one level deep to get each of the inner iterables. And then we loop a second level deep to get each item from each inner iterable.

To flatten a NumPy array of arrays, say arr , use the np. concatenate(arr). ravel() function call. The result will be a one-dimensional (1D) flattened NumPy array of values.

Flatten a NumPy array with reshape(-1)

You can also use reshape() to convert the shape of a NumPy array to one dimension. If you use -1 , the size is calculated automatically, so you can flatten a NumPy array with reshape(-1) . reshape() is provided as a method of numpy.

How list comprehension is faster? ›

List Comprehensions. This code will create a new list called squares that contains the square of each number in the original list. List comprehensions are often faster than loops because they use a more optimized internal mechanism for iterating over the collection.

List comprehensions return the entire list, and the generator expression returns only the generator object. The values will be the same as those in the list, but they will be accessed one at a time by using the next() function. This is what makes list comprehensions faster than generator expressions.

You use the flat() method for concatenating sub-arrays recursively into a single array. The flat() method takes a depth value as its parameter which is optional depending on the depth of the array you wish to flatten (concatenate). The flat() method takes in 1 as a depth by default.

A simple and straightforward solution is to append items from sublists in a flat list using two nested for loops. A more compact and Pythonic solution is to use chain() function from itertools module.

What is the difference between the Numpy flatten and ravel functions? The main functional distinction is that flatten is a function of an ndarray object and hence only works with genuine numpy arrays. ravel(), on the other hand, is a library-level function that may be invoked on any object that can be correctly parsed.

flatten always returns a copy. ravel returns a view of the original array whenever possible. This isn't visible in the printed output, but if you modify the array returned by ravel, it may modify the entries in the original array. If you modify the entries in an array returned from flatten this will never happen.

Space-time tradeoff. The fastest way to repeatedly lookup data with millions of entries in Python is using dictionaries. Because dictionaries are the built-in mapping type in Python thereby they are highly optimized.

The list is an ordered collection of data, whereas the dictionaries store the data in the form of key-value pairs using the hashtable structure. Due to this, fetching the elements from the list data structure is quite complex compared to dictionaries in Python. Therefore, the dictionary is faster than a list in Python.

How do I reduce the size of a list in Python? ›

Python offers a function called reduce() that allows you to reduce a list in a more concise way. The reduce() function applies the fn function of two arguments cumulatively to the items of the list, from left to right, to reduce the list into a single value.

Prototype - flatten() Method

This method returns a flat (one-dimensional) version of the array. Nested arrays are recursively injected inline. This can prove very useful when handling the results of a recursive collection algorithm.

The numpy. ndarray. flatten() function is used to get a copy of an given array collapsed into one dimension. This function is useful when we want to convert a multi-dimensional array into a one-dimensional array.

Flattening pandas dataframe means changing the shape of the dataframe to one dimension. You can say changing the values of the datasets to the list. Suppose you have to pandas dataframe with two-column and its four corresponding rows or records. Then all the columns and rows will flatten to a single list.

You can use the ndarray. flatten, in your example it would be matrix. flatten() . This returns the flattened version of your matrix.

There are three methods to convert list to array in python and these are, by using the array(), by using the numpy. array(), or by using the numpy. asarray().

Use the reshape() method to transform the shape of a NumPy array ndarray . Any shape transformation is possible. This includes but is not limited to transforming from a one-dimensional array to a two-dimensional array. By using -1 , the size of the dimension is automatically calculated.

How do you break a list in a list Python? ›

Using float() Function

float() function is the most common way to convert the string to the float value. The float function takes the parameter y, which is a string, and would then convert the string to a float value and return its float value.

In conclusion, we have seen that there are two ways to represent a list in Python: using square brackets [] and using the list() function. While both methods create a list, the square bracket method is faster and more efficient than using the list() function.

Regarding performance, we're also seeing similar results to the ones obtained for map() . List comprehensions are fastest, followed by filter() with a predefined filter function, and filter() with an ad-hoc lambda function comes in last.

Map function is faster than list comprehension when the formula is already defined as a function earlier. So, that map function is used without lambda expression.

The generator yields one item at a time — thus it is more memory efficient than a list. For example, when you want to iterate over a list, Python reserves memory for the whole list. A generator won't keep the whole sequence in memory, and will only “generate” the next element of the sequence on demand.

List comprehensions are faster than for loops to create lists. But, this is because we are creating a list by appending new elements to it at each iteration. This is slow.

Actually, list comprehension is much clearer and faster than filter+lambda, but you can use whichever you find easier. The first thing is the function call overhead: as soon as you use a Python function (whether created by def or lambda) it is likely that the filter will be slower than the list comprehension.

Data flattening is associated with transforming data into plain text files stored in file systems rather than in databases or data warehouses. Flat files are records that generally consist of single portions of data, which can be, for example, tabular spreadsheet files.

How to do data scaling in Python? ›

There are different methods for scaling data, in this tutorial we will use a method called standardization. Where z is the new value, x is the original value, u is the mean and s is the standard deviation. Now you can compare -2.1 with -1.59 instead of comparing 790 with 1.0.

Python has operations for rearranging tabular data, known as reshaping or pivoting operations. For example, hierarchical indexing provides a consistent way to rearrange data in a DataFrame. There are two primary functions in hierarchical indexing: stack(): rotates or pivots data from columns to rows.

Using .apply(_) and .concat(_) method

one can use these two methods for flattening an array. We pass two arguments to the apply(_) method - an empty array and the array we want to flatten(arr), then concatenate all the elements(sub-arrays) passed into an empty array. Thus obtaining a 1-dimensional array.

Flattening a Multi-dimensional array means transforming it into a one-dimensional array. The method used to arrive at this is the flatten! method. However, flatten! will not modify the original array or the array that calls the function.

flatMap is similar to flatten , but flatten only acts on the values of the arrays, while flatMap can act on values and indices of items in the array.

One method to flatten tuples of a list is by using the sum() method with empty lust which will return all elements of the tuple as individual values in the list. Then we will convert it into a tuple. Method 2: Another method is using a method from Python's itertools library.

Using a 'for' loop, we can make Python loop through the list and perform the task. This is the most efficient way to loop through a list with ease irrespective of whether the list contains 10 or 1000 items.

Empty lists are considered False in Python, hence the bool() function would return False if the list was passed as an argument. Other methods you can use to check if a list is empty are placing it inside an if statement, using the len() methods, or comparing it with an empty list.

The clear() method removes all the elements from a list.

What are the looping techniques on list in Python? ›

Speed. We can say that if we keep all other factors the same, recursion is slower than looping. This is so because recursion adds function calling overhead to its execution. And that makes it slower than iteration.

The for loop is probably the most common and well known type of loop in any programming language. For can be used to iterate through the elements of an array: For can also be used to perform a fixed number of iterations: By default the increment is one.

Use the if conditional statement to check whether the list object is pointing to the literal [] i.e checking whether the list is equal to [] or not. Print “Empty list”, if the condition is true. Else print “List is not empty”, if the condition is false.

How To Check if a List Is Empty in Python Using the len() Function. You can use the len() function in Python to return the number of elements in a data structure. Using the len() function, we printed the length of the people_list list which had four elements.

fromkeys() This is the quickest way to accomplish the goal of removing duplicates from the Python list. This method first removes duplicates before returning a dictionary that has been converted to a list. In addition, this method works well with strings.