5. Data Structures¶
This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.
5.1. More on Lists¶
The list data type has some more methods. Here are all of the methods of list objects:
Add an item to the end of the list. Equivalent to a[len(a):] = [x] .
list. extend ( iterable )
Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable .
Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x) .
Remove the first item from the list whose value is equal to x. It raises a ValueError if there is no such item.
Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)
Remove all items from the list. Equivalent to del a[:] .
Return zero-based index in the list of the first item whose value is equal to x. Raises a ValueError if there is no such item.
The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.
Return the number of times x appears in the list.
list. sort ( * , key = None , reverse = False )
Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation).
Reverse the elements of the list in place.
Return a shallow copy of the list. Equivalent to a[:] .
An example that uses most of the list methods:
You might have noticed that methods like insert , remove or sort that only modify the list have no return value printed – they return the default None . 1 This is a design principle for all mutable data structures in Python.
Another thing you might notice is that not all data can be sorted or compared. For instance, [None, ‘hello’, 10] doesn’t sort because integers can’t be compared to strings and None can’t be compared to other types. Also, there are some types that don’t have a defined ordering relation. For example, 3+4j < 5+7j isn’t a valid comparison.
5.1.1. Using Lists as Stacks¶
The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append() . To retrieve an item from the top of the stack, use pop() without an explicit index. For example:
5.1.2. Using Lists as Queues¶
It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).
To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:
5.1.3. List Comprehensions¶
List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.
For example, assume we want to create a list of squares, like:
Note that this creates (or overwrites) a variable named x that still exists after the loop completes. We can calculate the list of squares without any side effects using:
which is more concise and readable.
A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:
and it’s equivalent to:
Note how the order of the for and if statements is the same in both these snippets.
If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.
List comprehensions can contain complex expressions and nested functions:
5.1.4. Nested List Comprehensions¶
The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.
Consider the following example of a 3×4 matrix implemented as a list of 3 lists of length 4:
The following list comprehension will transpose rows and columns:
As we saw in the previous section, the inner list comprehension is evaluated in the context of the for that follows it, so this example is equivalent to:
which, in turn, is the same as:
In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:
See Unpacking Argument Lists for details on the asterisk in this line.
5.2. The del statement¶
There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:
del can also be used to delete entire variables:
Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.
5.3. Tuples and Sequences¶
We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range ). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.
A tuple consists of a number of values separated by commas, for instance:
As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.
Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable , and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples ). Lists are mutable , and their elements are usually homogeneous and are accessed by iterating over the list.
A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:
The statement t = 12345, 54321, ‘hello!’ is an example of tuple packing: the values 12345 , 54321 and ‘hello!’ are packed together in a tuple. The reverse operation is also possible:
This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.
5.4. Sets¶
Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.
Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set() , not <> ; the latter creates an empty dictionary, a data structure that we discuss in the next section.
Here is a brief demonstration:
Similarly to list comprehensions , set comprehensions are also supported:
5.5. Dictionaries¶
Another useful data type built into Python is the dictionary (see Mapping Types — dict ). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend() .
It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: <> . Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.
The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del . If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.
Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword.
Here is a small example using a dictionary:
The dict() constructor builds dictionaries directly from sequences of key-value pairs:
In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:
When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:
5.6. Looping Techniques¶
When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.
When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.
To loop over two or more sequences at the same time, the entries can be paired with the zip() function.
To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.
To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.
Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order.
It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.
5.7. More on Conditions¶
The conditions used in while and if statements can contain any operators, not just comparisons.
The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.
Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c .
Comparisons may be combined using the Boolean operators and and or , and the outcome of a comparison (or of any other Boolean expression) may be negated with not . These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C . As always, parentheses can be used to express the desired composition.
The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C . When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.
It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,
Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator := . This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.
5.8. Comparing Sequences and Other Types¶
Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:
Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.
Other languages may return the mutated object, which allows method chaining, such as d->insert("a")->remove("b")->sort(); .
How do I transpose a List? [duplicate]
How do I transpose them so they will be: [[1, 4], [2, 5], [3, 6]] ?
Do I have to use the zip function? Is the zip function the easiest way?
4 Answers 4
Using zip and *splat is the easiest way in pure Python.
Note that you get tuples inside instead of lists. If you need the lists, use map(list, zip(*l)) .
If you’re open to using numpy instead of a list of lists, then using the .T attribute is even easier:
The exact way of use zip() and get what you want is:
This code use list keyword for casting the tuples returned by zip into lists .
You can use map with None as the first parameter:
Unlike zip it works on uneven lists:
Then call map again with list as the first parameter if you want the sub elements to be lists instead of tuples:
(Note: the use of map with None to transpose a matrix is not supported in Python 3.x. Use zip_longest from itertools to get the same functionality. )
zip() doesn’t seem to do what you wanted, using zip() you get a list of tuples . This should work though:
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Транспонировать список списков
Один из способов сделать это с транспонированием NumPy. Для списка a:
Или другой без zip:
Эквивалентно решению Йены:
только для забавных действительных прямоугольников и предполагая, что m [0] существует
Эти методы работают в Python 2 или 3 и в нечетных или прямоугольных списках:
настройка
метод 1
- itertools.izip_longest() в Python 2
- itertools.zip_longest() в Python 3
Значение по умолчанию None . Спасибо @jena answer, где map() меняет внутренние кортежи на списки. Здесь он превращает итераторы в списки. Благодаря комментариям @Oregano и @badp .
метод 2
метод 3
Необычная альтернативная альтернатива @SiggyF работает с неравномерными списками (в отличие от первого, numpy транспонирования, который проходит через неравные списки), но None — единственное удобное значение заполнения.
(Нет, None, переданный на внутреннюю карту(), не является значением заливки, это означает что-то еще, это означает, что нет функции пропускать строки.)
Python: Transpose a List of Lists (5 Easy Ways!)
In this tutorial, you’ll learn how to use Python to transpose a list of lists, sometimes called a 2-dimensional array. By transposing a list of lists, we are essentially turning the rows of a matrix into its columns, and its columns into the rows. This is a very helpful skill to know when you’re learning machine learning.
In this tutorial, you’ll learn how to do this using for-loops, list comprehensions, the zip() function, the itertools library, as well as using the popular numpy library to accomplish this task.
The Quick Answer: Use Numpy’s .T Method
Table of Contents
What Does it Mean to Transpose a Python List of Lists?
Transposing arrays is a common function you need to do when you’re working on machine learning projects. But what exactly does it mean to transpose a list of lists in Python?
We can think of sublists in our list of lists in Python as the rows of a matrix. When we transpose a list of lists, we essentially turn the rows of our matrix into the columns, and turn the columns into the rows.
Let’s take a look at what this looks like visually:
Now let’s get started at learning how to transpose a two-dimensional array in Python.
Transpose a Python List of Lists using Numpy
Python comes with a great utility, numpy , that makes working with numerical operations incredibly simple! Numpy comes packaged with different object types, one of which is the numpy array . These arrays share many qualities with Python lists but also allow us to complete a number of helpful mathematical operations on them.
One of these operations is the transposing of data, using either the .transpose() method, which can also be shortened to just .T .
Before we can use any of these methods, however, we need to convert our list or our list of lists to a numpy array.
Let’s see how we can use numpy to transpose a list of lists:
From the example above, we can see how easy it is to use numpy to transpose a Python list of lists. What we did was:
- Convert our list of lists to a numpy array
- We then applied the .T method to transpose the data
- Finally, we applied the .tolist() method to turn our array back to a list of lists
In the next section, you’ll learn how to transpose a two-dimensional array using the built-in zip() function.
Want to learn how to transpose a Pandas Dataframe? Check out my in-depth tutorial with easy to follow along examples here.
Use the Zip Function to Transpose a List of Lists in Python
Let’s now take a look at how we can use the built-in zip() function to transpose a list of lists in Python. The zip() function is an incredibly helpful method that allows us to access the n th item of an iterable in sequence, as we loop over multiple iterable objects.
Now, this might sound a tad confusing, and it can be easier to see it in action than to understand it in theory, right off the bat. So let’s take a look at how we can do transpose a list of lists using the zip() function:
Let’s take a look at what we’ve done here:
- We created a list object using the list() function, out of zipping all the items in the list_of_lists object
- Finally, we used a list comprehension to turn each item of the list into a list. By default, the zip objects will return tuples, which may not always be ideal.
In the next section, you’ll learn how to use the itertools library to be able to turn lists of lists of different lengths into their transposed versions.
Want to dive a little deeper into the zip() function in Python? Check out this in-depth tutorial that covers off how to zip two lists together in Python, with easy to follow examples!
Use Itertools to Transpose a Python List of Lists
Sometimes, when working with Python lists of lists, you’ll encounter sublists that are of different lengths. In these cases, the zip() function will omit anything larger than the length of the shortest sublist.
Because of this, there are many times that we need to rely on the itertools library in order to transpose lists of lists where items have different lengths. In particular, we’ll use the itertool’s zip_longest() function to be able to include missing data in our transposed list of lists.
Again, this is much easier explained with an example, so let’s dive right in:
We can see here, that a fourth row has been added. We used the fillvalue= parameter to determine what to fill these missing gaps with. Much of this is the same as using the zip() function, but it does allow us to work around the limitations of that function.
In the next two sections, you’ll learn two more methods, which are more naive implementations using for-loops and list comprehensions, in order to transpose lists of lists.
Transpose a Python List of Lists using a For Loop
Python for-loops as incredible tools to help you better understand how some algorithms may work. While they may not always be the most efficient way of getting something done, they can be easy to follow and document in terms of what your code is doing exactly.
Let’s see how we can implement a Python for loop to transpose a two-dimensional array of data:
What we’ve done here is the following:
- We initialize an empty list called transposed
- We then loop over the length of the first list in the list of lists
- In this, we initialize a new empty list called row
- We then loop over each i th item and append to our list row
- Finally, we append that row to our transposed list
- This then repeats for each item in the sublists
In the next section, you’ll learn how to turn this for-loop implementation into a Pythonic list comprehension.
Want to learn more about how to write for loops in Python? This tutorial will teach you all you need to know, including some advanced tips and tricks!
Use a List Comprehension to Transpose a List of Lists in Python
In many cases, a Python for loop can be turned a very abbreviated list comprehension. While we increase the brevity of our code, we may sacrifice the readability of our code.
Let’s take a look at how we can transpose a list of lists using list comprehensions:
We can see here that we’ve saved quite a bit of space. We’ve not had to initialize two empty lists. That being said, this can be a bit more difficult to follow along with, especially as it’s much more difficult to provide guiding comments for each step of the way.
Want to learn more about Python list comprehensions? Check out my in-depth tutorial on them here. More of a visual learner? You’ll find an in-depth video on the topic inside the tutorial.
Conclusion
In this post, you learned how to transpose a Python list of lists, or a 2-dimensional array. You learned how to do this using the popular data science numpy library, as well as using the built-in zip() function, either alone or with the itertools library. You also learned some more naive methods of accomplishing this, using both for loops as well as Python list comprehensions.
To learn more about the numpy transpose method, check out the official documentation here.