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(); .
Convert List to Array Python
A list in python is a linear data structure that may hold a variety of elements of different types. A NumPy array is a data structure that can hold homogeneous (same type) items. 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() .
Convert List to Array in Python
Before moving to how we can convert a list to array in python, let's have a brief on the list and array in python.
A list in python is a linear data structure that may hold a variety of elements. By varied it can store strings, objects, integers, etc. in a single list. But on the other hand, a NumPy array is a data structure that can hold homogeneous items. So if an array is storing integers, it can only store integers. We can not add data such as string, or object to that integer array. But you may wonder where can we use such a data structure? The answer to that is there are some cases when we need to have a data structure that can hold only homogeneous items. Consider the case where we want to store the contact numbers. Have you seen a contact number as "98oi35hj"? No, we have contact numbers in form of integers as 1234567890. Therefore we limit the data type of the array to an integer. Hence if the user tries to enter a contact number as "98ghijo" we can simply return that contact number only accepts integers.
Methods to Convert List to Array Python
There are three methods to convert list to array in python. Let's understand them one by one,
Using array() + data type indicator
Python provides us with an inbuilt function array() that can be used to convert list to array in python. We have to pass the "data type" and the list to the function. This function will convert the list to the provided "data-type". For integers, we pass "i" as the "data-type".
Syntax
Parameters
typecode — This is a mandatory parameter that specifies the type of array that we have to create. For integer typecode is "i", for float it is "f", for double it is "d" and so on.
initializer — This is an optional parameter that is used to initialize the values of the array. This can be a value, a list, or an iterable of the proper type.
Let's see the program now,
Program
Output:
But what if we provided a string such as "h" to the list? Will it convert it to an array or will return an error? Let's also see an example of this.
Output:
As you can see in the output, we got an error stating that the function was expecting an integer but received a string. This is because in the list there's a string "h" and because of this string we received the error that "an integer is required (got type str)".
Using numpy.array()
In the second method, we can use the array() function provided by the numpy library. This function accepts a list and returns the array containing all the elements of the list.
How to Convert List to Array in Python
Here are the 3 methods to convert a list to an array in Python:
- Using “np.array()” method
- Using “np.asarray()” method
- Using “array() + data type indicator” method
Method 1: Using the np.array() method
To convert a list to an array in Python, you can use the “np.array()” method. The function takes a list as an argument and returns an array containing all the list elements.
Example
Output
In this example, we defined a list, which we converted into an array using the np.array() function and printed the array and its data type. To check variable data type, use the type() function.
Method 2: Using numpy.asarray() method
The np.asarray() is a numpy library function that takes a list as an argument, converts it into an array, and returns it.
The main difference between numpy.array() and numpy.asarray() is that the copy flag is False in the case of numpy.asarray() and True (by default) in the case of numpy.array().
Example
Output
Method 3: Using array() + data type indicator
You can use the array.array() method to convert a list to an array. To use the array() method from the array module, you need to import the array module and specify the data type and the list as arguments. For integers, we pass “i” as the “data-type”.
Example
Output
Conclusion
The best and an efficient way to convert a list to an array is to use the np.array() function.
The best way to convert an array to a list is to use the list() function.
How to Convert a List to an Array and Back in Python
Oluseye Jeremiah
Arrays and lists are two of the most commonly used data structures in Python.
You can use both arrays and lists to store collections of values, but they have some key differences.
For example, arrays are more efficient than lists for certain operations, such as mathematical operations on large collections of numerical data. But lists are more flexible and easier to work with in many cases.
Sometimes, you may need to convert between arrays and lists in Python. For example, you may have data stored in a list that you need to pass to a function that requires an array. Or you may have an array that you need to manipulate using list operations.
In this article, we will explore how to convert lists to arrays and arrays to lists in Python.
How to Convert a List to an Array in Python
To convert a list to an array in Python, you can use the array module that comes with Python’s standard library. The array module provides a way to create arrays of various types, such as signed integers, floating-point numbers, and even characters.
Here’s an example of how to convert a list to an array in Python:
In this code, we first import the array module. We then create a list called my_list containing the values 1 through 5.
Next, we create an array called my_array by calling the array() function and passing it two arguments: the type code ‘i’ , which specifies that we want an array of signed integers, and my_list , which is the list we want to convert.
When we print my_array , we should see the following output:
This shows that my_array is now an array containing the same values as my_list .
How to Convert an Array to a List in Python
To convert an array back to a list, we can use Python’s built-in list() function. Here’s an example of how to convert an array to a list in Python:
In this code, we first create an array called my_array containing the values 1 through 5. Next, we create a list called my_list by calling the list() function and passing it my_array .
When we print my_list , we should see the following output:
This shows that my_list is now a list containing the same values as my_array .
Conclusion
In this article, we explored how to convert arrays and lists in Python. You learned that you can use the array module to create arrays of various types and the list() function to convert arrays back to lists.
Converting between arrays and lists can be useful in many situations, such as when you need to pass data between functions or when you need to manipulate data using list operations. By understanding how to convert arrays and lists, you can work more effectively with data in Python.