Python min() and max() are built-in functions in python which returns the smallest number and the largest number of the list respectively, as the output. Python min() can also be used to find the smaller one in the comparison of two variables or lists. However, Python max() on the other hand is used to find the bigger one in the comparison of two variables or lists.
- Python List Min Index
- Python Find Minimum In List
- Python List Min Max
- Python List Minimum
- Python List Minus 1
Python List Min Index
Min(iterable,., key, default). Min(arg1, arg2,.args, key) Returns the smallest item in an iterable (eg, list) or the smallest of two or more arguments. The key argument specifies a one-argument ordering function like that used for sort. The default argument specifies an object to return if the provided iterable is empty. Python list method min returns the elements from the list with minimum value. Following is the syntax for min method −. Min(list) Parameters. List − This is a list from which min valued element to be returned. This method returns the elements from the list.
The syntax for python min() function is:
The syntax for python max() function is:
The iterable parameter can be an object like string, list, tuple or a map etc. The default parameter consists of a default value which is returned when the iterable parameter is null, otherwise an exception ValueError is thrown in the other case. The key is referred to as the parameter which checks for the order of sorting. Min() calculates the minimum number as an output and max() computes the maximum number out of all in the list.
Python program to understand the use of min() Function
The output of the above python program will be:
Python program to understand the use of max() Function
The output of the above python program will be:
Python min() and max() works well for data types such as string well too. Let us see a program below to understand the use of the min() function with strings:
Program Example 1:
The output of the above python program will be:
Program Example 2:
The output of the above python program will be:
Some Exceptions while using min() and max() built-in functions
TypeError Exception: This exception occurs while the user tries to compare the two data types. For example min((“abc”, “abcd”, 23, 12). In this case, the user will get run time exception as an error as it is impossible to find the smallest between two different data types. Hence, it is always advisable to avoid such errors and use proper data types while making comparisons or wanting an output.
Example to use the min() and max() functions in python
The output of the above python program will be:
More Post on Website
Python Find Minimum In List
This page documents the time-complexity (aka 'Big O' or 'Big Oh') of various operations in current CPython. Other Python implementations (or older or still-under development versions of CPython) may have slightly different performance characteristics. However, it is generally safe to assume that they are not slower by more than a factor of O(log n).
Generally, 'n' is the number of elements currently in the container. 'k' is either the value of a parameter or the number of elements in the parameter.
The Average Case assumes parameters generated uniformly at random.
Internally, a list is represented as an array; the largest costs come from growing beyond the current allocation size (because everything must move), or from inserting or deleting somewhere near the beginning (because everything after that must move). If you need to add/remove at both ends, consider using a collections.deque instead.
Operation | Average Case | |
Copy | O(n) | O(n) |
Append[1] | O(1) | O(1) |
Pop last | O(1) | O(1) |
Pop intermediate[2] | O(n) | O(n) |
Insert | O(n) | O(n) |
Get Item | O(1) | O(1) |
Set Item | O(1) | O(1) |
Delete Item | O(n) | O(n) |
Iteration | O(n) | O(n) |
Get Slice | O(k) | O(k) |
Del Slice | O(n) | O(n) |
Set Slice | O(k+n) | O(k+n) |
Extend[1] | O(k) | O(k) |
O(n log n) | O(n log n) | |
Multiply | O(nk) | O(nk) |
x in s | O(n) | |
min(s), max(s) | O(n) | |
Get Length | O(1) | O(1) |
A deque (double-ended queue) is represented internally as a doubly linked list. (Well, a list of arrays rather than objects, for greater efficiency.) Both ends are accessible, but even looking at the middle is slow, and adding to or removing from the middle is slower still.
Operation | Average Case | Amortized Worst Case |
Copy | O(n) | O(n) |
append | O(1) | O(1) |
appendleft | O(1) | O(1) |
pop | O(1) | O(1) |
popleft | O(1) | O(1) |
extend | O(k) | O(k) |
extendleft | O(k) | O(k) |
rotate | O(k) | O(k) |
remove | O(n) | O(n) |
See dict -- the implementation is intentionally very similar.
Operation | Average case | Worst Case | notes |
x in s | O(1) | O(n) | |
Union s|t | |||
Intersection s&t | O(min(len(s), len(t)) | O(len(s) * len(t)) | replace 'min' with 'max' if t is not a set |
Multiple intersection s1&s2&..&sn | (n-1)*O(l) where l is max(len(s1),..,len(sn)) | ||
Difference s-t | O(len(s)) | ||
s.difference_update(t) | O(len(t)) | ||
Symmetric Difference s^t | O(len(s)) | O(len(s) * len(t)) | |
s.symmetric_difference_update(t) | O(len(t)) | O(len(t) * len(s)) |
As seen in the source code the complexities for set difference s-t or s.difference(t) (set_difference()) and in-place set difference s.difference_update(t) (set_difference_update_internal()) are different! The first one is O(len(s)) (for every element in s add it to the new set, if not in t). The second one is O(len(t)) (for every element in t remove it from s). So care must be taken as to which is preferred, depending on which one is the longest set and whether a new set is needed.
- To perform set operations like s-t, both s and t need to be sets. However you can do the method equivalents even if t is any iterable, for example s.difference(l), where l is a list.
The Average Case times listed for dict objects assume that the hash function for the objects is sufficiently robust to make collisions uncommon. The Average Case assumes the keys used in parameters are selected uniformly at random from the set of all keys.
Note that there is a fast-path for dicts that (in practice) only deal with str keys; this doesn't affect the algorithmic complexity, but it can significantly affect the constant factors: how quickly a typical program finishes.
Python List Min Max
Operation | Average Case | Amortized Worst Case |
k in d | O(1) | O(n) |
Copy[3] | O(n) | O(n) |
Get Item | O(1) | O(n) |
Set Item[1] | O(1) | O(n) |
Delete Item | O(1) | O(n) |
Iteration[3] | O(n) | O(n) |
[1] = These operations rely on the 'Amortized' part of 'Amortized Worst Case'. Individual actions may take surprisingly long, depending on the history of the container.
Python List Minimum
[2] = Popping the intermediate element at index k from a list of size n shifts all elements afterk by one slot to the left using memmove. n - k elements have to be moved, so the operation is O(n - k). The best case is popping the second to last element, which necessitates one move, the worst case is popping the first element, which involves n - 1 moves. The average case for an average value of k is popping the element the middle of the list, which takes O(n/2) = O(n) operations.
Python List Minus 1
[3] = For these operations, the worst case n is the maximum size the container ever achieved, rather than just the current size. For example, if N objects are added to a dictionary, then N-1 are deleted, the dictionary will still be sized for N objects (at least) until another insertion is made.