I Made A Python Cheat Sheet for Data Structures and Algorithms (Useful for Leetcode)

Python Cheat Sheet

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Usage Guide

  • Keep this guide open beside you while solving problems and take a look as and when necessary.
  • Read through this once just to get an idea of the possibilities with Python.
  • (optional) Read this guide every few days or once a week for around 1 month. You’ll automatically have most syntax in your mind (spaced repetition).


  • Data Types
  • Operators and it’s precendence

Data Structures

Important data structures for Leetcode


Lists are used to store multiple items in a single variable

  • Operations Time Complexities
nums = [1,2,3]nums.index(1) # returns index
nums.append(1) # appends 1
nums.insert(0,10) # inserts 10 at 0th index
nums.remove(3) # removes all instances of 3
nums.copy(1) # returns copy of the list
nums.count(1) # returns no.of times '1' is present in the list
nums.extend(someOtherList) # ...
nums.pop() # pops last element [which element to pop can also be given as optional argument]
nums.reverse() # reverses original list (nums in this case)
nums.sort() # sorts list [does NOT return sorted list]
#Python's default sort uses Tim Sort, which is a combination of both merge sort and insertion sort.
It's pretty simple really:a[start:stop] # items start through stop-1
a[start:] # items start through the rest of the array
a[:stop] # items from the beginning through stop-1
a[:] # a copy of the whole array
There is also the step value, which can be used with any of the above:
a[start:stop:step] # start through not past stop, by step
The key point to remember is that the :stop value represents the first value that is not in the selected slice. So, the difference between stop and start is the number of elements selected (if step is 1, the default).
The other feature is that start or stop may be a negative number, which means it counts from the end of the array instead of the beginning. So:a[-1] # last item in the array
a[-2:] # last two items in the array
a[:-2] # everything except the last two items
Similarly, step may be a negative number:
a[::-1] # all items in the array, reversed
a[1::-1] # the first two items, reversed
a[:-3:-1] # the last two items, reversed
a[-3::-1] # everything except the last two items, reversed
Python is kind to the programmer if there are fewer items than you ask for. For example, if you ask for a[:-2] and a only contains one element, you get an empty list instead of an error. Sometimes you would prefer the error, so you have to be aware that this may happen.
Relation to slice() object
The slicing operator [] is actually being used in the above code with a slice() object using the : notation (which is only valid within []), i.e.:
is equivalent to:
a[slice(start, stop, step)]
Slice objects also behave slightly differently depending on the number of arguments, similarly to range(), i.e. both slice(stop) and slice(start, stop[, step]) are supported. To skip specifying a given argument, one might use None, so that e.g. a[start:] is equivalent to a[slice(start, None)] or a[::-1] is equivalent to a[slice(None, None, -1)].
While the :-based notation is very helpful for simple slicing, the explicit use of slice() objects simplifies the programmatic generation of slicing.


Dictionaries are used to store data values in key:value pairs. Info about collections.Counter() available below.

  • Operations Time Complexities
dict = {'a':1,'b':2,'c':3}dict.keys() # returns list of keys of dictionary
dict.values() # returns list of values of dictionary
dict.get('a') # returns value for any corresponding key
dict.items() # returns [('a',1),('b',2),('c',3)]
dict.copy() # returns copy of the dictionary
# NOTE : items() Returns view object that will be updated with any future changes to dict
dict.pop(KEY) # pops key-value pair with that key
dict.popitem() # removes most recent pair added
dict.setDefault(KEY,DEFAULT_VALUE) # returns value of key, if key exists, else default value returned
# If the key exist, this parameter(DEFAULT_VALUE) has no effect.
# If the key does not exist, DEFAULT_VALUE becomes the key's value. 2nd argument's default is None.
dict.update({KEY:VALUE}) # inserts pair in dictionary if not present, if present, corresponding value is overriden (not key)
# defaultdict ensures that if any element is accessed that is not present in the dictionary
# it will be created and error will not be thrown (which happens in normal dictionary)
# Also, the new element created will be of argument type, for example in the below line
# an element of type 'list' will be made for a Key that does not exist
myDictionary = defaultdict(list)


Python Counter is a container that will hold the count of each of the elements present in the container. The counter is a sub-class available inside the dictionary class. Specifically used for element frequencies

Pretty similar to dictionary, infact I use defaultdict(int) most of the time

from collections import Counter #(capital 'C')
# can also be used as 'collections.Counter()' in code
list1 = ['x','y','z','x','x','x','y', 'z']# Initialization
Counter(list1) # => Counter({'x': 4, 'y': 2, 'z': 2})
Counter("Welcome to Guru99 Tutorials!") # => Counter({'o': 3, ' ': 3, 'u': 3, 'e': 2.....})
# Updating
counterObject = collections.Counter(list1)
counterObject.keys() = [ 'x' , 'y' , 'z' ]
most_common_element = counterObject.most_common(1) # [('x', 4)]
counterObject.update("some string") # => Counter({'o': 3, 'u': 3, 'e': 2, 's': 2})
counterObject['s'] += 1 # Increase/Decrease frequency
# Accessing
frequency_of_s = counterObject['s']
# Deleting
del couterObject['s']


A double-ended queue, or deque, has the feature of adding and removing elements from either end.

  • Operations Time Complexities
from collections import dequequeue = deque(['name','age','DOB'])queue.append("append_from_right") # Append from right
queue.pop() # Pop from right
queue.appendleft("fromLeft") # Append from left
queue.popleft() # Pop from left
queue.index(element,begin_index,end_index) # Returns first index of element b/w the 2 indices.
queue.remove() # removes first occurrance
queue.count() # obvious
queue.reverse() # reverses order of queue elements


As we know the Heap Data Structure is used to implement the Priority Queue ADT. In python we can directly access a Priority Queue implemented using a Heap by using the Heapq library/module.

  • Operations Time Complexities
import heapq # (minHeap by Default)nums = [5, 7, 9, 1, 3]heapq.heapify(nums) # converts list into heap. Can be converted back to list by list(nums).
heapq.heappush(nums,element) # Push an element into the heap
heapq.heappop(nums) # Pop an element from the heap
#heappush(heap, ele) :- This function is used to insert the element mentioned in its arguments into heap. The order is adjusted, so as heap structure is maintained.
#heappop(heap) :- This function is used to remove and return the smallest element from heap. The order is adjusted, so as heap structure is maintained.
# Other Methods Available in the Library
# Used to return the k largest elements from the iterable specified
# The key is a function with that accepts single element from iterable,
# and the returned value from that function is then used to rank that element in the heap
heapq.nlargest(k, iterable, key = fun)
heapq.nsmallest(k, iterable, key = fun)


A set is a collection which is unordered, immutable, unindexed, No Duplicates.

  • Operations Time Complexities
set = {1,2,3}set.add(item)
set.discard(item) | set.remove(item) # removes item | remove will throw error if item is not there, discard will not
set.pop() # removes random item (since unordered)
set.isdisjoint(anotherSet) # returns true if no common elements
set.issubset(anotherSet) # returns true if all elements from anotherSet is present in original set
set.issuperset(anotherSet) # returns true if all elements from original set is present in anotherSet
set.difference(anotherSet) # returns set containing items ONLY in first set
set.difference_update(anotherSet) # removes common elements from first set [no new set is created or returned]
set.intersection(anotherSet) # returns new set with common elements
set.intersection_update(anotherSet) # modifies first set keeping only common elements
set.symmetric_difference(anotherSet) # returns set containing all non-common elements of both sets
set.symmetric_difference_update(anotherSet) # same as symmetric_difference but changes are made on original set
set.union(anotherSet) # ...
set.update(anotherSet) # adds anotherSet without duplicate


A tuple is a collection which is ordered, unchangeable and can contain duplicate values

  • Operations Time Complexities
  • Similar to list
tuple = (1,2,3,1)tuple.count(1) # returns occurence of an item
tuple.index(1) # returns index of 1 in array


# ** split Function **
#The split() method breaks up a string at the specified separator and returns a list of strings.
text = 'Python is a fun programming language'
# split the text from space
print(text.split(' '))
#convert string to list
# Output: ['Python', 'is', 'a', 'fun', 'programming', 'language']# ** count Function **
#The count() method returns the number of occurrences of a substring in the given string.
message = 'python is popular programming language'
# number of occurrence of 'p'
print('Number of occurrence of p:', message.count('p')) # Output: Number of occurrence of p: 4
#The isnumeric() method returns True if all characters in a string are numeric characters. If not, it returns False.
s = '1242323'
print(s.isnumeric()) #Output: True
#The find() method returns the index of first occurrence of the substring (if found). If not found, it returns -1.
# check the index of 'fun'
print(message.find('fun')) # Output: 12
#The isalnum() method returns True if all characters in the string are alphanumeric (either alphabets or numbers). If not, it returns False.name = "M3onica Gell22er "
print(name.isalnum()) # Output : False
#The isalpha() method returns True if all characters in the string are alphabets. If not, it returns False
name = "Monica"
print(name.isalpha()) #output true
#other imp functions
string.strip([chars]) #The strip() method returns a copy of the string by removing both the leading and the trailing characters (based on the string argument passed).
string.upper() #he upper() method converts all lowercase characters in a string into uppercase characters and returns it.
string.lower() #The lower() method converts all uppercase characters in a string into lowercase characters and returns it.

Python integer division behaves differently with -ve numbers ex: -3//2 will give -2 answer instead of -1 so always use int(-3/2) for integer division in problems


The Modulo Operation (%) With Negative Numbers in Python

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