A Cheat Sheet 📜 to revise Python syntax in less time. Particularly useful for solving Data Structure and Algorithmic problems or a quick overview before an interview.
Lists are used to store multiple items in a single variable
Operations Time Complexities
nums = [1,2,3]nums.index(1)# returns indexnums.append(1)# appends 1nums.insert(0,10)# inserts 10 at 0th indexnums.remove(3)# removes all instances of 3nums.copy(1)# returns copy of the listnums.count(1)# returns no.of times '1' is present in the listnums.extend(someOtherList)# ...nums.pop()# pops last element [which element to pop can also be given as optional argument]nums.pop(0)# removes first element of listnums.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.# Two ways to reverse sort:nums.sort(reverse =True)nums =sorted(nums, reverse =True)# Comparatornums = [ ('hi',2), ('ignored',1), ('just passing by',0) ]nums.sort(key=lambdax: x[1])Post-sort of nums array:[ ('just passing by',0), ('ignored',1), ('hi',2) ]
It's pretty simple really:a[start:stop]# items start through stop-1a[start:]# items start through the rest of the arraya[:stop]# items from the beginning through stop-1a[:]# a copy of the whole arrayThere is also the step value, which can be used withany of the above:a[start:stop:step]# start through not past stop, by stepThe key point to remember is that the :stop value represents the first valuethat isnotin the selected slice. So, the difference between stop and start isthe number of elements selected (if step is1, the default).The other feature is that start or stop may be a negative number, which meansit counts from the end of the array instead of the beginning. So:a[-1]# last item in the arraya[-2:]# last two items in the arraya[:-2]# everything except the last two itemsSimilarly, step may be a negative number:a[::-1]# all items in the array, reverseda[1::-1]# the first two items, reverseda[:-3:-1]# the last two items, reverseda[-3::-1]# everything except the last two items, reversedPython is kind to the programmer if there are fewer items than you ask for. Forexample,if you ask for a[:-2]and a only contains one element, you get anempty list instead of an error. Sometimes you would prefer the error, so youhave to be aware that this may happen.Relation to slice()objectThe slicing operator []is actually being used in the above code with a slice()object using the :notation (which is only valid within []), i.e.:a[start:stop:step]is equivalent to:a[slice(start, stop, step)]Slice objects also behave slightly differently depending on the number ofarguments, similarly to range(), i.e. both slice(stop)andslice(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]isequivalent to a[slice(None, None, -1)].While the :-based notation is very helpful for simple slicing, the explicit useof slice() objects simplifies the programmatic generation of slicing.
Dictionary
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}len(dict)# length of dictionary, O(1) time complexity, O(1) space complexity, # In Python, a variable is maintained inside the container(here the dictionary) # that holds the current size of the container.for key indict:print"the key name is"+ key +"and its value is"+ dict[key]for key indict:print(key)# prints A \n B \n C# https://stackoverflow.com/a/5905090 # for _ in dict: # does the same thing as# for _ in dict.keys() # same as above, only thing .keys() is good for is a listdict.keys()# returns list of keys of dictionarydict.values()# returns list of values of dictionarydict.get('a')# returns value for any corresponding keydict.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 dictdict.pop(KEY)# pops key-value pair with that keydict.popitem()# removes most recent pair addeddict.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 existmyDictionary =defaultdict(list)
Counter
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, in fact I usedefaultdict(int)most of the time
from collections import Counter #(capital 'C')# can also be used as 'collections.Counter()' in codelist1 = ['x','y','z','x','x','x','y','z']# InitializationCounter(list1)# => Counter({'x': 4, 'y': 2, 'z': 2})Counter("Welcome to Guru99 Tutorials!")# => Counter({'o': 3, ' ': 3, 'u': 3, 'e': 2.....})# UpdatingcounterObject = 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# Accessingfrequency_of_s = counterObject['s']# Deletingdel couterObject['s']
Deque
A double-ended queue, or deque, has the feature of adding and removing elements from either end.
Operations Time Complexities
#in BFS(Breadth-first search) or other algorithms where we have to pop or add elements to the begining , deque is the best option
#we can also use list, but list.pop(0) is O(n) operation where as dequeu.popleft() is O(1)from collections import dequequeue =deque(['name','age','DOB'])queue.append("append_from_right")# Append from rightqueue.pop()# Pop from rightqueue.appendleft("fromLeft")# Append from leftqueue.popleft()# Pop from leftqueue.index(element,begin_index,end_index)# Returns first index of element b/w the 2 indices.queue.insert(index,element)queue.remove()# removes first occurrancequeue.count()# obviousqueue.reverse()# reverses order of queue elements
Heapq
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 heapheapq.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 heapheapq.nlargest(k, iterable, key = fun)heapq.nsmallest(k, iterable, key = fun)#Max heap in python #By default heapq in python is min heap, #if we want to use max heap we can simply invert the value of the keys and use heapq. #For example, turn 1000.0 into -1000.0 and 5.0 into -5.0.#The easiest and ideal solution#Multiply the values by -1#All the highest numbers are now the lowest and vice versa.#Just remember that when you pop an element to multiply it with -1 in order to get the original value again.#Example: import heapqheap = []heapq.heappush(heap, 1*(-1))heapq.heappush(heap, 10*(-1))heapq.heappush(heap, 20*(-1))print(heap)The output will look like:[-20,-1,-10]#when popping element multiply it with -1max_element =-heapq.heappop(heap)print(max_element)Output will be:20# Priority Queue (just add a tuple, first index is the compared value)https://stackoverflow.com/questions/3954530/how-to-make-heapq-evaluate-the-heap-off-of-a-specific-attributeheapq.heappush(heap, (5, 'accessory value')heapq.heappush(heap, (3, 'string not compared')heapq.heappush(heap, (6, 'just the value on the left')print(heap[0]) Will output:(3, 'string not evaluated')
Sets
A set is a collection which is unordered, immutable, unindexed, No Duplicates.
set={1,2,3}# new set cannot be made by {}, only with set()newSet =set()if3inset:print(True)len(set)# 3set.add(item)set.remove(item)set.discard(item)|set.remove(item)# removes item | remove will throw error if item is not there, discard will notset.pop()# removes random item (since unordered)set.isdisjoint(anotherSet)# returns true if no common elementsset.issubset(anotherSet)# returns true if all elements from anotherSet is present in original setset.issuperset(anotherSet)# returns true if all elements from original set is present in anotherSetset.difference(anotherSet)# returns set containing items ONLY in first setset.difference_update(anotherSet)# removes common elements from first set [no new set is created or returned]set.intersection(anotherSet)# returns new set with common elementsset.intersection_update(anotherSet)# modifies first set keeping only common elementsset.symmetric_difference(anotherSet)# returns set containing all non-common elements of both setsset.symmetric_difference_update(anotherSet)# same as symmetric_difference but changes are made on original setset.union(anotherSet)# ...set.update(anotherSet)# adds anotherSet without duplicate
Tuples
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 itemtuple.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 spaceprint(text.split(' '))# Output: ['Python', 'is', 'a', 'fun', 'programming', 'language']# String Builder instead of l += 'foo' < VERY SLOW sincePython strings are immutable, # so repeatedly using s += more will allocate lots of successively larger strings. # .join will generate the final string in one go from its constituent parts.# That said, if you're aiming for something like Java's StringBuilder, # the canonical Python idiom is to add items# to a list and then use str.join to concatenate them all at the end:l = []l.append('foo')l.append('bar')l.append('baz')s =''.join(l)# https://stackoverflow.com/a/4435194# convert string to lists="abcd"s=list(s)print(s)# Output: ['a', 'b', 'c', 'd']# ** count Function **# The count() method returns the number of occurrences of a substring in the given string.# Examplemessage ='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 Falsename ="Monica"print(name.isalpha())#output true# other important functionsstring.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() # The 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.
string.islower() # The islower() method returns True if all cased characters in the string are lowercase and there is at least one cased character, False otherwise.
string.isdigit()string.isupper() # The isupper() method returns True if all cased characters in the string are uppercase and there is at least one cased character, False otherwise.
Built-in or Library functions
Functions to iterate over list / other iterable (tuple, dictionaries)
**map(fun, iter)**# fun : It is a function to which map passes each element of given iterable.# iter : It is a iterable which is to be mapped.**zip(list,list)**for elem1,elem2 inzip(firstList,secondList):# will merge both lists and produce tuples with both elements# Tuples will stop at shortest list (in case of both lists having different len)# Example'''a = ("John", "Charles", "Mike")b = ("Jenny", "Christy", "Monica")x = zip(a, b)# use the tuple() function to display a readable version of the result:print(tuple(x))o/p: (('John', 'Jenny'), ('Charles', 'Christy'), ('Mike', 'Monica'))'''**any(list)** [ OPPOSITE IS =>**all()** ]any(someList)# returns true if ANY element in list is true [any string, all numbers except 0 also count as true]**enumerate(list|tuple)**# [when you need to attach indexes to lists or tuples ]enumerate(anyList)# ['a','b','c'] => [(0, 'a'), (1, 'b'), (2, 'c')]**filter(function|list)**filter(myFunction,list)# returns list with elements that returned true when passed in function*****************import bisect ************************* bisect.bisect(list,number,begin,end)**O(log(n))# [ returns the index where the element should be inserted # such that sorting order is maintained ]a = [1,2,4]bisect.bisect(a,3,0,4)# [1,2,4] => 3 coz '3' should be inserted in 3rd index to maintain sorting order# Other variants of this functions are => bisect.bisect_left() | bisect.bisect_right()# they have same arguments. Suppose the element we want to insert is already present# in the sorting list, the bisect_left() will return index left of the existing number# and the bisect_right() or bisect() will return index right to the existing number# ** bisect.insort(list,number,begin,end) ** O(n) to insert# ** bisect.insort_right(list,number,begin,end) ** # ** bisect.insort_left(list,number,begin,end) ** The above 3 functions are exact same of bisect.bisect(), the only differenceis that they return the sortedlist after inserting andnot the index. Theleft()right() logic is also same as above.
Getting ASCII value of a character
**ord(str)**# returns ascii value of the character , Example ord("a") = 97**chr(int)**# return character of given ascii value , Example chr(97) = "a"
Clean Code Tips
Doc Strings - Documentation for your functions in the interview to look slick 😎
A docstring is short for documentation string.
Python docstrings (documentation strings) are the string literals that appear right after the definition of a function, method, class, or module.
Triple quotes are used while writing docstrings. For example:
def double(num):
"""Function to double the value"""
return 2*num
Docstrings appear right after the definition of a function, class, or a module. This separates docstrings from multiline comments using triple quotes.
The docstrings are associated with the object as their __doc__ attribute.
So, we can access the docstrings of the above function with the following lines of code:
def double(num):
"""Function to double the value"""
return 2*num
print(double.__doc__)
Output
Function to double the value
Use Assert keyword in python for testing edge cases. Looks more professional.
Definition and Usage
The assert keyword is used when debugging code.
The assert keyword lets you test if a condition in your code returns True, if not, the program will raise an AssertionError.
You can write a message to be written if the code returns False, check the example below.
x ="hello"#if condition returns False, AssertionError is raised:assert x =="goodbye","x should be 'hello'"
ALWAYS be aware of any code snippet that is being REPEATED in your solution. MODULARITY #1 Priority. Refactoring is also an important part of interview.
This is usually asked as a follow up after coding the solution. Are there any changes you want to make to this solution?
# Python program showing how to# multiple input using split# taking two inputs at a timex, y =input("Enter a two value: ").split()print("Number of boys: ", x)print("Number of girls: ", y)print()# taking three inputs at a timex, y, z =input("Enter a three value: ").split()print("Total number of students: ", x)print("Number of boys is : ", y)print("Number of girls is : ", z)print()# taking two inputs at a timea, b =input("Enter a two value: ").split()print("First number is {} and second number is {}".format(a, b))print()# taking multiple inputs at a time# and type casting using list() functionx =list(map(int, input("Enter a multiple value: ").split()))print("List of students: ", x)
# Python program showing# how to take multiple input# using List comprehension# taking two input at a timex, y = [int(x)for x ininput("Enter two value: ").split()]print("First Number is: ", x)print("Second Number is: ", y)print()# taking three input at a timex, y, z = [int(x)for x ininput("Enter three value: ").split()]print("First Number is: ", x)print("Second Number is: ", y)print("Third Number is: ", z)print()# taking two inputs at a timex, y = [int(x)for x ininput("Enter two value: ").split()]print("First number is {} and second number is {}".format(x, y))print()# taking multiple inputs at a timex = [int(x)for x ininput("Enter multiple value: ").split()]print("Number of list is: ", x)# taking multiple inputs at a time separated by commax = [int(x)for x ininput("Enter multiple value: ").split(",")]print("Number of list is: ", x)
Syntax :
math.log(a,Base)
Parameters :a : The numeric value
Base : Base to which the logarithm has to be computed.
Return Value :
Returns natural log if 1 argument is passed and log with
specified base if 2 arguments are passed.
Exceptions :
Raises ValueError is a negative no. is passed as argument.
import math# Printing the log base e of 14print ("Natural logarithm of 14 is : ", end="")print (math.log(14))# Printing the log base 5 of 14print ("Logarithm base 5 of 14 is : ", end="")print (math.log(14,5))
Finding the ceiling and the floor value
Ceil value means the smallest integral value greater than the number and the floor value means the greatest integral value smaller than the number. This can be easily calculated using the ceil() and floor() method respectively.
# Python code to demonstrate the working of# ceil() and floor()# importing "math" for mathematical operationsimport matha =2.3# returning the ceil of 2.3 (i.e 3)print ("The ceil of 2.3 is : ", end="")print (math.ceil(a))# returning the floor of 2.3 (i.e 2)print ("The floor of 2.3 is : ", end="")print (math.floor(a))
Other Important functions
# Constants# Print the value of Euler e (2.718281828459045)print (math.e)# Print the value of pi (3.141592653589793)print (math.pi)print (math.gcd(b, a))print (pow(3,4))# print the square root of 4print(math.sqrt(4))a = math.pi/6b =30# returning the converted value from radians to degreesprint ("The converted value from radians to degrees is : ", end="")print (math.degrees(a))# returning the converted value from degrees to radiansprint ("The converted value from degrees to radians is : ", end="")print (math.radians(b))
**bin(int)**bin(anyNumber)# Returns binary version of number**divmod(int,int)**divmod(dividend,divisor)# returns tuple like (quotient, remainder)
Python cmp_to_key function to sort list with custom compare function
When providing a custom comparator, it should generally return an integer/float value that follows the following pattern (as with most other programming languages and frameworks):
return a negative value (< 0) when the left item should be sorted before the right item
return a positive value (> 0) when the left item should be sorted after the right item
return 0 when both the left and the right item have the same weight and should be ordered "equally" without precedence
from functools import cmp_to_keysorted(mylist, key=cmp_to_key(compare))# Exampledefcompare(item1,item2):iffitness(item1)<fitness(item2):return-1eliffitness(item1)>fitness(item2):return1else:return0
Python integer division doesn’t work properly with -ve numbers ex: -3//2 will give -2 answer instead of -1 so always use int(-3/2) for integer division in problems
