Elements of programming interviews in python pdf download

Elements of programming interviews in python pdf download

Elements of Programming Interviews in Python The Insiders' Guide,Adnan Aziz, Tsung-Hsien Lee, Amit Prakash

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Ice Cream at the Facebook Sweet Shop is always fun. Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software. Updated to reflect changing techniques and trends, this new fourth edition provides insider guidance on the unique interview process that today's programmers face. Online coding contests are being used to screen candidate pools of thousands, take-home projects have become commonplace, and employers are even evaluating a candidate's public code repositories at GitHub—and with competition becoming increasingly fierce, programmers need to shape themselves into the ideal candidate well in advance of the interview.

This edition combines a thoroughly revised basis in classic questions involving fundamental data structures and algorithms with problems and step-by-step procedures for new topics including probability, data science, statistics, and machine learning which will help you fully prepare for whatever comes your way. Programming Interviews Exposed teaches you the interview skills you need to stand out as the best applicant to help you get the job you want. The pressure is on during the interview process but with the right preparation, you can walk away with your dream job. This classic book uncovers what interviews are really like at America's top software and computer companies and provides you with the tools to succeed in any situation. The authors take you step-by-step through new problems and complex brainteasers they were asked during recent technical interviews. The problem-solving process is clearly illustrated so you'll be able to easily apply what you've learned during crunch time.

You'll also find expert tips on what questions to ask, how to approach a problem, and how to recover if you become stuck. All of this will help you ace the interview and get the job you want. What you will learn from this book Tips for effectively completing the job application Ways to prepare for the entire programming interview process How to find the kind of programming job that fits you best Strategies for choosing a solution and what your approach says about you How to improve your interviewing skills so that you can respond to any question or situation Techniques for solving knowledge-based problems, logic puzzles, and programming problems Who this book is for This book is for programmers and developers applying for jobs in the software industry or in IT departments of major corporations.

Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved. This book is about coding interview questions from software and Internet companies. It covers five key factors which determine performance of candidates: 1 the basics of programming languages, data structures and algorithms, 2 approaches to writing code with high quality, 3 tips to solve difficult problems, 4 methods to optimize code, 5 soft skills required in interviews. The basics of languages, algorithms and data structures are discussed as well as questions that explore how to write robust solutions after breaking down problems into manageable pieces. It also includes examples to focus on modeling and creative problem solving. Interview questions from the most popular companies in the IT industry are taken as examples to illustrate the five factors above. Besides solutions, it contains detailed analysis, how interviewers evaluate solutions, as well as why they like or dislike them.

The author makes clever use of the fact that interviewees will have limited time to program meaningful solutions which in turn, limits the options an interviewer has. So the author covers those bases. ElementWithCachedMax x, x if self. empty else max x, self. max Each of the specified methods has time complexity O 1. The additional space complexity is O n , regardless of the stored keys. We can improve on the best-case space needed by observing that if an element e being pushed is smaller than the maximum element already in the stack, then e can never be the maximum, so we do not need to record it.

We cannot store the sequence of maximum values in a separate stack because of the possibility of duplicates. We resolve this by additionally recording the number of occurrences of each maximum value. See Figure 5. max , self. append x if len self. MaxWithCount x, 1 The worst-case additional space complexity is O n , which occurs when each key pushed is greater than all keys in the primary stack. However, when the number of distinct keys is small, or the maximum changes infrequently, the additional space complexity is less, O 1 in the best-case. The time complexity for each specified method is still O 1.

Both stacks are initially empty, and their progression is shown from left-to-right, then top-to-bottom. The top of the auxiliary stack holds the maximum element in the stack, and the number of times that element occurs in the stack. The auxiliary stack is denoted by aux. If the queue is empty, dequeue typically returns null or throws an exception. Elements are added enqueued and removed dequeued in first-in, first-out order. The most recently inserted element is referred to as the tail or back element, and the item that was inserted least recently is referred to as the head or front element. A queue can be implemented using a linked list, in which case these operations have O 1 time complexity. The queue API often includes other operations, e. dequeue 3 2 0 2 0 2 0 4 enqueue 4 a Initial configuration.

b Queue a after dequeue. c Queue b after enqueuing 4. A deque, also sometimes called a double-ended queue, is a doubly linked list in which all insertions and deletions are from one of the two ends of the list, i. An insertion to the front is commonly called a push, and an insertion to the back is commonly called an inject. A deletion from the front is commonly called a pop, and a deletion from the back is commonly called an eject. Different languages and libraries may have different nomenclature. Queues boot camp In the following program, we implement the basic queue API—enqueue and dequeue—as well as a max-method, which returns the maximum element stored in the queue. The basic idea is to use composition: add a private field that references a library queue object, and forward existing methods enqueue and dequeue in this case to that object. append x def dequeue self : return self. pop 0 def max self : return max self. The time complexity of finding the maximum is O n , where n is the number of entries.

For example, queues are ideal when order needs to be preserved. deque class. append e pushes an element onto the queue. Similarly, q[-1] will retrieve, but not remove, the element at the back of the queue. popleft will remove and return the element at the front of the queue. In particular, each node in a binary tree has a depth, which is its distance from the root. Given a binary tree, return an array consisting of the keys at the same level. For example, you should return hhi, h6, 6i, h, , 2, i, h28, 0, 3, 1, 28i, h17, , i, hii for the binary tree in Figure 6. Hint: First think about solving this problem with a pair of queues. Solution: A brute force approach might be to write the nodes into an array while simultaneously computing their depth. Now we can sort this array using a stable sorting algorithm with node depth being the sort key. The time complexity is dominated by the time taken to sort, i. Intuitively, since nodes are already presented in a somewhat ordered fashion in the tree, it should be possible to avoid a full-blow sort, thereby reducing time complexity.

Furthermore, by processing nodes in order of depth, we do not need to label every node with its depth. append curr. left , curr. The space complexity is O m , where m is the maximum number of nodes at any single depth. Variant: Write a program which takes as input a binary tree and returns the keys in top down, alternating left-to-right and right-to-left order, starting from left-to-right. For exam- ple, if the input is the tree in Figure 6. Variant: Write a program which takes as input a binary tree and returns the keys in a bottom up, left-to-right order.

For example, if the input is the tree in Figure 6. Variant: Write a program which takes as input a binary tree with integer keys, and returns the average of the keys at each level. Rabin, Formally, a binary tree is either empty, or a root node r together with a left binary tree and a right binary tree. The subtrees themselves are binary trees. The left binary tree is sometimes referred to as the left subtree of the root, and the right binary tree is referred to as the right subtree of the root. Binary trees most commonly occur in the context of binary search trees, wherein keys are stored in a sorted fashion Chapter 11 on Page However, there are many other applications of binary trees: at a high level, binary tree are appropriate when dealing with hierarchies.

Figure 6. Node A is the root. Nodes B and I are the left and right children of A. The node depths range from 0 to 5. Node M has the highest depth 5 of any node in the tree, implying the height of the tree is 5. Often the node stores additional data. If a node is a left or a right child of p, we say it is a child of p. Note that with the exception of the root, every node has a unique parent. Usually, but not universally, the node object definition includes a parent field which is null for the root. Observe that for any node there exists a unique sequence of nodes from the root to that node with each node in the sequence being a child of the previous node.

This sequence is sometimes referred to as the search path from the root to the node. The parent-child relationship defines an ancestor-descendant relationship on nodes in a binary tree. Specifically, a node is an ancestor of d if it lies on the search path from the root to d. If a node is an ancestor of d, we say d is a descendant of that node. Our convention is that a node is an ancestor and descendant of itself. A node that has no descendants except for itself is called a leaf. The depth of a node n is the number of nodes on the search path from the root to n, not including n itself. The height of a binary tree is the maximum depth of any node in that tree. A level of a tree is all nodes at the same depth. See Figure 6. As concrete examples of these concepts, consider the binary tree in Figure 6. Node I is the parent of J and O. Node G is a descendant of B. The search path to L is hA, I, J, K, Li. The depth of N is 4. Node M is the node of maximum depth, and hence the height of the tree is 5.

The height of the subtree rooted at B is 3. The height of the subtree rooted at H is 0. Nodes D, E, H, M, N, and P are the leaves of the tree. A full binary tree is a binary tree in which every node other than the leaves has two children. A perfect binary tree is a full binary tree in which all leaves are at the same depth, and in which every parent has two children. A complete binary tree is a binary tree in which every level, except possibly the last, is completely filled, and all nodes are as far left as possible. This terminology is not universal, e. It is straightforward to prove using induction that the number of nonleaf nodes in a full binary tree is one less than the number of leaves. A complete binary tree on n nodes has height blog nc. A left-skewed tree is a tree in which no node has a right child; a right-skewed tree is a tree in which no node has a left child. In either case, we refer to the binary tree as being skewed. A key computation on a binary tree is traversing all the nodes in the tree.

Traversing is also sometimes called walking. Here are some ways in which this visit can be done. An inorder traversal of the binary tree in Figure 6. A preorder traversal of the binary tree in Figure 6. A postorder traversal of the binary tree in Figure 6. Let T be a binary tree of n nodes, with height h. Implemented recursively, these traversals have O n time complexity and O h additional space complexity. The space complexity is dictated by the maximum depth of the function call stack. If each node has a parent field, the traversals can be done with O 1 additional space complexity. Binary trees boot camp A good way to get up to speed with binary trees is to implement the three basic traversals—inorder, preorder, and postorder. left Inorder : Processes the root after the traversal of left child and before the traversal of right child.

right Postorder : Processes the root after the traversals of left and right children. data The time complexity of each approach is O n , where n is the number of nodes in the tree. Although no memory is explicitly allocated, the function call stack reaches a maximum depth of h, the height of the tree. Therefore, the space complexity is O h. The minimum value for h is log n complete binary tree and the maximum value for h is n skewed tree. Recursive algorithms are well-suited to problems on trees. Remember to include space implic- itly allocated on the function call stack when doing space complexity analysis.

Please read the introduction to Chapter 12 if you need a refresher on recursion. Some tree problems have simple brute-force solutions that use O n space solution, but subtler solutions that uses the existing tree nodes to reduce space complexity to O 1. Consider left- and right-skewed trees when doing complexity analysis. Note that O h com- plexity, where h is the tree height, translates into O log n complexity for balanced trees, but O n complexity for skewed trees. If each node has a parent field, use it to make your code simpler, and to reduce time and space complexity. Table 6. A perfect binary tree is height-balanced, as is a complete binary tree. A height-balanced binary tree does not have to be perfect or complete—see Figure 6. Write a program that takes as input the root of a binary tree and checks whether the tree is height- balanced.

Hint: Think of a classic binary tree algorithm. Solution: Here is a brute-force algorithm. Compute the height for the tree rooted at each node x recursively. The basic computation is to compute the height for each node starting from the leaves, and proceeding upwards. For each node, we check if the difference in heights of the left and right children is greater than one. We can store the heights in a hash table, or in a new field in the nodes. This entails O n storage and O n time, where n is the number of nodes of the tree. We can solve this problem using less storage by observing that we do not need to store the heights of all nodes at the same time. balanced : Left subtree is not balanced. balanced : Right subtree is not balanced.

balanced The program implements a postorder traversal with some calls possibly being eliminated because of early termination. Specifically, if any left subtree is not height-balanced we do not need to visit the corresponding right subtree. The function call stack corresponds to a sequence of calls from the root through the unique path to the current node, and the stack height is therefore bounded by the height of the tree, leading to an O h space bound. The time complexity is the same as that for a postorder traversal, namely O n.

Variant: Write a program that returns the size of the largest subtree that is complete. Variant: Define a node in a binary tree to be k-balanced if the difference in the number of nodes in its left and right subtrees is no more than k. Design an algorithm that takes as input a binary tree and positive integer k, and returns a node in the binary tree such that the node is not k-balanced, but all of its descendants are k-balanced. For example, when applied to the binary tree in Figure 6. Fredman and R. Tarjan, A heap is a specialized binary tree.

Specifically, it is a complete binary tree as defined on Page The keys must satisfy the heap property—the key at each node is at least as great as the keys stored at its children. See Figure 7. The array representation for the max-heap in Figure 7. A max-heap supports O log n insertions, O 1 time lookup for the max element, and O log n deletion of the max element. The extract-max operation is defined to delete and return the maximum element. Searching for arbitrary keys has O n time complexity. The min-heap is a completely symmetric version of the data structure and supports O 1 time lookups for the minimum element.

Note that the root hold the maximum key, b After the deletion of max of the heap in a. Deletion is per- Figure 7. Your program must compute the k longest strings in the sequence. All that is required is the k longest strings—it is not required to order these strings. As we process the input, we want to track the k longest strings seen so far. Out of these k strings, the string to be evicted when a longer string is to be added is the shortest one. A min-heap not a max-heap! is the right data structure for this application, since it supports efficient find-min, remove-min, and insert. In the program below we use a heap with a custom compare function, wherein strings are ordered by length. islice stream , k ] heapq. Therefore, if there are n strings in the input, the time complexity to process all of them is O n log k.

Use a heap when all you care about is the largest or smallest elements, and you do not need to support fast lookup, delete, or search operations for arbitrary elements. A heap is a good choice when you need to compute the k largest or k smallest elements in a collection. For the former, use a min-heap, for the latter, use a max-heap. Table 7. nlargest k, L heapq. If you need to build a max-heap on integers or floats, insert their negative to get the effect of a max-heap using heapq. Each trade is encoded by a line in the following format: ,AAPL,30, Lines within each file are sorted in increasing order of time. The remaining values are the stock symbol, number of shares, and price. You are to create a single file containing all the trades from the files, sorted in order of increasing trade times. The individual files are of the order of 5— megabytes; the combined file will be of the order of five gigabytes.

In the abstract, we are trying to solve the following problem. Write a program that takes as input a set of sorted sequences and computes the union of these sequences as a sorted sequence. For example, if the input is h3, 5, 7i, h0, 6i, and h0, 6, 28i, then the output is h0, 0, 3, 5, 6, 6, 7, 28i. Hint: Which part of each sequence is significant as the algorithm executes? Solution: A brute-force approach is to concatenate these sequences into a single array and then sort it. The time complexity is O n log n , assuming there are n elements in total. The brute-force approach does not use the fact that the individual sequences are sorted. We can take advantage of this fact by restricting our attention to the first remaining element in each sequence.

Specifically, we repeatedly pick the smallest element amongst the first element of each of the remaining part of each of the sequences. A min-heap is ideal for maintaining a collection of elements when we need to add arbitrary values and extract the smallest element. For ease of exposition, we show how to merge sorted arrays, rather than files. As a concrete example, suppose there are three sorted arrays to be merged: h3, 5, 7i, h0, 6i, and h0, 6, 28i. For simplicity, we show the min-heap as containing entries from these three arrays. In practice, we need additional information for each entry, namely the array it is from, and its index in that array. The min-heap is initialized to the first entry of each array, i. We extract the smallest entry, 0, and add it to the output which is h0i.

Then we add 6 to the min-heap which is {3, 0, 6} now. We chose the 0 entry corresponding to the third array arbitrarily, it would be a perfectly acceptable to choose from the second array. Next, extract 0, and add it to the output which is h0, 0i; then add 6 to the min-heap which is {3, 6, 6}. Next, extract 3, and add it to the output which is h0, 0, 3i; then add 5 to the min-heap which is {5, 6, 6}. Next, extract 5, and add it to the output which is h0, 0, 3, 5i; then add 7 to the min-heap which is {7, 6, 6}. Next, extract 6, and add it to the output which is h0, 0, 3, 5, 6i; assuming 6 is selected from the second array, which has no remaining elements, the min-heap is {7, 6}.

Next, extract 6, and add it to the output which is h0, 0, 3, 5, 6, 6i; then add 28 to the min-heap which is {7, 28}. Next, extract 7, and add it to the output which is h0, 0, 3, 5, 6, 6, 7i; the min-heap is {28}. Next, extract 28, and add it to the output which is h0, 0, 3, 5, 6, 6, 7, 28i; now, all elements are processed and the output stores the sorted elements. merge method which takes multiple inputs. Then there are no more than k elements in the min-heap. Both extract-min and insert take O log k time. Hence, we can do the merge in O n log k time. The space complexity is O k beyond the space needed to write the final result. In particular, if the data comes from files and is written to a file, instead of arrays, we would need only O k additional storage. Alternatively, we could recursively merge the k files, two at a time using the merge step from merge sort. There would be log k stages, and each has time complexity O n , so the time complexity is the same as that of the heap-based approach, i.

Brin and L. Page, Search algorithms can be classified in a number of ways. Is the underlying collection static or dynamic, i. Is it worth spending the com- putational cost to preprocess the data so as to speed up subsequent queries? Are there statistical properties of the data that can be exploited? Should we operate directly on the data or transform it? In this chapter, our focus is on static data stored in sorted order in an array. Data structures appropriate for dynamic updates are the subject of Chapters 7, 9, and The first collection of problems in this chapter are related to binary search. The second collection pertains to general search. This has O n time complexity. Fundamentally, binary search is a natural elimination-based strategy for searching a sorted array. The idea is to eliminate half the keys from consideration by keeping the keys in sorted order.

If the search key is not equal to the middle element of the array, one of the two sets of keys to the left and to the right of the middle element can be eliminated from further consideration. Questions based on binary search are ideal from the interviewers perspective: it is a basic technique that every reasonable candidate is supposed to know and it can be implemented in a few lines of code. On the other hand, binary search is much trickier to implement correctly than it appears—you should implement it as well as write corner case tests to ensure you understand it properly. Many published implementations are incorrect in subtle and not-so-subtle ways—a study re- ported that it is correctly implemented in only five out of twenty textbooks. Binary search can be written in many ways—recursive, iterative, different idioms for condi- tionals, etc. A disadvantage of binary search is that it requires a sorted array and sorting an array takes O n log n time.

However, if there are many searches to perform, the time taken to sort is not an issue. Many variants of searching a sorted array require a little more thinking and create opportunities for missing corner cases. Searching boot camp When objects are comparable, they can be sorted and searched for using library search functions. Typically, the language knows how to compare built-in types, e. However, user-defined types used in sorted collections must explicitly implement comparison, and ensure this comparison has basic properties such as transitivity. If the comparison is implemented incorrectly, you may find a lookup into a sorted collection fails, even when the item is present. Suppose we are given as input an array of students, sorted by descending GPA, with ties broken on name.

In the program below, we show how to use the library binary search routine to perform fast searches in this array. In particular, we pass binary search a custom comparator which compares students on GPA higher GPA comes first , with ties broken on name. Binary search is an effective search tool. It is applicable to more than just searching in sorted arrays, e. If your solution uses sorting, and the computation performed after sorting is faster than sorting, e. Table 8. Specifically, assuming a is a sorted list. This call returns the index of the first entry that is greater than or equal to the targeted value. If all elements in the list are less than x, the returned value is len a. This call returns the index of the first entry that is greater than the targeted value.

If all elements in the list are less than or equal to x, the returned value is len a. In an interview, if it is allowed, use the above functions, instead of implementing your own binary search. The following problem has a slight twist on this. Write a method that takes a sorted array and a key and returns the index of the first occurrence of that key in the array. For example, when applied to the array in Figure 8. Hint: What happens when every entry equals k? Solution: A naive approach is to use binary search to find the index of any element equal to the key, k. After finding such an element, we traverse backwards from it to find the first occurrence of that element.

The binary search takes time O log n , where n is the number of entries in the array. Traversing backwards takes O n time in the worst-case—consider the case where entries are equal to k. The fundamental idea of binary search is to maintain a set of candidate solutions. For the current problem, if we see the element at index i equals k, although we do not know whether i is the first element equal to k, we do know that no subsequent elements can be the first one. We start with all indices as candidates, i. The midpoint index, 4 contains k. Therefore we can now update the candidate set to [0, 3], and record 4 as an occurrence of k. We update the candidate set to [2, 3]. The value at the midpoint 2 is 2, so we update the candidate set to [3, 3]. Since the value at this midpoint is , we update the first seen occurrence of k to 3. Now the interval is [3, 2], which is empty, terminating the search—the result is 3.

Variant: Design an efficient algorithm that takes a sorted array and a key, and finds the index of the first occurrence of an element greater than that key. Call an index i a local minimum if A[i] is less than or equal to its neighbors. How would you efficiently find a local minimum, if one exists? Variant: Write a program which takes a sorted array A of integers, and an integer k, and returns the interval enclosing k, i. Variant: Write a program which tests if p is a prefix of a string in an array of sorted strings. The reduction in space is accomplished by exploiting the possibil- ity that a small fraction of errors of commission may be tolerable in some applications. Bloom, A hash table is a data structure used to store keys, optionally, with corresponding values. Inserts, deletes and lookups run in O 1 time on average. The underlying idea is is to store keys in an array. The hash code is an integer computed from the key by a hash function.

If the hash function is chosen well, the objects are distributed uniformly across the array locations. The standard mechanism to deal with collisions is to maintain a linked list of objects at each array location. A new array with a larger number of locations is allocated, and the objects are moved to the new array. A hash table is qualitatively different from a sorted array—keys do not have to appear in order, and randomization specifically, the hash function plays a central role. Compared to binary search trees discussed in Chapter 11 , inserting and deleting in a hash table is more efficient assuming rehashing is infrequent.

One disadvantage of hash tables is the need for a good hash function but this is rarely an issue in practice. Similarly, rehashing is not a problem outside of realtime systems and even for such systems, a separate thread can do the rehashing. A hash function has one hard requirement—equal keys should have equal hash codes. This may seem obvious, but is easy to get wrong, e. In addition, a hash function should be efficient to compute. As a rule, you should avoid using mutable objects as keys. First, the hash function should examine all the characters in the string. It should give a large range of values, and should not let one character dominate e. We would also like a rolling hash function, one in which if a character is deleted from the front of the string, and another added to the end, the new hash code can be computed in O 1 time. In some applications, a trie, which is a tree data structure that is used to store a dynamic set of strings, has computational advantages.

Unlike a BST, nodes in the tree do not store a key. Hash tables boot camp We introduce hash tables with two examples—one is an application that benefits from the algorith- mic advantages of hash tables, and the other illustrates the design of a class that can be used in a hash table. An application of hash tables Anagrams are popular word play puzzles, whereby rearranging letters of one set of words, you get another set of words. Crossword puzzle enthusiasts and Scrabble players benefit from the ability to view all possible anagrams of a given set of letters. Suppose you were asked to write a program that takes as input a set of words and returns groups of anagrams for those words.

Each group must contain at least two words. Since anagrams do not depend on the ordering of characters in the strings, we can perform the test by sorting the characters in the string. Two words are anagrams if and only if they result in equal strings after sorting. We can form the described grouping of strings by iterating through all strings, and comparing each string with all other remaining strings. If two strings are anagrams, we do not consider the second string again. This leads to an O n2 m log m algorithm, where n is the number of strings and m is the maximum string length.

Looking more carefully at the above computation, note that the key idea is to map strings to a representative. Given any string, its sorted version can be used as a unique identifier for the anagram group it belongs to. What we want is a map from a sorted string to the anagrams it corresponds to. Anytime you need to store a set of strings, a hash table is an excellent choice. The sorted strings are keys, and the values are arrays of the corresponding strings from the original input. defaultdict list for s in dictionary : Sorts the string , uses it as a key , and then appends the original string as another value into hash table. join sorted s ]. Sorting all the keys has time complexity O nm log m. The insertions add a time complexity of O nm , yielding O nm log m time complexity in total. Design of a hashable class Consider a class that represents contacts. For simplicity, assume each contact is a string. Two contacts should be equal if they contain the same set of strings, regardless of the ordering of the strings within the underlying list.

Multiplicity is not important, i. In order to be able to store contacts in a hash table, we first need to explicitly define equality, which we can do by forming sets from the lists and comparing the sets. In our context, this implies that the hash function should depend on the strings present, but not their ordering; it should also consider only one copy if a string appears in duplicate form. It should be pointed out that the hash function and equals methods below are very inefficient. In practice, it would be advisable to cache the underlying set and the hash code, remembering to void these values on updates. Since the set type is mutable , it cannot be hashed. Therefore we use frozenset. return hash frozenset self. Hash codes are often cached for performance, with the caveat that the cache must be cleared if object fields that are referenced by the hash function are updated. Hash tables have the best theoretical and real-world performance for lookup, insert and delete.

Each of these operations has O 1 time complexity. The O 1 time complexity for insertion is for the average case—a single insert can take O n if the hash table has to be resized. Consider using a hash code as a signature to enhance performance, e. Consider using a precomputed lookup table instead of boilerplate if-then code for mappings, e. When defining your own type that will be put in a hash table, be sure you understand the relationship between logical equality and the fields the hash function must inspect. Specifi- cally, anytime equality is implemented, it is imperative that the correct hash function is also implemented, otherwise when objects are placed in hash tables, logically equivalent objects may appear in different buckets, leading to lookups returning false, even when the searched item is present. Table 9. defaultdict, and collections. The difference between set and the other three is that is set simply stores keys, whereas the others store key-value pairs. All have the property that they do not allow for duplicate keys, unlike, for example, list.

In a dict, accessing value associated with a key that is not present leads to a KeyError exception. Have you ever Wanted to work at an exciting futuristic company? Struggled with an interview problem that could have been solved in 15 minutes? Wished you could study real-world computing problems? If so, you need to read Elements of Programming Interviews EPI. EPI is your comprehensive guide to interviewing for software development roles. The core of EPI is a collection of over problems with detailed solutions. The problems are representative of interview questions asked at leading software companies. The problems are illustrated with figures, tested programs, and additional variants.

edu no longer supports Internet Explorer. To browse Academia. edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. This document is a sampling of our book, Elements of Programming Interviews in Python EPI. Its purpose is to provide examples of EPI's organization, content, style, topics, and quality. The sampler focuses solely on problems; in particular, it does not include three chapters on the nontechnical aspects of interviewing. We'd love to hear from you-we're especially interested in your suggestions as to where the exposition can be improved, as well as any insights into interviewing trends you may have. You can buy EPI Python at Amazon. com paperback. ahmed azarnia. ALI MOULAEI NEJAD.

What motivated me to write these notes are i As a teacher, I feel that the sequence in which the topics are exposed has a significant impact on the appreciation and understanding of the subject. ii Most textbooks have far too much material for one semester and often intimidate an average student. Even when I pick and choose topics, the ones not covered have a distracting effect on the reader. iii Not prescribing a textbook in a course that I have done in the past creates insecurity among many students who are not adept at writing down notes as well as participating in class discussions so important for a course like algorithms.

As a corollary, this may make it easier for some of the students to skip some lectures. iv Gives me some flexibility about asking students to fill up some of the gaps left deliberately or inadvertently in my lectures. v Gives my students some idea of the level of formalism I expect in the assignments and exams - this is somewhat instructor dependent. vi The students are not uniformly mature so that I can demand formal scribing. I am sure that many of my colleagues have felt about one or more of the above reasons before they took the initiative at some point as is evidenced by the availability of many excellent notes that are accessible via internet.

This is a first draft that I am making available in the beginning of the semester and I am hoping to refine and fill up some of the incomplete parts by the middle of this semester. The notes are likely to contain errors, in particular, typographic. I am also relying on the power of collective scrutiny of some of the brightest students to point these out and I will endeavour to update this every week. I have also promised myself that I will not get carried away in adding to the present version beyond 1xx pages. Sandeep Sen July Cliff Shaffer. We study data structures so that we can learn to write more efficient programs. But why must programs be efficient when new computers are faster every year? The reason is that our ambitions grow with our capabilities. Instead of rendering efficiency needs obsolete, the modern revolution in computing power and storage capability merely raises the efficiency stakes as we computerize more complex tasks.

The quest for program efficiency need not and should not conflict with sound design and clear coding. dineshkumar t. Niroj Paudel. Marko Pantic. Miressa Beyene. Hui Liu. b0aza alnaim. Nurullah Shahin. Jose Luis Garavito Alejos. Naila Kase. ajith kumar. Shyam Sapkota. dmo shangla. Mahesh Kumar. Islamic Story. zuuzu koofi. Anglers world Kashmir. Alayjyoti Banerjee. Vivek Jha. rajput bunty. In Vino Veritas. Log in with Facebook Log in with Google. Remember me on this computer. Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up. Download Free PDF. Elements of Programming Interviews in Python The Insiders' Guide. dilip chaudhary. See Full PDF Download PDF.

Related Papers. data structure and object oriented programing. Download Free PDF View PDF. Lecture Notes for Algorithm Analysis and Design. Introduction Data structure and solutions. A Practical Introduction to Data Structures and Algorithm Analysis Third Edition Java Version. computer powerpoint. Data Structures and Algorithm Analysis Edition 3. Data Structures and Algorithms - Rance D. Necaise Previously, he was a professor at the Department of Electrical and Computer Engineering at The University of Texas at Austin, where he conducted research and taught classes in applied algorithms. He received his Ph. from The University of California at Berkeley; his undergraduate degree is from Indian Institutes of Technology Kanpur.

He has worked at Google, Qualcomm, IBM, and several software startups. When not designing algorithms, he plays with his children, Laila, Imran, and Omar. Tsung-Hsien Lee is a Senior Software Engineer at Uber. Previously, he worked as a Software Engineer at Google and as Software Engineer Intern at Facebook. He received both his M. and undergraduate degrees from National Tsing Hua University. He has a passion for designing and implementing algorithms. He likes to apply algorithms to every aspect of his life. He takes special pride in helping to organize Google Code Jam and Amit Prakash is a co-founder and CTO of ThoughtSpot, a Silicon Valley startup.

Previously, he was a Member of the Technical Staff at Google, where he worked primarily on machine learning problems that arise in the context of online advertising. Before that he worked at Microsoft in the web search team. from The University of Texas at Austin; his undergraduate degree is from Indian Institutes of Technology Kanpur. When he is not improving business intelligence, he indulges in his passion for puzzles, movies, travel, and adventures with Nidhi and Aanya. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form, or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior consent of the authors. The views and opinions expressed in this work are those of the authors and do not necessarily reflect the official policy or position of their employers.

We typeset this book using LATEX and the Memoir class. We used TikZ to draw figures. Allan Ytac created the cover, based on a design brief we provided. The companion website for the book includes contact information and a list of known errors for each version of the book. If you come across an error or an improvement, please let us know. com Distributed under the Attribution-NonCommercial-NoDerivs 3. Brooks, A program updates variables in memory according to its instructions. Variables come in types—a type is a classification of data that spells out possible values for that type and the operations that can be performed on it.

A type can be provided by the language or defined by the programmer.

Elements of Programming Interviews in Python: The Insiders’ Guide (PDF),Newest Books

WebDownload Elements Of Programming Interviews In Python: The Insiders’ Guide [PDF] Type: PDF Size: MB Download as PDF Download Original PDF This document Webin python pdf. elements of programming interviews the. elements of programming interviews in python the insiders. elements of programming interviews in python WebElements Of Programming Interviews In Python: The Insiders’ Guide [PDF] This document was uploaded by our user. The uploader already confirmed that they had the Webcompetition, nor does it impress the interviewer in an interview of company like Google, Microsoft, etc. The most difficult questions asked in competitions and interviews, are Webalgo/Elements of Programming interviews (new).pdf. Go to file. Cannot retrieve contributors at this time. MB WebOct 11,  · Download Elements Of Programming Interviews In Pythonfull books in PDF, epub, and Kindle. Read online free Elements Of Programming Interviews In ... read more

The second stage is similar to the first one, the difference being that we move elements greater than the pivot to the end of the array. For the recursive call starting at the subtree rooted at K, the constraint is [23, 43]. What we want is a map from a sorted string to the anagrams it corresponds to. next If key was not present in the list , L will have become null. Remember to include space implic- itly allocated on the function call stack when doing space complexity analysis. If each node has a parent field, the traversals can be done with O 1 additional space complexity.

This has O n time complexity. If e1 and e2 are S-expressions so is e1e2. Page 5 To my Aziz, father,lshrat for gk:ing tne my lifelong loae of learning Adnan Aziz To my parents, Hsicn-Kuo ke andTseng-Hsi"a Li, for the eaerlasting support and looe they gioe me Tsung-Hsien Lee To rny parents, Manju Shree and Arun Praknsh, the most loaing parents I can imagine Amit Prakash. This leads the following brute-force algorithm. Skip to content. Amit Prakash is a co-founder and CTO of ThoughtSpot, a Silicon Valley startup.