Data Science Blog Posts

Data structures are the data structured in a way for efficient use by the users. As the computer program relies hugely on the data and also requires a large volume of data for its performance, therefore it is highly important to arrange the data. This arrangement of data in organized structures is known as a data structure. Storing of the data in data structures allows the access, modifications, and other operations that can be carried over the data elements. The arrangement of the data is mainly done in a computer and therefore proper algorithms are required to carry on operations with the data structures. Reducing space and decreasing the time complexity of different tasks is the main aim of data structures. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. The most important points in a data structure are: A large amount of data is organized through every type of data structure. A particular principle is followed by every data structure. The basic principle of the data structure should be followed even if any operations are carried out over the data structure.  Arrangement of the data within a data structure can follow different orders. A data structure is therefore classified according to the way of arrangement of the data. Basically, there are two types of data structure. Primitive data structure Non-primitive data structure  The primitive type of data structure includes the predefined data structures such as char, float, int, and double. The non-primitive data structures are used to store the collection of elements. This data structure can be further categorized into Linear data structure Non-Linear data structure.  Read: Learn the differences between linear and non linear data structure What is Linear Data Structure: Definition and Characteristics It is a type of data structure where the arrangement of the data follows a linear trend. The data elements are arranged linearly such that the element is directly linked to its previous and the next elements. As the elements are stored linearly, the structure supports single-level storage of data. And hence, traversal of the data is achieved through a single run only. Characteristics It is a type of data structure where data is stored and managed in a linear sequence.  Data elements in the sequence are linked to one after the other. Implementation of the linear structure of data in a computer’s memory is easy as the data is organized sequentially. Array, queue. Stack, linked list, etc. are examples of this type of structure. The data elements stored in the data structure have only one relationship. Traversal of the data elements can be carried out in a single run as the data elements are stored in a single level. There is poor utilization of the computer memory if a structure storing data linearly is implemented. With the increase in the size of the data structure, the time complexity of the structure increases. Must read: Learn excel online free! These structures can therefore be summarized as a type of data structure where the elements are stored sequentially and follow the order where: Only one first element is present which has one next element. Only one last element is present which has one previous element. All the other elements in the data structure have a previous and a next element Our learners also read: Data structures and Algorithms free course! upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses You can use linear data structure in C, C++, Python, JavaScript, or any other programming language you are familiar with. If you get baffled when given a list of linear structures and wonder which of the following is a linear data structure, here’s a rundown of the types.  Types of Linear Data Structures Operations performed on linear data structure include insertion, deletion, searching, traversing, and sorting. All these operations serve as the foundation for linear data structures. Discussed below are the linear data structure types and the corresponding operations on linear data structures that can be performed. You can also look for linear data structure examples to develop a robust idea.  1. Array The array is that type of structure that stores homogeneous elements at memory locations which are contiguous. The same types of objects are stored sequentially in an array. The main idea of an array is that multiple data of the same type can be stored together. Before storing the data in an array, the size of the array has to be defined. Any element in the array can be accessed or modified and the elements stored are indexed to identify their locations. An array can be explained with the help of a simple example of storing the marks for all the students in a class. Suppose there are 20 students, then the size of the array has to be mentioned as 20. Marks of all the students can then be stored in the created array without the need for creating separate variables for marks for every student. Simple traversal of the array can lead to the access of the elements. Arrays incorporate the use of zero-based indexing techniques. This means users can access the first element with an index of 0, the second with an index of 1, and so on. Another remarkable feature of this linear data structure is that arrays provide a constant time of O(1) to access the elements, which means that it takes equal time to access any element in the series, disregarding the size of the array. Types of array: One-dimensional array: This is a simple form of array that contains elements, all of which are the same type of data, in a single row.  Two-dimensional array: This is also known as a matrix. This type of data structure has rows and columns and appears like a grid. The elements can be accessed using two indices- one for column and one for row.  Multi-dimensional array: These arrays have more than two dimensions.  Operations Performed on Arrays: The following operations can be performed on arrays: Accessing an element: Accessing an element by its index is an essential operation that can be performed on arrays. It is a constant time operation with a time complexity of O(1).  Inserting or deleting elements: Inserting elements at the end of an array is a constant-time operation having complexity O(1). But inserting an element at the beginning takes O(n) time since all the elements have to be shifted.  The same goes for deletion of elements in an array. Searching for elements: For unsorted data, linear search takes O(n) time, and for sorted data, binary search takes O(logn) time.  2. Linked list The linked list is that type of data structure where separate objects are stored sequentially. Every object stored in the data structure will have the data and a reference to the next object. The last node of the linked list has a reference to null. The first element of the linked list is known as the head of the list. There are many differences between a linked list to the other types of data structures. These are in terms of memory allocation, the internal structure of the data structure, and the operations carried on the linked list.  Getting to an element in a linked list is a slower process compared to the arrays as the indexing in an array helps in locating the element. However, in the case of a linked list, the process has to start from the head and traverse through the whole structure until the desired element is reached. In contrast to this, the advantage of using linked lists is that the addition or deletion of elements at the beginning can be done very quickly.  Our learners also read: Free Python Course with Certification There are three types of linked lists: Single Linked List: This type of structure has the address or the reference of the next node stored in the current node. Therefore, a node which at the last has the address and reference as a NULL. Example: A->B->C->D->E->NULL. A Double Linked List: As the name suggests, each node has two references associated with it. One reference directs to the previous node while the second reference points to the next node. Traversal is possible in both directions as reference is available for the previous nodes. Also, explicit access is not required for deletion. Example: NULL<-A<->B<->C<->D<->E->NULL. Linked List which is circular: The nodes in a circular linked list are connected in a way that a circle is formed. As the linked list is circular there is no end and hence no NULL. This type of linked list can follow the structure of both singly or doubly. There is no specific starting node and any node from the data can be the starting node. The reference of the last node points towards the first node. Example: A->B->C->D->E. Properties of a linked list are: Access time: O(n) Searching time: O(n) Adding element: O(1)  Deleting  an Element : O(1)  3. Stack The stack is another type of structure where the elements stored in the data structure follow the rule of LIFO (last in, first out) or FILO (First In Last Out). Two types of operations are associated with a stack i.e. push and pop. Push is used when an element has to be added to the collection and pop is used when the last element has to be removed from the collection. Extraction can be carried out for only the last added element. Types of stack: There are two types of stacks:  Fixed-size stack: This kind of stack does not grow or shrink. Once full, any attempt to add an element will lead to an overflow error. Similarly, an attempt to remove an element will also display an underflow error. Dynamic size stack: This kind of stack can grow or shrink. When a stack is full or empty, it can automatically resize to accommodate a new element or shrink in size.  Other operations are:  top(): This operation helps to return the element that has been inserted last and is at the top without removing it.  size(): This operation indicates the total number of elements the stack contains. isEmpty(): This operation helps to identify if a stack is empty.  Properties of a stack are: Adding element: O(1) deleting element:  O(1) Accessing Time: O(n) [Worst Case] Only one end allows inserting and deleting an element. Examples of the stack include the removal of recursion. In scenarios where a word has to be reversed, or while using editors when the word that was last typed will be removed first (using an undo operation), stacks are used. If you want to try interesting data structure projects, click to read this article. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 4. Queue Queue is the type of data structure where the elements to be stored follow the rule of First In First Out (FIFO). The particular order is followed for performing the required operations over the elements. The difference of a queue from that of a stack lies in the removal of an element, where the most recently added object is removed first in a stack. Whereas, in the case of a queue, the element that was added first is removed first. Following is a list of the different types of queues: Input restricted queue: In this kind of queue, one can only insert inputs from one end. Deletion, however, can be done from both ends.  Output restricted queue: This is just the reverse of input restricted queues. Here, the input can be taken from both ends, but deletion can only be done from one end.  Circular queue: In this kind of queue, the first and the last positions are connected to one another, resulting in a circular structure.  Double-ended queue: This kind of operation supports insertion and deletion from both ends.  Priority queue: In this kind of queue, elements can be accessed based on priority assigned to them. Both the end of the data structure is used for the insertion and the removal of data. The two main operations governing the structure of the queue are enqueue, and dequeue. Enqueue refers to the process where inserting an element is allowed to the collection of data and dequeue refers to the process where removal of elements is allowed, which is the first element in the queue in this case. Properties of a queue are: Inserting an element: O(1) Deleting an element: O(1) Accessing Time: O(n) Other queue operations are: peek() or front(): This helps to acquire the data element available at the queue’s front node without actually eliminating it.  rear(): This operation returns an element at the rear without it being removed.  ifNull(): Finds out if a queue is empty.  ifFull(): Finds out if a queue is full.  Have a look at this linear data structure example.  Example of the queue: Similar to those queues made while waiting for the bus or anywhere, the data structure too follows the same pattern. We can imagine a person waiting for the bus and standing at the first position as the person that came to the queue first. This person will be the first one who will get onto a bus, i.e. exit the queue. Queues are applied when multiple users are sharing the same resources and they have to be served on the basis of who has come first on the server.  Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Conclusion An increase in the size of the data has necessitated the efficient use of data structures in computer programs. Data if not organized in a structured manner, the performance of tasks over the elements becomes difficult. For a hassle-free operation, it is always important to organize it so that easy and effective operations can be carried out by computer programs. If the data elements are organized in sequential order then it is known as a linear data structure whereas if the data elements are arranged in a non-linear way, it is termed a non-linear structure.  Wide application of data structure has been observed in machine learning languages, real-life problems, etc. People, who are dreaming to work in this field, should be able to master these concepts. If you are want to learn more, then check out the upGrad Executive PG Programme in Data Science which provides a platform to transform you into successful data scientists. Designed for any mid-level professionals, the data science course will expose you to all the theoretical and practical knowledge required for your success. So why wait for other options, when success is just a click away. If any assistance is required, we will be happy to help you.

As a working professional, you are familiar with terms like data, database, information, processing, etc. You must have also come across terms like data mining and data warehouse. We’ll talk about those two terms in detail later on, but there’s a far more elaborate methodology that encompasses the two terms mentioned above: KDD. Before diving into the fundamentals of KDD, let’s get an insight into data mining. What is Data Mining? Data mining is the process of obtaining information from a vast dataset. It helps explore and identify user patterns and trends in datasets and uses algorithms to extract patterns from them. The interdisciplinary approach of data mining uses statistics, AI, ML, and database technology methods.  Data mining has found its importance across many industries, such as finance, healthcare, education, etc. For instance, in the retail industry, data mining uses data-gathering techniques to examine the trends in purchasing and creates models to anticipate customers’ behavior. It is used to: Predict cancellations according to the clients’ past data.  Offer product or service recommendations based on past usage.  Using past transactional data spot and stop any fraudulent behavior.  Customers are grouped based on similar purchasing history; this helps in sending personalized marketing messages.   Data mining and knowledge discovery are essential for businesses to identify patterns and make strategic decisions for the growth of the business.  What is KDD? KDD is referred to as Knowledge Discovery in Database and is defined as a method of finding, transforming, and refining meaningful data and patterns from a raw database in order to be utilised in different domains or applications. The above statement is an overview or gist of KDD, but it’s a lengthy and complex process which involves many steps and iterations. Now before we delve into the nitty-gritty of KDD, let’s try and set the tone through an example. Suppose, there’s a small river flowing nearby and you happen to be either one of a craft enthusiast, a stone collector or a random explorer. Now, you have prior knowledge that a river bed is full of stones, shells and other random objects. This premise is of the utmost importance without which one can’t reach the source. Must read: Free excel courses! Next, depending on whom you happen to be, the needs and requirements may vary. This is the second most important thing to understand. So, you go ahead and collect stones, shells, coins or any artefacts that might be lying on the river bed. But that brings along dirt and other unwanted objects along as well, which you’ll need to get rid of in order to have the objects ready for further use. At this stage, you might need to go back and collect more items as per your needs, and this process will repeat a few times or be completely skipped as per the conditions. The collected objects need segregation into different types to better suit your application and are further required to be cut, polished or painted. This stage is called the transformation stage. During this process, you gain an understanding of, for example, where you are more likely to find bigger stones of certain colouration – whether near the bank or deeper in the river, whether the artefacts are probable to be found upstream or downstream and so on. Data mining is an important part when you learn data science. This helps in decoding patterns which can help in more efficient and quicker completion of tasks. What you eventually end up with is the discovery of knowledge that is refined, reliable and highly specific to your application. Now, let’s dive into KDD in data mining in detail. Read: Data Mining Salary in India What is KDD in Data Mining? KDD in data mining is a programmed and analytical approach to model data from a database to extract useful and applicable ‘knowledge’. Data mining forms the backbone of KDD and hence is critical to the whole method. It utilises several algorithms that are self-learning in nature to deduce useful patterns from the processed data. The process is a closed-loop constant feedback one where a lot of iterations occur between the various steps as per the demand of the algorithms and pattern interpretations. What is the Use of the Knowledge Discovery Process in Data Mining? The knowledge discovery process in data mining aims to identify hidden relationships, patterns, and trends in the sample data, which can be used for making predictions, recommendations, and decisions. KDD is an interdisciplinary and broad field used in many industries like healthcare, finance, e-commerce, marketing, etc.  KDD in data mining is necessary for businesses and organizations since it enables them to get new knowledge and insights from the data. Knowledge discovery in databases can assist in improving customer experience, enhance the decision-making process, optimize operations, support strategic planning, and drive business growth.  Essential Terms for Understanding KDD in Data Mining If you seek an elaborate understanding of ‘what is KDD process?’, you must first get acquainted with certain terms. Some of these terms are listed below: Databases: Databases are organized collections of data stored on a computer. The information is structured since it is challenging to understand unstructured data, mainly from a large dataset.  Data mart: A data mart can be defined as a collection of interrelated databases. There can be different data sources, such as transactional data, operational data, client data, etc. The data passes through several stages. In the extraction stage, all the data is collected from various sources, followed by the transformational stage, where the data is sorted, processed, and then standardized. Finally, it is placed into the data mart.  Pattern: The valuable output from the analysis represents the dataset’s trends. KDD helps identify non-trivial patterns, which usually cannot be observed easily.  Knowledge: Interpreting and understanding the patterns helps decision-makers develop the necessary knowledge. The acquired knowledge must be novel and valuable. Ensuring that the knowledge is non-trivial, practical, and unique is crucial as it helps authorities make essential decisions about the business.  Expectation from KDD To explain the process of KDD, you must understand what the process is expected to achieve. These expectations include: Non-trivial: For example, you can find that people who purchase luxury items are wealthier than people who don’t. But, if you can discover the specific life events that might lead to purchasing luxury products, then the knowledge discovery is non-trivial.  Implicit knowledge: There are generally two sorts of knowledge: explicit and implicit. Explicit knowledge is usually apparent and evident. However, the knowledge embedded within the data is implicit. Implicit knowledge involves unwritten processes, trends, and rules that are challenging to discover. Previously unknown: KDD is expected to discover something new. The analysis will not be worth it if it only provides known information. The KDD process should provide information from the previously unknown dataset, allowing decision-makers to make a more informed decision.  Useful: Lastly, the knowledge should have practicality; otherwise, it will be useless. Hence, analysts have to sift through the data to find the pattern necessary for the business.  Steps Involved in a Typical KDD Process 1. Goal-Setting and Application Understanding This is the first step in the process and requires prior understanding and knowledge of the field to be applied in. This is where we decide how the transformed data and the patterns arrived at by data mining will be used to extract knowledge. This premise is extremely important which, if set wrong, can lead to false interpretations and negative impacts on the end-user. 2. Data Selection and Integration After setting the goals and objectives, the data collected needs to be selected and segregated into meaningful sets based on availability, accessibility importance and quality. These parameters are critical for data mining because they make the base for it and will affect what kinds of data models are formed. upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4   3. Data Cleaning and Preprocessing This step involves searching for missing data and removing noisy, redundant and low-quality data from the data set in order to improve the reliability of the data and its effectiveness. Certain algorithms are used for searching and eliminating unwanted data based on attributes specific to the application. Must read: Data structures and algorithm free! 4. Data Transformation This step prepares the data to be fed to the data mining algorithms. Hence, the data needs to be in consolidated and aggregate forms. The data is consolidated on the basis of functions, attributes, features etc. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 5. Data Mining This is the root or backbone process of the whole KDD. This is where algorithms are used to extract meaningful patterns from the transformed data, which help in prediction models. It is an analytical tool which helps in discovering trends from a data set using techniques such as artificial intelligence, advanced numerical and statistical methods and specialised algorithms. Our learners also read: Free Online Python Course for Beginners 6. Pattern Evaluation/Interpretation Once the trend and patterns have been obtained from various data mining methods and iterations, these patterns need to be represented in discrete forms such as bar graphs, pie charts, histograms etc. to study the impact of data collected and transformed during previous steps. This also helps in evaluating the effectiveness of a particular data model in view of the domain. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 7. Knowledge Discovery and Use This is the final step in the KDD process and requires the ‘knowledge’ extracted from the previous step to be applied to the specific application or domain in a visualised format such as tables, reports etc. This step drives the decision-making process for the said application. Read about: Data Mining Techniques You Should Know About Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Conclusion In today’s world, data is being generated from numerous sources of different types and in different formats, for example, economic transactions, biometrics, scientific, pictures and videos etc. With such huge amounts of information being traded each moment, a technique is of utmost importance which can extract the juice and provide reliable, high quality, and effective data for use in various fields for decision making. This is where KDD is so useful. If you are curious to learn about data science, check out upGrad & IIIT-B’s Executive PG Programme in Data Science. which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Data types are an essential concept in the python programming language. In Python, every value has its own python data type. The classification of data items or to put the data value into some sort of data category is called Data Types. It helps to understand what kind of operations can be performed on a value. If you are a beginner and interested to learn more about data science, check out our data science certification from top universities. In the Python Programming Language, everything is an object. Data types in Python represents the classes. The objects or instances of these classes are called variables. Let us now discuss the different kinds of data types in Python.  Built-in Data Types in Python Binary Types: memoryview, bytearray, bytes Boolean Type: bool Set Types: frozenset, set Mapping Type: dict Sequence Types: range, tuple, list Numeric Types: complex, float, int Text Type: str If you are using Python, check data type using the syntax type (variable). Get a detailed insight into what are the common built-in data types in Python and associated terms with this blog.   Our learners also read – free online python course for beginners! 1. Python Numbers We can find complex numbers, floating point numbers and integers in the category of Python Numbers. Complex numbers are defined as a complex class, floating point numbers are defined as float and integers are defined as an int in Python. There is one more type of datatype in this category, and that is long. It is used to hold longer integers. One will find this datatype only in Python 2.x which was later removed in Python 3.x.  “Type()” function is used to know the class of a value or variable. To check the value for a particular class, “isinstance()” function is used.  Must read: Data structures and algorithms free course! Integers: There is no maximum limit on the value of an integer. The integer can be of any length without any limitation which can go up to the maximum available memory of the system.  Integers can look like this: >>> print(123123123123123123123123123123123123123123123123123 + 1) 123123123123123123123123123123123123123123123123124 Floating Point Number: The difference between floating points and integers is decimal points. Floating point number can be represented as “1.0”, and integer can be represented as “1”. It is accurate up to 15 decimal places. Complex Number: “x + yj” is the written form of the complex number. Here y is the imaginary part and x is the real part. 2. Python List An ordered sequence of items is called List. It is a very flexible data type in Python. There is no need for the value in the list to be of the same data type. The List is the data type that is highly used data type in Python. List datatype is the most exclusive datatype in Python for containing versatile data. It can easily hold different types of data in Python.   Lists are among the most common built-in data types in Python. Like arrays, they are also collections of data arranged in order. The flexibility associated with this type of data is remarkable.  It is effortless to declare a list. The list is enclosed with brackets and commas are used to separate the items.  A list can look like this: >>> a = [5,9.9,’list’] One can also alter the value of an element in the list. Complexities in declaring lists: Space complexity: O(n) Time complexity: O(1) How to Access Elements in a Python List   Programmers refer to the index number and use the index operator [ ] to access the list items. In Python, negative sequence indexes represent the positions placed at the end of the array.  Therefore, negative indexing means starting from the items at the end, where -1 means the last item, -2 means the second last item, and so on.  How to Add Elements to a Python List   There are three methods of adding elements to a Python list: Method 1: Adding an element using the append() method  Using the append() method, you can add elements in this Python data type. This is ideally suited when adding only one element at a time. Loops are used to add multiple elements using this method. Both the time and space complexity for adding elements in a list using the append() method is O(1).  Method 2: Adding an element using the insert() method  Unlike the append() method, the insert() method takes two arguments: the position and the value. In this case, the time complexity is O(n), and space complexity is O(1).  Method 3: Adding an element using extend() method Alongside the append() and insert() methods, there is one more method used to add elements to a Python list known as the extend() method. The extend() method helps add multiple elements at the end of the list simultaneously. Here, the time complexity is O(n), and the space complexity is O(1).  How to Remove Elements from a Python List Removing elements from a Python list can be done using two methods: Method 1: Removing elements using the remove() method This built-in function can be used to remove elements from a Python list. Only one element can be removed at a time using this function.  If the element whose removal has been requested does not exist in the list, an error message pops up. Removing elements using the remove() method takes a time complexity of O(n) and a space complexity of O(1).  Method 2: Removing elements using pop() method  The pop() function can also help eliminate and return an element from this Python data type. However, by default, the function only removes the last element of the list.  If you want to remove any element from any specific position, provide the index of the element to be removed in the argument of the pop() function.  In this functionality, the time complexity for removing the last element is O(1)/O(n) O(1), and that for removing the first and middle elements is O(n). The space complexity in this case is O(1).  3. Python Tuple A Tuple is a sequence of items that are in order, and it is not possible to modify the Tuples. The main difference list and tuples are that tuple is immutable, which means it cannot be altered. Tuples are generally faster than the list data type in Python because it cannot be changed or modified like list datatype. The primary use of Tuples is to write-protect data. Tuples can be represented by using parentheses (), and commas are used to separate the items.  Tuples can look like this: >>> t = (6,’tuple’,4+2r) In the case of a tuple, one can use the slicing operator to extract the item, but it will not allow changing the value.  Data Frames in Python Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses upGrad’s Exclusive Data Science Webinar for you – document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 4. Python Strings A String is a sequence of Unicode characters. In Python, String is called str. Strings are represented by using Double quotes or single quotes. If the strings are multiple, then it can be denoted by the use of triple quotes “”” or ”’. All the characters between the quotes are items of the string. One can put as many as the character they want with the only limitation being the memory resources of the machine system. Deletion or Updation of a string is not allowed in python programming language because it will cause an error. Thus, the modification of strings is not supported in the python programming language. A string can look like this: >>> s = “Python String” >>> s = ”’a multi-string Strings are also immutable like tuples and items can be extracted using slicing operators []. If one wants to represent something in the string using quotes, then they will need to use other types of quotes to define the string in the beginning and the ending. Such as:  >>> print(“This string contains a single quote (‘) character.”) This string contains a single quote (‘) character. >>> print(‘This string contains a double quote (“) character.’) This string contains a double quote (“) character. Our learners also read: Excel online course free! 5. Python Set The Collection of Unique items that are not in order is called Set. Braces {} are used to defined set and a comma is used to separate values. One will find that the items are unordered in a set data type. Duplicates are eliminated in a set and set only keeps unique values. Operations like intersection and union can be performed on two sets.  Python set will look like this: >>> a = {4,5,5,6,6,6} >>> a  {4, 5, 6} The slicing operator does not work on set because the set is not a collection of ordered items, and that is why there is no meaning to the indexing of set. Python Developer Tools Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? 6. Python Dictionary Dictionary is a type of python data type in which collections are unordered, and values are in pairs called key-value pairs. This type of data type is useful when there is a high volume of data. One of the best functions of Dictionaries data type is retrieving the data for which it is optimized. The value can only be retrieved if one knows the key to retrieve it.  Braces {} (curly brackets) are used to define dictionaries data type in Python. A Pair in the dictionary data type is an item which is represented as key:value. The value and the key can be of any data type. Python Dictionary can look like this: >>> d = {3:’key’,4:’value’} 7. Boolean Type There can be only two types of value in the Boolean data type of Python, and that is True or False.  It can look like this: >>> type(True) <class ‘bool’> >>> type(False) <class ‘bool’> The true value in the Boolean context is called “truthy”, and for false value in the Boolean context, it is called “falsy”. Truthy is defined by the objects in boolean, which is equal to True, and in the same way, Falsy is defined by the objects equal to falsy. One can also evaluate Non-Boolean objects in a Boolean context. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Conclusion If you are reading this article, you are probably learning Python or trying to become a Python developer. We hope this article helped you learn about the data types in Python.  If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science.

The communication network is expanding, and so the people are using the internet! Businesses are going digital for efficient management. The data generated on the internet is rising, and thus datasets are becoming complex. It is essential to organise, manage, access and analyse the data carefully and efficiently, a data structure is the most helpful technique, and the article focuses on the same! Data Structure In computer science, data structures are the basis for abstract data types (ADT), where ADT are the logical form of the data type. The physical layout of the data type is implemented using the data structure. Different data structure types are used for different kinds of applications; some are specialised in particular tasks.  The data structure is a collection of data values and relationships among them, operations and functions applicable to the data.  It assists in organising, managing and storing data in a particular format. Thus, users can have easy access and modify the data efficiently.  Data structures help to manage large amounts of data, such as massive databases. Efficient algorithms are built based on efficient data structures. Besides efficient storage, data structures are also responsible for the efficient retrieval of information from stored memory. It includes an array, Linked List, Pointer, Searching, Stack, Graph, Queue, Structure, Programs, Sorting and so forth. The article covers the concept of Searching in Data Structure and its methods. Two examples of algorithms are explained in detail to understand the concept clearly. To gain further knowledge, skills and expertise, online courses on data structure are available, mentioned at the end of the article.  What is Searching in Data Structure? The process of finding the desired information from the set of items stored in the form of elements in the computer memory is referred to as ‘searching in data structure’. These sets of items are in various forms, such as an array, tree, graph, or linked list. Another way of defining searching in the data structure is by locating the desired element of specific characteristics in a collection of items.  Our learners also read: Data structures and Algorithms free course! Searching Methods in Data Structures Searching in the data structure can be done by implementing searching algorithms to check for or retrieve an element from any form of stored data structure. These algorithms are categorised based on their type of search operation, such as: Sequential search The array or list of elements is traversed sequentially while checking every component of the set. For example, Linear Search. Interval Search Algorithms designed explicitly for searching in sorted data structures are included in the interval search. The efficiency of these algorithms is far better than linear search algorithms. For example, Binary Search, Logarithmic Search. These methods are examined based on the time taken by an algorithm to search an element matching the search item in the data collections and are given by, The best possible time The average time The worst-case time The primary concerns are regarding worst-case times that lead to guaranteed predictions of the algorithm’s performance and are also easy to calculate compared to average times.  upGrad’s Exclusive Data Science Webinar for you – document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 To illustrate examples and concepts in this article, ‘n’ items in the data collection in any data format are considered. Dominant operations are used to simplify analysis and algorithm comparison. For searching in a data structure, a comparison is a dominant operation, which is denoted by O() and pronounced as “big-Oh” or “Oh”.    Must read: Learn excel online free! There are numerous searching algorithms in a data structure such as linear search, binary search, interpolation search, jump search, exponential search, Fibonacci search, sublist search, the ubiquitous binary search, unbounded binary search, recursive function for substring search, and recursive program to search an element linearly in the given array. The article is restricted to linear and binary search algorithms and their working principles. Let’s get detailed insight into the linear search and binary search in the data structure. Linear Search The linear search algorithm searches all elements in the array sequentially. Its best execution time is one, whereas the worst execution time is n, where n is the total number of items in the search array. It is the most simple search algorithm in data structure and checks each item in the set of elements until it matches the search element until the end of data collection. When data is unsorted, a linear search algorithm is preferred. Linear search has some complexities as given below: Space Complexity Space complexity for linear search is O(n) as it does not use any extra space where n is the number of elements in an array. Time Complexity *Best- case complexity = O(1) occurs when the search element is present at the first element in the search array. *Worst- case complexity = O(n) occurs when the search element is not present in the set of elements or array. *Average complexity = O(n) is referred to when the element is present somewhere in the search array. Example, Let’s take an array of elements as given below: 45, 78, 12, 67, 08, 51, 39, 26 To find ‘51’ in an array of 8 elements given above, a linear search algorithm will check each element sequentially till its pointer points to 51 in the memory space. It takes O(6) time to find 51 in an array. To find 12, in the above array, it takes O(3), whereas, for 26, it requires O(8) time.   Binary Search This algorithm finds specific items by comparing the middlemost items in the data collection. When a match occurs, it returns the index of the item. When the middle item is greater than the item, it searches for a central item of the left sub-array. In contrast, if the middle item is smaller than the search item, it explores the middle of the item in the right sub-array. It continues searching for an item until it finds it or until the sub-arrays size becomes zero. Binary search needs sorted order of items. It is faster than a linear search algorithm. It works on the divide and conquers principle. Run-time complexity = O(log n)  The binary search algorithm has complexities as given below: Worst-case complexity = O (n log n) Average complexity = O (n log n) Best case complexity = O (1) Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Example, Let’s take a sorted algorithm of 08 elements: 08, 12, 26, 39, 45, 51, 67, 78 To find 51 in an array of the above elements, The algorithm will divide an array into two arrays, 08, 12, 26, 39 and 45, 51, 67, 78 As 51 is greater than 39, it will start searching for elements on the array’s right side. It will further divide the into two such as 45, 51 and 67, 78 As 51 is smaller than 67, it will start searching left of that sub-array. That subarray is again divided into two as 45 and 51. As 51 is the number matching to the search element, it will return its index number of that element in the array. It will conclude that the search element 51 is located at the 6th position in an array. Binary search reduces the time to half as the comparison count is reduced significantly than the linear search algorithm.  Read: Types of Data Structures in Python Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Interpolation Search It is an improved variant of the binary search algorithm and works on the search element’s probing position. Similar to binary search algorithms, it works efficiently only on sorted data collection.  Worst execution time = O(n) When the target element’s location is known in the data collection, an interpolation search is used. To find a number in the telephone directory, if one wants to search Monica’s telephone number, instead of using linear or binary search, one can directly probe to memory space storage where names start from ‘M’.  Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Hashing One of the most widely used searching techniques in data structure, the underlying method of hashing transforms how we access and retrieve data. Fundamental to hashing are hash functions, which convert input data into fixed-size values called hashes. Hashing allows constant access, providing a direct path to the element of interest. To explain searching in data structure, let’s examine the intricacies of creating hash functions and overcoming potential obstacles like collisions as we go into the principles of hashing. Understanding Hashing Hashing is essentially the same as a secret code for data. An input (or key) is passed to a hash function, which converts it into a fixed-length string of characters—typically a combination of integers and letters. The generated hash is then used to search data structures, usually an array, as an index or address to find the corresponding data. Compromises in hashing Hashing has trade-offs, even if it has constant-time access appeal. The quality of the hash function determines how efficient hashing is; poorly constructed methods can increase collisions and reduce performance. Furthermore, overly complicated hash functions could introduce computational costs. Selecting the best hash function and collision resolution plan requires considering the dataset’s unique properties and anticipated usage patterns. One must strike a balance between simplicity, efficiency, and uniform distribution. Depth-First Search (DFS) When we move from what is searching in data structure in linear structures to the more complex domain of trees, Depth-First Search (DFS) becomes a key method to investigate tree branch searching in DS.  The structural diversity of trees and graphs is easily accommodated by DFS in algorithms for searching. The implementation is elegant because of its recursive nature, which mimics the innate recursive structure of trees. The traversal’s depth-first design is advantageous when focusing on taking a path and working your way to the end rather than examining other options. Let’s examine the versatility and effectiveness of DFS for searching operation in data structure by exploring its uses in various tree-based structures, such as binary trees and graphs.  How Depth-First Search Works DFS investigates as thoroughly as possible along one branch, starting with the root of a tree or a selected node in a graph, and then turns around to examine other branches. The process continues till every node has been visited. Applications of DFS in binary trees DFS fits in well with the structure of binary trees. It performs well when the objective is to search for a specific element or navigate the whole depth of the tree. Pre-order, in-order, and post-order DFS variations provide flexibility in capturing various facets of the tree’s contents and structure. Preorder DFS: Visits the live node before any offspring. To create a sorted list for binary search trees, in-order DFS visits the left child first, then the current node, and lastly, the right child. Post-order DFS: Frequently employed to remove nodes from a tree, it visits the children before the current node. Breadth-First Search (BFS) Breadth-First Search (BFS) is a logical and systematic way to explore a tree’s levels. In contrast to Depth-First Search (DFS), BFS chooses a different approach by focusing on the shallowest levels before going deeper.  Let’s examine the complexities of BFS, how to use it for search in data structure, its benefits, and applications. How Breadth-First Search Works BFS goes through a tree or graph level by level, methodically investigating every node at each level before going on to the next. The method ensures a thorough examination of the entire structure by gradually covering each level, starting from the root (or a selected node). BFS uses a queue data structure to keep track of the node processing order, which promotes a systematic and well-organized traversal. Applications of BFS Shortest Path Finding: BFS works exceptionally well when determining the shortest path is essential. BFS determines the shortest path from the root to any reachable node by methodically investigating levels. Because of this feature, BFS is an excellent option for applications such as navigation systems and network routing. Least Spanning Trees: BFS helps determine a graph’s least spanning trees. By methodically examining the graph, BFS finds the edges that make up the minimum spanning tree—a tree that spans all nodes with the lowest feasible total edge weight. Connected Components: BFS is skilled at locating connected components while working with undirected graphs. BFS assists in classifying nodes into discrete connected components by beginning at a node and investigating every reachable node. Benefits of BFS Optimal Path Finding: BFS ensures that the shortest path will always be used to reach a target node first. Because of its optimality, it is recommended in situations where accuracy and efficiency are crucial. Whole Investigation: BFS ensures that every level in a tree or graph is thoroughly and methodically investigated. When processing or analyzing every node in an organized way is the objective, this feature is helpful. Easily Implemented: Compared to more intricate traversal algorithms, BFS is comparatively simple to build due to its simplicity. Its popularity across various applications can be attributed in part to its simplicity. Conclusion Searching in data structures refers to finding a given element in the array of ‘n’ elements. There are two categories, viz. Sequential search and interval search in searching. Almost all searching algorithms are based on one of these two categories. Linear and binary searches are the two simple and easy-to-implementing algorithms in which binary works faster than linear algorithms. Though linear search is most straightforward, it checks each element until it finds a match to the search element, thus efficient when data collection is not sorted correctly. But, if the data collection is sorted and the length of an array is considerable, then binary search is faster. The data structure is an essential part of computer programming while dealing with datasets. Programmers and developers need to keep updating and upskilling themselves with basics and updates in computer programming techniques. Programmers dealing with data structure should opt for courses often.   If you are curious to learn more about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Introduction Have you ever wondered if there is an alternative to write those complex If-else statements in Python? If you do not want multiple ‘If’ statements to clutter your code, you should consider using the Switch case statement that provides a cleaner and quicker way to implement control flow in your code. Unlike C++, Java, Ruby, and other programming languages, Python does not provide a switch case statement, but it offers few workarounds to make this statement work. For example, Python allows you to create your code snippets that work like Python Switch case statements in the other programming languages. You will get to know more about the ways of implementing switch-case statements later in this blog. If you are interested to learn more about python, check out our data science courses. What is a Switch Statement in Python? In general, the switch is a control mechanism that tests the value stored in a variable and executes the corresponding case statements. Switch case statement introduces control flow in your program and ensures that your code is not cluttered by multiple ‘if’ statements. Hence, your code looks meticulous and transparent to viewers. It is a wonderful programming feature that programmers use to implement the control flow in their code. Switch case statement works by comparing the values specified in the case statements with variables in your code. A Python switch case statement enables a computer to select one of several possible execution routes depending on the value of a specified expression, which helps to streamline decision-making procedures. Now, the question arises ‘does Python have switch case statement?’. We should know that switch-case is not supported natively by Python; nonetheless we can get comparable results with alternative methods. Our learners also read – python free courses! Method 1: If-Elif Expressions The Basis: Basic Structure for if-elif The most common way to simulate switch case in Python is to use if-elif statements. More than 15.7 million developers use Python as their main coding language. Using an input expression as a guide, this fundamental method generates a sequence of if-elif conditions representing many scenarios. An alternative method of managing several conditions, which differs from the traditional if-elif structure, is demonstrated in the Python switch statement example as illustrated below: Dot Net Copy the programming switch_case_example(input_value) def:     If ‘case1’ is the value of input_value:         #Code in case #1     If input_value is equal to ‘case2’:         #Code in case #2     If input_value == ‘case3’, then         # Case 3 code     #… more elif criteria as necessary     alternatively:         # Default scenario when none of the parameters are met You can use this structure to run particular code blocks according to the value of input_value. Even while this method works, it may become burdensome when the number of cases increases. Code Simplification: Maximizing if-elif Chains If-elif chains may grow cumbersome as the number of cases rises, affecting the code’s readability and maintainability. We investigate methods to optimize and arrange these chains to address this. Using the if-elif statement’s fall-through feature is one method. It enables a case to move on to the next if its condition is not met. When many cases should run the same code, this Python case statement example helps: Dot Net The code def optimized_switch_case(input_value) should be copied.     If input_value is present in (‘case1’, ‘case2’, ‘case3’), then         # Programming for cases 1, 2, and 3. elif input_value == ‘case4’:         # Programming for case number four: elif input_value == ‘case5’         # Case 5 code #… more elif conditions as required else:         # Default scenario when none of the parameters are met This method shortens the code and eliminates redundancy. Elegant Python: Employing Dictionaries for Case Switch in Python The dictionaries in Python provide a sophisticated and efficient way to create switch-case logic. You can make a dictionary where the values correspond to the related code blocks, and the keys represent cases in place of a sequence of if-elif expressions, as illustrated below. Dot Net Copy the programming Switch case using dictionary def input_value(input_value):     cases: ~         ‘case1’: print(“Code for case1”), lambda         ‘case2’: print(“Code for case2”), lambda         ‘case3’: print(“Code for case3”), lambda         #… additional instances as required     # If input_value is not in cases, default case     cases.get(lambda: print(“Default case”), input_value)Then This method adheres to Python’s clear and expressive design principles while also streamlining and simplifying your code. Now that you know the answer to “is there switch case in Python?” and “Does Python support switch case?”, you can use Python switch syntax skillfully by becoming proficient in these if-elif statement strategies. You can then modify your approach according to your code’s complexity and scalability requirements. Method 2: Employing Cases in Functions Making the Most of Functions: Determining Case Functions Using functions as cases is another way to simulate Python switch case syntax. This method gives each scenario its own function, improving the code’s readability and modularity. Let’s see how to apply this strategy in practice. Step 1: Define the Case Functions Provide distinct functions that capture the particular behavior related to each scenario. For instance: Dot Net Copy the programming case1() def:     print(“Case 1: Executing Code”) case2() def     print(“Case 2: Executing Code”) case3() def:     print(“Case 3 is being executed”) #… provide more case functions as necessary The organization and maintainability of the code are enhanced by the fact that each function contains the logic relevant to a particular scenario. Step 2: Construct a Mapping Dictionary Create a dictionary now that associates case names with their corresponding functions: Dot Net Replicate the code case_functions = { ‘case1’: case1, ‘case2’: case2, ‘case3’: case3, #… other cases as required } This dictionary links case names to their corresponding functions as a lookup table. Execution in Motion: Execution in Motion of a Case You can dynamically execute case functions based on user input if you have functions representing cases and a dictionary that maps case names to functions in place. Your switch in Python gains flexibility from its dynamic execution. Step 3: Conduct Dynamic Case Functions  Create a system that receives input from the user, looks up the appropriate function in the dictionary, and then runs it. Here’s one instance: Dot Net Replicate this code: function execute_case(case_name):     # Use the dictionary to get the matching function: case_function = case_functions.get(case_name)     In the event that case_function: case_function(), #execute the case function     alternatively:         output(“Case not found”) Using user_input as an example, input(“Enter a case:”) run the case (user input) By seamlessly integrating with user input, this system enables the execution of certain case functions in response to the case name entered. Using this approach results in an expandable and modular code structure. It, therefore, only takes declaring or changing functions to add or modify situations, improving the maintainability and scalability of your application. The case statement in Python emphasizes being clean and modular to be aligned with the use of functions as cases. Method 3: Listing Switch-Like Behavior The Enum Class in Python: Generating Enumerations With the help of Python’s Enum class, we can easily create enumerations and include a case switch in Python with structure programs. Enumerations provide a neat and structured method of expressing different situations within a program. They are simply a collection of named values. Step 1: Establish a List Let’s begin by reviewing the fundamentals of making enumerations with a select case in Python. You may define an enumeration like this by using the enum module: Dot Net Copy the programming from a list import. List MyEnum(Enum) class:     Case No. 1     Case No. 2     Case No. 3     #… add more instances as necessary Here, CASE1, CASE2, and CASE3 represent the three separate cases comprising the enumeration MyEnum. A distinct value is assigned to each case switch in Python, giving your code a concise and comprehensible depiction of the various situations it may run into. Using the Force: Switch-Case with Listings Now that we have our enumeration, let’s investigate how to use it to mimic a select case in Python switch-case expressions.  Step 2: Using Enumerations in Switch-Like Statements Enumerations provide a more Pythonic solution to conventional switch-case structures. The following is an illustration of how to use enumerations within a case statement in Python: Dot Net Copy the programming Switch case using enum (case) def:     # When using Python 3.10 and later, a match statement     case of match:         MyEnum.CASE1 case:             print(“CASE1’s executing code”)         a MyEnum case.Case #2             print(“CASE2’s executing code”)         MyEnum.CASE3 case:             print(“CASE3 is being executed”)         issue _:             “Default case” is printed. # Usage example: Using enum, switch case (MyEnum.CASE2) In this Python switch example, we design a switch-case-like structure using a match statement (first introduced in Python 3.10). Because every case is defined explicitly, the code is easier to comprehend and retains the expressiveness for which Python is renowned. The Advantages of Listing There are various benefits to using enumerations in Python for switch-case-like behavior. Readability: Using enumerations to express various scenarios is a clear and self-documenting method. Maintainability: The code stays orderly, and adding or changing cases is simple. Pythonic: This method adheres to the simplicity and clarity of Python switch syntax. Using enumerations improves the readability and beauty of your Python code and offers a reliable workaround for situations requiring switch-case logic. This approach is especially effective if your application deals with a limited number of unique scenarios. Method 4: Applying Decorators to Switch-Like Action In Python, decorators can be very useful tools. Let’s understand the use of decorators to construct a switch-case mechanism to improve the code’s maintainability and organization. Creating Case Decorators in Design Discover the world of designing case decorators. Learn how to create and use decorators, specialized functions that offer a logical and structured switch-case logic for better maintainability and readability of Python scripts. Selecting Cases Dynamically with Decorators A versatile system that may be tailored to various conditions is achieved by combining decorators and dynamic case selection. This combination makes modifications adaptable and improves system responsiveness. Illustrative examples will demonstrate how to use these techniques in real-world Python programming. How to Implement Python Switch Case Statement If you have always coded in languages like C++ or Java, you may find it odd that Python does not have a switch case statement. Instead, Python offers numerous workarounds like a dictionary, Python classes, or Python lambda functions to implement switch-case statements.  If you want to know the exact reason behind not having a switch case statement in python, then you should check PEP 3103.  Before diving deep into these alternatives, let us first see how a switch case function typically works in other programming languages. Must read: Free excel courses! In the below example, we have used the C programming language switch (monthOfYear) {     case 1:         printf(“%s”, January);         break;     case 2:         printf(“%s”, February);         break;     case 3:         printf(“%s”, March);         break;     case 4:         printf(“%s”, April);         break;     case 5:         printf(“%s”, May);         break;     case 6:         printf(“%s”, June);         break;     case 7:         printf(“%s”, July);         break;    case 8:         printf(“%s”, August);         break;     case 9:         printf(“%s”, September);         break;     case 10:         printf(“%s”, October);         break;     case 11:         printf(“%s”, November);         break;     case 12:         printf(“%s”, December);         break;     default:         printf(“Incorrect month”);         break;     } Now, let us go further into Python switch case function alternatives and understand how these alternatives work with the help of examples. Read: Career Opportunities in Python: Everything You Need To Know Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Using Dictionary Mapping If you are familiar with other programming languages, then you must be knowing that the dictionary uses key-value pairs to store a group of objects in memory. When you are using a dictionary as an alternative to switch-case statements, keys of the key-value pair work as a case.  The following example shows the implementation of the switch case statement using a dictionary. Here, we are defining a function month() to print which month, a month of the year is. First, start by creating case statements and write individual functions for each case. Make sure that you write a function that tackles the default case. def january():     return “January” def february():     return “February” def march():     return “march” def april():     return “April” def may():     return “may” def june():     return “June” def july():     return “July” def august():     return “august” def september():     return “September” def october():     return “October” def november():     return “November”  def december():     return “December” def default():     return “Incorrect month” Next, create a dictionary object in Python and store all the functions that you have defined in your program. switcher = {     0: ‘january’,     1: ‘february’,     2: ‘march’,     3: ‘april’,     4: ‘may’,     5: ‘june’,     6: ‘july’,     7: ‘august’,     8: ‘september’,     9: ‘october’,     10: ‘november’,     11: ‘december’     } Lastly, create a switch function in your program that should accept integer as an input, performs a dictionary lookup, and invokes the corresponding functions. def month(monthOfYear):     return switcher.get(monthOfYear, default)() The complete code will look like this def january():     return “January” def february():     return “February” def march():     return “march” def april():     return “April” def may():     return “may” def june():     return “June” def july():     return “July” def august():     return “august” def september():     return “September” def october():     return “October” def november():     return “November”  def december():     return “December” def default():     return “Incorrect month”      switcher = {     0: ‘january’,     1: ‘february’,     2: ‘march’,     3: ‘april’,     4: ‘may’,     5: ‘june’,     6: ‘july’,     7: ‘august’,     8: ‘september’,     9: ‘october’,     10: ‘november’,     11: ‘december’     } def month(monthOfYear):     return switcher.get(monthOfYear, default)() print(switch(1)) print(switch(0)) The above code prints the following output February January Also Read: 42 Exciting Python Project Ideas & Topics for Beginners Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis upGrad’s Exclusive Data Science Webinar on the Future of Consumer Data in an Open Data Economy – document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 Using Python Classes You can also use Python classes as an alternative to implementing switch-case statements. A class is an object constructor that has properties and methods. Let us understand this further with the help of the same above example. Here, we will define a switch method inside a Python switch class. Must read: Data structures and algorithm free! Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Example First, we will define a switch method inside a Python switch class that takes a month of the year as an argument, converts the result into a string.   class PythonSwitch:     def month(self, monthOf Year):         default = “Incorrect month”         return getattr(self, ‘case_’ + str(monthOf Year), lambda: default)() Note: In the above example, we have used two things: keyword lambda and getattr() method.  We use the lambda keyword to define an anonymous function in Python. Lambda keyword invokes the default function when a user enters invalid input. getattr() method is used to invoke a function in Python. Now, create individual functions for each case. def january(self):         return “January”       def february(self):         return “February”    def march(self):         return “March”        def april(self):         return “April”       def may(self):         return “May”       def june(self):         return “June”    def july(self):         return “July”       def august(self):         return “August”       def september(self):         return “September”    def october(self):         return “October”       def november(self):         return “November”       def december(self):         return “December” The complete code will look like this class PythonSwitch:     def month(self, monthOf Year):         default = “Incorrect month”         return getattr(self, ‘case_’ + str(monthOf Year), lambda: default)()     def january(self):         return “January”       def february(self):         return “February”       def march(self):         return “March”        def april(self):         return “April”       def may(self):         return “May”       def june(self):         return “June”    def july(self):         return “July”       def august(self):         return “August”       def september(self):         return “September”    def october(self):         return “October”       def november(self):         return “November”       def december(self):         return “December” my_switch = PythonSwitch() print (my_switch.month(1)) print (my_switch.month(10)) The above code prints the following output January October Check out: Python Developer Salary in India Conclusion In this blog, you have learned about switch-case statements, what are the alternatives of switch-case statements, and how to use them. As explained above, Python does not have an in-built switch case function, but you can always use these alternatives to make your code look neat and clean and get better performance.  If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Data structures serve as the backbone of efficient data organization and management within computer systems. They play a pivotal role in computer algorithms and software development, contributing significantly to designing streamlined and effective programs. Data structures are indispensable across various domains in computer science, ranging from Artificial Intelligence to Operating Systems.  One of the most widely used non-linear data structures is trees. Trees portray a hierarchical arrangement, rendering ordering information irrelevant, unlike their linear counterparts, such as arrays, stacks, queues, and linked lists.  Comprising nodes and pointers, a tree’s structure involves parent nodes with left and right children, presenting a versatile and fundamental concept in data organization. This brings us to the topic we will discuss extensively in this blog – binary tree representation in data structure. Amongst different types of data structures are binary trees that come with more uses than most of the other types. Their most notable applications include peer-to-peer programming, search, cryptography, network routers with higher bandwidth than others, and 3D video games. We will now discuss in detail what binary trees in data science are, what are their types, and how are they represented.  What are binary trees? If you have worked on normal trees before or even know about their basics, you would know that there are no restrictions when it comes to the number of children that different nodes are allowed to have in these trees. Binary trees are a little different in this sense. Every parent or node in binary trees can have a maximum of only two children.  In binary tree representation, this limitation, however, simplifies the structure, making binary trees highly efficient in scenarios where quick data retrieval is essential. The binary nature allows for straightforward decision-making processes commonly used in searching algorithms. Despite the restriction on the number of children, the versatility and speed of binary trees make them crucial elements in various computer science applications. All nodes in a binary tree have three primary components –  a data element  This component has the actual information or value held by the node. It could represent various types of data, such as numbers, strings, or other relevant content, depending on the context of the tree. a right reference  The right reference is a pointer or reference that directs to the node’s right child. The right child is a node positioned to the right of the current node within the binary tree hierarchy. This reference helps traverse and explore the tree’s right branch. a left reference  Similar to the right reference, the left reference is a pointer or reference that points to the node’s left child. The left child is positioned to the left of the current node, forming the left branch of the binary tree. This reference is important for navigating and analyzing the tree’s left subtree. In combination, these three components create a cohesive structure, allowing for efficient organization, storage, and data retrieval within the binary tree representation. The data element holds the pertinent information, while the right and left references guide the relationships between nodes, forming the foundation for the tree’s hierarchical arrangement. The node that lies at the top of the tree is referred to as the root node. Parent nodes are those that have children. Children nodes and parent nodes are connected to each other through references. Nodes that don’t have any children are referred to as leaf nodes. It is clearly evident that nodes in binary trees can have one child, two children, or no children at all. Binary trees aren’t linear data structures like queues, arrays, stacks, and linked lists. They are hierarchical data structures instead.  Check out: Data Science Project Ideas for Beginners Important properties of nodes in binary trees A better understanding of these properties will help you in making the most of this discussion on binary trees. The depth of different nodes is defined as the number of nodes that exist on the way that connects the root to a particular node. That is why the depth of the root node is 0. On the other hand, the height of different nodes in a binary tree is the number of nodes that lie in the path that connects a particular node with the root node. That is why the height of leaf nodes is 0.  As you can clearly see, the depth of a node is measured by starting from the root node and then going down to reach that node. On the other hand, when it comes to calculating the height, we start at the node in question and then journey towards the root node. Both the times, we start at 0. There are people who also measure height and depth from1 and not from 0, which isn’t wrong and is just what different people prefer.  Now the maximum depth of a node is defined as the depth of a binary tree. Similarly, the maximum height of a node is defined as the height of a binary tree. So the height and depth of a binary tree are always the same.  Learn more: Data Structures & Algorithm in Python What is a binary search tree?  A binary search tree is the most common of all the other types of binary trees. It is a specialized binary tree that comes with properties that are different and more useful than any other form of a binary tree. What exactly is a binary search tree or BST? Just as its name suggests, a binary search tree is used to search data in the tree. A BST comes with properties that allow it to facilitate efficient searches. A BST is a binary tree that has the key of the node that is smaller and greater than nodes in the right sub-tree and nodes in the left sub-tree respectively.  Our learners also read: Free excel courses! upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Advantages of binary search tree There are several advantages of binary search tree that contribute to their widespread use in computer science and algorithm design: Efficient search operations: The binary search tree structure enables quick and efficient search operations. The binary nature of the tree ensures that at each node, the left subtree contains values smaller than the node, and the right subtree contains values greater. This property allows for a binary search algorithm, reducing the search space at each step. Simple insertion and deletion: Inserting and deleting elements in a binary search tree is quite straightforward. The binary structure allows for easy maintenance of the order, and the tree can be rebalanced if needed to maintain optimal search performance. Ordered data retrieval: In-order traversal of a binary search tree results in sorted data retrieval. This property is valuable in scenarios where data needs to be accessed or processed in sorted order without additional sorting operations. Space efficiency: Compared to other data structures like arrays, binary search trees are more space-efficient, especially when dealing with dynamic datasets. Nodes are allocated as needed, and memory is not pre-allocated, making it flexible for varying data sizes. Versatility in applications: Binary search trees find applications in various domains, including databases, symbol tables, and compilers. Their efficient search and ordered retrieval properties make them versatile where these operations are critical. Balanced binary search trees: When balanced, binary search trees ensure optimal performance in terms of search, insertion, and deletion operations. Balanced trees, such as AVL trees or Red-Black trees, maintain a balanced structure, preventing the tree from degenerating into a linked list and ensuring logarithmic time complexity for operations. Representation of binary trees Binary tree representation in data structure can be carried out in different ways, each with distinct pros and cons. The prominent methods are binary tree representation using linked list (Linked representation), storage representation of binary tree (Sequential representation), and linear representation. 1. Linked representation Binary trees in linked representation are stored in the memory as linked lists. These lists have nodes that aren’t stored at adjacent or neighboring memory locations and are linked to each other through the parent-child relationship associated with trees.  In this representation, each node has three different parts –  pointer that points towards the right node,  pointer that points towards the left node,  data element.  This is the more common representation. All binary trees consist of a root pointer that points in the direction of the root node. When you see a root node pointing towards null or 0, you should know that you are dealing with an empty binary tree. The right and left pointers store the address of the right and left children of the tree.  Our learners also read: Free Python Course with Certification 2. Sequential representation Although it is simpler than linked representation, its inefficiency makes it a less preferred binary tree representation of the two. The inefficiency lies in the amount of space it requires for the storage of different tree elements. The sequential representation uses an array for the storage of tree elements. The number of nodes a binary tree has defines the size of the array being used. The root node of the binary tree lies at the array’s first index. The index at which a particular node is stored will define the indices at which the right and left children of the node will be stored. An empty tree has null or 0 as its first index.  Also read: Free data structures and algorithm course! Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Types of binary trees Full binary trees: Full binary trees are those binary trees whose nodes either have two children or none. In other words, a binary tree becomes a full binary tree when apart from leaves, all its other nodes have two children. Complete binary trees: Complete binary trees are those that have all their different levels completely filled. The only exception to this could be their last level, whose keys are predominantly on the left. A binary heap is often taken as an example of a complete binary tree. Perfect binary trees: Perfect binary trees are binary trees whose leaves are present at the same level and whose internal nodes carry two children. A common example of a perfect binary tree is an ancestral family tree. Pathological degenerate binary trees: Degenerate trees are those binary trees whose internal nodes have one child. Their performance levels are similar to linked lists. Learn more about the types of binary tree. Read: The Six Most Commonly Used Data Structures in R Benefits of binary trees An ideal way to go with the hierarchical way of storing data Reflect structural relationships that exist in the given data set Make insertion and deletion faster than linked lists and arrays  A flexible way of holding and moving data Are used to store as many nodes as possible Are faster than linked lists and slower than arrays when comes to accessing elements Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Conclusion In this blog, we have discussed what binary trees in data structures are as well as talked about their types, their representations, and their benefits. The two major uses of the trees are for searching and storing data, and hence they are integral to the study of Data Science and its related fields.   Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis If you are curious to learn about binary trees in data structures, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Introduction When you buy a product from an e-commerce site, you will often find that you will be asked to review the product and provide feedback on the services. Likewise, when you interact with a customer care agent, your conversation is recorded, and again you are requested to give your feedback and suggestions. When you open a webpage, you are asked if you would like to enable cookies. In today’s day and age, businesses are constantly gathering information.  Every time we interact with a business, the interaction is converted to data by the company. Any information businesses can collect and collate is beneficial for their future decision-making. All businesses have their objectives and goals and it is based on these that they collect data from their customers.  But how does an organisation make sense of the mountain of data that is available to them? It is humanly impossible to brush through all the information to draw relevant insights that would be useful for a business. The time and effort to comb through data manually is immense.  The modern business highly depends on the data that is generated by consumers and users. Converting data into meaningful information is essential to understand and analyze the business insight that will help drive the business into profitable actions. That is where Business Intelligence (BI) comes into effect. You may be wondering, what exactly does Business Intelligence mean? Business Intelligence (BI) combines data mining, data visualisation, business analytics, process analysis, performance benchmarking, and data tools and infrastructure to uncover actionable insights from business data in user-friendly formats such as graphs, dashboards, charts, and reports. By presenting past and current trends in easy-to-understand visual formats, BI allows businesses to make effective management and business decisions, thus, increasing their competitive advantage.  The field is competitive. But if you want to leave your mark and crack the interview for a Business Intelligence industry position, follow our list of Business Intelligence interview questions and sail through the interview without a sweat. These Business Intelligence analyst interview questions are specifically designed to offer you a glance at what you’re going to face when dealing with recruiters. This article will provide all the necessary business intelligence interview questions and answers for your interview preparation. Source The Business Intelligence industry and its professionals rapidly evolve with time and space with no specific signs of slowing down. It is because almost every large enterprise is heavily dependent on data-driven analysis and its predictions.  Hence, every company is transforming its decision-making abilities through Business Intelligence tools. So, we have compiled the most commonly asked Business Intelligence interview questions and answers. These questions are popularly asked in the top companies and can help you clear the Business Analyst interviews. Business Intelligence Interview Questions And Answers  Source Here the top business intelligence interview questions and answers 1. What do you understand by Business Intelligence? (This is one of the most common business intelligence interview questions) This is among the common business intelligence interview questions for freshers. The term Business Intelligence refers to a collective meaning, including technologies, tools, applications, practices for the data collection, and providing that data to the users, especially to help in running the business or a part of it. In other words, the business analyst’s reports generated and compiled using the Business Intelligence approaches are consumed by the higher management administrative and business executives to make better decisions for the overall maturity of the business. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 2. What are the primary objectives of Business Intelligence? The primary objectives of Business Intelligence are: Business Intelligence is leverage to make the following enterprise-level decisions. Business Intelligence helps in identifying the wrong tracks and approaches of a business. Business Intelligence can cluster the data for analysis and then compile them to monitor corrective actions. Business Intelligence is also useful for determining whether a company is executing as per plan. Identification and extraction of trends and insights from business are possible using Business Intelligence tools. Source 3. Can you explain the concept of data modeling in BI and why it is essential? Data modeling is the act of constructing a visual representation of the relationships between the many types of data that make up a database or data warehouse. Tables, relationships, and characteristics must be defined. Because it offers a clear framework for organizing and accessing data, data modeling is crucial to BI because it aids analysts in understanding the structure of the data and how it may be utilized for reporting and analysis. 4. What are the popular Business Intelligence (BI) tools used by Business Analysts? The popular Business Intelligence (BI) tools used by Business Analysts are: Microsoft BI Cognos MicroStrategy Tableau SAS Business Objects OBIEE Hyperion Read: MBA Interview Question 5. How will you implement a BI system in your professional approach? There are three steps to implement a BI system: Extract the raw data from the corporate database. The data might be available across various heterogeneous databases. Then, the data is cleaned to put them in the data-warehouse by linking the table and forming the data cubes. Lastly, using BI systems, business analysts can extract business insights, request ad-hoc reports, analyze those clean datasets, and predict the business decisions. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? 6. How will you define OLAP (Online Analytical Processing)? (This is one of the most common business intelligence interview questions) OLAP (Online Analytical Processing) is a technological concept applied in various BI tools and applications that helps in executing complex analytical calculations. OLAP analyzes the trends, performs intricate calculations (like aggregation, summation, count, average, min, max), and carries out sophisticated data modeling in a BI system. OLAP systems’ primary objective is to diminish the response time of queries and improve the effectiveness of the calculated reports. 7. How will you define OLTP (Online Transaction Processing)? OLTP (Online Transaction Processing) systems are the vast collection of small data transactions like insert, delete, and update. These are operational databases that produce quick processing of a query. It also determines the integrity and consistency of data. It is the number of transactions per second that helps in measuring the efficiency of an OLTP system. Learn data science course online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 8. Define the term Data Warehousing? (Another common business intelligence interview questions) Data Warehousing is the repository system used to analyze and report data from various heterogeneous sources and forms. These data will be available from the oracle database, SQL Server, Postgres, or a simple excel sheet. The warehouse uses one central mechanism called the repository, through which the business analyst can fetch all historical reports associated with that data. Our learners also read: Top Python Courses for Free 9. What is the difference between structured and unstructured data in the context of Business Intelligence? Data that is arranged in a set manner is referred to as structured data. Spreadsheets and databases frequently include structured data. It is ideal for conventional BI analysis since it is well-organized and simple to query.  Contrarily, unstructured data has no established structure and might consist of text, pictures, videos, and social media postings. For the purpose of gaining insightful knowledge from unstructured data analysis, sophisticated methods like sentiment analysis and natural language processing (NLP) are required. 10. Mentioned some characteristics of the Data warehouse A data warehouse is a separate database responsible for storing historical information records and is kept separate from an operational database. Processed and analyzed data from a data warehouse helps make top management strategic and tactical decisions based on the analysis. Analyzing data in the data warehouse helps the business analysts and users see the current business trends. The data warehouse is also responsible for consolidating historical data analysis. Read: Python Interview Question and Answer 11. What are the key advantages of using BI systems? (Another common business intelligence interview question) The key advantages of using BI systems are: It helps boost productivity and makes it possible to create a business report with just a single click. It also helps increase the visibility of the data analysis and possibly identify those areas that demand attention. As per the organization’s goals, the BI system sets the accountability. BI systems automate several tasks by offering predictive analysis, benchmarking, modeling figures, and statistical calculations using different methodologies. 12. What is ETL (Extract, Transform, Load) in the context of BI, and why is it important? This comes under the most frequently asked Business Intelligence interview questions. Extract, Transform, and Load, or ETL, is a critical step in the business intelligence process. The processes of extraction and transformation involve collecting data from multiple sources, cleaning, organizing, and enriching the data, and loading entails putting the cleaned, structured, and enhanced data into a data warehouse for analysis. ETL is crucial because it guarantees that data is precise, consistent, and prepared for analysis. 13. Mention two disadvantages of Business Intelligence Systems. (Another common business intelligence interview questions) The two disadvantages of Business Intelligence Systems are: The BI systems are costly, so using them for small and medium scale enterprises will prove expensive. Implementing BI systems for the data warehouse is complicated. Hence, the complexity of using it is another drawback of it. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 14. What are aggregates? Aggregates are a form of data found in the aggregate table. To calculate these aggregates, various aggregate functions such as min, max, count average, etc. are used. 15. How does self-service BI benefit organizations, and what challenges may arise with its implementation? With the help of BI, business users may produce reports and carry out data analysis without the assistance of IT or data professionals. It enables flexibility and prompt decision-making.  To guarantee that users derive accurate and relevant insights from the data, however, hurdles might include concerns with data governance, possible data errors, and the need for appropriate training. 16. Explain in one line the meaning of granularity. Granularity tells us about the level (high to low) of information residing in the table. The lower the granularity is, the low-level information it contains. Also Read: Machine Learning Interview Question and Answer Here are a few more  BI developer interview questions to keep your preparation on track! 17. What are the differences between OLAP and OLTP? One of the most common business intelligence analyst interview questions is to outline the differences between OLAP and OLTP. The differences are as follows: Purpose: As the name suggests, Online Analytical Processing  (OLAP) serves the purpose of ‘analysing’ large-scale, complex data to enable efficient decision-making and problem-solving. On the other hand, Online Transactional Processing is optimised for processing a large volume of real-time transactions quickly.  Data source: OLAP comprises consolidated data that draws on multiple datasets like different OLTP databases. OLTP uses traditional DBMS to store a large volume of short real-time transactions. Processing time: OLTP generally has a very fast processing time in comparison to OLAP. The former processes a large volume of real-time transactions while OLAP processes complex data which can vary in size and complexity affecting its processing time.  Backup and Recovery: OLAP can be backed less frequently as they don’t constantly modify current data and even if data is lost, it may be recovered from OLTP databases. In contrast, OLTP requires frequent backups since they are transactional and updated in real-time to maintain the efficient running of the business.   End-user: The final user of the OLAP are knowledge workers, data analysts, and data scientists. On the other hand, OLTP is used by client-facing personnel and frontline workers (cashiers, hotel desk clerks) and self-service customer applications (travel, online banking services).  18. What is dimensional modeling and how is it relevant to Business Intelligence? If you are a BI developer, you should be prepared for one of the most common BI developer interview questions. Dimensional modelling is a data organisation method that organises data into different levels or dimensions. It comprises facts and dimensions tables. It enables BI reporting, query, and analysis which allows users to analyse data across several dimensions using common calculations. It makes data retrieval easier through optimization for select operations. If you’re applying for the position of analyst, this is one of the popularly asked business intelligence analyst interview questions 19. What is the role of a BI dashboard, and why is it valuable for decision-makers? A BI dashboard is a visual tool that displays measurements, key performance indicators (KPIs), and data trends in an approachable manner. It enables decision-makers to swiftly evaluate the state of their company and make wise decisions based on current information. Dashboards offer a concentrated view of important data, fostering the use of data in decision-making. 20. What are the differences between a fact table and a dimension table?  One of the most important Business Intelligence interview questions for experienced candidates is to identify the differences between a fact table and a dimension table. Although the two are connected, the differences are as follows: A fact table is at the centre of the star or snowflake schema, while the dimension table remains connected to the fact table as an important part of the star or snowflake schema.  The fact table contains the quantitative data that needs to be analysed. On the other hand, the dimension table contains descriptive information, that is, attributes or dimensions describing the objects of the fact table.  Fact table is a vertical table while the dimension table is horizontal.  While a dimension table contains hierarchical attributes, the fact table does not.  There are more dimension tables than fact tables in a schema.  Conclusion The competition in the field of Business Intelligence is tough. The industry is always on the lookout for competent professionals. If you want to put your best foot forward and get ahead of your competitors, it is crucial that you brush up on your interview skills, and practicing Business Intelligence interview questions for experience can be an excellent way to do so. All these essential Business Intelligence interview questions and answers will help you boost the business analyst job interview. upGrad provides various business analyst courses and programs. If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Python GUI projects offer a great way to become proficient in Python programming, and they include a wide range of exciting options, including Python tkinter projects with source code. They allow students to achieve academic goals while moving towards their desired careers. webinar Most organized training programs include activity-based methods to encourage an in-depth understanding of technical subjects. Moreover, conceptual and practical knowledge goes a long way in enriching professional growth. So, we have assembled some project examples for you to explore. If you want to get expertise on Python, learn more about our data science courses. Python as a one of the top programming languages is favored by researchers and industry leaders alike. Its advanced libraries and file extensions enable developers to build state-of-the-art tools for real-world problems. These applications span fields like IT, business, education, and entertainment.  Why GUI Programming with Python? Users interact with electronic devices and digital applications through graphical elements, such as icons, buttons, and windows. Also, the GUI communicates a programmer’s vision to the end-users.  Python aids efficient GUI programming and lends user-friendliness to the software design. It supports a wide range of cross-platform frameworks in addition to being compatible with popular operating systems like Windows, Linux, and Mac. Moreover, its GUI toolkits include TK, GTK, QT, and wxWidgets, which come with more features than other platform-specific kits.  Our learners also read: Python online course free! If you are looking to write an application quickly, you can consider using Tkinter with Python. Besides the time efficiency, its straightforward syntax and flexibility distribution capability make it a go-to choice for GUIs.  Python provides multiple options for developing GUI. tkinter is the most commonly used GUI method. It represents a standard Python interface added to the Tk GUI toolkit provided with Python. Python with tkinter is the simplest and fastest way to develop GUI applications. So, creating a python GUI using tkinter is easy. How to create a tkinter app: Follow these steps to create a tkinter app. 1) Import the tkinter module: Create the main window Add w widgets to the main window Implement the event Trigger on the widgets. The process of importing tkinter is identical to importing any other module in the Python code. The module name is ‘tkinter’ in Python 3.x and ‘Tkinter’ in Python 2.x. 2) Import tkinter: There are two primary methods available to create the used which the user needs to remember while creating the python tkinter projects with GUI. Tk(screenName=None,  baseName=None,  className=’Tk’,  useTk=1): tkinter provides the above method to create a main window. You can modify the className to any other name to modify the window’s name. The fundamental code used to develop the application’s main window is: m=tkinter.Tk() (here m is the name of the main window object) mainloop(): This method is used when your application is prepared to run. It is an infinite loop used to execute the application, wait for an event to occur, and finally process the event as far as the window is not shut down. m.mainloop() tkinter also provides access to the widgets’ geometric configuration that can consolidate the widgets in the parent windows. Three geometry manager classes are mentioned below. pack() method: It classifies the widgets in blocks before including them in the parent widget. grid() method: It classifies the widgets in a grid structure before including them in the parent widget. place() method: It classifies the widgets by including them in specific positions guided by the programmer. What Can you learn with Python GUI projects? In the year 2023, engaging and useful apps will be developed through a variety of Python project ideas that offer opportunities for the enhancement of your abilities in Python GUI at a beginner’s level. The GUI of your application is like a public vision-board of your ideas. When you implement a project, you pick up a number of things about clean, aesthetic, and functional design that might otherwise skip your attention. We have listed some of these elements below: Layout managers and widgets Frames and Windows (comprising a suitable presentation of widgets) Menus, input buttons, and entry fields GUI for a MySQL database Pop-ups and user prompts Window decorations (such as images and text labels)  GUI forms and Matplotlib (2D charts)  Extension with external libraries Unit-testing GUI Python 3 installation using Tkinter Event-driven programming Must read: Learn excel online free! Now, let us discuss some examples. Do not forget to install a code editor and the Tk package with updated modules before you begin with full force. Learn about: SQL vs Python: Difference Between SQL and Python Top Python libraries for GUI programming: You can use any of these toolkits in python GUI programming. 1) Tkinter: It is a standard Python package used for GUI programming. It is built on top of the Tk interface to develop python tkinter projects. 2) PyQt: It is a Python toolkit binding of the Qt toolkit. Qt is a C++ framework used by Python to execute a cross-platform PyQt toolkit in the form of a plug-in. 3) wxPython: wxPython is a cross-platform GUI toolkit and is a wrapper for the API wxWidgets. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Python GUI Projects 1. Table Analysis Using Pandas While highlighting Python GUI examples you can consider the Python-based pandastable library to store tabular data. The table widget allows you a structured visual without having to write extensive code. The interface is similar to a spreadsheet, which comes with configurable plottings. With the pandas DataFrame, beginners can handle large volumes of data and try their hands at interactive use of web applications. Read about: Top 12 Fascinating Python Applications in Real-World 2. An Application for Practicing Trading The trading view-trainer application offers an innovative learning environment for stock market enthusiasts to become proficient traders. It uses historical data from the cloud platform, TradingView. Also, the tv-trainer app takes care of both market order and limit order, which is active in most investment exchanges. Read more about cloud computing ultimate guide for beginners. 3. A Tool for Converting Dictionary Files This project is called PyGlossary. It is compatible with the most modern OS and requires Python version 3.6 or higher. If you want to edit dictionary databases or convert their themes into different formats, this is the tool to get started. upGrad’s Exclusive Data Science Webinar for you – How upGrad helps for your Data Science Career? document.createElement('video'); https://cdn.upgrad.com/blog/alumni-talk-on-ds.mp4 4. Text Annotation Tool If you are looking for a lightweight text span interface, have a look at YEDDA. This project was previously called SUTDAnnotator, named after the institute where Jie Yang developed it. YEDDA comes with two interfaces – one for annotation and the other for result analysis (admin interface). The tool aims to automate the process of annotating text, symbols, and emojis by hand. It works well with your standard operating systems and covers a wide range of natural languages, from English to Chinese.  Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 5. Real-time Analysis of Human Vitals This project works in two main phases. Firstly, it monitors the human body parameters using electronic devices, such as web-cameras and sensors. Then, it analyses the vital signs to present the information on a screen. Today, many wearable technologies and smartphones have built-in applications that do this. Further, connectivity tools can allow you to forward this information and receive telemedicine services. If you want to use Python GUI programming for a similar project, you can easily find studies, research papers on the internet, along with the source codes.  Check out: Artificial Intelligence Project Ideas 6. A Detection System for Traffic Signal Violation The Global Report on World Safety by the WHO reveals that India accounts for about11 percent of the world’s accident-related deaths, claiming as many as 1.5 lives annually. Traffic violations form a significant component of this problem. And since the capacity of the designated officials is limited, novel technologies can help create a more integrated solution in this regard.  Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? You can look at developing a full-fledged application to detect behavior anomalies on the road. GitHub has the full project along with the GUI for your practice. The system uses YOLOv3 (algorithm to detect objects from video footage) and the Tkinter toolkit. If you implement this project from scratch, you can also acquire an in-depth understanding of how computer vision can solve real-life challenges. Know more: GitHub vs GitLab: Difference Between GitHub and GitLab 7. A scale Chart for Musical Compositions Python 3 and Tk can be used for building a creative application that visually represents different scales, notes, modes, and keys. Such tools are particularly common for string instruments like the guitar. Here, the users can navigate different scales (major, natural minor, harmonic minor, pentatonic, blues, etc.) and chords (5 chords, major, minor, diminished, augmented, and so on) on a 24-fret chart. You can check out the GuitarScaleChart project to get inspiration.  8. A Design Solution for Consumer Electronics This project is concerned with a general 3D GUI for television receivers. The interactive design builds on the 2D model and incorporates the existing elements into the three-dimensional world. Published by IEEE, the paper explores how the interactive utilities of TV applications can be enhanced, and ultimately, how consumer experiences can be enriched.  Read: Python Project Ideas & Topics 9. Private Smart Home Design Project As IoT gains pace in today’s connected world, Python GUI projects on smart homes have become quite popular. Raspberry Pi documentation is freely available on the internet to help you in the process. With in-built WiFi and Bluetooth support, you can easily create a mini-computer for home automation and security support.  10. Study on Electro-pneumatic Trainers Based on PIC and GUI Utility Pneumatic control systems are an essential component of industrial applications. There are various academic papers and articles that describe the functioning of the two main parts, which are: A Programmable Integrated Circuit (PIC) microcontroller A Visual Basic (VB) platform Such systems can serve as the building blocks of vision-based robots. If you go into the nitty-gritty, you will find that the transmitted signals are received, decoded (with the help of the PIC), and finally displayed using programs like MATLAB. You can use this information to either pursue a research project or a hands-on experiment.  11. Weather Forecast App Create a Python GUI program that showcases the current weather forecasts, utilizing APIs such as OpenWeatherMap for information retrieval and user-friendly presentation. This project offers beginners an opportunity to delve into data retrieval, API integration, and dynamic GUI modifications. 12. Personal Diary App Develop an advanced diary application. This software should allow users to input and safeguard private information. It must prioritize privacy through the utilization of data encryption and password protection. Novices, in particular, will gain comprehensive knowledge about fundamental aspects of data security within GUI apps via their involvement with this project. 13. Virtual Classroom Scheduler To meet the escalating demand for online education, develop a virtual classroom scheduler. Teachers and students can utilize this GUI program to manage their online lessons, assignments, and meetings effectively, thus enhancing user interaction and data management capabilities. 14. Budget Tracker Utilizing Python’s GUI frameworks, create a budget tracking program. Upon inputting their income and spending, the software actively calculates users’ budget status. This project underscores the significance of data entry forms and fundamental financial computations, which are both critical elements in understanding personal finance at its core. 15. Recipe Book Create a digital recipe book using a Python GUI. It empowers users to swiftly add, search for, and view recipes. Novices find this project an exceptional learning opportunity as it needs the utilization of databases. 16. Language Learning Flashcards To facilitate language learning, create a cutting-edge flashcard app that allows users to generate and review text and image based study aids. The project strategically amalgamates GUI design with data administration to support novices in understanding the correlation between interface presentation and operational functionality. Also Read: 15 Interesting MATLAB Project Ideas & Topics For Beginners Uses of Python GUI Here are a few popular uses of Python GUI: i.) Mobile application development Mobile applications present interfaces for users to list, comment, post, or interact in different ways. They are the finest examples of Python GUIs. ii.) Games development: Popular games like Flappy Bird and Mount & Blade are developed using Python GUI. Its stunning graphics and interactivity make sure games can significantly leverage GUIs to enhance user enjoyment. iii.) Human-machine interfaces in industries GUIs are extensively used in utility and entertainment. They are also used in industries through Human Machine Interfaces (HMIs). HMIs are the types of GUIs that help operators to understand industrial monitoring and control systems. They also allow the rectification of any glitches in operating conditions. The corresponding python GUI projects with source code help you develop industrial applications at affordable prices. Top Python GUI frameworks: One of the reasons why Python is quite popular for GUI programming is the availability of frameworks. Several options are available for developers. But here are some of the famous Python GUI frameworks to help you get started on your Python GUI project. 1) Tkinter: Tkinter is a standard Python package used for GUI programming. It is built on the Tk interface to develop python tkinter projects. For those wondering, ‘what is tkinter in Python?’ – it’s a powerful tool for creating graphical user interfaces. It is the de-facto GUI toolkit for many Python developers. As the best GUI for Python, many beginner Python developers choose it as their foremost GUI programming framework. Its unique aspect is the modularity with widgets. Every widget happens as a customizable component. The widgets can be merged to develop a comprehensive GUI. 2) PySide2 / Qt5: PySide2 and Qt5 are not completely the same framework. However, they were developed by the same corporation under Qt for the Python project.  Both these frameworks have almost99.9% similar APIs. They are renowned for their APIs that streamline the work of Python developers. They come with tons of examples and documentation that help beginners and experts to develop their application in python tkinter projects. 3) PyGUI: PyGUI (Python GUI) allows developers to develop user interfaces using native elements for Python applications. It operates with a lightweight API due to minimal extra code between the Python app and the platform it operates on. Hence, it is an excellent option if you want a fundamental solution for Python GUI programming. 4) PySimpleGUI: Mike B developed PySimpleGUI in 2018 to simplify GUI programming for Python beginners. It blends the features of multiple famous Python GUI frameworks like Tkinter and Qt. It does this by offering standardized code that beginners can utilize to develop GUIs lego-style. It suggests that even beginners can effortlessly create elegant and intuitive interfaces without dealing with the framework’s complex features. So, it is one of the easiest python GUI projects with source code for beginners. Examples of Other Projects Here’s some more Python project ideas: Fitness Tracker Design a fitness monitoring application that empowers users to gauge their progress and record their trials. Utilize charts and graphs specifically for the presentation of fitness statistics. This undertaking seamlessly mixes GUI design with data visualization techniques, an intersection crucial in contemporary software development. Task Manager Design a task management program empowering users to seamlessly add, remove, and organize tasks. Enrich its functionality with features such as alerts and due dates. This endeavor enhances not only user interaction but also their abilities in scheduling tasks and organizing data. Conclusion Python is an easy language for beginners to learn and master. Over the years, it has experienced a rising demand among programmers and coders. And naturally, the contributors and teaching communities have taken the cue to provide adequate guidance. We tried to give you a peek into this vast landscape with the above compilation on Python GUI projects. If you are curious to learn about python, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Pandas is a BSD-licensed and open-source Python library offering high-performance, easy-to-use data structures, and data analysis tools. Python with Pandas is used in a wide array of disciplines, including economics, finance, statistics, analytics, and more. In this article, we have listed some essential pandas interview questions and NumPy interview questions that a python learner must know. If you want to learn more about python, check out our data science programs. What are the Different Job Titles That Encounter Pandas and Numpy Interview Questions? Here are some common job titles that often encounter pandas in python interview questions. 1. Data Analyst Data analysts often use Pandas to clean, preprocess, and analyze data for insights. They may be asked about their proficiency in using Pandas for data wrangling, summarization, and visualization. 2. Data Scientist Data scientists use Pandas extensively for preprocessing and exploratory data analysis (EDA). During interviews, they may face questions related to Pandas for data manipulation and feature engineering. 3. Machine Learning Engineer When building machine learning models, machine learning engineers leverage Pandas for data preparation and feature extraction. They may be asked Pandas-related questions in the context of model development. 4. Quantitative Analyst (Quant) Quants use Pandas for financial data analysis, modeling, and strategy development. They may be questioned on their Pandas skills as part of the interview process. 5. Business Analyst Business analysts use Pandas to extract meaningful insights from data to support decision-making. They may encounter Pandas interview questions related to data cleaning and visualization. 6. Data Engineer Data engineers often work on data pipelines and ETL processes where Pandas can be used for data transformation tasks. They may be quizzed on their knowledge of Pandas in data engineering scenarios. 7. Research Analyst Research analysts across various domains, such as market research or social sciences, might use Pandas for data analysis. They may be assessed on their ability to manipulate data using Pandas. 8. Financial Analyst Financial analysts use Pandas for financial data analysis and modeling. Interview questions might focus on using Pandas to calculate financial metrics and perform time series analysis. 9. Operations Analyst Operations analysts may use Pandas to analyze operational data and optimize processes. Questions might revolve around using Pandas for efficiency improvements. 10. Data Consultant Data consultants work with diverse clients and datasets. They may be asked Pandas questions to gauge their adaptability and problem-solving skills in various data contexts. What is the Importance of Pandas in Data Science? Pandas is a crucial library in data science, offering a powerful and flexible toolkit for data manipulation and analysis. So, let’s explore Panda in detail: – 1. Data Handling Pandas provides essential data structures, primarily the Data Frame and Series, which are highly efficient for handling and managing structured data. These structures make it easy to import, clean, and transform data, often the initial step in any data science project. 2. Data Cleaning Data in the real world is messy and inconsistent. Pandas simplifies the process of cleaning and preprocessing data by offering functions for handling missing values, outliers, duplicates, and other data quality issues. This ensures that the data used for analysis is accurate and reliable. 3. Data Exploration Pandas facilitate exploratory data analysis (EDA) by offering a wide range of tools for summarizing and visualizing data. Data scientists can quickly generate descriptive statistics, histograms, scatter plots, and more to gain insights into the dataset’s characteristics. 4. Data Transformation Data often needs to be transformed to make it suitable for modeling or analysis. Pandas support various operations, such as merging, reshaping, and pivoting data, essential for feature engineering and preparing data for machine learning algorithms. 5. Time Series Analysis Pandas are particularly useful for working with time series data, a common data type in various domains, including finance, economics, and IoT. It offers specialized functions for resampling, shifting time series, and handling date/time information. 6. Data Integration It’s common to work with data from multiple sources in data science projects. Pandas enable data integration by allowing easy merging and joining of datasets, even with different structures or formats. Pandas Interview Questions & Answers Question 1 – Define Python Pandas. Pandas refer to a software library explicitly written for Python, which is used to analyze and manipulate data. Pandas is an open-source, cross-platform library created by Wes McKinney. It was released in 2008 and provided data structures and operations to manipulate numerical and time-series data. Pandas can be installed using pip or Anaconda distribution. Pandas make it very easy to perform machine learning operations on tabular data. Question 2 – What Are The Different Types Of Data Structures In Pandas? Panda library supports two major types of data structures, DataFrames and Series. Both these data structures are built on the top of NumPy. Series is a one dimensional and simplest data structure, while DataFrame is two dimensional. Another axis label known as the “Panel” is a 3-dimensional data structure and includes items such as major_axis and minor_axis. Source Question 3 – Explain Series In Pandas. Series is a one-dimensional array that can hold data values of any type (string, float, integer, python objects, etc.). It is the simplest type of data structure in Pandas; here, the data’s axis labels are called the index. Question 4 – Define Dataframe In Pandas. A DataFrame is a 2-dimensional array in which data is aligned in a tabular form with rows and columns. With this structure, you can perform an arithmetic operation on rows and columns. Our learners also read: Free online python course for beginners! Question 5 – How Can You Create An Empty Dataframe In Pandas? To create an empty DataFrame in Pandas, type import pandas as pd ab = pd.DataFrame() Also read: Free data structures and algorithm course! Question 6 – What Are The Most Important Features Of The Pandas Library? Important features of the panda’s library are: Data Alignment Merge and join Memory Efficient Time series Reshaping Read: Dataframe in Apache PySpark: Comprehensive Tutorial Question 7 – How Will You Explain Reindexing In Pandas? To reindex means to modify the data to match a particular set of labels along a particular axis. Various operations can be achieved using indexing, such as- Insert missing value (NA) markers in label locations where no data for the label existed. Reorder the existing set of data to match a new set of labels. upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 Question 8 – What are the different ways of creating DataFrame in pandas? Explain with examples. DataFrame can be created using Lists or Dict of nd arrays. Example 1 – Creating a DataFrame using List import pandas as pd     # a list of strings     Strlist = [‘Pandas’, ‘NumPy’]     # Calling DataFrame constructor on the list     list = pd.DataFrame(Strlist)     print(list)    Must read: Learn excel online free! Example 2 – Creating a DataFrame using dict of arrays import pandas as pd     list = {‘ID’: [1001, 1002, 1003],’Department’:[‘Science’, ‘Commerce’, ‘Arts’,]}     list = pd.DataFrame(list)     print (list)    Check out: Data Science Interview Questions Question 9 – Explain Categorical Data In Pandas? Categorical data refers to real-time data that can be repetitive; for instance, data values under categories such as country, gender, codes will always be repetitive. Categorical values in pandas can also take only a limited and fixed number of possible values.  Numerical operations cannot be performed on such data. All values of categorical data in pandas are either in categories or np.nan. This data type can be useful in the following cases: If a string variable contains only a few different values, converting it into a categorical variable can save some memory. It is useful as a signal to other Python libraries because this column must be treated as a categorical variable. A lexical order can be converted to a categorical order to be sorted correctly, like a logical order. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Question 10 – Create A Series Using Dict In Pandas. import pandas as pd     import numpy as np     ser = {‘a’ : 1, ‘b’ : 2, ‘c’ : 3}     ans = pd.Series(ser)     print (ans)    Question 11 – How To Create A Copy Of The Series In Pandas? To create a copy of the series in pandas, the following syntax is used: pandas.Series.copy Series.copy(deep=True) * if the value of deep is set to false, it will neither copy data nor the indices. Question 12 – How Will You Add An Index, Row, Or Column To A Dataframe In Pandas? To add rows to a DataFrame, we can use .loc (), .iloc () and .ix(). The .loc () is label based, .iloc() is integer based and .ix() is booth label and integer based. To add columns to the DataFrame, we can again use .loc () or .iloc (). Question 13 – What Method Will You Use To Rename The Index Or Columns Of Pandas Dataframe? .rename method can be used to rename columns or index values of DataFrame Question 14 – How Can You Iterate Over Dataframe In Pandas? To iterate over DataFrame in pandas for loop can be used in combination with an iterrows () call. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Question 15 – What Is Pandas Numpy Array? Numerical Python (NumPy) is defined as an inbuilt package in python to perform numerical computations and processing of multidimensional and single-dimensional array elements.  NumPy array calculates faster as compared to other Python arrays. Question 16 – How Can A Dataframe Be Converted To An Excel File? To convert a single object to an excel file, we can simply specify the target file’s name. However, to convert multiple sheets, we need to create an ExcelWriter object along with the target filename and specify the sheet we wish to export. Question 17 – What Is Groupby Function In Pandas? In Pandas, groupby () function allows the programmers to rearrange data by using them on real-world sets. The primary task of the function is to split the data into various groups. Also Read: Top 15 Python AI & Machine Learning Open Source Projects Frequently Asked Python Pandas Interview Questions For Experienced Candidates Till now, we have looked at some of the basic pandas questions that you can expect in an interview. If you are looking for some more advanced pandas interview questions for the experienced, then refer to the list below. Seek reference from these questions and curate your own pandas interview questions and answers pdf. 1. What do we mean by data aggregation? One of the most popular numpy and pandas interview questions that are frequently asked in interviews is this one. The main goal of data aggregation is to add some aggregation in one or more columns. It does so by using the following Sum- It is specifically used when you want to return the sum of values for the requested axis. Min-This is used to return the minimum values for the requested axis. Max- Contrary to min, Max is used to return a maximum value for the requested axis.  2. What do we mean by Pandas index?  Yet another frequently asked pandas interview bit python question is what do we mean by pandas index. Well, you can answer the same in the following manner. Pandas index basically refers to the technique of selecting particular rows and columns of data from a data frame. Also known as subset selection, you can either select all the rows and some of the columns, or some rows and all of the columns. It also allows you to select only some of the rows and columns. There are mainly four types of multi-axes indexing, supported by Pandas. They are  Dataframe.[ ] Dataframe.loc[ ] Dataframe.iloc[ ] Dataframe.ix[ ] 3. What do we mean by Multiple Indexing? Multiple indexing is often referred to as essential indexing since it allows you to deal with data analysis and analysis, especially when you are working with high-dimensional data. Furthermore, with the help of this, you can also store and manipulate data with an arbitrary number of dimensions.  These are some of the most common python pandas interview questions that you can expect in an interview. Therefore, it is important that you clear all your doubts regarding the same for a successful interview experience. Incorporate these questions in your pandas interview questions and answers pdf to get started on your interview preparation! Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Conclusion We hope the above-mentioned Pandas interview questions and NumPy interview questions will help you prepare for your upcoming interview sessions. If you are looking for courses that can help you get a hold of Python language, upGrad can be the best platform.  If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.