Chapter 4 – 100 Most Asked Numpy Interview Questions
100 Most Asked Numpy Interview Questions
Welcome to the 2200 questions series from The Data Monk, in this series we will cover all the topics in a Question-Answer mode that are required for anyone who wants to make a career in the following field:-
– Data Analysis
– Business Analysis
– Business Intelligence Engineering
– Machine Learning
– Data Science
– Product Analysis
– Data Engineering
– Risk Analysis
These 2200 questions are useful for anyone who is in their 2nd-3rd year of engineering to 8-10 years of experience in the IT industry( be it QA/Development/Support) and are willing to make a career in Analytics.
100 Most Asked Numpy Interview Questions
Why Analytics is a domain for you?
If you want to make a handsome switch with a good package then Analytics is for you because of the following reasons:-
– It is a high-paying job
– It is interesting as you will have a good impact on the growth of the organization
– It involves a lot of things like requirement gathering, building logic, making ETL, pipeline creation, reporting to the CXOs, and so on. So, it is a very impactful role
– It has a HUGE demand in the future as the data will keep on growing and so will your role
How much does an analytics role pay?
The CTC of the role will depend on multiple factors but just to give you a glimpse of it:-
“Anyone from a tier 2-3 college with good knowledge of the material that we are providing will have a fair chance to bag something like 15+ LPA for a fresher. The more you grind the better you get and the CTC grows with experience.”
Now coming back to why you should try The Data Monk for your Analytics journey.
Why The Data Monk?
We are a group of 30+ Analytics Engineers working in various product-based companies like Zomato, Ola, OYO, Google, Rapido, Uber, Ugam, BYJUs, etc. and we observed that people do not have a well-structured way to enhance their knowledge. There are multiple courses here and there, but no one has consolidated what needs to be learned to move to the analytics domain.
Further, there are courses from Large institutes where they charge you something like 2-5 lacks and try to teach you everything from Data structure to SQL to Power BI to ML. You do not have to spend so much on these topics.
We followed a very old-school way, take a topic and solve 100-200 questions on these topics. Learn them, understand them, and revise them. This should be enough for you to crack that domain.
For example, if I am a very beginner in SQL, then I will just try to solve 200 questions starting from the definition to advance level questions. After solving and revising these questions I should have a good amount of knowledge to answer 6 out of 10 questions asked in an interview and going by that calculation I can be a strong candidate in 5-7 out of 10 companies.
See, by the end, you need to convert a job first and then keep on learning in the organization.
Most of the books are on questions like ‘250 questions to crack SQL interview’ and this will cost you around 250 rupees, take the book, understand, and learn it. This small amount can bag you a 15 LPA job 🙂
You can trust us as we have guided more than 1000 people to make a career in Analytics
2200 Analytics Interview Questions
Coming back to the topic, below is the list of 250 SQL questions to Ace any Analytics Interview
Chapter 1 – SQL – 250 SQL questions to Ace any Analytics Interview
Chapter 2 – Python – 200 Most Asked Python Interview Questions
Chapter 3 – Pandas – 100 Most Asked Pandas Interview Questions with solution
Chapter 4 – Numpy – 100 Most Asked Numpy Interview Questions with solution
Most Asked Numpy Interview Questions
558. What is NumPy, and what are its main features?
559. How do you create a NumPy array from a Python list?
560. How do you perform element-wise arithmetic operations between two NumPy arrays?
561. How do you calculate the dot product of two NumPy arrays?
562. What is broadcasting in NumPy, and how does it work?
563. What are some common statistical functions available in NumPy, and how do you use them?
564. Define the mean function in numpy and give a simple example
565. Define the median function in numpy and give a simple example
567. Define the var function in numpy and give a simple example
568. Define the min function in numpy and give a simple example
569. Define the max function in numpy and give a simple example
570. How do you select elements from a NumPy array based on a conditional expression?
571. How do you reshape a NumPy array?
572. Convert a multidimensional array to 1D array
573. How do you perform matrix operations in NumPy, such as matrix multiplication and inversion?
574. What is Matrix Inversion?
575. What is Determinant calculation?
576. How do you save and load NumPy arrays from disk?
577. How do you concatenate two or more NumPy arrays horizontally?
578. How do you concatenate two or more NumPy arrays vertically?
579. How do you concatenate two or more NumPy arrays arbitarily?
580. How do you create a masked array in NumPy, and what is its purpose?
581. What is a shallow copy in a NumPy array?
582. What is a deep copy in NumPy?
583. How do you generate random numbers in NumPy, and what are some common distributions you can sample from?
584. How do you sort a NumPy array in ascending or descending order?
585. How do you perform element-wise logical operations between two NumPy arrays?
586. How do you compute the Fourier transform of a signal using NumPy?
587.How to use NumPy with SciPy?
588. How to use NumPy with matplotlib?
Coding Questions
589. Write a NumPy code snippet to create an array of zeros with shape (3, 4).
590. Write a NumPy code snippet to create an array of ones with shape (2, 5).
591. Write a NumPy code snippet to create an array of evenly spaced values between 0 and 10 with a step size of 2.
592. Write a NumPy code snippet to create a random array with shape (2, 3) and values between 0 and 1.
593. Write a NumPy code snippet to calculate the sum of all elements in a two-dimensional array.
594. Write a NumPy code snippet to calculate the mean of all elements in a one-dimensional array.
595. Write a NumPy code snippet to calculate the standard deviation of all elements in a one-dimensional array.
596. Write a NumPy code snippet to calculate the dot product of two one-dimensional arrays.
597. Write a NumPy code snippet to reshape a one-dimensional array into a two-dimensional array with 3 rows and 2 columns.
598. Write a NumPy code snippet to find the index of the maximum value in a one-dimensional array.
Data Cleaning using Numpy
599. How do you remove missing or null values from a NumPy array?
600. How can you identify and remove outliers in a NumPy array?
601. What are some common techniques for normalizing data in a NumPy array?
602. How do you sort a NumPy array, and what are some of the options for customizing the sort?
603. What are some functions in NumPy that are commonly used for data cleaning?
604. How can you handle duplicate values in a NumPy array?
605. What is the difference between slicing and indexing in NumPy, and how are they used for data cleaning?
606. How can you concatenate two NumPy arrays, and what are some of the considerations when doing so?
607. How can you reshape a NumPy array, and what are some common use cases for doing so in data cleaning?
608. How do you create a NumPy array with specific dimensions and data types?
609. How do you access specific elements in a NumPy array?
610. How can you perform basic arithmetic operations on NumPy arrays?
611. How can you create a mask for a NumPy array based on specific conditions?
612. How can you apply a function to specific elements in a NumPy array?
613. How can you combine two or more NumPy arrays to create a new array?
614. How can you save a NumPy array to a file, and how can you load a saved array back into Python?
615. How can you calculate dot products and matrix multiplication using NumPy?
616. How can you apply linear algebra operations (e.g., inverse, determinant) to a NumPy array?
617. What happens if you try to reshape a NumPy array with the wrong number of elements?
618. What is the difference between NumPy’s broadcasting rules and Python’s broadcasting rules?
619. How can you modify the data type of a NumPy array?
620. How can you perform element-wise comparison between two NumPy arrays with different shapes?
621. What is the difference between NumPy’s views and copies, and how can you determine which one you have?
622. How can you create a custom data type in NumPy, and what are some use cases for doing so?
623. What are some best practices for optimizing performance when working with large NumPy arrays?
624. How can you handle missing or invalid data in a NumPy array?
625. What are some ways to create a NumPy array, and when would you use each one?
626. How can you access and modify individual elements of a NumPy array?
627. What is the difference between slicing and indexing in NumPy, and how can you use them to extract subsets of an array?
628. How can you perform mathematical operations on a NumPy array, and what are some common functions for doing so?
629. What is broadcasting in NumPy, and how can you use it to perform element-wise operations on arrays with different shapes?
630. How can you reshape a NumPy array, and what are some common use cases for doing so?
631. What is a masked array in NumPy, and how can you use it to handle missing data?
632. How can you stack and concatenate NumPy arrays, and what are some use cases for doing so?
633. What are some best practices for optimizing performance when working with large NumPy arrays?
Numpy Advance Interview Questions
634. What are some of the performance benefits of using NumPy over pure Python when working with numerical data?
635. What is a view in NumPy, and how does it differ from a copy?
636. How can you use NumPy to perform linear algebra operations, such as matrix multiplication and solving systems of equations?
637. What are some of the built-in functions in NumPy for generating random numbers, and how can you use them to simulate data?
638. What is the difference between a structured array and a record array in NumPy, and what are some use cases for each?
639. How can you use NumPy to perform Fourier transforms, and what are some applications of Fourier analysis in signal processing and image processing?
640. How can you use NumPy to perform interpolation, and what are some use cases for doing so?
641. What are some of the limitations of NumPy, and how can you work around them?
642. What are some best practices for organizing and structuring code when working with NumPy, especially when dealing with large, complex arrays?
643. Write a NumPy code snippet to create an array of 100 random integers between 0 and 10.
644. Write a NumPy code snippet to calculate the element-wise sum of two arrays of the same shape.
645. Write a NumPy code snippet to compute the inner product of two one-dimensional arrays
646. Write a NumPy code snippet to find the indices of the maximum and minimum values in a two-dimensional array.
647. Write a NumPy code snippet to reshape a one-dimensional array into a two-dimensional array with 4 rows and 5 columns
648. Write a NumPy code snippet to calculate the correlation coefficient between two arrays of the same length.
649. Write a NumPy code snippet to create a diagonal matrix with the elements 1, 2, and 3 on the diagonal.
650. Write a NumPy code snippet to sort a one-dimensional array in ascending order.
651. Write a NumPy code snippet to calculate the element-wise product of two arrays of the same shape.
652. Write a NumPy code snippet to create a mask that selects all elements of an array that are greater than 5.
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