**Introduction**

NumPy is a python-based, open-source package primarily used for array processing. It is known for its high performance and efficiency, it excels in handling N-dimensional array objects. NumPy is widely used for scientific computations, mathematical operations, statistical analysis, and offers robust broadcasting capabilities.

So, here's a list of some often-asked **NumPy interview questions** and answers that you might want to review before your next interview.

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**NumPY Interview Questions for Freshers**

Here are some of the important Numpy Interview Questions for Freshers.

**1. What is NumPy?**

It is hands down one of the most asked numpy interview questions. NumPy, aka Numerical Python, is a free and open-source Python library. It's made to work with matrices and multidimensional arrays to conduct complicated mathematical, image processing, quantum computing, and statistical operations, among other things. Indexing, slicing, splitting, joining, reshaping, and other complicated manipulations are all possible with NumPy N-dimensional arrays.

NumPy is an excellent choice for data science applications because of its powerful built-in features. Because the demand for data scientists and machine learning professions is increasing, studying NumPy and carving a golden career path with it is a sensible choice.

**2. Why is NumPy favored over other programming languages and tools like IDL, Matlab, Octave, or Yorick?**

NumPy is a high-performance library for scientific calculations written in the Python programming language. Because it is open-source and free, it is chosen over IDL, Matlab, Octave, or Yorick. It also outperforms a generic programming language when it comes to linking Python's interpreter to C/C++ and Fortran code because it employs Python, which is a general-purpose programming language.

NumPy works with multi-dimensional arrays and matrices, allowing for complicated mathematical operations to be performed on them.

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**3. How do you count the number of times a particular value appears in an array of integers?**

The bincount() function can be used to count the number of times a given value appears. It's worth noting that the bincount() function can take either positive integers or boolean expressions as an argument. Negative integers aren't allowed to be utilized.

Use the NumPy.bincount function (). The resulting array is as follows:

```
import numpy as np
arr = np.array([0, 5, 4, 0, 4, 4, 3, 0, 0, 5, 2, 1, 1, 9])
print(np.bincount(arr))
```

**Output:**

**4. The input NumPy array is shown below. Column two should be removed and replaced with the new column listed below.**

```
import numpy as np
sa = np.array([[1,2,3],[4,5,6],[7,8,9]])
newColumn = NumPy.array([[0,0,0]])
```

**Expected Output: **

```
Printing Original Array:
[[1 2 3]
[4 5 6]
[7 8 9]]
Printing Array after deleting col 2 on axis 1
[[1 3]
[4 6]
[7 9]]
Printing Array after inserting col 2 on axis 1
[[1 0 3]
[4 0 6]
[7 0 9]]
```

**Solution:**

```
import numpy as np
print("Printing Original Array:")
sa = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(sa)
print("Printing Array after deleting col 2 on axis 1")
sa = np.delete(sa , 1, axis = 1)
print(sa)
arr = np.array([[0,0,0]])
print("Printing Array after inserting col 2 on axis 1")
sa = np.insert(sa , 1, arr, axis = 1)
print(sa)
```

**Output: **

**Also read -** __Important Numpy Functions for ML__

**5. What Is The Distinction Between Numpy & Scipy?**

NumPy would be perfect if it only had the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, and so on. SciPy would house all numerical codes. However, compatibility is one of NumPy's main goals, therefore it tries to keep all features supported by any of its predecessors. As a result, NumPy includes several linear algebra routines that are better appropriately found in SciPy. In any case, SciPy has more advanced linear algebra modules, as well as a variety of additional numerical techniques. You should probably install both NumPy and SciPy if you're undertaking scientific computing with Python. The majority of new features belong in SciPy, not NumPy.

**6. Is it possible to use eye() function to generate diagonal values?**

```
import numpy as np
arr = np.eye(4)
print("\ndiaglonal values : \n",arr)
```

**Output: **

**7. Is Python 3.x supported by Numpy and Scipy?**

Python 2.x , as well as Python 3.2 and newer, are supported by NumPy and SciPy. NumPy 1.5.0 was the first version of NumPy to support Python 3. SciPy version 0.9.0 introduces Python 3 support.

**8. Is it possible to use diag() to create a square matrix?**

```
import numpy as np
arr = np.diag([1,2,3,4,5,6])
print("\n square matrix: \n",arr)
```

**Output: **

**9. What are the benefits of NumPy Arrays over (nested) Python lists?**

When compared to NumPy arrays, Python's lists, despite being extremely efficient containers capable of a wide range of functions, have many drawbacks. Vectorized operations, such as element-by-element addition and multiplication, are not conceivable.

Because they support objects of various types, they also require Python to retain the type information of each element. This means that every time an operation on an element is performed, a type dispatching code must be run. In addition, each iteration would have to go through type checks and require Python API accounting, resulting in C loops carrying very few actions.

**10. Is SIMD used by NumPy?**

NumPy has a flexible working mechanism that allows it to take advantage of CPU SIMD features to give quicker and more stable performance across all platforms.

**11. In NumPy, how do I change the data type of an array?**

```
import numpy as np
# Float to Integer
ar = np.array([3.3, 4.2, 5.1])
newar = ar.astype('i')
print(newar)
print(newar.dtype)
```

**Output: **

**12. In NumPy, what is the difference between copy and view?**

Copy :

A replica of the original array is returned.

Do not use the original array's data or memory location.

Any changes made to the copy won't appear in the original.

```
import numpy as np
ar = np.array([1,2,3,4])
a= ar.copy()
a[0] = 5
print(ar)
print(a)
```

**Output: **

View:

Returns a visual representation of the original array.

The data and memory location of the original array is used.

Any changes made to the copy will also be reflected in the original.

```
import numpy as np
arr = np.array([1,2,3,4])
a= arr.view()
#changing a value in the original array
arr[0] = 100
print(arr)
print(a)
```

**Output:**

**13. Why not just have a Matrix Multiplication Operator?**

The @ symbol will be defined as a matrix multiplication operator in Python 3.5, and Numpy and Scipy will take advantage of it. PEP 465 was dedicated to this addition. To compensate for the unavailability of this operator in earlier versions of Python, separate matrix and array types were created.

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