Numpy Interview Questions
Q1. What is Numpy?
 Numpy is a flexible, optimized, and one of the most important packages for array processing in Python. It is the fundamental package for scientific computing with Python.
Q2. Why is Numpy used in Python?
 NumPy is a Python package for Scientific Computing. The NumPy package is used for a variety of operations. ndarray is a multidimensional array that stores values of the same datatype. These arrays are indexed similarly to Sequences, beginning with zero. NumPy is a Python package for Scientific Computing.
Q3. Where is the use of Numpy?
 NumPy is an opensource Python numerical library. NumPy includes data structures such as multidimensional arrays and matrices. It can perform a variety of mathematical operations on arrays, including trigonometric, statistical, and algebraic routines. NumPy is a Numeric and Numarray extension.
Q4. How many dimensions can Numpy process?

In NumPy, an array with N dimensions is known as a ndarray.

ndarray is a multidimensional container that holds elements that are all the same type and size.

The number of dimensions in a ndarray is also referred to as its 'rank.'

The size of the array in each dimension is defined by a tuple of integers called shape.'

A 'dtype' object defines the data type of elements in ndarray.

Q5. Data Types supported by NumPy?
 numpy.bool_ : bool
 numpy.byte : signed char
 numpy.ubyte : unsigned char
 numpy.short : short
 numpy.ushort : unsigned short
 numpy.intc : int
 numpy.uintc : unsigned int
 numpy.int_ : long
 numpy.uint : unsigned long
 numpy.longlong : long long
Q6. What is array slicing?
 By mentioning the lower and upper limits, a portion of the array is selected by slicing. Slicing generates views from the original array rather than copying them.
Q7. Given an array a = [[1,2,3],[3,4,5],[23, 45,1] find the sum of every row in array a.

print(a.sum(axis=0)) # [27, 51, 9]
Q8. Features of Numpy?
 Contains an Ndimensional array object
 It is interoperable; compatible with many hardware and computing platforms
 Works extremely well with array libraries; sparse, distributed or GPU
 Ability to perform complicated (broadcasting) functions
 Tools that enable integration with C or C++ and Fortran code
 Ability to perform highlevel mathematical functions like statistics, Fourier transform, sorting, searching, linear algebra, etc
Q9. Why is the Numpy array preferred over Lists?
 Lists in Python, while extremely efficient containers capable of a variety of functions have several limitations when compared to NumPy arrays. Vectorized operations such as elementwise addition and multiplication are not possible.
Q10. How to delete a particular column and insert a new column in the NumPy array with code?
 Suppose we have a NumPy array:
[[351 532 633] [72 12 22] [43 841 156]]
new column to add:
[ 1 2 3 ]
Code:
import NumPy as np input_array = np.array([[351,532,633],[72,12,22],[43,84,156]]) new_col = np.array([[1,2,3]]) arr = np.delete(input_array , 1, axis = 1) arr = np.insert(arr , 1, new_col, axis = 1) print(arr) #prints [[351,532,633],[1,2,3],[43,84,156]}