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 open-source 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?
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In NumPy, an array with N dimensions is known as a ndarray.
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ndarray is a multidimensional container that holds elements that are all the same type and size.
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The number of dimensions in a ndarray is also referred to as its 'rank.'
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The size of the array in each dimension is defined by a tuple of integers called shape.'
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A 'dtype' object defines the data type of elements in ndarray.
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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.
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print(a.sum(axis=0)) # [27, 51, 9]
Q8. Features of Numpy?
- Contains an N-dimensional 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 high-level 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 element-wise 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]}
