Below are **7 Numpy questions** which can be asked in a Python Related job interview.

**Q. 1 – What is Numpy?**

The **numpy** is a module which is responsible for effectively storing and processing data at a faster rate as compared to normal array. The advantage of **numpy** is support of large number of in built mathematical operations as compared to other programming languages. Also, the support to represent n dimensions is also possible with **numpy**.

**Q. 2 – How to Install Numpy?**

As numpy is an external Python module, that’s why you need to use **pip** to install it. Just write **python3 -m pip install numpy** on terminal/Command line of your PC, this will download as well install numpy.

**Q. 3 – How to create Single dimension numpy array?**

```
import numpy as np
list1 = [1, 2.5, 8, 0, 1]
arr1 = np.array(list1)
print(arr1) # Prints out [1. 2.5 8. 0. 1. ]
```

**Q. 4 – What attributes are provided by numpy?**

- ndim => As
**numpy**provides**n**dimensions, we can get how many dimensions currently the array is having with**ndim**. - shape => Indicates number of rows and columns which again can be in different dimensions.
- dtype => Indicates data type of elements stored in
**numpy**.

```
import numpy as np
ip = [[1, 2, 3, 4], [5, 6, 7, 8]]
numpy_array = np.array(ip)
print(numpy_array)
print("Number of Dimensions in Numpy array are =>", numpy_array.ndim)
print("Shape of Numpy array is =>", numpy_array.shape)
print("Data Types in Numpy array are =>", numpy_array.dtype)
```

**Output of Above Code**

```
[[1 2 3 4]
[5 6 7 8]]
Number of Dimensions in Numpy array are => 2
Shape of Numpy array is => (2, 4)
Data Types in Numpy array are => int64
```

**Q. 5 – What utility methods are provided by numpy for creating different elements?**

**np.zeros()**=> Creates a numpy array only having zeros as elements. For example –**np.zeros((3, 3))**creates a three-by-three dimensional numpy array just containing zeros only.**np.ones()**=> Creates a numpy array only having ones as elements. For example –**np.ones((4, 4))**creates a four-by-four dimensional numpy array just containing ones only.**np.eye()**=> Creates a numpy array having**ones at diagonals and zeros elsewhere**. For example –**np.eye(4, 5)**will creates a four-by-five dimensional numpy array having ones at diagonals, zeros elsewhere.**np.arange()**=> Create a single or**n**dimension array in which numbers are populated starting from 0 to the number specified as parameter. For example –**np.arange(7)**will be returns**array([0, 1, 2, 3, 4, 5, 6])**

**Q. 6 – Explain various simple mathematical operations which can be done on numpy?**

```
import numpy as np
numpy_array1 = np.array([[1., 2., 3.],[4., 5., 6.]])
numpy_array2 = np.array([[2., 3., 4.], [4., 5., 6.]])
print("Adding two numpy arrays")
print(numpy_array1 + numpy_array2)
print("\n")
print("Subtracting two numpy arrays")
print(numpy_array1 - numpy_array2)
print("\n")
print("Reversing a numpy array")
print(1 / numpy_array1)
print("\n")
print("Reversing a numpy array")
print(1 / numpy_array2)
print("\n")
print("Taking under root of numpy array")
print(numpy_array1 ** 0.5)
```

**Output of Above Code**

```
Adding two numpy arrays
[[ 3. 5. 7.]
[ 8. 10. 12.]]
Subtracting two numpy arrays
[[-1. -1. -1.]
[ 0. 0. 0.]]
Reversing a numpy array
[[1. 0.5 0.33333333]
[0.25 0.2 0.16666667]]
Reversing a numpy array
[[0.5 0.33333333 0.25 ]
[0.25 0.2 0.16666667]]
Taking under root of numpy array
[[1. 1.41421356 1.73205081]
[2. 2.23606798 2.44948974]]
```

**Q. 7 – How to Transposing a Numpy Array?**

Numpy array can be transposed by **numpy_array.T **code statement.

```
import numpy as np
numpy_array1 = np.array([[1., 2., 3.],[4., 5., 6.]])
print(numpy_array1.T)
```

**Output of Above Code**

```
[[1. 4.]
[2. 5.]
[3. 6.]]
```