NumPy’s `np.zeros`

is a function used to create a NumPy array filled with zeros. This function is particularly useful when you need to initialize an array with a specific shape and set all its elements to zero. It is a part of the NumPy library, which is widely used for numerical and scientific computing in Python.

**Usage and Purpose:**

The primary purpose of np.zeros is to create arrays with a pre-defined shape and fill them with zeros. It is commonly used in various scientific, engineering, and data analysis applications for tasks such as:

**Data Initialization:**You can use np.zeros to initialize arrays before filling them with actual data, making it easier to set up data structures for computations.**Matrix and Array Operations:**In linear algebra and matrix operations, creating zero-filled arrays of specific dimensions is a common practice.**Image Processing:**In image processing, you may need to create blank images or masks represented as arrays filled with zeros.

**Advantages of np.zeros:**

**Efficiency:**NumPy arrays are implemented in C and are highly efficient, making`np.zeros`

a fast and memory-efficient way to create zero-filled arrays.**Ease of Use:**The function is easy to use, requiring you to specify the desired shape as an argument.**Compatibility:**NumPy arrays created using`np.zeros`

can seamlessly integrate with other NumPy functions and libraries for further data manipulation and analysis.

**Disadvantages of np.zeros:**

**Fixed Value:**`np.zeros`

initializes the array with zeros, so it may not be suitable if you need to initialize with other values.**Data Type:**By default,`np.zeros`

creates arrays with floating-point data types. If you need integer values, you’ll need to specify the`dtype`

argument.

Example Using np.zeros with a Website Data Scenario:

Suppose you want to create a NumPy array to represent the daily website views for “freshers.in” for a week (7 days). You can use `np.zeros`

for this task:

```
import numpy as np
# Views for website : www.freshers.in
# Create an array representing daily website views for a week (7 days)
views_per_day = np.zeros(7, dtype=int) # Assign sample data for website views
views_per_day[0] = 1200
views_per_day[1] = 1500
views_per_day[2] = 1100
views_per_day[3] = 1400
views_per_day[4] = 1600
views_per_day[5] = 900
views_per_day[6] = 1300
print("Daily Website Views for freshers.in:")
print(views_per_day)
```

In this example:

We import NumPy as `np`

.

We use `np.zeros(7, dtype=int)`

to create an integer NumPy array with 7 elements initialized to zero.

We then assign sample data to represent daily website views.

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