# Solved: Python NumPy squeeze function Example with axis

In the world of data science and programming, Python has quickly become a popular language due to its simplicity, readability, and versatility. In this article, we will dive deep into the Python NumPy library and its powerful squeeze function. We’ll be discussing how to take advantage of its features to manipulate and analyze data effortlessly. Read on to discover how you can solve complex problems using the NumPy squeeze function with examples, including a step-by-step explanation of the code.

To help illustrate this topic, let’s think about a modern catwalk scenario. As a fashion expert, you know how important it is to choose the perfect outfit that will captivate the audience, representing a harmony of styles, looks, and trends in one ensemble.

## Understanding NumPy Library

• NumPy (Numerical Python) is an open-source library that is incredibly useful for performing mathematical and logical operations on large, multidimensional arrays and matrices.
• It offers excellent support for various mathematical functions, statistical operations, and linear algebra routines.
• NumPy’s syntax is very similar to Python’s list, but it performs faster and requires less memory.

Just as combinations of garments, colors, and fashion history influence the style of an outfit, libraries and functions in Python play a crucial role in solving programming challenges.

## NumPy Squeeze Function

In the world of fashion, the perfect style is all about making the pieces fit together seamlessly. Similarly, the NumPy squeeze function allows us to remove single-dimensional entries from the shape of an input array.

```import numpy as np

sample_array = np.array([[[0], [1], [2]]])
squeezed_array = np.squeeze(sample_array)
print(squeezed_array)
```

The above code snippet removes the single-dimensional entries from the shape of the sample_array, resulting in a one-dimensional array.

## Understanding Axis in NumPy Squeeze Function

An important aspect of the NumPy squeeze function is the use of the axis parameter. It allows us to selectively specify which dimensions to squeeze, rather than removing all single-dimensional entries.

To get a better understanding of the concept, let’s once again think of it in terms of style and fashion. An outfit could consist of layers and accessories which are assembled along specific axes or directions (top-to-bottom, front-to-back). Similarly, when working with the squeeze function, we can imagine each axis representing a particular aspect of the array shape.

```import numpy as np

sample_array_2 = np.array([[[1], [2], [3]], [[4], [5], [6]]])

squeezed_array_axis = np.squeeze(sample_array_2, axis=1)
print(squeezed_array_axis)
```

In this example, specifying axis=1 causes the function to remove only the single-dimensional entries along the second axis. This selective removal of dimensions is analogous to selecting specific layers of the outfit without disrupting the other dimensions.

In conclusion, understanding the NumPy library and its powerful squeeze function has the potential to significantly enhance your Python programming abilities in data manipulation and analysis. Just as a fashion expert embraces the variety of styles, looks, and trends, a skilled developer embraces the versatility of Python libraries and functions to create efficient and elegant solutions.

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