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# In Python, what is NumPy? How is it used?

In this article, we will learn about NumPy in python and how is it used in various applications.

## What is NumPy?

**NumPy** is a Python library designed to work efficiently with arrays in Python. It is fast, simple to learn, and efficient in storage. It also improves the way data is handled for the process. Numpy is used to generate an n-dimensional array. To use NumPy, we simply import it into our program, and then we can easily use it’s functionality in our code.

NumPy is a popular Python package for **scientific computing** and **statistical analysis**. Its arrays are grids of values from the same data-type.

It also has functions for working with **linear algebra, the Fourier transform,** and **matrices**.

We can turn many algorithms into functions that can be applied to arrays. NumPy has applications that extend beyond its own. It is a very diverse library with numerous applications in other fields.

Numpy works well with Data Science, Data Analysis, and Machine Learning. It also serves as a foundation for other Python libraries. These libraries make advantage of NumPy's features to expand their capabilities.

### Features of Numpy

An effective N-dimensional array object

Contains Advanced (broadcasting) functions

Has C/C++ and Fortran code integration tools

Numpy provides Linear algebra, Fourier transform, and random number capabilities.

## Why Should You Use NumPy?

Lists in Python serve the same purpose as arrays, but they are slower to process.

NumPy strives to provide array objects that are up to 50 times faster than typical Python lists.

The array object in NumPy is named ndarray, and it comes with several helper methods that make working with ndarray simple.

Arrays are often utilized in data science, where speed and resources are critical.

## Applications of Numpy

### An alternative to lists and arrays in Python

Numpy arrays are comparable to Python lists. Numpy arrays, like lists in Python, are homogeneous sets of elements. The fact that NumPy arrays are **homogeneous** is their most essential feature.

This distinguishes them from Python arrays. It preserves uniformity for mathematical operations that would be impossible to perform with heterogeneous items. Another advantage of utilizing NumPy arrays is the enormous number of functions that can be applied to them.

Due to the heterogeneous nature of Python arrays, these functions could not be performed.

### NumPy uses very little memory

Arrays are objects in NumPy. Python constantly deletes and builds these objects based on the requirements. As a result, memory allocation is lower as compared to Python lists. NumPy has features to prevent memory waste in the data buffer.

It has functions like copies, viewing, and indexing that can save a lot of memory. Indexing aids in returning the view of the original array, allowing for data reuse. It also identifies the element's data type, which helps with code optimization.

### Using NumPy to work with multidimensional arrays

In NumPy, we can also generate multi-dimensional arrays. These arrays have several rows and columns and they are multi-dimensional because they have more than one column. A multi-dimensional array is used to create matrices.

These matrices are simple to use or work. The usage of matrices also makes the code more memory efficient. To execute various operations on these matrices, we have a matrix module.

### Perform Mathematical operations with NumPy

Working with NumPy also offers simple routines for doing mathematical operations on an array of data collection. In NumPy, we have several modules for performing fundamental and special mathematical functions.

It supports - Linear Algebra, bitwise operations, Fourier transform, arithmetic operations, string operations, and so on.

## Applications of Numpy Array

### Manipulation of Shapes

If the output delivers the same number of elements, users can customize the array dimensions at runtime. On the array, we use the **np.reshape()** function. The reshape function can be used to execute a variety of tasks. For example, we use it to broadcast two different arrays.

### Array Creation

We can create an array data set to use in implementing various functions. Using the **np.arrange()** function, we can also produce a specified set of numbers for the array elements. The Reshape function can be used to create a new set of dimensions.

We can also use the random method to create an array with randomly generated values. Similarly, we may use the **linspace()** function to create arrays with identical element spacing.

Arrays with pre-filled ones or zeroes can be created. The default data type is float64, but we can change it with the **dtype** option.

### Array Dimensions

Numpy supports both one-dimensional and multidimensional arrays. Some functions impose restrictions on multidimensional arrays. The arrays must subsequently be converted into one-dimensional arrays. Using **np.ravel()**, we may convert a multidimensional array to a single dimension.

## Applications of Numpy with Other Libraries

### Using NumPy with Pandas

Pandas is one of the most essential data analysis libraries in Python. They are known for their excellent performance, quick analysis, and data cleansing. We use it to alter data structures and analyze data.

It is made up of a data frame object. It works with NumPy to speed up computations. When we combine these libraries, we have a highly useful resource for scientific computations.

### Using NumPy with Matplotlib

Matplotlib is a NumPy module. It is an extremely useful tool for working with graphical representations. It has a large range of functions for plotting and manipulating graphs.

This combination can take the place of MatLab's functions. It is used to generate the results graphs. We extend it further by utilizing graphic toolkits such as PyQt and wxPython.

### Using NumPy with Scipy

Scipy is a Python open-source library. It is Python's most important scientific library. It is based on the functionality of NumPy. SciPy includes enhanced features for scientific computations.

It can be combined with NumPy to improve mathematical performance. The combination facilitates the execution of difficult scientific operations.

### Using NumPy with Tkinter

Tkinter is a GUI standard library. Tkinter is used for the graphical representation of NumPy data. Its integration with NumPy allows for the creation of quick and simple GUIs. The use of Tkinter in combination with NumPy is simple. The array objects can be simply converted into image objects.

### Conclusion

Finally, we've seen Numpy's uses and applications. NumPy is a core Python package with a massive set of features. It has a wide range of built-in features and a flexible nature Its advantages include lower memory consumption and faster runtime behaviour. Numpy operates with the same smoothness regardless of dataset size.

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