Why Numpy?



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Numpy Applications

1. An alternative for lists and arrays in Python

Arrays in Numpy are equivalent to lists in python. Like lists in python, the Numpy arrays are homogenous sets of elements. The most important feature of NumPy arrays is they are homogenous in nature.

This differentiates them from python arrays. It maintains uniformity for mathematical operations that would not be possible with heterogeneous elements. Another benefit of using NumPy arrays is there are a large number of functions that are applicable to these arrays.

These functions could not be performed when applied to python arrays due to their heterogeneous nature.

2. NumPy maintains minimal memory

Arrays in NumPy are objects. Python deletes and creates these objects continually, as per the requirements. Hence, the memory allocation is less as compared to Python lists. NumPy has features to avoid memory wastage in the data buffer.

It consists of functions like copies, view, and indexing that help in saving a lot of memory. Indexing helps to return the view of the original array, which implements the reuse of the data. It also specifies the data type of the elements which leads to code optimization.

3. Using NumPy for multi-dimensional arrays

We can also create multi-dimensional arrays in NumPy.These arrays have multiple rows and columns. These arrays have more than one column that makes these multi-dimensional. Multi-dimensional array implements the creation of matrices.

These matrices are easy to work with. With the use of matrices, the code also becomes memory efficient. We have a matrix module to perform various operations on these matrices.

4. Mathematical operations with NumPy

Working with NumPy also includes easy-to-use functions for mathematical computations on the array data set. We have many modules for performing basic and special mathematical functions in NumPy.

There are functions for Linear Algebra, bitwise operations, Fourier transform, arithmetic operations, string operations, etc.

Numpy Array Applications

1. Shape Manipulations

Users can change array dimensions at runtime if the output produces the same number of elements. We apply np.reshape(…)function on the array. The reshape function is useful for performing various operations. For eg, we use it when we want to broadcast two dissimilar arrays.

2. Array Generation

We can generate array of data set for implementing various functions. We can also generate a predefined set of numbers for the array elements using the np.arange(…)function. Reshape function is useful to generate a different set of dimensions.

We can also use the random function to generate an array having random values. Similarly, we can use linspace() function to generate arrays having similar spacing in elements.

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We can create arrays with pre-filled ones or zeroes. The default data type is set to be float64 but we can edit the data type using dtype() option.

3. Array Dimensions

Numpy consists of both one and multidimensional arrays. Some functions have restrictions on multidimensional arrays. It is then necessary to transform those arrays into one-dimensional arrays. We can transform multi-dimensional to single dimension using np.ravel(..)

Numpy Applications with Other Libraries

1. NumPy with Pandas

Pandas is one of the most important libraries in python for data analysis. Pandas provide high performance, fast analysis, and data cleaning. We use it to manipulate data structures and have data analysis tools.

It consists of a data frame object. It interoperates with NumPy for faster computations. When we use both the libraries together it is a very helpful resource for scientific computations.

2. NumPy with Matplotlib

Matplotlib is a module in NumPy. It is a very helpful tool to work with graphical representations. It consists of a wide range of functions to plot graphs and also manipulate them.

This combination can replace the functionalities of MatLab. It is used to generate the graphs of the results. We enhance it further with the use of graphic toolkits like PyQt and wxPython.

3. NumPy with SciPy

Scipy is an open-source library in Python. It is the most important scientific library in python. It has been built upon the functionalities of NumPy.There are advanced functionalities in SciPy for scientific computations.

We can combine it with NumPy for greater mathematical performance. The combination helps in the implementation of complex scientific operations.

4. NumPy with Tkinter

Tkinter is a standard library for GUI. We use Tkinter for the GUI representation of the NumPy data. Its combination with NumPy can implement fast and easy GUIs. The use of Tkinter along with NumPy is user-friendly. We can easily convert the array objects into image objects.

Benefits of Numpy

Here are the top four benefits that NumPy can bring to your code:

1. More speed: NumPy uses algorithms written in C that complete in nanoseconds rather than seconds.

2. Fewer loops: NumPy helps you to reduce redundant loops and keep from getting tangled up in iteration indices.

3. Clearer code: Without loops, your code will look more like the equations you’re trying to calculate.

4. Better quality: There are thousands of contributors working to keep NumPy fast, friendly, and bug-free.

Because of these benefits, NumPy is the de facto standard for multidimensional arrays in Python data science, and many of the most popular libraries are built on top of it. Learning NumPy is a great way to set down a solid foundation as you expand your knowledge into more specific areas of data science.

Summary

To conclude, we have seen Numpy applications. NumPy is a core Python library with a tremendous amount of functionalities. It has a vast range of built-in functionalities

and an extensible nature. It has the benefits of smaller memory consumption and faster runtime behavior. Numpy works with the same smoothness regardless of the size of the dataset. It has uses in various other developing fields like machine learning and data science.

We can also combine it with other libraries to further enhance its applications. It has separate modules to perform special and complex mathematical functions.

For more information please refer to the following link;

https://numpy.org/doc/stable/user/quickstart.html

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