If you’re a newbie programmer, you may have questions like, what is the difference between Python and NumPy? Or what is NumPy in Python? Python, a popular programming language, allows programmers to express concepts in fewer lines of code and is more readable. Python lists are used to implement scalar and matrix calculations. The performance can be better when compared to other programming languages.
Several libraries have emerged to maintain the ease of use of Python while allowing for efficient numerical calculations. One such library is NumPy, the first Python library to provide efficient numerical computations.
Continue reading this article to understand the differences between Python lists and NumPy arrays and the practical applications of both.
Python is a widely used programming language. Guido van Rossum developed it in 1991. According to a survey, approximately 80% of developers use Python as their primary coding language.
Python runs on a variety of platforms (Windows, Mac, Linux, Raspberry Pi, etc.). Similar to English, Python has a simple syntax. Unlike some other programming languages, Python allows programmers to write programs in fewer lines. Using Python, you can execute code immediately after you write it, thanks to its interpreter system. As a result, prototyping is very fast. It is possible to treat Python procedurally, object-oriented, or functionally.
The Python language was designed for readability, and it has some similarities to the English language with influences from mathematics. New lines are used to complete commands in Python, as opposed to semicolons or parentheses in other programming languages. The scope of loops, functions, and classes in Python is defined by indentation, which uses whitespace. Curly brackets are commonly used for this purpose in other programming languages.
Among many other uses, the following are the main applications of Python.
Python 3 is the latest major version of Python. However, Python 2 is still quite popular, even though it no longer receives anything other than security updates. You can write Python code in an Integrated Development Environment, such as Thonny, Pycharm, Netbeans, or Eclipse, which is particularly useful when managing large Python file collections.
Python can be applied in various fields, including but not limited to the following:
1. AI and Machine Learning:
The stability, flexibility, and simplicity of Python make it perfect for machine learning (ML) and artificial intelligence (AI) projects. The Python language is popular among data scientists, and many Python libraries and packages are available for machine learning and AI.
2. Data Analytics:
Python programming is also used in data analytics, another rapidly developing field. It is becoming more and more important to be able to collect, manipulate, and organize data.
It makes sense to use Python for data science and analytics. The language is easy-to-learn, flexible, and well-supported, making data analysis relatively quick and easy. The program is useful for manipulating large amounts of data and performing repetitive tasks.
3. Data Visualization:
The visualization of data is another popular and growing area of interest. Python’s strengths are once again emphasized. Python offers a variety of graphing libraries with many features.
You can find a library to suit your needs, regardless of whether you want a simple graphical representation or an interactive plot. Plotly and Pandas Visualization are examples.
4. Game Development:
Although Python is not an industry-standard in game development, it does have its uses. Using the language, you can create simple games, which makes it a useful tool for quickly prototyping. It is also possible to perform certain functions (such as creating dialogue trees) in Python.
Search engine optimization (SEO) is another surprising application of Python. In this area, automation is often beneficial, which Python can certainly provide. When it comes to implementing changes across multiple pages or categorizing keywords, Python can help.
Those who work in SEO should also consider emerging technologies like natural language processing (NLP). Python is a very useful tool to develop these NLP skills and understand how people search and how search engines return results.
The use of Python in finance is increasing, especially in quantitative and qualitative analysis. In addition to automating workflows across different data sources, it can be used to determine asset price trends and predictions. Several libraries are available to assist with compiling and processing information in Python, as mentioned earlier. This makes it one of the most popular languages in the finance industry.
When you ask ‘what is Python used for?’ you may not have expected the design to be in the list of Python applications. Python is used to develop graphic design applications. Surprisingly, the language is used in 2D imaging software like Paint Shop Pro and Gimp. The versatility of Python can even be seen in 3D animation software such as Lightwave, Blender, and Cinema 4D.
What Is NumPy, and Why Is it Used in Python?
NumPy is Python’s fundamental package for scientific computing. It is a Python library that provides a multidimensional array object, a wide array of routines for performing fast array operations, and various derived objects (such as masked arrays and matrices). The library offers many functions: mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, linear algebra, basic statistics, and random simulations.
The ndarray object is at the heart of the NumPy package. It is an n-dimensional array that contains homogeneous data types. Many operations are compiled into the code for faster execution. There are functions in NumPy’s outer namespace that mirror many of its methods so that programmers can code in their preferred paradigm.
Among Python’s most popular multi-dimensional data interchange languages are NumPy array dialects and NumPy ndarray classes.
The following are some usages of NumPy in Python:
1. Shape Manipulations:
Changing the array dimensions at runtime is possible if the output has the same number of elements. np.reshape(…) is applied to the array. Various operations can be performed with the reshape function. A simple example would be broadcasting two dissimilar arrays.
2. Array Generation:
Using np.arrange(…), we can create a predefined set of numbers for the array elements. The random function can generate an array of random values. To generate arrays with similar spacing in elements, we can use the linspace function.
We can create arrays with pre-filled zeros or ones. We can edit the default data type using dtype, which is set to float64 by default.
3. Array Dimensions:
NumPy supports both one-dimensional arrays and multidimensional arrays. Some functions restrict multidimensional arrays. The arrays must then be transformed into one-dimensional arrays. np.ravel(..) performs this function.
1. Disaster Management (np.fft.fft):
In the np.fft.fft (array, axis=None) function, the discrete Fourier transform is computed, and a complex ndarray is returned. But what is the Fourier transform actually? The Fourier Transform transforms any set of values or functions into a summation of harmonic functions.
2. Mechanical Vibrations (np.linalg.eig):
It returns the eigenvalues and eigenvectors of a squared array using the np.linalg.eig (array) function. In matrices, eigenvectors are vectors that multiply by any correct ordered matrix to give the multiple of the same eigenvector. The constant value of which it is multiple is the eigenvalue.
3. Physics (np.meshgrid):
The np.meshgrid(*xi, copy=True, sparse=False, indexing=’xy’) function returns coordinate vectors from coordinate matrices. Combining several one-dimensional arrays creates a multidimensional array.
Understanding The Difference Between list and NumPy array in Python
|Python Lists||NumPy Arrays|
|Python’s core library provides a list of functions||Python’s core library for scientific computing is NumPy|
|A variety of data types can be found in it||Contains similar datatypes|
|Python includes this function by default||To access NumPy arrays, we need NumPy libraries|
|Both homogeneous and heterogenous||It is homogenous|
|The list cannot be operated element by element||Element-wise operation is possible in arrays|
|It is by default one-dimensional. Sometimes we can create an N-dimensional list. However, the process is lengthy||We can create an N-dimensional array by using numpy.array() function|
|Compared to NumPy Array, it requires more memory||It requires smaller memory consumption|
|In the memory, items are stored at random locations||In its memory, items are stored in a sequential manner|
|Comparatively, it is slower than NumPy Array||It is faster than Python lists|
|There is no optimism function in it||The optimism function is present|
A popular programming language, Python uses one of its libraries called NumPy to perform functions faster. You can do better numerical computations with NumPy. NumPy is also numerical Python and a library for working with arrays. Aside from providing comprehensive mathematical functions, it also contains linear algebra routines, Fourier transforms, and other functions. NumPy is used in Python for this reason. As given in the article above, though NumPy is used in Python, they have many differences, and their applications will also differ.
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