Python For Data Science
The data science programming requirements demand a very versatile, but flexible language, that is simple to write the code, but that can handle highly complex mathematical processing. Python is more suitable for such requirements since it has already been established as a language for general computing as well as for scientific computing. More than that, it is continuously being updated in the form of a new addition to its infinity of libraries destined to different programming requirements. Next, we will discuss these python resources, which makes it the preferred language for data science.
- A simple and easy to learn the language, which results in fewer lines of code than other similar languages, such as R. Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion in the overall flow of the program.
- It is cross-platform so that the same code works in several environments without the need for any change. What makes it perfect to be used in a multi-environment configuration easily
- Executes faster than other similar languages used for data analysis such as R and MATLAB
- Its excellent memory management capacity, especially garbage collection, makes it in versatile to manage the volume of transformation, cutting, data cutting and data visualization with great volume.
- The most important thing is that Python has a very large collection of libraries that serve as special purpose analysis tools. For example – the NumPy package deals with scientific computing and its matrix needs much less memory than the conventional python list to manage numerical data.
- Python has packages that can directly use the code of other languages such as Java or C. This helps to optimize code performance through existing code of other languages each time it provides a better result.
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Why Learn Python For Data Science?
Python is, without a doubt, the most suitable language for a data scientist. I have listed some points that will help you understand why people use Python for Data Science: Python is a free, flexible and powerful open source language Python reduces development time by half with its simple and easy syntax of lero With Python, you can perform the manipulation, analysis and visualization of data Python provides powerful libraries for machine learning applications and other scientific calculations
What is Python? Is Python for Data Science only?
I’m going to keep the theoretical part short. But there are two things you need to know about Python before you start using it. First, Python is a general-purpose programming language and is not just for Data Science. This means that you do not need to learn every part of it to be a great data scientist. At the same time, if you learn the basics well, you will understand other programming languages as well – which is always very useful if you work in IT. Secondly, Python is a high level language. This means that, in terms of CPU time, it is not the most efficient language on the planet. But, on the other hand, it was made to be simple, “easy to use” and easy to interpret. So what you can lose with the CPU time, you can win again in engineering time.
Best Python Data Science Frameworks
As we have summarized before, NumPy is the abbreviation of Numerical Python. It is the most popular library and base for high-level tools in Python programming for data science. A deep understanding of the NumPy arrays helps in the effective use of Pandas for data scientists. NumPy is versatile since it can work with matrices and multidimensional matrices. NumPy has many internal functions related to statistics, numerical computation, linear algebra, Fourier transform, etc. NumPy is the standard library for scientific computing with powerful tools for integration with C and C ++. If you want to master the science of data, then the NumPy is the library you must learn.
It is an open source library used for the computation of several modules, such as image processing, integration, interpolation, special functions, optimizations, algebra linear, Fourier transform, clustering, and many other tasks. This library is used with NumPy to run an efficient numerical computation.
This popular library is used for machine learning in data science with various classification, regression, and clustering algorithms, which provide support vector machines, naive Bayes, gradient improvement and logical regression. SciKit is designed to interoperate with SciPy and NumPy.
Pandas is popularly known for providing data frames in Python. This is a powerful library for data analysis, compared to other domain-specific languages, such as R. By using Pandas, it is easier to handle missing data, it supports working with data indexed differently from several different characteristics and it supports the automatic alignment of data. It also provides tools for data analysis and data structures such as merging, modeling or cutting of data sets and is also very effective in working with data related to time series, providing robust tools to load Excel data, files simple, data banks and fast format HDF5.
Matplotlib stands for Mathematical Plotting Library in Python. This is a library that is used primarily for data visualization, including 3D graphics, histograms, image graphics, scatter charts, bar charts and power spectra with interactive features for zoom and pan for publication in different formats. Print. It supports almost all platforms, such as Windows, Mac, and Linux. This library also serves as an extension for the NumPy library. Matplotlib has a pyplot module that is used in views, which is often compared to MATLAB.