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Today, I will talk to you about what Python libraries can help you build data science applications, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following for you. I hope you can get something according to this article.

Data science is one-third of the world, and Python is one of them. Below, we will introduce seven Python libraries that can help you build your first data science application. Numpy

In many data science projects, array is the most important data type. NumPy is a software library that supports a large number of multi-dimensional arrays and matrix operations, and it is a necessary tool for many machine learning developers and researchers. It is one of the most basic data science libraries in Python. It is the basis of a large number of Python mathematical and scientific computing packages, such as NumPy, which is used in the pandas library we will talk about later.

Pandas

Pandas library is specially used for data analysis, which fully draws lessons from the relevant concepts of Python standard library NumPy. It allows you to load, clean, and manipulate data, and to perform some kind of cleanup and manipulation on the project. You can use alternative methods such as SQL for data manipulation and database management, but Pandas is simpler and more suitable for data scientists who want to be developers (or at least MVP developers).

Keras or PyTorch

At present, the two main deep learning libraries, Keras and Pytorch, have received a lot of attention because they are relatively easy to use in neural network models. These two libraries make it easy for users to test different neural network architectures and even build their own neural network architectures. Keras is a model computing framework of neural network, which has no weight calculation and supports multiple AI frameworks. Pytorch is a machine learning framework, which has more flexibility and control than Keras, but without any complex declarative programming, it is a good choice if you want to learn more about the machine learning pytorch library.

Plotly

Plotly is a new generation of Python data visualization development library, which provides perfect interactive ability and flexible rendering options. Plotly can draw different types of graphics. Compared with other drawing libraries in Python, it is more professional, easier to use and more flexible. Plotly takes data visualization to a new level. Plotly has built-in complete interactive capabilities and editing tools, supports both online and offline modes, and provides a stable API for integration with existing applications that can display data charts in web browsers or store local copies.

SciKitLearn

SciKitLearn is a special module for machine learning and is a toolkit for many types of machine learning models and preprocessing tools. It contains most of the common machine learning methods, including classification, regression, unsupervised, data dimensionality reduction, data preprocessing and so on. As a Python open source framework specifically for machine learning, Scikitlearn can provide very good help to developers to a certain extent. It internally implements a variety of mature algorithms, easy to install and use, rich examples, and tutorials and documentation are also very detailed.

Ipywidgets

In order for users to have a better experience, developers must choose between a traditional-looking user interface and a web-based user interface. When building, you can use libraries such as PyQT or TkInter to build traditional user interfaces. But it's best to use ipywidgets to provide a rich set of widgets for Jupyter notebooks and develop web applications that can run on browsers.

Requests

Requests package is used to obtain the content of the website, using the HTTP protocol, is recognized as the best http request library for python.

Today, many data science applications use API (Application programming Interface). Simply put, through API, you can request server applications to provide you with access to the database or perform specific tasks for you. Requests is a library that talks to API. Today, it's hard to be a data scientist without using API.

With the above seven Python libraries, developers can build data science applications that people use, and if you are proficient in these tools, you can build mvp in a few hours and test ideas with real users. After that, if you decide to extend your application, you can use more professional tools such as Flask and Django in addition to the HTML, CSS, and JS code.

After reading the above, do you have any further understanding of what Python libraries are available to help you build data science applications? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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