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This article mainly explains "what are the practical Python libraries". The content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn what the practical Python libraries are.

Data collection

Most data analysis projects begin with data collection and extraction. In some cases, the company may provide relevant data sets when dealing with existing problems for the company. But sometimes, there may be no ready-made data, which needs to be collected by the data engineer. The most common situation is that data engineers need to find data on their own from the Internet.

1. Scrapy

If you want to write a Python web crawler to extract information from a web page, Scrapy is probably the first Python library that most people think of. For example, users can extract comments from all restaurants in a city or collect all comments about a product on an online shopping site.

The most common use of the library is to use it to identify interesting information patterns that appear on Web pages, whether in the form of URL or XPath. Once the pattern of this information is sorted out, Scrapy can help users automatically extract the information they need and organize it into tabular or JSON-formatted data structures.

You can easily install Scrapy using pip.

2. Selenium

The original intention of Selenium designers was to build it into an automated website testing framework, but developers found it more effective as a web data crawling tool.

Selenium usually comes in handy after users have interacted with each other on the site they are interested in. For example, users may need to sign up for an account on the site, log in to their own account, and click a few buttons or links to find the content they want.

The above link is defined as the JavaScript function. In this case, it may not be easy to apply Scrapy or Beautiful Soup, but you can easily do it with Selenium.

It should be noted, however, that Selenium runs much slower than a normal grab library. This is because Selenium initializes browsers like Chrome and simulates all the behavior defined by the browser code.

Therefore, when dealing with URL schema or Xpaths, it is best to use Scrapy or Beautiful Soup, and do not use Selenium as a last resort.

3. BeautifulSoup

Beautiful Soup is another Python library that can be used to collect site content. It is generally believed that the time required to learn BeautifulSoup is much shorter than that of Scrapy. In addition, Beautiful Soup is more suitable for relatively small-scale problems or one-time tasks.

Scrapy requires users to develop their own "crawlers" and operate them through the command line, while using Beautiful Soup only needs to import its functions into the computer and use them online. Therefore, users can even apply Beautiful Soup to their own Jupyternotebook.

Data cleaning and transformation

There is no need to dwell on the importance of data cleaning and transformation, and there are already many excellent Python libraries that can handle this problem perfectly. The author will choose several as data scientists or analysts must know for a brief introduction.

4. Pandas

It may be a bit superfluous to mention Pandas here, as long as you are a practitioner who has processed data, it is impossible not to use Pandas.

Users can use Pandas to manipulate data within the Pandas data framework. Pandas also has a large number of built-in functions to help users convert data.

Needless to say, it is essential to learn Python,Pandas well.

5. Numpy

Image source: medium

Numpy, like Pandas, is an indispensable Python library for both ordinary users and data scientists and analysts.

Numpy expands the object list of Python into a comprehensive multi-dimensional sequence. At the same time, Numpy also has a large number of built-in mathematical functions, which can meet almost all the operation requirements of users. In general, users can use Numpy sequences as matrices and perform matrix operations.

The author believes that when most data scientists start writing Python code, the first step is to enter the following:

Import numpy as np import pandas as pd

Therefore, it is understandable to say that the above two libraries are the most popular among Python users.

6. Spacy

Image source: medium

Spacy may not be as famous as the last two libraries. Numpy and Pandas are mainly used to deal with numerical data and structured data, while Spacy can help users convert free text into structured data.

Spacy is one of the most popular natural language processing libraries. After crawling a large number of product reviews from shopping websites, we need to extract useful information from them in order to analyze them. Spacy contains a large number of built-in functions, which can provide a lot of help to the work of users. Examples include lexical analyzers, named individual identification, and specific text detection.

Another highlight of Spacy is that it supports multiple language versions. Its website claims that the library is available in more than 55 languages.

Data visualization

Data visualization is an indispensable part of data analysis, and only by visualizing the results can the data content be interpreted.

7. Matplotlib

Image source: scriptverse

Matplotlib is the most comprehensive Python data visualization library. Some people think that the interface of Matplotlib is ugly, but the author believes that as the most basic Python data visualization library, Matplotlib can provide the greatest possibility for users' visualization goals.

Developers using JavaScript also have their own preferred visualization libraries, but when dealing with tasks that involve a large number of customized features that are not supported by advanced libraries, developers must use D3.js. The same is true of Matplotlib.

8. Plotly

Image source: pngitem

Although the author firmly believes that in order to visualize data, it is necessary to master Matplotlib, but in most cases readers prefer to use Plotly, because using Plotly can produce the most colorful images with the least amount of code.

Whether you want to construct a 3D surface map, a scatter map based on a map, or an interactive animation, Plotly can meet the requirements in the shortest possible time.

Plotly also provides a table studio where users can upload their visualization to an online repository for future editing.

Data modularization

Data analysts engaged in modularization are often referred to as senior analysts. Nowadays, machine learning is not a new concept. Python is generally considered to be the most commonly used language for machine learning, so there are a large number of excellent libraries to support use in Python.

9. Scikit Learn

Image source: kindpng

Before indulging in "deep learning", everyone should start their own machine learning journey by using Scikit Learn. Scikit Learn has six main modules with the following functions:

Data preprocessing

Dimension reduction

Data regression

Data classification

Data clustering analysis

Model selection

As long as you can make good use of Scikit Learn, you can be regarded as an excellent data scientist.

10. Tensorflow

Tensorflow is an open source machine learning library launched by Google. Its most popular feature is the data stream image on Tensorboard.

Tensorboard is a web-based dashboard that visualizes data learning streams and results, which is useful for troubleshooting and presentation.

11. PyTorch

Image source: mattolpinski

PyTorch is an open source library released by Facebook and used as a common machine learning framework for Python. The statement of PyTorch is more suitable for Python than Tensorflow. Because of this, it is also easier to learn to use PyTorch.

As a library focused on deep learning, PyTorch also has a wealth of application program interface functions and built-in functions to help data scientists train their deep learning models more quickly.

Audio and image recognition

Machine learning can process not only numbers, but also audio and images (video is often thought of as a combination of many frames of images). Therefore, when dealing with these multimedia data, the above machine learning library is far from enough.

12. OpenCV

Image source: opencv

OpenCV is the most commonly used image and video recognition library. It is no exaggeration to say that OpenCV allows Python to completely replace Matlab in the field of image and video recognition.

OpenCV provides a variety of application programming interfaces, and it supports not only Python, but also Java and Matlab. OpenCV's excellent processing power makes it well received in the computer industry and academic research.

13. Librosa

Image source: github

Librosa is a very powerful Python library for audio and sound processing. Librosa can be used to extract various parts from the audio band, such as rhythm, rhythm and beat.

Extremely complex algorithms such as Laplacia segmentation can be easily used with only a few lines of code after using Librosa.

Web page

Python used to be the darling of web development before it was widely used in the field of data science. As a result, there are many libraries for web development.

14. Django

If you want to use Python to develop a web service backend, Django has always been the best choice. Django's design philosophy is to build a high-level framework for a website in a few lines of code.

Django connects directly to most well-known databases so that users can save time in establishing connections and developing data models. Users of Django only need to focus on business logic without worrying about being manipulated by create, update, read, and delete (Create,update,retrieve and delete, CURD), because Django is a database-driven framework.

15. Flask

Flask is a lightweight web development framework for Python. Its most valuable feature is that it can easily carry out customized processing that can meet any demand.

Thank you for your reading, these are the contents of "what are the practical Python libraries?" after the study of this article, I believe you have a deeper understanding of what the practical Python library has, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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