foreword
Python crawler data analysis is a technology that uses the Python programming language and related libraries to collect data on the Internet, and process, analyze and visualize the data. Python crawler data analysis technology is widely used in data mining, business intelligence, market research, public opinion analysis and other fields. This article will introduce the basic concepts, common libraries and practical cases of Python crawler data analysis.
1. The basic concept of Python crawler data analysis
1.1 Reptiles
A crawler is an automated program that simulates human behavior on the Internet and obtains data from web pages. Crawlers can obtain web page content through the HTTP protocol and extract required data from it. The workflow of a crawler usually includes the following steps:
(1) Send an HTTP request to obtain the content of the web page;
(2) Analyze the content of the web page and extract the required data;
(3) Save the data locally or in the database.
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1.2 Data Analysis
Data analysis refers to the processing, analysis and visualization of data to discover the laws and trends in the data, so as to provide support for decision-making. Data analysis usually includes the following steps:
(1) Data cleaning, removing useless and abnormal data;
(2) Data processing, processing and converting data;
(3) Data analysis, statistics and analysis of data;
(4) Data visualization, which displays the data in the form of charts and other forms.
1.3 Python crawler data analysis
Python crawler data analysis refers to the use of Python programming language and related libraries to obtain data on the Internet, and to process, analyze and visualize the data. Python crawler data analysis technology can help us quickly obtain a large amount of data and conduct in-depth analysis on the data, so as to discover the laws and trends in the data and provide support for decision-making.
2. Python crawler data analysis common library
2.1 requests library
The requests library is a library for sending HTTP requests in Python, which can easily obtain web page content. The requests library provides a simple and easy-to-use API, which can easily send GET, POST and other requests and get the response content. The following is a sample code for sending a GET request using the requests library:
import requests
url = 'https://www.baidu.com'
response = requests.get(url)
print(response.text)
2.2 BeautifulSoup library
The BeautifulSoup library is a library for parsing HTML and XML documents in Python, which can easily extract data from web pages. The BeautifulSoup library provides an easy-to-use API to easily parse HTML and XML documents and extract the required data. The following is a sample code for parsing an HTML document using the BeautifulSoup library:
from bs4 import BeautifulSoup
从 bs4 进口美丽汤
import requests
url = 'https://www.baidu.com'
response = requests.get(url)
响应 = requests.get（URL）
soup = BeautifulSoup(response.text, 'html.parser')
print(soup.title.string)
2.3 pandas library
The pandas library is a library for data processing and analysis in Python, which can easily process and convert data. The pandas library provides two data structures, DataFrame and Series, for convenient data processing and analysis. Here is a sample code to read a CSV file using the pandas library:
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head())
2.4 matplotlib library
The matplotlib library is a library for data visualization in Python, which can easily display data in the form of charts and other forms. The matplotlib library provides a simple and easy-to-use API that makes it easy to draw various types of charts. The following is a sample code for drawing a line chart using the matplotlib library:
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y)
plt.show()
1. Introduction to Python
The following content is the basic knowledge necessary for all application directions of Python. If you want to do crawlers, data analysis or artificial intelligence, you must learn them first. Anything tall is built on primitive foundations. With a solid foundation, the road ahead will be more stable.All materials are free at the end of the article！！！
Include:
Computer Basics
python basics
Python introductory video 600 episodes:
Watching the zero-based learning video is the fastest and most effective way to learn. Following the teacher's ideas in the video, it is still very easy to get started from the basics to the in-depth.
2. Python crawler
As a popular direction, reptiles are a good choice whether it is a part-time job or as an auxiliary skill to improve work efficiency.
Relevant content can be collected through crawler technology, analyzed and deleted to get the information we really need.
This information collection, analysis and integration work can be applied in a wide range of fields. Whether it is life services, travel, financial investment, product market demand of various manufacturing industries, etc., crawler technology can be used to obtain more accurate and effective information. use.
Python crawler video material
3. Data Analysis
According to the report "Digital Transformation of China's Economy: Talents and Employment" released by the School of Economics and Management of Tsinghua University, the gap in data analysis talents is expected to reach 2.3 million in 2025.
With such a big talent gap, data analysis is like a vast blue ocean! A starting salary of 10K is really commonplace.
4. Database and ETL data warehouse
Enterprises need to regularly transfer cold data from the business database and store it in a warehouse dedicated to storing historical data. Each department can provide unified data services based on its own business characteristics. This warehouse is a data warehouse.
The traditional data warehouse integration processing architecture is ETL, using the capabilities of the ETL platform, E = extract data from the source database, L = clean the data (data that does not conform to the rules), transform (different dimension and different granularity of the table according to business needs) calculation of different business rules), T = load the processed tables to the data warehouse incrementally, in full, and at different times.
5. Machine Learning
Machine learning is to learn part of the computer data, and then predict and judge other data.
At its core, machine learning is "using algorithms to parse data, learn from it, and then make decisions or predictions about new data." That is to say, a computer uses the obtained data to obtain a certain model, and then uses this model to make predictions. This process is somewhat similar to the human learning process. For example, people can predict new problems after obtaining certain experience.
Machine Learning Materials:
6. Advanced Python
From basic grammatical content, to a lot of in-depth advanced knowledge points, to understand programming language design, after learning here, you basically understand all the knowledge points from python entry to advanced.
At this point, you can basically meet the employment requirements of the company. If you still don’t know where to find interview materials and resume templates, I have also compiled a copy for you. It can really be said to be a systematic learning route for nanny and .
But learning programming is not achieved overnight, but requires long-term persistence and training. In organizing this learning route, I hope to make progress together with everyone, and I can review some technical points myself. Whether you are a novice in programming or an experienced programmer who needs to be advanced, I believe that everyone can gain something from it.
It can be achieved overnight, but requires long-term persistence and training. In organizing this learning route, I hope to make progress together with everyone, and I can review some technical points myself. Whether you are a novice in programming or an experienced programmer who needs to be advanced, I believe that everyone can gain something from it.
Data collection
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