Hello everyone, the editor is here to answer the following questions for you, the list of 70 python hand-practicing projects for python crawlers, and the 80 python hand-practicing projects Baidu Netdisk. Let us take a look today!
Foreword : If someone asks: "Is Python still popular?" "Of course, it is very popular." "How long can it be popular?" "I don't know."
With the development of technology, many programming languages have been derived, but no one language can be in a monopoly position (we are now in the era of Java). Python is undoubtedly the hottest language at present. The main reason is that it is easy to learn. There is no complicated logical relationship, which has attracted the attention and learning of a large number of approved programmers/programmers, but many people are at a loss for employment directions after finishing the basic part, because there are too many employment directions. Vertical fields include Python development, Web full-stack, Python crawler engineers, etc. The direction of expansion can be automated testing, data analysis, and if you go to the high end, you can also choose big data, artificial intelligence, etc.
The seemingly prosperous employment market, although half a foot in the enterprise, has stumbled because many people have stumbled on the threshold of project experience. Therefore, in order to solve the vast number of PY friends who want to enter Python or are already in the pit, I spent more than ten hours sorting out the top ten Python classic employment training projects, which meet the employment standards of enterprises.
Python entry-level projects
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Project cases:
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Statistical directory file disk usage
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Drawing patterns via Python
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Picture conversion stick figure
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Use technical points:
1. Python development environment and introduction to Python
2. Comparison of Python language with other languages
3. Basic syntax, input, output, variables, comments, indentation, PEP8 specification
4. Boolean, number, string, list, tuple, dictionary, set
5. Process control branch structure
6. Process control loop structure
7. Function definition, call, return value, scope
8. Keyword parameters, default value parameters, variable parameters, anonymous functions, recursive functions
9. File opening and closing, file reading and writing, file directory related operations, serialization
Exercise goal: master the basic syntax of Python
Practice effect display:
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Project cases:
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Crack verification code identification
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Video conversion character animation
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Use technical points:
1. Classes and instances, access restrictions, properties and methods, member properties and class properties
2. Inheritance and polymorphism, @property, decorator
3. Slicing, list comprehension, iteration
4. map/reduce, decorator, generator, iterator, heap and stack
5. import statement, from/import statement, __name__ attribute, custom module, package, installation and use of third-party modules
6. try except exception handling, unit testing
7. UTF8 、UNICODE、ASC
Exercise Objectives: Master programming and data structures
Python Advanced Project
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Project 3: The front and back of the online micro-course mall system
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Project cases:
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Route-Map User Home Page
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Maintain database with Django proxy
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Use Django's model class to manage micro-course users
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database visualization system
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Registration and automatic login function
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Phishing net csrf attack case
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Use technical points:
1. Routing and model class implementation template
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Environment build
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Basic Route Maps and Namespaces
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Passing and receiving of regular route mapping parameters
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reverse resolution processor
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Request object and Response object
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Context and Template Calls
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Template Layer Basic Syntax
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Template Filter Details
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Template reuse and block extraction
2. Model class implementation
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Definition of tables and fields
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Common Field Constraints
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Data migration and maintenance
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Addition, deletion and modification of model classes
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Query method of model class
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Use of QuerySet
3. Django framework
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Cookie security and life cycle
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The principle and use of Session
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Django connects to Redis service
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Submitting and receiving form data
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The principle of csrf cross-domain attack
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Csrf cross-domain attack example and defense
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one-to-many operation
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many-to-many operation
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Django self-association
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Middleware Django Middle-war application
Exercise objectives: understand data extraction strategy/familiar with crawler principle and implementation process/data crawling based on single task/select data crawling projects of Scrapy-Redis distributed asynchronous framework/select solutions for anti-crawling strategies in the industry/based on Distributed asynchronous frame grabbing
Project effect display:
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Item 4 Crawl popular articles from a certain portal
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Project Five Consulting Company Bidding Information Collection Platform
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Project 6 distributed architecture crawling bidding information collection platform
case:
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Product classification information extraction on e-commerce platform
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urllib parameter encoding and encryption
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Masquerading of request headers
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mock login
Related technical points:
1. Data extraction and cleaning strategy
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regular expression
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re module use cases
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xpath syntax
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lxml module in Python
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Baidu's anti-crawling strategy and solution for xpath crawlers
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JsonPath uses
2. urllib and anti-crawling strategy
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Http request protocol
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The urllib module uses
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Get requests and URL encoding
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Http post request
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Request object in urllib
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Request header masquerading strategy
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Proxy IP of anti-crawling strategy
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Simulated login of anti-climbing strategy
3. The principle of scrapy framework
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The core principle of Scrapy asynchronous framework
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Scrapy project creation and configuration
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Scrapy asynchronous crawling
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Pipeline pipeline file
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Middleware middleware
4.Scrapy-Redis distributed crawler
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Redis uses
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Scrapy-Redis component principle
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Scrapy-Redis configuration
Practice objectives: business logic analysis/Model layer development/backend data rendering of product home page/user personal page management/shopping cart function improvement/video transmission authority and agreement/creation of super administrator/backstage management home page display setting/model data visualization operation /Classification filtering and fuzzy query/Optimization of data visualization page
Crawl data display:
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Item 7 Server log data cleaning analysis
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Project 8 Meteorological data analysis
Use technical points
1. Principles of data science and data processing
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Principles of Data Science
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Data processing flow
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Jupyter notebook, a good assistant for data analysis
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Data Science Module Numpy
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Statistical Analysis Module Pandas
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Data Quality Analysis
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Data characteristic analysis
2. Feature engineering
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Seeing the big picture through real data
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Select performance metrics, check assumptions, acquire data (create workspace, quickly view data structures, create test sets)
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Explore the mysteries of data from data visualization (visualize data, find correlations, experiment with different combinations of attributes)
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Preparations before machine learning training (data cleaning, custom converters, feature scaling, conversion pipelines)
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Select and train a model (evaluate training set, cross-validation, analyze best model and its errors, test set evaluation)
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Model tuning
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Analyzing the best model and test set evaluation
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System Maintenance and Monitoring
Exercise objectives: data analysis and data mining, machine learning/Jupyter notebook installation and use, magic command/Numpy matrix and random number generation, ndarray basic operations, ndarray merging and splitting, matrix operations, aggregation operations, arg operations, comparison operations /Pandas data structure, data selection and operation, loading various data, sorting and merging, data summary, data grouping and pivot table, time series/data visualization/data acquisition and loading, data cleaning/data content processing and Principles of Analysis/Feature Engineering
Employment Direction: [Python Data Analyst]
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Project Nine Online Auction Data Analysis of First-line E-Commerce
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Project 10 Internet user background and identity association mining actual combat
case:
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Spam classifier implementation
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MNIST digit image recognition
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Online Auction Data Analysis of First-tier E-Commerce Companies
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Internet user background and identity association mining
Related technical points:
1. Machine Learning
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Principles of machine learning (loss function convex optimization)
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Key problems in machine learning (insufficient training data, poor quality, irrelevant features, overfitting, underfitting)
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Classification training and multi-class classifiers
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Performance assessment (measurement accuracy, precision and recall, ROC curve)
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Linear regression (standard equation, computational complexity)
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Regular linear model (ridge regression, logistic regression, probability estimation, decision boundary) Section 9: Support vector machine (linear SVM, nonlinear SVM)
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Dimensionality reduction (projection, manifold learning, PCA)
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Clustering algorithm Kmeans
2. Mass data processing and mining
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Hadoop massive data implementation principle
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Map Reduce idea to transform data key-value
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Hive persistent application in data statistical analysis
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PySpark and SparkSQL
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Linked Data Mining
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Association Rules Apriori Algorithm
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Association Analysis Solution for Massive Data
Practice objectives: Hadoop principle/Map Reduce conversion implementation/association mining algorithm model/pyspark use machine learning/common algorithm model/common concepts of machine learning/data dimensionality reduction/association based on massive data
Employment Direction: [Python Machine Learning and Big Data]
About Python Technical Reserve
It is good to learn Python whether it is employment or sideline business to make money, but to learn Python, you still need a study plan. Finally, everyone will share a full set of Python learning materials to help those who want to learn Python!
1. Learning routes in all directions of Python
The route of all directions in Python is to organize the commonly used technical points of Python to form a summary of knowledge points in various fields. Its usefulness lies in that you can find corresponding learning resources according to the above knowledge points to ensure that you learn more comprehensively.
2. Learning software
If a worker wants to do a good job, he must first sharpen his tools. The commonly used development software for learning Python is here, which saves you a lot of time.
3. Introductory learning video
When we watch videos and learn, we can’t just move our eyes and brain without using our hands. A more scientific learning method is to use them after understanding. At this time, the hands-on project is very suitable.
4. Practical cases
Optical theory is useless, you have to learn to follow along, and you have to do it yourself, so that you can apply what you have learned to practice. At this time, you can learn from some actual combat cases.
5. Interview materials
We must learn Python to find high-paying jobs. The following interview questions are the latest interview materials from first-line Internet companies such as Ali, Tencent, and Byte, and Ali bosses have given authoritative answers. After finishing this set The interview materials believe that everyone can find a satisfactory job.
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