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Today, I will talk to you about how to use Python for big data mining and analysis, many people may not know much about it. In order to make you understand better, the editor has summarized the following contents for you. I hope you can get something according to this article.

Big data is everywhere. In this day and age, whether you like it or not, you may encounter it in the process of running a successful business.

What is big data?

Big data is just as it seems-there is a lot of data. Individually, the insights you can get from a single piece of data are limited. But combining complex mathematical models with powerful TB-level data can create insights that humans cannot produce. The value that big data analysis provides to business is intangible and transcends human capabilities every day.

The first step in big data's analysis is to collect the data itself, which is known as "data mining". Most enterprises deal with GB-level data, including user data, product data and geographic location data. Today, I will take you to explore how to use Python for big data mining and analysis.

Why choose Python?

The biggest advantage of Python is that it is easy to use. This language has intuitive syntax and is a powerful multi-purpose language. This is very important in big data's analytical environment, and many enterprises are already using Python, such as Google,YouTube, Disney and so on. Also, Python is open source, and there are many class libraries for data science.

Now, if you really want to use Python for big data analysis, there is no doubt that you need to know the syntax of Python, understand regular expressions, know what tuples, strings, dictionaries, dictionary deductions, lists and list deductions are-- this is just the beginning.

Data analysis process

Generally, a data analysis project can be implemented according to the steps of "data acquisition-data storage and extraction-data preprocessing-data modeling and analysis-data visualization". According to this process, the subdivision knowledge points that each part needs to master are as follows:

There are two main ways to obtain external data.

The first is to obtain external public data sets, some scientific research institutions, enterprises, governments will open some data, you need to go to specific websites to download this data. These data sets are usually relatively perfect and of relatively high quality.

Another way to get external data is crawlers.

For example, you can get the recruitment information for a position on the recruitment website through a crawler, the rental information of a city on the rental website, the list of movies with the highest score on Douban, and the ranking of Zhihu likes and NetEase Yun music reviews. Based on the data crawled on the Internet, you can analyze certain industries and certain groups of people.

Before crawling, you need to know some basic knowledge of Python: elements (lists, dictionaries, tuples, etc.), variables, loops, functions.

And how to use Python libraries (urllib, BeautifulSoup, requests, scrapy) to implement web crawlers.

After mastering the basic crawlers, you also need some advanced skills, such as regular expressions, using cookie information, simulated user login, packet analysis, building proxy pools, etc., to deal with the anti-crawler restrictions of different sites.

Data access: SQL language

When dealing with data less than 10,000, Excel has no problem with general analysis. Once the amount of data is large, it will be inadequate, and the database can solve this problem very well. And most enterprises will store data in the form of SQL.

As the most classical database tool, SQL provides the possibility for the storage and management of massive data, and greatly improves the efficiency of data extraction. You need to master the following skills:

Extract data in a specific situation

Add, delete, check and change the database

Grouping aggregation of data, how to establish relationships between multiple tables

Data preprocessing: Python (pandas)

Most of the time, the data we get is not clean, such as repetition, missing, outliers and so on. At this time, we need to clean the data and deal with the data that affect the analysis in order to obtain more accurate analysis results.

For data preprocessing, it is no problem to learn the use of pandas (Python package) to deal with general data cleaning. The knowledge points to be mastered are as follows:

Lecting: data access

Missing value handling: delete or populate missing data rows

Duplicate value processing: judgment and deletion of repeated values

Exception handling: clear unnecessary spaces and extreme, abnormal data

Related operations: descriptive statistics, Apply, histogram, etc.

Merge: a merge operation that conforms to various logical relationships

Grouping: data partition, function execution, data reorganization

Reshaping: quickly generate PivotTable report

Probability Theory and knowledge of Statistics

The knowledge points to be mastered are as follows:

Basic statistics: mean, median, mode, percentile, extreme value, etc.

Other descriptive statistics: skewness, variance, standard deviation, significance, etc.

Other statistical knowledge: population and samples, parameters and statistics, ErrorBar

Probability distribution and hypothesis testing: various distributions and hypothesis testing processes

Other knowledge of probability theory: conditional probability, Bayesian, etc.

With the basic knowledge of statistics, you can use these statistics to do basic analysis. You can use Seaborn, matplotlib, etc. (python package) to do some visual analysis, through a variety of visual statistical charts, and get instructive results.

Python data analysis

Master the method of regression analysis, through linear regression and logical regression, in fact, you can regression analysis of most of the data, and draw relatively accurate conclusions. The knowledge points to be mastered in this part are as follows:

Regression analysis: linear regression, logical regression

Basic classification algorithms: decision tree, random forest.

Basic clustering algorithm: k-means...

The Foundation of feature Engineering: how to optimize the Model with feature selection

Parameter adjustment method: how to adjust the parameter optimization model

Python data analysis package: scipy, numpy, scikit-learn, etc.

At this stage of data analysis, focus on the methods of regression analysis, most of the problems can be solved, using descriptive statistical analysis and regression analysis, you can get a good analysis conclusion.

Of course, as you increase your practice, you may encounter some complex problems, and you may need to understand some more advanced algorithms: classification, clustering.

Then you will know which algorithm model is more suitable for different types of problems. For the optimization of the model, you need to know how to improve the prediction accuracy through feature extraction and parameter adjustment.

You can realize the whole process of data analysis, data mining modeling and analysis through the scikit-learn library in Python.

Summary:

In fact, data mining is not a dream, 5 steps can make you become a Python crawler master!

After reading the above, do you have any further understanding of how to use Python for big data mining and analysis? 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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