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This article mainly introduces "Python+BI how to crawl cherries data". In daily operation, I believe many people have doubts about Python+BI how to crawl cherries data. The editor consulted all kinds of data and sorted out simple and easy operation methods. I hope to help you answer the doubts of "Python+BI how to crawl cherries data"! Next, please follow the small series to learn together!

I crawled 3000 pieces of data on Taobao with python, and then imported them into FineBI for visual analysis. Finally, I produced the following visual report:

Let me show you how it works:

I. Data acquisition

It is a cliché to crawl data on Taobao with Python. Search for "Che Li Zi" directly on Taobao. You can see in the commodity page below that the labels we mainly crawl this time are "commodity name,""price,""number of payers,""store name,""shipping address," etc.:

Press F12, bring up the background to view the source code, and find different commodity label codes, such as price is "price g_price g_price-highlight">,"number of payers is"deal-cnt", etc.:

After understanding the code structure of the web page, the next step is to write the code directly in python. The specific process is not described in detail. Some of the code is as follows:

After crawling the data, import it into Excel, and then after simple data cleaning and processing in Excel, finally get a completed data table:

II. Data analysis

Although python can also achieve the function of data analysis, but need to knock code, learning cost and difficulty are relatively large, it is better to directly use professional data analysis tools for analysis, such as common such as FineBI, Tableau, PowerBI and so on.

FineBI is a well-known local data analysis tool in China. Compared with tableau, the biggest advantage of these foreign tools is that they are simple and flexible. They can realize various analysis operations only by dragging and dropping with the mouse. They basically do not need to write code and are very friendly to novices.

In fact, FineBI is essentially an enterprise-level business data analysis platform. In addition to data analysis, it can also realize data management, data platform construction and other functions. I will not introduce it in detail here. If you are interested, I will introduce it in the next article.

With the excel source table, first we import Excel into FineBI:

Then click "Create Dashboard" in the upper left corner of the page to enter the visual background:

The next step is to enter the dashboard for visualization operation. The basic steps are "Select chart type-Select indicators and dimensions-Drag to specified coordinate axis-Beautify details." For example, if I want to create a visual map, first select chart type as "Area Map", and then select indicators and dimensions. However, there is no geographical latitude in the original data table, so I need to create it myself:

Finally, we drag to the specified coordinate axis, and then beautify the details to complete a visual map:

Similarly, other visualizations can be created according to our own requirements, which will not be discussed in detail here.

III. Data visualization

1. Sales distribution of cherries

It can be seen that the largest sales volume of domestic cherries comes from Shanghai, Zhejiang, Guangdong provinces, Xizang, Qinghai, Inner Mongolia and other provinces have no sales volume. Basically, the sales volume in coastal areas is higher than that in Inner Mongolia.

2. Sales volume of each province

It is more obvious through the bar chart that Shanghai has more than 200,000 sales, almost the sum of Zhejiang, Guangdong and Sichuan.

3. Sales volume in each city

After screening out the top ten cities in sales volume and the average price of cherries in each city, we can see that the sales volume and price in Shanghai are the highest, and we can see how strong Shanghai's purchasing power is.

4. Price range of cherries

In the data table, the price range is divided into "below 50,""50-100,""100-150,""150-200,""200-500,""above 500," etc. It can be seen that the price range with the largest proportion is "50-100," which should belong to civilian price; it is worth noting that the price proportion of "200-500" is also higher than "100-150."

5. Sales volume and price of each store

It can be seen that the highest sales volume is basically flagship stores, and the highest average price is basically around 600-800.

At this point, the study of "Python+BI how to crawl car data" is over, hoping to solve everyone's doubts. Theory and practice can better match to help you learn, go and try it! If you want to continue learning more relevant knowledge, please continue to pay attention to the website, Xiaobian will continue to strive to bring more practical articles for everyone!

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