Python + bi crawls 3000 pieces of cheriki data and discovers these secrets

Soft sails 2021-02-23 17:07:24
python bi crawls pieces cheriki


It's said that the price of cherizi has dropped sharply recently , A lot of people dreamed of it before “ Chelizi is free ”, Now it's all possible . In fact, the price of cheriki has dropped , The main reason is that the cost of import freight is greatly reduced , In order to find the best way to buy cherizi , I decided to use it. python+BI Data analysis .

So I use it on Taobao python Crawling away 3000 Data , And then it's imported into FineBI Visual analysis in the , Finally, the following visual report was produced :

Now let me show you the operation process :

One 、 Data acquisition

use Python Data crawling on Taobao is a commonplace operation , Search directly on Taobao “ Cherry ”, You can see in the product page below , Our main tag this time is “ Name of commodity ”、“ Price ”、“ Number of payers ”、“ Shop name ”、“ Shipping address ” etc. :

Press down F12, Call up the background to view the source code , Find different product tag codes , For example, what is the price “price g_price g_price-highlight">”, The number of payers is “deal-cnt” etc. :

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

After crawling the data, import it into Excel in , And then in Excel After simple data cleaning and processing , Finally, we get a completed data sheet :

Two 、 Data analysis

python Although it can also realize the function of data analysis , But you need to knock the code , The cost and difficulty of learning are relatively high , It's better to directly use professional data analysis tools for analysis , For example, common ones like FineBI、Tableau、PowerBI etc. .

Now I'm going to take FineBI For example ,FineBI It 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 、 flexible , All you need to do is drag and drop with the mouse , There's basically no code to write , Very friendly to novices .

Actually FineBI It is essentially an enterprise level business data analysis platform , In addition to data analysis , And data management 、 Data platform building and other functions , I won't go into details here , If you are interested, I'll introduce it next .

With excel Source table , First we will Excel Import to FineBI in :

Then click on the top left corner of the page “ Create a dashboard ”, You can enter the visualization background :

Next, go to the dashboard for visualization , The basic steps are “ Choose chart type —— Choose indicators and dimensions —— Drag to the specified axis —— Beautify the details ”, For example, I want to create a visual map , First, select the chart type as “ Regional map ”, Then you have to choose indicators and dimensions , But there is no geographic latitude in the original data table , So you need to create it yourself :

Last , We drag it to the specified axis , Then beautify the details to make a visual map :

And so on , Other visual charts can also be made according to our own needs , I won't go into details here .

3、 ... and 、 Data visualization

1、 Distribution of sales volume of cherizi

It can be seen that the biggest sales volume of cherizi in China comes from Shanghai , And Zhejiang 、 Guangdong Province , Tibet 、 qinghai 、 There are no sales in Inner Mongolia and other provinces , Basically, sales in coastal areas are higher than those in inland areas .

2、 Sales situation of each province

It's more obvious through the bar chart , The sales volume in Shanghai is 20 More than ten thousand , It's almost Zhejiang 、 guangdong 、 The sum of Sichuan .

3、 The sales of each city

We've screened out the top 10 cities in terms of sales , And the average car price per city , It can be seen that the sales volume and price in Shanghai are the highest , We can see how strong the purchasing power of Shanghai is ;

4、 The price range of chelizi

The data table divides the price range into “50 following ”、“50-100”、“100-150”、“150-200”、“200-500”、“500 above ” etc. , It can be seen that the price range with the largest proportion is “50-100”, It should be civilian price ; It is worth noting that “200-500” The price share of is also higher than “100-150”.

5、 The sales volume and price of each store

It can be seen that the flagship stores have the highest sales volume , The highest average price is basically 600-800 about ;

Four 、 summary

Because there's not a lot of data , So I didn't do too much data analysis this time , You can take the data yourself , stay FineBI There's a lot more analysis going on .

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本文为[Soft sails]所创,转载请带上原文链接,感谢
https://pythonmana.com/2021/02/20210223170531226a.html

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