Python + SeaTable | Get company financing and other information from Qixinbao website to SeaTable table

This case is based on the real needs of an investment company.

As an investment company, it is necessary to update the financing information of the company concerned in a timely manner, so how to solve this problem? Under normal circumstances, we only need to obtain a series of public information of the company from websites such as Qixinbao, including industrial and commercial information, shareholder legal person, financing information, etc. However, it is obviously inefficient and inconvenient to log in to the website each time to inquire one by one. However, if you use the SeaTable form to record the company name, unified credit code and other information, and then use the Qixinbao API to obtain the company's financing information to the SeaTable form, you can be efficient and clear at a glance. And you can manually modify and maintain some missing or inaccurate information from Qixinbao.

Below, this article will share a case based on the above-mentioned real needs of an investment company. It will show how to obtain company information through Qixinbao's external API interface and quickly integrate it into the SeaTable table. Includes the following:

data synchronization

After logging in to SeaTable first, add a table to write basic company information, such as the company name and unified credit code as basic data. The "Company" sub-table in the following figure:

Next, create a sub-table "Financing Information" in the form for subsequent filling in financing and other information. Then insert columns for this subtable, such as financing round, unified credit code, financing date, financing amount, etc. As shown below:

Then, use the unified credit code in the "Company" subtable to request data from Qixinbao's API. At this time, you need to apply for the APP_KEY and SECRET_KEY of the API on Qixinbao yourself, and you need to add the local IP address to the IP whitelist of Qixinbao. After these preparations are done, the financing data can be written in Python combined with the relevant API of SeaTable. The following is the algorithm logic of data synchronization:

• For each company in the "Company" sub-table, use Qixinbao API to obtain the company's financing data, including financing amount, financing round, financing date, etc.
• Compare the "Financing Information" sub-table:

If a financing record does not exist, add it;

If it already exists, but the information in the table is inconsistent, it is considered that the information in the table has been manually updated by the user and will not be modified;

If it already exists and the information is consistent, it will not be processed.

For the specific process code, please refer to the complete case script here. The table after the data is written is as follows (the financing amount is for demonstration purposes only):

Data processing (quick links to associated records of subtables)

After completing the data acquisition and synchronization above, you can use the "Auto-Add Link" in SeaTable's "Data Processing" function to match the rules based on the "Uniform Credit Code" column of the "Company" and "Financing Information" sub-tables , to quickly add linked records to these two subtables with one click to achieve the purpose of association. The operation is as follows:

Click the "..." button on the table toolbar and select "Automatically add links" in "Data Processing", as shown below:

Then, according to the matching rule, select the "Uniform Credit Code" column in the two sub-tables to be equal, and then click Run. As shown below:

After clicking Run, the two sub-tables will automatically generate link columns and associate the corresponding records at the same time. That is, the "Company" table is automatically associated with all financing records for each unified credit code in the "Financing Information" table. As shown in the "Financing Information" column in the figure below (click the record in the cell to enter to view and edit row details). As shown below:

At the same time, the "Financing Information" table will also be automatically linked to the associated records in the "Company" table, that is, each row of financing records is associated with which company it belongs to (click the record in the "Company" column cell to view and edit details). As shown below:

Data Analysis and Visualization

In the SeaTable table, as shown in the figure above, you can filter out data from different angles by adding a table view, which is convenient for quick switching and viewing. In addition, this also facilitates data analysis and visualization of data (views) from different angles. For example, using the "Statistics" function of the table can quickly create statistical tables and basic charts; using the "Advanced Statistics" plugin (one-click addition from the "Plugins" on the right side of the table) can quickly create more types of statistical analysis charts, Complete more dimensions and forms of data visualization for the financing table. Charts can be exported. As shown below:

Summarize

In summary, using SeaTable convenient data management, complete Python API functions, powerful data analysis and visualization capabilities, combined with simple Python scripts, we can flexibly present the information we want efficiently and intuitively It can save a lot of manpower calculation and even application development costs, and easily realize more efficient and higher level office automation. Of course, this is just a reference case, we can also use Python to combine other rich features of SeaTable to achieve more workflows and applications.