This article teaches you how to use the IP attribution query API for statistical analysis

introduction

In the Internet age, IP address plays a very important role, it can help us track website visitors, optimize network services and so on. The IP attribution goes a step further, it can help us precisely locate the geographical location of the IP address, and provides great help for statistical analysis, network security, market research and other fields.

In today's article, I will explain in detail how to use the IP attribution query API for statistical analysis~

Steps for usage

Detailed explanation of each step:

Select IP home interface: First, you need to choose a reliable IP home interface or service provider. These interfaces usually provide an API (application programming interface) that allows you to query the attribution information of the IP address programmatically.
Obtain IP address data: Next, you need to obtain IP address data for statistical analysis. This data can be collected by you yourself, or obtained from other sources, such as website access logs, application logs, etc. Make sure you have legal access to and use of this data.
Query IP attribution: Use the selected IP attribution interface to send each IP address to the interface for query by calling its API. The interface will return the attribution information of the IP address, such as country, province/state, city, etc.
Organizing and storing data: Organizing and associating the queried IP attribution information with the original IP address data. You can create a database table or use other data storage methods for subsequent statistical analysis.
Perform statistical analysis: With the compiled IP attribution data, you can perform various statistical analysis. For example, you can calculate the number of users in different regions, the proportion of visits between regions, the geographical distribution of user behavior, etc. These analyzes can be performed using statistical software (such as data analysis libraries for R, Python) or commercial analysis tools.
Interpret and apply results: Based on the results of statistical analyses, you can interpret and apply the data. For example, you can adjust marketing strategies according to the distribution of users in different regions, or adjust the positioning of website content according to the proportion of regional visits.

How to choose an IP attribution query API that meets your needs? Here I recommend the API of APISpace. Regarding the IP attribution query API, there are high-precision version , district and county level , city level , IP attribution-IPv6 city level , IP attribution-IPv6 district and county level ** multiple versions , with more options. Next, I will take the IP attribution-IPv4 district and county-level API as an example to teach you how to use it~

IP attribution-IPv4 district and county level API usage tutorial

The first step, register and obtain an API key

After registering and logging in to APISpace , you can see the [Free Trial] button on the IP attribution-IPv4 county-level API details page , and click to get the corresponding free times.

After the registration is successful, we click [My API ] in the page navigation menu to enter the [Access Control ] page, and we can see the key provided by the platform.

Generally speaking, after successful registration, there is a certain free trial. After the trial, we can purchase more times on the platform.

The second step, integrating the API

After obtaining the API key, we can refer to the documentation or sample code provided by the API provider to integrate the IP attribution query function in the code.

On the APISpace website, we can get its sample code on the IP attribution details page.

The sample code using Python as an example is as follows:

import http.client

conn = http.client.HTTPSConnection("eolink.o.apispace.com")

payload = ""

headers = {
    "X-APISpace-Token":"",
    "Authorization-Type":"apikey"
}

conn.request("GET","/ipguishu/ip/geo/v1/district?ip=1.45.124.145&coordsys=WGS84", payload, headers)

res = conn.getresponse()

data = res.read()

print(data.decode("utf-8"))

The third step, test IP attribution query

We enter the IP address of the test on the test page of the IP attribution, and then we can see the relevant IP information returned.

The return example is as follows:

{
    "code": "Success",
    "data": {
        "continent": "亚洲",
        "country": "中国",
        "zipcode": "100005",
        "timezone": "UTC+8",
        "accuracy": "区县",
        "owner": "北京维瑞智盛软件开发有限公司",
        "isp": "北京海讯达通信有限公司",
        "source": "数据挖掘",
        "areacode": "CN",
        "adcode": "110101",
        "asnumber": "45083",
        "lat": "39.930941",
        "lng": "116.409243",
        "radius": "6.9215",
        "prov": "北京市",
        "city": "北京市",
        "district": "东城区"
    },
    "charge": true,
    "msg": "查询成功",
    "ip": "1.45.124.145",
    "coordsys": "WGS84"
}

conclusion

It should be noted that the accuracy and timeliness of the IP attribution interface may vary. In addition, for users who use tools such as proxy servers or VPNs to hide their IP addresses, the IP attribution may not be accurate. Therefore, when conducting statistical analysis, these factors should be considered comprehensively to obtain more reliable results.