如何根据IP范围过滤数据帧 [英] How to filter dataframe based on ip range

问题描述

我有2列的数据框.我想根据json文件中存在的ip范围过滤此数据帧.

I have dataframe which has 2 columns. I want to filter this dataframe based on ip ranges present in json file.

ip_ranges.json

[
    {"start": "45.43.144.0", "end": "45.43.161.255"}
    {"start": "104.222.130.0", "end": "104.222.191.255"}
    ...
]

数据框:

ip,p_value
97.98.173.96,3.7
73.83.192.21,6.9
...

注意:ip_range.json包含10万个元素，我的数据框有30万行.

Note: ip_range.json contains 100k elements and my dataframe has 300k rows.

目前，我是这样实现的

• 创建了python列表以存储每个范围内的所有ip.例如["45.43.144.0"，"45.43.144.1"，"45.43.144.2"，...，"45.43.161.255"].对于所有IP范围，都采用类似的方式.
• 从此列表中删除了重复的元素
• 使用此列表构造的数据框
• 在"ip"上合并了两个数据框

此过程对于一小部分ip_ranges可以正常工作.但是，对于大量的ip_ranges而言，该过程需要更长的时间才能完成.

This process works fine for small set of ip_ranges. But for large set of ip_ranges, the process takes longer time to complete.

是否有更好的方法可以更有效地执行此操作?

Is there any better approach to perform this more efficiently?

推荐答案

只是一个想法:将您放入具有From和To列的数据框ip_range中.使用提供的快速代码，例如df中的那些)转换为十进制数字. >这里.

Just an idea: Put you ranges into a dataframe ip_range with columns From and To. Convert all ip-addresses (the ones in df, too) to decimal numbers with the fast code provided for example here.

现在可以快速生成范围:

Now generating the ranges can be done fast:

ip_range['Rng'] = ip_range.apply(lambda x: np.arange(x.From, x.To+1), axis=1)

这些范围可以转换为DataFrame:

These ranges can be converted into a DataFrame:

ips = pd.DataFrame(itertools.chain(*ip_range['Rng']))

此DataFrame可以轻松地与df合并.

This DataFrame can easily be merged with df.

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