Zaraki Kenpachi
2020-02-01 04:01
首先使用2个数据帧为每个IP查找状态，然后根据此字典数据创建新列并加载到原始df中。
我认为可以以更紧凑的方式完成此操作，但仍然可以完成。
import pandas as pd
data = [[1, 'CA', '192.168.1.0/24'], [2, 'TX', '172.68.7.0/24'], [3, 'NY', '61.141.47.0/24']]
df = pd.DataFrame(data, columns=['ID', 'State', 'IP_subnet'])
# replace end of IP
df['IP_subnet'] = df['IP_subnet'].str.replace(r'.0/24', '')
data2 = [[1, '61.141.47.1'], [2, '192.168.1.48'], [3, '172.68.7.124'], [4, '40.32.123.212']]
df2 = pd.DataFrame(data2, columns=['ID', 'IP'])
# match IP with state
data = {}
for index, row in df.iterrows():
ww = df2[df2['IP'].str.contains(row['IP_subnet'])]
data[ww['IP'].values[0]] = row['State']
# create State column
state_data = []
for index, row in df2.iterrows():
if row['IP'] in data:
state_data.append(data.get(row['IP']))
else:
state_data.append('NaN')
df2['State'] = state_data
输出：
ID IP State
0 1 61.141.47.1 NY
1 2 192.168.1.48 CA
2 3 172.68.7.124 TX
3 4 40.32.123.212 NaN
热门帖子
相关问题
热门github
1
2
4
The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.
(翻译：用于生成式 AI 的 Python 风险识别工具 (PyRIT) 是一个开放式访问自动化框架，使安全专业人员和机器学习工程师能够主动发现其生成式 AI 系统中的风险。)
5
7
8
9
Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.
(翻译：Mamba 是一种新的状态空间模型架构，在信息密集型数据（例如语言建模）上显示出良好的性能，而之前的二次模型在 Transformers 方面存在不足。它基于结构化状态空间模型的进展，并本着FlashAttention的精神进行高效的硬件感知设计和实现。)
10
11
13
谢谢您的帮助！！