Pandas 在 2 个数据帧上使用 netaddr 来查看 ip 列是否属于带有布尔结果的

Question

我正在使用 netaddr python 库。我有 2 个数据帧，一个带有可转换为 CIDR 表示法的 IP 范围，另一个带有我想查看它们是否属于任何范围的 IP 地址。创建范围数据框：import pandas as pdimport netaddrfrom netaddr import *a = {'StartAddress': ['65.14.88.64', '148.77.37.88', '65.14.41.128', '65.14.40.0'], 'EndAddress': ['65.14.88.95', '148.77.37.95','65.14.41.135', '65.14.40.255']}df1 = pd.DataFrame(data=a)#Convert range to netaddr cidr formatdef rangetocidr(row):    return netaddr.iprange_to_cidrs(row.StartAddress, row.EndAddress)    df1["CIDR"] = df1.apply(rangetocidr, axis=1)df1    StartAddress  EndAddress    CIDR0   65.14.88.64   65.14.88.95   [65.14.88.64/27]1   148.77.37.88  148.77.37.95  [148.77.37.88/29]2   65.14.41.128  65.14.41.135  [65.14.41.128/29]3   65.14.40.0    65.14.40.255  [65.14.40.0/24]df1["CIDR"].iloc[0][IPNetwork('65.14.88.64/27')]创建 IP 数据帧：b = {'IP': ['65.13.88.64', '148.65.37.88','65.14.88.65','148.77.37.93','66.15.41.132']}df2 = pd.DataFrame(data=b)#Convert ip to netaddr formatdef iptonetaddrformat (row):    return netaddr.IPAddress(row.IP)df2["IP_Format"] = df2.apply(iptonetaddrformat, axis=1)df2    IP            IP_Format0   65.13.88.64   65.13.88.641   148.65.37.88  148.65.37.882   65.14.88.65   65.14.88.653   148.77.37.93  148.77.37.934   66.15.41.132  66.15.41.132df2["IP_Format"].iloc[0]IPAddress('65.13.88.64')我期待中添加一列df2，如果IP地址是从CIDR块df1。所以它看起来像：df2    IP            IP_Format     IN_CIDR0   65.13.88.64   65.13.88.64   False1   148.65.37.88  148.65.37.88  False2   65.14.88.65   65.14.88.65   True3   148.77.37.93  148.77.37.93  True4   66.15.41.132  66.15.41.132  False我更愿意仅使用 2 个数据帧中的列来执行此操作，但已通过将列转换为列表并使用以下内容进行了尝试，但这似乎不起作用：df2list = repr(df2[['IP_Format']])df1list = df[['CIDR']]def ipincidr (row):    return netaddr.largest_matching_cidr(df2list, df1list)df2['INRANGE'] = df2.apply(ipincidr, axis=1)

Answer 1

以下解决方案基于这样的假设：只有第四组 IP 发生变化，而前三组 IP 保持不变，如问题所示。

# Splitting IP into 2 parts __.__.__ and __. 

# Doing this for IP from df2 along with Start and End columns from df1

ip = pd.DataFrame(df2.IP.str.rsplit('.', 1, expand=True))

ip.columns = ['IP_init', 'IP_last']

start = pd.DataFrame(df1.StartAddress.str.rsplit('.', 1, expand=True))

start.columns = ['start_init', 'start_last']

end = pd.DataFrame(df1.EndAddress.str.rsplit('.', 1, expand=True))

end.columns = ['end_init', 'end_last']

df = pd.concat([ip, start, end], axis=1)

# Checking if any IP belongs to any of the given blocks, if yes, note their index

index = []

for idx, val in enumerate(df.itertuples()):

    for i in range(df.start_init.count()):

        if df.loc[idx, 'IP_init'] == df.loc[i, 'start_init']:            

            if df.loc[idx, 'IP_last'] >= df.loc[i, 'start_last'] 

                  and df.loc[idx, 'IP_last'] <= df.loc[i, 'end_last']:

                index.append(idx)

                break

# Creating column IN_CIDR and marking True against the row which exists in IP block

df2['IN_CIDR'] = False

df2.loc[index, 'IN_CIDR'] = True

df2

    IP            IP_Format     IN_CIDR

0   65.13.88.64   65.13.88.64   False

1   148.65.37.88  148.65.37.88  False

2   65.14.88.65   65.14.88.65   True

3   148.77.37.93  148.77.37.93  True

4   66.15.41.132  66.15.41.132  False

注意 - 您也可以使用which results np.whereto 跳过第一次迭代，因此您以后可以只关注行，从而减少开销。np.where(df.IP_init.isin(df.start_init), True, False)[False, False,  True,  True, False]True