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How can I bind multiple IPs to Python?

I have a list of ~ 300 IP addresses that I would like to build on a world map. Can you explain how I can do this with Python?

EDIT: I'm also interested in the question visualization part

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3 answers

You can use hostip.info API . For instance:

http://api.hostip.info/get_html.php?ip=64.233.160.0

Thus, your Python code using urllib2will look like this:

import urllib2
f = urllib2.urlopen("http://api.hostip.info/get_html.php?ip=64.233.160.0")
data = f.read()
f.close()

Then extract the data from the result.

If you need longitude and latitude, use the flag position=true:

http://api.hostip.info/get_html.php?ip=64.233.160.0&position=true
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GeoIP, , . Python API.

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Python 3.x, , , IP-; pd.series/dataframe - .

, API , API Google Maps, .

, .

TL;DR: geolite2.

1. geolite2 geolite2

# !pip install maxminddb-geolite2
import time
from geolite2 import geolite2
geo = geolite2.reader()
df_1 = train_data.loc[:50,['IP_Address']]

def IP_info_1(ip):
    try:
        try:
        x = geo.get(ip)
    except ValueError:   #Faulty IP value
        return np.nan
    try:
        return x['country']['names']['en'] if x is not None else np.nan
    except KeyError:   #Faulty Key value
        return np.nan

s_time = time.time()
# map IP --> country
#apply(fn) applies fn. on all pd.series elements
df_1['country'] = df_1.loc[:,'IP_Address'].apply(IP_info_1)
print(df_1.head(), '\n')
print('Time:',str(time.time()-s_time)+ \n')

print(type(geo.get('48.151.136.76')))


       IP_Address         country
0   48.151.136.76   United States
1    94.9.145.169  United Kingdom
2   58.94.157.121           Japan
3  193.187.41.186         Austria
4   125.96.20.172           China 

Time: 0.09906983375549316s 

<class 'dict'>

2. DbIpCity ip2geotools

# !pip install ip2geotools
import time
s_time = time.time()
from ip2geotools.databases.noncommercial import DbIpCity
df_2 = train_data.loc[:50,['IP_Address']]
def IP_info_2(ip):
    try:
        return DbIpCity.get(ip, api_key = 'free').country
    except:
        return np.nan
df_2['country'] = df_2.loc[:, 'IP_Address'].apply(IP_info_2)
print(df_2.head())
print('Time:',str(time.time()-s_time)+'s')

print(type(DbIpCity.get('48.151.136.76',api_key = 'free')))


       IP_Address country
0   48.151.136.76      US
1    94.9.145.169      GB
2   58.94.157.121      JP
3  193.187.41.186      AT
4   125.96.20.172      CN

Time: 80.53318452835083s 

<class 'ip2geotools.models.IpLocation'>

The reason why the huge time difference can be related to the data structure of the output data, i.e. , a direct subset of dictionaries, seems to be much more efficient than indexing from a specialized object ip2geotools.models.IpLocation.

In addition, the output of the 1st method is a dictionary containing data on geolocation, which are a subset, respectively, to obtain the necessary information:

x = geolite2.reader().get('48.151.136.76')
print(x)

>>>
    {'city': {'geoname_id': 5101798, 'names': {'de': 'Newark', 'en': 'Newark', 'es': 'Newark', 'fr': 'Newark', 'ja': 'ニューアーク', 'pt-BR': 'Newark', 'ru': ''}},

 'continent': {'code': 'NA', 'geoname_id': 6255149, 'names': {'de': 'Nordamerika', 'en': 'North America', 'es': 'Norteamérica', 'fr': 'Amérique du Nord', 'ja': '北アメリカ', 'pt-BR': 'América do Norte', 'ru': ' ', 'zh-CN': '北美洲'}}, 

'country': {'geoname_id': 6252001, 'iso_code': 'US', 'names': {'de': 'USA', 'en': 'United States', 'es': 'Estados Unidos', 'fr': 'États-Unis', 'ja': 'アメリカ合衆国', 'pt-BR': 'Estados Unidos', 'ru': '', 'zh-CN': '美国'}}, 

'location': {'accuracy_radius': 1000, 'latitude': 40.7355, 'longitude': -74.1741, 'metro_code': 501, 'time_zone': 'America/New_York'}, 

'postal': {'code': '07102'}, 

'registered_country': {'geoname_id': 6252001, 'iso_code': 'US', 'names': {'de': 'USA', 'en': 'United States', 'es': 'Estados Unidos', 'fr': 'États-Unis', 'ja': 'アメリカ合衆国', 'pt-BR': 'Estados Unidos', 'ru': '', 'zh-CN': '美国'}}, 

'subdivisions': [{'geoname_id': 5101760, 'iso_code': 'NJ', 'names': {'en': 'New Jersey', 'es': 'Nueva Jersey', 'fr': 'New Jersey', 'ja': 'ニュージャージー州', 'pt-BR': 'Nova Jérsia', 'ru': '-', 'zh-CN': '新泽西州'}}]}
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