United States Ip Addresses
Ip Address By Country 2021 – World Population Review
Every machine on a network has a unique identifier. Most computers and networks use the TCP/IP protocol as the standard for communicating on a network, which uses an IP address to identify a computer. An Internet Protocol address, or an IP address, is a numerical label assigned to each device connected to a computer network that uses the Internet Protocol for communication.
There are two standards for IP addresses. IP Version 4 (IPv4) and IP Version 6 (IPv6). IPv4 uses 32 binary bits to create a single unique address on the network. An IPv4 address is expressed by four numbers separated by decimals. IPv6 uses 128 binary bits to create a single unique address that is expressed by eight groups of hexadecimal numbers separated by colons. All computers with IP addresses have an IPv4 address and many are starting to incorporate the newer IPv6 addresses.
There are about 4. 294 billion IP addresses the IPv4 protocol, 600 million of which are reserved and cannot be used for public routing. The Internet Assigned Numbers Authority (IANA) allocates the rest of the IP addresses to countries. Allocation does not necessarily correlate with population numbers.
Of the over 4 billion IP addresses, 1, 541, 605, 760 are allocated to the United States, the highest number of any country. This comes out to about 4, 911 IP addresses per 1, 000 people. These comprise about 35. 9% of the total number of IP addresses.
China has the second-highest number of IP addresses of 330, 321, 408, about 7. 7% of the total number of IP addresses. China is followed by Japan with 202, 183, 168 and the United Kingdom with 123, 500, 144. Germany has the fifth-highest number of IP addresses with 118, 132, 104.
Vatican City, which has the smallest population of any sovereign state of fewer than 1, 000 people, has 17, 920 IP addresses. This equates to 21, 435 IP addresses per 1, 000 people.
Factored into the 4. 294 million IP addresses are over 875 million bogons, which are bogus (or fake) IP addresses on a computer network. Bogons include IP packets with addresses that are not in any range allocated or delegated by the IANA. Most Internet service providers and firewalls filter out bogons as they have no use and are usually the result of accidental or malicious misconfiguration.
Below is the number of IP addresses in every country, as well as the number of IP addresses 1, 000 people in the population and the percentage of total IP addresses each country possesses.
Identifying country by IP address – Stack Overflow
Here is my solution in Python 3. x to return geo-location info given a dataframe containing IP Address(s); efficient parallelized application of function on vectorized is the way to go.
Will contrast performance of two popular libraries to return location.
TLDR: use geolite2 method.
1. geolite2 package from geolite2 library
Input
#! pip install maxminddb-geolite2
import time
from geolite2 import geolite2
geo = ()
df_1 = [:50, [‘IP_Address’]]
def IP_info_1(ip):
try:
x = (ip)
except ValueError: #Faulty IP value
return
return x[‘country’][‘names’][‘en’] if x is not None else
except KeyError: #Faulty Key value
s_time = ()
# map IP –> country
#apply(fn) applies fn. on all elements
df_1[‘country’] = [:, ‘IP_Address’](IP_info_1)
print((), ‘\n’)
print(‘Time:’, str(()-s_time)+’s \n’)
print(type((’48. 151. 136. 76′)))
Output
IP_Address country
0 48. 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
2. DbIpCity package from ip2geotools library
#! pip install ip2geotools
from ncommercial import DbIpCity
df_2 = [:50, [‘IP_Address’]]
def IP_info_2(ip):
return (ip, api_key = ‘free’). country
except:
df_2[‘country’] = [:, ‘IP_Address’](IP_info_2)
print(())
print(‘Time:’, str(()-s_time)+’s’)
print(type((’48. 76′, api_key = ‘free’)))
0 48. 76 US
1 94. 169 GB
2 58. 121 JP
3 193. 186 AT
4 125. 172 CN
Time: 80. 53318452835083s
A reason why the huge time difference could be due to the Data structure of the output, i. e direct subsetting from dictionaries seems way more efficient than indexing from the specicialized object.
Also, the output of the 1st method is dictionary containing geo-location data, subset respecitively to obtain needed info:
x = ()(’48. 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’: ‘Ньюарк’}},
‘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’: ‘Северная Америка’, ‘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’: ‘США’, ‘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’: ‘США’, ‘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’: ‘Нью-Джерси’, ‘zh-CN’: ‘新泽西州’}}]}
DNS servers in United States
198. 6. 1. 4
701
UUNET
—
2021-10-11 11:01:34 UTC
valid
100%
Whois
184. 57. 117. 125
Columbus
10796
TWC-10796-MIDWEST
2021-10-11 11:01:27 UTC
DNSSEC
95%
38. 98. 193. 246
Philadelphia
174
COGENT-174
2021-10-11 11:01:19 UTC
50. 204. 103. 135
Palo Alto
7922
COMCAST-7922
2021-10-11 11:01:17 UTC
198. 146
2021-10-11 11:01:10 UTC
198. 3
50. 234. 50. 134
Murfreesboro
2021-10-11 11:01:09 UTC
50. 231. 115. 22
UNKNOWN
2021-10-11 11:01:06 UTC
66. 92. 159. 2
11696
NBS11696
Version: r17936/18112
2021-10-11 11:00:47 UTC
99%
173. 14. 65. 89
Elk Grove
2021-10-11 11:00:33 UTC
61%
162. 155. 248. 54
Owensboro
dnsmasq-2. 82
2021-10-11 11:00:32 UTC
77%
71. 66. 130. 90
unbound 1. 4. 22
2021-10-11 11:00:27 UTC
96%
50. 239. 88. 9
Stockbridge
dnsmasq-2. 80
198. 142
2021-10-11 11:00:19 UTC
67%
198. 122
2021-10-11 11:00:18 UTC
194. 124. 76. 14
New York
62240
Clouvider Limited
2021-10-11 11:00:12 UTC
64. 81. 79. 2
17184
ATL-CBEYOND
2021-10-11 11:00:05 UTC
97%
107. 191. 48. 176
Elk Grove Village
20473
AS-CHOOPA
Served by PowerDNS –
2021-10-11 10:30:36 UTC
49%
169. 53. 182. 120
36351
SOFTLAYER
2021-10-11 10:30:28 UTC
79%
4. 0. 53
3356
LEVEL3
Version: recursive-main/20717463
2021-10-11 10:30:27 UTC
65. 49. 37. 195
6939
HURRICANE
2021-10-11 10:28:49 UTC
10%
185. 9. 142
dnsmasq-2. 50
2021-10-11 10:28:43 UTC
69%
208. 253. 106
Cleveland
19009
ONECLEVELAND
9. 8. 2rc1-RedHat-9. 2-0. 17. rc1. el6_4. 4
2021-10-11 10:28:14 UTC
72%
9. 9
Berkeley
19281
QUAD9-AS-1
Q9-U-7. 2
2021-10-11 10:28:08 UTC
52%
8. 26. 56. 10
23393
NUCDN
2021-10-11 10:27:54 UTC
64. 107. 45. 5
Calumet City
394031
SOUTHSUBURBANCOLLEGEOFCOOKCOUNTY
2021-10-11 10:27:26 UTC
92%
23. 19. 245. 88
396362
LEASEWEB-USA-NYC
cryptostorm
2021-10-11 10:17:21 UTC
104. 255. 175. 2
Bend
397373
H4Y-TECHNOLOGIES
2021-10-11 10:17:14 UTC
158. 51. 199
Ventura
397880
SKYWIRE-ASN
2021-10-11 10:17:09 UTC
68%
23. 27. 15
Richardson
63949
Linode, LLC
dnsmasq-pi-hole-2. 80
2021-10-11 10:16:51 UTC
32%
34. 74. 77. 242
North Charleston
15169
GOOGLE
dnsmasq-pi-hole-2. 85
2021-10-11 10:09:01 UTC
12%
3. 122
Ashburn
14618
AMAZON-AES
2021-10-11 10:08:49 UTC
22%
173. 7. 140
Lima
Version: recursive-main/22386077
2021-10-11 10:07:11 UTC
173. 161. 17
Chagrin Falls
dnsmasq-2. 40
2021-10-11 10:06:54 UTC
45%
71. 67. 219. 165
Minster
2021-10-11 10:06:50 UTC
74. 139. 108. 134
2021-10-11 10:06:44 UTC
98. 102. 227
Louisville
90%
162. 126
West Chester
2021-10-11 10:06:43 UTC
19%
98. 212. 37
Rockford
74. 218. 28
Independence
2021-10-11 10:06:39 UTC
50. 216. 97
Minneapolis
2021-10-11 10:06:38 UTC
85%
174. 99. 18. 188
Raleigh
11426
TWC-11426-CAROLINAS
67. 66
De Pere
2021-10-11 10:06:37 UTC
93%
98. 52. 125
Kaukauna
50. 235. 225
Needville
dnsmasq-2. 78
98%
216. 2
2021-10-11 10:06:36 UTC
2620:fe::11
Q9-B-7. 2
2021-10-11 10:06:02 UTC
198. 24. 75. 66
2021-10-11 10:05:45 UTC
84%
71. 192. 50
Shreve
2021-10-11 10:05:32 UTC
65%
2001:4860:4860::64
2021-10-11 10:05:21 UTC
96. 93. 140
Fort Lauderdale
2021-10-11 10:05:16 UTC
76. 190. 33. 28
Dayton
Microsoft DNS 6. 7601 (1DB1446A)
2021-10-11 10:05:12 UTC
91%
2001:428:101:100:205:171:3:65
209
CENTURYLINK-US-LEGACY-QWEST
2021-10-11 09:17:24 UTC
209. 211. 254. 18
Rock Springs
55044
LRCSNET
Microsoft DNS 6. 7601 (1DB15F75)
2021-10-11 09:14:19 UTC
129. 71. 12
Charleston
7925
WVNET
2021-10-11 09:13:43 UTC
206. 222. 38
3549
LVLT-3549
Version: recursive-main/18640994
2021-10-11 09:10:29 UTC
149. 112. 10
Q9-P-7. 2
2021-10-11 05:27:54 UTC
82%
198. 1
2021-10-11 05:27:53 UTC
73%
198. 195
2021-10-11 05:27:52 UTC
54%
64. 233. 207. 16
Evansville
12083
WOW-INTERNET
9. 11. 0-P3
2021-10-11 05:25:05 UTC
48%
96. 91. 132
Knoxville
dnsmasq-2. 83
2021-10-11 02:07:10 UTC
71. 238. 94. 130
Oregon City
[SECURED]
89%
50. 126
Roslindale
2021-10-11 02:07:08 UTC
162. 241. 94
31863
DACEN-2
9. 5-P4-5. 1+deb10u2-Debian
2021-10-11 01:20:24 UTC
66%
2001:4b8:2:101::602
14654
WAYPORT
None of your business
2021-10-11 01:17:17 UTC
2001:4b8:3:201::902
2021-10-11 01:17:15 UTC
96. 64. 245
Manteca
2021-10-10 19:13:28 UTC
98. 194. 182
Houston
73. 44. 186. 96
Palatine
2021-10-10 19:13:27 UTC
2607:f130:0:f8::3085:e961
35916
MULTA-ASN1
2021-10-10 14:14:33 UTC
70. 251. 123
Chatsworth
20001
TWC-20001-PACWEST
2021-10-10 14:11:04 UTC
74. 62. 109. 178
2021-10-10 14:10:54 UTC
51%
216. 41. 2
2021-10-10 14:10:48 UTC
98. 213. 66
2021-10-10 14:10:45 UTC
74%
208. 105. 189. 156
Lincoln
11351
TWC-11351-NORTHEAST
2021-10-10 14:07:10 UTC
71%
198. 228. 33
8100
ASN-QUADRANET-GLOBAL
9. 5-4~bpo70+1-Debian
2021-10-10 11:25:13 UTC
74. 84. 170
Ridgecrest
30036
MEDIACOM-ENTERPRISE-BUSINESS
2021-10-10 11:21:30 UTC
67. 170. 171. 40
Banks
2021-10-10 10:07:11 UTC
28%
50. 217. 25. 205
Carrollton
2021-10-10 10:07:05 UTC
73. 2. 182
Davis
24. 123. 138. 90
Powell
2021-10-10 06:06:43 UTC
94%
103. 196. 38. 39
San Francisco
40138
MDNET
2021-10-10 05:06:46 UTC
103. 38
2021-10-10 05:06:45 UTC
73. 78. 152
Harrisonburg
2021-10-10 04:06:47 UTC
192. 180. 34. 135
Cuyahoga Falls
dnsmasq-2. 39
2021-10-10 03:30:14 UTC
64%
73. 121. 146. 21
Galax
dnsmasq-2. 75
2021-10-10 03:23:28 UTC
74. 73. 12
Milwaukee
2021-10-10 03:20:13 UTC
66. 65
Cedar Knolls
dnsmasq-2. 48
2021-10-10 03:16:56 UTC
4. 2
2021-10-10 03:16:55 UTC
204. 97. 10
1239
SPRINTLINK
2021-10-09 23:10:06 UTC
216. 28. 69
16524
METTEL
2021-10-09 18:07:01 UTC
216. 33
2021-10-09 18:07:00 UTC
64. 132. 250
2021-10-09 18:06:57 UTC
45. 90. 30. 226
34939
nextdns, Inc.
2021-10-09 18:06:51 UTC
8. 20. 247. 2
2021-10-09 16:06:47 UTC
64. 135. 20
13645
BROADBANDONE
9. 4-P2-RedHat-9. 4-18. P2. fc20
2021-10-09 04:17:02 UTC
64. 69. 35
7029
WINDSTREAM
SERVFAIL
2021-10-09 04:16:59 UTC
64. 100. 68
199. 16. 19
Brownwood
53646
HARRIS-BROADBAND
2021-10-08 22:29:17 UTC
46%
66. 13. 250
College Station
19108
SUDDENLINK-COMMUNICATIONS
2021-10-08 22:28:58 UTC
80%
Whois
Frequently Asked Questions about united states ip addresses
Can you tell country by IP address?
No you can’t – IP addresses get reallocated and reassigned from time to time, so the mapping of IP to location will also change over time.Mar 21, 2012
What is USA server address?
DNS servers in United StatesIP AddressLocationSoftware / Version129.71.254.12 dns.state.wv.us.Charleston—198.190.61.1Othello—24.181.107.229 southerncontrols.com.Montgomerydnsmasq-2.80185.66.9.142 bloodyhawks.ru.New Yorkdnsmasq-2.5056 more rows
