Added use of IPGeoSearch Module

Author: MatthiasRathbun

README.md

@@ -1,10 +1,30 @@
 # IPGeo
 
+## About IPGeo
+
+The IPGeo repository on GitHub is a free version of the IP geolocation Module found on [Our Webpage](http://ipgeo.azurewebsites.net/).
+
+Paid versions are described more in detail on [Our Webpage](http://ipgeo.azurewebsites.net/).
+
+Our Free Version Includes 10 searches a month. Exceeding the 10 Searches a month will lead to an error as the server will not send back the IP Search Data.
+
+Feel Free to check out the [IPGeoSearch](https://github.com/MatthiasRathbun/IPGeo-Search) library on GitHub for more ways to use our API!
+
 ## Requirements
 
-Before running on your local computer, make sure you have ```python 3.6+``` with the latest version of pandas installed and ```ipList.txt``` in the same folder as the python file.
+Before running on your local computer, make sure you have `python 3.6+` with the latest version of `pandas` and `IPGeoSearch` installed and `ipList.txt` in the same folder as the python file.
+
+To Install Pandas, run
+
+```cmd
+pip install pandas
+```
+
+To Install IPGeoSearch, run
 
-To Install Pandas, run ```pip install pandas```.
+```cmd
+pip install IPGeoSearch
+```
 
 Request.txt should be formatted in one IP address per line like such:
 
@@ -18,19 +38,19 @@ Request.txt should be formatted in one IP address per line like such:
 
 ## How To Use
 
-### Python
+### Python Example
 
- The ```results``` folder will have all the ```.json``` and ```.csv``` files stored in it after running. ```.json``` files are found in ```results/json/``` while the ```.csv``` files are found in ```results/csv/```
+ The `results` folder will have all the `.json` and `.csv` files stored in it after running.
 
-To query IP, run
+To query IP using our sample file, run
 
 ```cmd
 python request.py
 ```
 
 in your command prompt environment with the pandas installed.
 
-Files named ```result-ip-x.x.x.x.csv``` and ```result.json-x.x.x.x.json``` are saved which have the relevant information regarding your IP Search. Files are named with the IP in the file Name. An example csv for a search of Google's IP ```8.8.8.8``` would be named ```result-ip-8.8.8.8.csv```.
+Files named `x.x.x.x.csv` and `x.x.x.x.json` are saved which have the relevant information regarding your IP Search. Files are named with the IP in the file Name. An example csv for a search of Google's IP `8.8.8.8` would be named `8.8.8.8.csv`.
 
 Errors are more than likely caused by a non existent IP in the data base.
 
@@ -43,11 +63,3 @@ curl https://ipgeo.azurewebsites.net/try -H "Content-type:application/json" -X P
 ```
 
 Using Curl is not recommended as it will not automatically parse the json into a csv and will not automate requests.
-
-## About IPGeo
-
-The IPGeo repository on GitHub is a free version of the IP geolocation Module found on [Our Webpage](http://ipgeo.azurewebsites.net/).
-
-Paid versions are described more in detail on [Our Webpage](http://ipgeo.azurewebsites.net/).
-
-Our Free Version Includes 10 searches a month. Exceeding the 10 Searches a month will lead to an error as the server will not send back the IP Search Data.

request.py

@@ -1,95 +1,7 @@
-import requests
-import json
-import ast
-import pandas as pd
-import time
+from IPGeoSearch import search
 
-def flatten_json(nested_json):
-    """
-        Flatten json object with nested keys into a single level.
-        Args:
-            nested_json: A nested json object.
-        Returns:
-            The flattened json object if successful, None otherwise.
-    """
-    out = {}
-
-    def flatten(x, name=''):
-        if type(x) is dict:
-            for a in x:
-                flatten(x[a], name + a + '_')
-        elif type(x) is list:
-            i = 0
-            for a in x:
-                flatten(a, name + str(i) + '_')
-                i += 1
-        else:
-            out[name[:-1]] = x
-
-    flatten(nested_json)
-    return out
-#Sets Post URL
-url = 'https://ipgeo.azurewebsites.net/try'
-
-#opening list of IP's
 with open('ipList.txt', 'r') as f:
-    ipList = [line.strip() for line in f]
+    ips = [line.strip() for line in f]
     f.close()
-#checking if IP List is compatible with version
-if len(ipList) > 10:
-    print("Your IP List is longer than 10 entires, which is more than alloted for your version. Sending it would result in an error from the server.")
-    print("Please shorten your list so that all your IP's may be processed.")
-    time.sleep(5)
-    exit()
-
-#Recursively sending requests to the server
-for ip in ipList:
-    ipsearch = "{\n\t\"ip\":\""+ip+"\"\n}"
-    authentication = {'Content-Type': "application/json"}
-    res = requests.post(url, data=ipsearch, headers=authentication)
-    res = res.text
-    with open("results/json/result-ip-"+ip+".json", 'w') as result:
-        result.write(res)
-        result.close()
-        with open("results/json/result-ip-"+ip+".json", 'r') as result:
-            data = json.load(result)
-            data = flatten_json(data)
-            ipAddress = [""+ip+""]
-            df = pd.Series(data).to_frame().T
-            df['ip'] = ipAddress
-            #processing and removing unnecessary fields from the result
-            if set(['city_names_en','subdivisions_0_names_en']).issubset(df.columns):
-                df = df[['ip','city_names_en','subdivisions_0_names_en','country_names_en','continent_names_en','location_latitude','location_longitude',
-                'autonomous_system_number','autonomous_system_organization','isp','organization','organization_type','isic_code','naics_code','connection_type'
-                ,'ip_routing_type', 'line_speed']]
-                df = df.rename(columns={'city_names_en':'City','subdivisions_0_names_en':'State/Province','country_names_en':'Country','continent_names_en':'Continent',
-                'location_latitude':'Latitude','location_longitude':'Longitude','autonomous_system_number':'ASN','autonomous_system_organization':'ASO',
-                'isp':'ISP', 'organization':'Organization','organization_type':'Organization Type','isic_code':'ISIC','naics_code':'NAICS'
-                ,'connection_type':'Connection Type','ip_routing_type':'IP Routing Type','line_speed':'Line Speed'})
-            elif 'city_names_en' in df:
-                df = df[['ip','city_names_en','country_names_en','continent_names_en','location_latitude','location_longitude',
-                'autonomous_system_number','autonomous_system_organization','isp','organization','organization_type','isic_code','naics_code','connection_type',
-                'ip_routing_type','line_speed']]
-                df = df.rename(columns={'city_names_en':'City','country_names_en':'Country','continent_names_en':'Continent',
-                'location_latitude':'Latitude','location_longitude':'Longitude','autonomous_system_number':'ASN','autonomous_system_organization':'ASO',
-                'isp':'ISP', 'organization':'Organization','organization_type':'Organization Type','isic_code':'ISIC','naics_code':'NAICS'
-                ,'connection_type':'Connection Type','ip_routing_type':'IP Routing Type','line_speed':'Line Speed'})
-            elif 'subdivisions_0_names_en' in df:
-                df = df[['ip','subdivisions_0_names_en','country_names_en','continent_names_en','location_latitude','location_longitude',
-                'autonomous_system_number','autonomous_system_organization','isp','organization','organization_type','isic_code','naics_code','connection_type',
-                'ip_routing_type','line_speed']]
-                df = df.rename(columns={'subdivisions_0_names_en':'State/Province','country_names_en':'Country','continent_names_en':'Continent',
-                'location_latitude':'Latitude','location_longitude':'Longitude','autonomous_system_number':'ASN','autonomous_system_organization':'ASO',
-                'isp':'ISP', 'organization':'Organization','organization_type':'Organization Type','isic_code':'ISIC','naics_code':'NAICS'
-                ,'connection_type':'Connection Type','ip_routing_type':'IP Routing Type','line_speed':'Line Speed'})
-            else:
-                df = df[['ip','country_names_en','continent_names_en','location_latitude','location_longitude',
-                'autonomous_system_number','autonomous_system_organization','isp','organization','organization_type','isic_code','naics_code','connection_type',
-                'ip_routing_type','line_speed']]
-                df = df.rename(columns={'country_names_en':'Country','continent_names_en':'Continent',
-                'location_latitude':'Latitude','location_longitude':'Longitude','autonomous_system_number':'ASN','autonomous_system_organization':'ASO',
-                'isp':'ISP', 'organization':'Organization','organization_type':'Organization Type','isic_code':'ISIC','naics_code':'NAICS'
-                ,'connection_type':'Connection Type','ip_routing_type':'IP Routing Type','line_speed':'Line Speed'})
-            df.to_csv(path_or_buf="results/csv/result-ip-"+ip+".csv", sep =',', index = False)
-            result.close()
 
+search.search(ipList=ips,path='results/')