Hi there,
I'm trying to use the API with the Targeting Idea Service to get
"targeted_monthly_searches" for various search terms: e.g., react.js,
vue.js, angular.js. The trick is that I clearly get different results
between, say, react.js and reactjs. So I'd prefer the aggregated related
search term result be returned (i.e., the search traffic for "react.js" OR
"reactjs"). I assume this is what occurs when match type is "broad," but
I'm not sure how to specify different types of match when using the
Targeting Idea Service (I see that the Traffic Estimator Service offers the
ability to specify matchType as "BROAD", but this doesn't seem to be an
option with the Targeting Idea Service.
Also, is there a way to put filtering in the query? I.e., when you're
searching YouTube or Google you can be more specific by including OR, AND,
and NOT parameters. But there doesn't seem to be any way to do that beyond
just specifying matchType = BROAD. But does "broad" match type even
function as desired (giving you, for example, an aggregate result for
vuejs/vue.js/"vue js"/etc.?
############# Here's what works:
from googleads import adwords
import pandas as pd
adwords_client = adwords.AdWordsClient.LoadFromStorage()
PAGE_SIZE = 10
# Initialize appropriate service.
targeting_idea_service = adwords_client.GetService(
'TargetingIdeaService', version='v201609')
# Construct selector object and retrieve related keywords.
offset = 0
stats_selector = {
'searchParameters': [
{
'xsi_type': 'RelatedToQuerySearchParameter',
'queries': ['react.js', 'reactjs', 'vuejs', 'vue.js']
},
{
# Language setting (optional).
# The ID can be found in the documentation:
#
https://developers.google.com/adwords/api/docs/appendix/languagecodes
'xsi_type': 'LanguageSearchParameter',
'languages': [{'id': '1000'}],
},
{
# Network search parameter (optional)
'xsi_type': 'NetworkSearchParameter',
'networkSetting': {
'targetGoogleSearch': True,
'targetSearchNetwork': False,
'targetContentNetwork': False,
'targetPartnerSearchNetwork': False
}
}
],
'ideaType': 'KEYWORD',
'requestType': 'STATS',
'requestedAttributeTypes': ['KEYWORD_TEXT',
'TARGETED_MONTHLY_SEARCHES'],
'paging': {
'startIndex': str(offset),
'numberResults': str(PAGE_SIZE)
}
}
stats_page = targeting_idea_service.get(stats_selector)
# Parse results to pandas dataframe
stats_pd = pd.DataFrame()
if 'entries' in stats_page:
for stats_result in stats_page['entries']:
stats_attributes = {}
for stats_attribute in stats_result['data']:
#print (stats_attribute)
if stats_attribute['key'] == 'KEYWORD_TEXT':
kt = stats_attribute['value']['value']
else:
for i, val in enumerate(stats_attribute['value'][1]):
data = {'keyword': kt,
'year': val['year'],
'month': val['month'],
'count': val['count']}
data = pd.DataFrame(data, index = [i])
stats_pd = stats_pd.append(data, ignore_index=True)
print(stats_pd)
######## And this doesn't (but would be appealing):
from googleads import adwords
import pandas as pd
adwords_client = adwords.AdWordsClient.LoadFromStorage()
PAGE_SIZE = 10
# Initialize appropriate service.
targeting_idea_service = adwords_client.GetService(
'TargetingIdeaService', version='v201609')
# Construct selector object and retrieve related keywords.
offset = 0
stats_selector = {
'searchParameters': [
{
'xsi_type': 'RelatedToQuerySearchParameter',
'queries': [
{'term': 'react js', 'matchType': 'BROAD'},
{'term': 'reactjs', 'matchType': 'PHRASE'},
{'term': 'react.js', 'matchType': 'EXACT'}
]
},
{
# Language setting (optional).
# The ID can be found in the documentation:
#
https://developers.google.com/adwords/api/docs/appendix/languagecodes
'xsi_type': 'LanguageSearchParameter',
'languages': [{'id': '1000'}],
},
{
# Network search parameter (optional)
'xsi_type': 'NetworkSearchParameter',
'networkSetting': {
'targetGoogleSearch': True,
'targetSearchNetwork': False,
'targetContentNetwork': False,
'targetPartnerSearchNetwork': False
}
}
],
'ideaType': 'KEYWORD',
'requestType': 'STATS',
'requestedAttributeTypes': ['KEYWORD_TEXT',
'TARGETED_MONTHLY_SEARCHES'],
'paging': {
'startIndex': str(offset),
'numberResults': str(PAGE_SIZE)
}
}
stats_page = targeting_idea_service.get(stats_selector)
# Parse results to pandas dataframe
stats_pd = pd.DataFrame()
if 'entries' in stats_page:
for stats_result in stats_page['entries']:
stats_attributes = {}
for stats_attribute in stats_result['data']:
#print (stats_attribute)
if stats_attribute['key'] == 'KEYWORD_TEXT':
kt = stats_attribute['value']['value']
else:
for i, val in enumerate(stats_attribute['value'][1]):
data = {'keyword': kt,
'year': val['year'],
'month': val['month'],
'count': val['count']}
data = pd.DataFrame(data, index = [i])
stats_pd = stats_pd.append(data, ignore_index=True)
print(stats_pd)
######### Or this would be a nice solution:
from googleads import adwords
import pandas as pd
adwords_client = adwords.AdWordsClient.LoadFromStorage()
PAGE_SIZE = 10
# Initialize appropriate service.
targeting_idea_service = adwords_client.GetService(
'TargetingIdeaService', version='v201609')
# Construct selector object and retrieve related keywords.
offset = 0
stats_selector = {
'searchParameters': [
{
'xsi_type': 'RelatedToQuerySearchParameter',
'queries': [
{'term': 'vue js|vue.js|vuejs', 'as': 'vue.js'},
{'term': 'react js|react.js|reactjs', 'as':
'react.js'},
{'term': 'angular js|angular.js|angularjs',
'as': 'angular.js'}
]
},
{
# Language setting (optional).
# The ID can be found in the documentation:
#
https://developers.google.com/adwords/api/docs/appendix/languagecodes
'xsi_type': 'LanguageSearchParameter',
'languages': [{'id': '1000'}],
},
{
# Network search parameter (optional)
'xsi_type': 'NetworkSearchParameter',
'networkSetting': {
'targetGoogleSearch': True,
'targetSearchNetwork': False,
'targetContentNetwork': False,
'targetPartnerSearchNetwork': False
}
}
],
'ideaType': 'KEYWORD',
'requestType': 'STATS',
'requestedAttributeTypes': ['KEYWORD_TEXT',
'TARGETED_MONTHLY_SEARCHES'],
'paging': {
'startIndex': str(offset),
'numberResults': str(PAGE_SIZE)
}
}
stats_page = targeting_idea_service.get(stats_selector)
# Parse results to pandas dataframe
stats_pd = pd.DataFrame()
if 'entries' in stats_page:
for stats_result in stats_page['entries']:
stats_attributes = {}
for stats_attribute in stats_result['data']:
#print (stats_attribute)
if stats_attribute['key'] == 'KEYWORD_TEXT':
kt = stats_attribute['value']['value']
else:
for i, val in enumerate(stats_attribute['value'][1]):
data = {'keyword': kt,
'year': val['year'],
'month': val['month'],
'count': val['count']}
data = pd.DataFrame(data, index = [i])
stats_pd = stats_pd.append(data, ignore_index=True)
print(stats_pd)
--
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