Dear Google Ads Team,
I hope this email finds you well. I am writing to report an issue I
encountered while working with the Google Ads API.
Currently, I am attempting to pull data including the "PAUSED" status from
the backend by adding the appropriate segment resource in my query.
However, I have encountered difficulties in retrieving this specific status
data.
Despite my efforts to incorporate the necessary segment resource, I have
not been successful in obtaining the desired information. It seems that the
query is not returning the "PAUSED" status data as expected.
I would greatly appreciate it if you could provide assistance in resolving
this issue. Could you please guide me on the correct approach or provide
any additional resources that could help me in pulling the "PAUSED" status
data effectively?
Your prompt attention to this matter would be highly appreciated as it is
crucial for the successful execution of my project.
Thank you very much for your assistance. I look forward to your response
and resolution of this issue.
i have attached my query here, please go through it
Best regards,
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Also find us on our blog:
https://googleadsdeveloper.blogspot.com/
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def google_ads_url_df():
ads_query = "SELECT campaign.name, campaign.id,
ad_group.name,ad_group_ad.status ,ad_group.id, ad_group_ad.ad.name,
ad_group_ad.ad.id, metrics.clicks, metrics.conversions, metrics.average_cost,
metrics.average_cpc, metrics.ctr, metrics.impressions, metrics.interactions,
metrics.cost_micros, metrics.cost_per_conversion,
metrics.conversions_from_interactions_rate,
segments.date,ad_group_ad.ad.final_urls FROM ad_group_ad WHERE
ad_group_ad.status!='REMOVED' AND segments.date BETWEEN '%s' AND '%s' " %
(prev_date, end_date)
search_request.query = ads_query
response = ga_service.search_stream(search_request)
campain_id_list = []
campain_name_list = []
ad_group_status_list=[]
ad_group_id_list = []
ad_group_name_list = []
ad_group_ad_ad_name_list = []
ad_group_ad_ad_id_list = []
metrics_clicks_list = []
metrics_cost_list=[]
metrics_ctr_list = []
metrics_impressions_list = []
metrics_conversions_list = []
metrics_cost_per_conversion_list = []
metrics_conversions_from_interactions_rate_list = []
metrics_average_cost_list = []
metrics_average_cpc_list = []
segments_date_list = []
url=[]
for batch in response:
for row in batch.results:
campaign = row.campaign
ad_group = row.ad_group
metrics = row.metrics
segments = row.segments
ad_group_ad = row.ad_group_ad
campain_id_list.append(campaign.id)
campain_name_list.append(campaign.name)
ad_group_status_list.append(ad_group_ad.status)
ad_group_id_list.append(ad_group.id)
ad_group_name_list.append(ad_group.name)
ad_group_ad_ad_name_list.append(ad_group_ad.ad.name)
ad_group_ad_ad_id_list.append(ad_group_ad.ad.id)
metrics_clicks_list.append(metrics.clicks)
metrics_cost_list.append(metrics.cost_micros)
metrics_ctr_list.append(metrics.ctr)
metrics_impressions_list.append(metrics.impressions)
metrics_conversions_list.append(metrics.conversions)
metrics_cost_per_conversion_list.append(metrics.cost_per_conversion)
metrics_conversions_from_interactions_rate_list.append(metrics.conversions_from_interactions_rate)
metrics_average_cost_list.append(metrics.average_cost)
metrics_average_cpc_list.append(metrics.average_cpc)
segments_date_list.append(segments.date)
original_string = ad_group_ad.ad.final_urls[0]
url.append(original_string)
ads_df = DataFrame()
ads_df['campaign_id'] = campain_id_list
ads_df['campaign_name'] = campain_name_list
ads_df['ad_group_status'] =ad_group_status_list
ads_df['ad_group_id'] = ad_group_id_list
ads_df['ad_group_name'] = ad_group_name_list
# ads_df['ad_group_ad_ad_name'] = ad_group_ad_ad_name_list
ads_df['ad_group_ad_ad_id'] = ad_group_ad_ad_id_list
ads_df['metrics_clicks'] = metrics_clicks_list
ads_df['metrics_impressions'] = metrics_impressions_list
ads_df['metrics_cost']=metrics_cost_list
ads_df['cost']=ads_df['metrics_cost']/1000000
ads_df['metrics_ctr'] = metrics_ctr_list
ads_df['metrics_conversions'] = metrics_conversions_list
ads_df['metrics_cost_per_conversion'] = metrics_cost_per_conversion_list
ads_df['metrics_conversions_from_interactions_rate'] =
metrics_conversions_from_interactions_rate_list
ads_df['metrics_average_cost'] = metrics_average_cost_list
ads_df['metrics_average_cpc'] = metrics_average_cpc_list
ads_df['segments.date'] = segments_date_list
ads_df["url"]=url
if not ads_df.empty:
pattern = r'utm_campaign=(.*?)&'
ads_df['utm_campaign'] = ads_df['url'].apply(lambda x: re.search(pattern,
x).group(1) if re.search(pattern, x) else None)
pattern = r'utm_content=(.*?)&'
ads_df['utm_content'] = ads_df['url'].apply(lambda x: re.search(pattern,
x).group(1) if re.search(pattern, x) else None)
pattern = r'utm_medium=(.*?)&'
ads_df['utm_medium'] = ads_df['url'].apply(lambda x: re.search(pattern,
x).group(1) if re.search(pattern, x) else None)
pattern = r'utm_source=(.*?)(&|$)'
ads_df['utm_source'] = ads_df['url'].apply(lambda x: re.search(pattern,
x).group(1) if re.search(pattern, x) else None)
return None if ads_df.empty else spark.createDataFrame(ads_df)
