def get_campaign_insights(self, date_preset=None):
campaign = Campaign(self.campaign_id)
params = {
'date_preset': date_preset,
}
insights = campaign.get_insights(
params=params,
fields=list(general_insights.values()) +
list(target_insights.values()))
if bool(insights):
spend = insights[0].get(Field.spend)
impressions = insights[0].get(Field.impressions)
self.campaign_insights.update({Field.spend: int(spend)})
self.campaign_insights.update(
{Field.impressions: int(impressions)})
for act in insights[0].get(Field.actions):
if act["action_type"] in conversion_metrics:
self.campaign_insights.update({
conversion_metrics[act["action_type"]]:
int(act["value"])
})
self.campaign_insights.update({
'cost_per_' + conversion_metrics[act["action_type"]]:
float(spend) / float(act["value"])
})
return self.campaign_insights
def fetch_campaign_data(self, campaign_id):
campaign = Campaign(campaign_id)
campaign_datas = campaign.get_insights(fields=[
AdsInsights.Field.campaign_id,
AdsInsights.Field.campaign_name,
AdsInsights.Field.spend,
AdsInsights.Field.impressions,
AdsInsights.Field.unique_actions,
AdsInsights.Field.reach],
params={
'breakdowns':
['hourly_stats_aggregated_by_audience_time_zone'],
'time_range': self.time_range})
return campaign_datas
def fetch_campaign_data(campaign_id):
campaign = Campaign(campaign_id)
campaign_datas = campaign.get_insights(
fields=[
AdsInsights.Field.campaign_id, AdsInsights.Field.campaign_name,
AdsInsights.Field.spend, AdsInsights.Field.impressions,
AdsInsights.Field.unique_actions, AdsInsights.Field.reach
],
params={
'breakdowns': ['hourly_stats_aggregated_by_audience_time_zone'],
'time_range': {
'since': yesterday,
'until': today
}
})
for campaign_data in campaign_datas:
result = {}
result['spend'] = campaign_data['spend'] if\
'spend' in campaign_data else ''
result['impression_count'] = campaign_data['impressions'] if\
'impressions' in campaign_data else ''
result['reach_count'] = campaign_data['reach'] if\
'reach' in campaign_data else ''
result['ad_compaign_id'] = campaign_data['campaign_id'] if\
'campaign_id' in campaign_data else ''
result['stat_dt'] = campaign_data['date_start']
print(result['stat_dt'])
result['stat_hour'] = \
str(int(campaign_data['hourly_stats_aggregated_by_audience_time_zone'][0:2]) + 1)
try:
for actions in campaign_data['unique_actions']:
if actions['action_type'] == 'link_click':
result['link_click_count'] = actions['value']
if actions[
'action_type'] == 'offsite_conversion.fb_pixel_purchase':
result['effective_count'] = actions['value']
except Exception as e:
result['effective_count'] = ''
result['link_click_count'] = ''
result['create_time'] =\
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
yield result
def device():
today = datetime.date.today()
fields1 = [
'account_name', 'campaign_name', 'campaign_id', 'reach',
'cost_per_action_type:post_reaction', 'action:post_reaction',
'unique_actions:post_reaction', 'inline_link_clicks',
'action:post_engagement', 'unique_outbound_clicks:outbound_click',
'cost_per_action_type:post_engagement', 'ctr', 'cost_per_unique_click',
'action:landing_page_view', 'unique_actions:landing_page_view',
'action:offsite_conversion.custom.281094269070676',
'cost_per_action_type:page_engagement',
'cost_per_unique_action_type:page_engagement', 'device_platform',
'action:offsite_conversion.custom.280669215754025',
'cost_per_unique_action_type:link_click', 'website_ctr:link_click',
'cpc', 'unique_actions:link_click', 'unique_actions:page_engagement',
'date_stop', 'unique_inline_link_clicks',
'cost_per_action_type:link_click', 'inline_post_engagement',
'unique_inline_link_click_ctr',
'cost_per_unique_action_type:landing_page_view',
'unique_link_clicks_ctr', 'action:page_engagement',
'cost_per_inline_post_engagement', 'action:link_click',
'cost_per_outbound_click:outbound_click', 'unique_ctr',
'cost_per_action_type:offsite_conversion.custom.280669215754025',
'impressions', 'unique_actions:post_engagement',
'cost_per_unique_action_type:post_engagement', 'inline_link_click_ctr',
'cost_per_action_type:landing_page_view',
'cost_per_action_type:offsite_conversion.custom.281094269070676',
'clicks', 'cost_per_unique_inline_link_click',
'outbound_clicks:outbound_click', 'date_start',
'instant_experience_clicks_to_open', 'unique_clicks',
'cost_per_unique_outbound_click:outbound_click', 'frequency',
'instant_experience_outbound_clicks',
'outbound_clicks_ctr:outbound_click',
'unique_outbound_clicks_ctr:outbound_click', 'spend', 'cpp',
'instant_experience_clicks_to_start', 'cpm', 'objective',
'cost_per_inline_link_click'
]
for x in range(1, 8):
date1 = today - datetime.timedelta(days=x)
date1 = str(date1)
arr = date1.split("-")
year = arr[0]
month = arr[1]
params = {
'time_range': {
'since': date1,
'until': date1
},
'breakdowns': ['device_platform']
}
fields = [
'account_name',
'campaign_name',
'campaign_id',
'reach',
'frequency',
'impressions',
'clicks',
'cpm',
'ctr',
'spend',
'actions',
'canvas_avg_view_percent',
'canvas_avg_view_time',
'conversion_rate_ranking',
'conversion_values',
'conversions',
'cost_per_action_type',
'cost_per_conversion',
'cost_per_estimated_ad_recallers',
'cost_per_inline_link_click',
'cost_per_inline_post_engagement',
'cost_per_outbound_click',
'cost_per_thruplay',
'cost_per_unique_action_type',
'cost_per_unique_click',
'cost_per_unique_inline_link_click',
'cost_per_unique_outbound_click',
'cpc',
'cpp',
'engagement_rate_ranking',
'estimated_ad_recall_rate',
'estimated_ad_recallers',
'full_view_impressions',
'full_view_reach',
'inline_link_click_ctr',
'inline_link_clicks',
'inline_post_engagement',
'instant_experience_clicks_to_open',
'instant_experience_clicks_to_start',
'instant_experience_outbound_clicks',
'mobile_app_purchase_roas',
'objective',
'outbound_clicks',
'outbound_clicks_ctr',
'place_page_name',
'quality_ranking',
'social_spend',
'unique_actions',
'unique_clicks',
'unique_ctr',
'unique_inline_link_click_ctr',
'unique_inline_link_clicks',
'unique_link_clicks_ctr',
'unique_outbound_clicks',
'unique_outbound_clicks_ctr',
'video_30_sec_watched_actions',
'video_avg_time_watched_actions',
'video_p100_watched_actions',
'video_p25_watched_actions',
'video_p50_watched_actions',
'video_p75_watched_actions',
'video_p95_watched_actions',
'video_play_actions',
'video_play_curve_actions',
'video_thruplay_watched_actions',
'website_ctr',
]
f = open("account_id",
"r") #importing the account id from external file
acc = f.read()
my_account = AdAccount(acc)
campaignids = my_account.get_campaigns()
#print(campaignids)
list_d = []
l = []
y = 0
for i in range(len(campaignids)):
#print("loop ran ", i)
try:
c_id = campaignids[i]["id"]
campaign = Campaign(c_id)
camp_insights = campaign.get_insights(fields=fields,
params=params)
j = 0
#print(camp_insights)
dic_camp = {}
for item in camp_insights: #converting to dictionary
dic_camp = dict(item)
#print("converted to dictionary")
#print(dic_camp)
#flattening of data
try:
for each_action in dic_camp["actions"]:
dic_camp[
"action:" +
each_action['action_type']] = each_action['value']
del dic_camp["actions"]
except KeyError:
continue
try:
for each_action in dic_camp["cost_per_action_type"]:
dic_camp[
"cost_per_action_type:" +
each_action['action_type']] = each_action['value']
del dic_camp["cost_per_action_type"]
except KeyError:
continue
try:
for each_action in dic_camp["cost_per_outbound_click"]:
dic_camp[
"cost_per_outbound_click:" +
each_action['action_type']] = each_action['value']
del dic_camp["cost_per_outbound_click"]
except KeyError:
continue
try:
for each_action in dic_camp["cost_per_unique_action_type"]:
dic_camp[
"cost_per_unique_action_type:" +
each_action['action_type']] = each_action['value']
del dic_camp["cost_per_unique_action_type"]
except KeyError:
continue
try:
for each_action in dic_camp[
"cost_per_unique_outbound_click"]:
dic_camp[
"cost_per_unique_outbound_click:" +
each_action['action_type']] = each_action['value']
del dic_camp["cost_per_unique_outbound_click"]
except KeyError:
continue
try:
for each_action in dic_camp["outbound_clicks"]:
dic_camp[
"outbound_clicks:" +
each_action['action_type']] = each_action['value']
del dic_camp["outbound_clicks"]
except KeyError:
continue
try:
for each_action in dic_camp["outbound_clicks_ctr"]:
dic_camp[
"outbound_clicks_ctr:" +
each_action['action_type']] = each_action['value']
del dic_camp["outbound_clicks_ctr"]
except KeyError:
continue
try:
for each_action in dic_camp["unique_actions"]:
dic_camp[
"unique_actions:" +
each_action['action_type']] = each_action['value']
del dic_camp["unique_actions"]
except KeyError:
continue
try:
for each_action in dic_camp["unique_outbound_clicks"]:
dic_camp[
"unique_outbound_clicks:" +
each_action['action_type']] = each_action['value']
del dic_camp["unique_outbound_clicks"]
except KeyError:
continue
try:
for each_action in dic_camp["unique_outbound_clicks_ctr"]:
dic_camp[
"unique_outbound_clicks_ctr:" +
each_action['action_type']] = each_action['value']
del dic_camp["unique_outbound_clicks_ctr"]
except KeyError:
continue
try:
for each_action in dic_camp["website_ctr"]:
dic_camp[
"website_ctr:" +
each_action['action_type']] = each_action['value']
del dic_camp["website_ctr"]
except KeyError:
continue
#print(dic_camp)
list_d.append(dic_camp)
except IndexError:
continue
if list_d:
filename1 = "FacebookAds/landing_device/" + "device_" + date1 + ".csv"
filename2 = "FacebookAds/processing_device/csv/" + year + "/" + month + "/" + "publisher_device_" + date1 + ".csv"
importtos3.write_to_s3(list_d, fields1, 'paragon-datalake',
filename1, filename2)
return list_d
FacebookAdsApi.init(access_token=token)
#Account ID
me = AdAccountUser(fbid='me')
my_account = me.get_ad_accounts()[0]
Account_id = my_account['id']
#initializing accountid to classs object
class Account(object):
def __init__(self, account_id):
self.account_id = account_id
new_acc = Account(account_id=Account_id)
campaigns = my_account.get_campaigns()
print(campaigns)
campaign = Campaign(new_acc.account_id)
params = {'data_preset': 'this_year'}
fields = [
'campaign_name', 'adset_name', 'adset_id', 'impressions', 'spend', 'reach',
'actions', 'action_values'
]
#getting insights
insights = campaign.get_insights(fields, params)
print(insights)