使用Facebook Marketing API不会暂停广告见解
问题内容：
我写了这个脚本，返回了包含其统计信息的广告列表，但很显然，我仅获得有效广告的见解，而不是暂停的见解-对于暂停的，我只是获得广告系列名称及其ID！
我尝试使用如下所示的过滤器，但无法正常工作：
’‘
first = "https://graph.facebook.com/v3.2/act_105433210/campaigns?filtering=[{'field':'effective_status','operator':'IN','value':['PAUSED']}]&fields=created_time,name,effective_status,insights{spend,impressions,clicks}&access_token=%s"% token
然后我检查使用：
result = requests.get(first)
content_dict = json.loads(result.content)
print(content_dict)
这是我得到的输出示例：
{'data': [{'created_time': '2019-02-15T17:24:29+0100', 'name': '20122301-FB-BOOST-EVENT-CC SDSDSD', 'effective_status': 'PAUSED', 'id': '6118169436761'}
只有活动的名称，而没有见解！是否有人之前曾检索过有关暂停的广告/广告系列的统计信息/见解？
谢谢 ！
请检查我的python脚本的其他文章：我无法使用Python和Facebook Marketing
API来获取我所有Facebook广告系列的统计信息
问题答案：
经过几天的挖掘，我终于想出了一个脚本，该脚本确实用于提取3年的Facebook广告见解，从而避免了Facebook API的速率限制。
首先，我们导入所需的库：
from facebookads.api import FacebookAdsApi
from facebookads.adobjects.adsinsights import AdsInsights
from facebookads.adobjects.adaccount import AdAccount
from facebookads.adobjects.business import Business
import datetime
import csv
import re
import pandas as pd
import numpy as np
import matplotlib as plt
from google.colab import files
import time
请注意，提取见解之后，我将它们保存在Google Cloud存储中，然后保存在Big Query表中。
access_token = 'my-token'
ad_account_id = 'act_id'
app_secret = 'app_s****'
app_id = 'app_id****'
FacebookAdsApi.init(app_id,app_secret, access_token=access_token, api_version='v3.2')
account = AdAccount(ad_account_id)
然后，以下脚本调用api并检查我们确实达到的速率限制：
import logging
import requests as rq
#Function to find the string between two strings or characters
def find_between( s, first, last ):
try:
start = s.index( first ) + len( first )
end = s.index( last, start )
return s[start:end]
except ValueError:
return ""
#Function to check how close you are to the FB Rate Limit
def check_limit():
check=rq.get('https://graph.facebook.com/v3.1/'+ad_account_id+'/insights?access_token='+access_token)
usage=float(find_between(check.headers['x-ad-account-usage'],':','}'))
return usage
现在，这是您可以运行以提取最近X天的数据的整个脚本！
Y = number of days
for x in range(1, Y):
date_0 = datetime.datetime.now() - datetime.timedelta(days=x )
date_ = date_0.strftime('%Y-%m-%d')
date_compact = date_.replace('-', '')
filename = 'fb_%s.csv'%date_compact
filelocation = "./"+ filename
# Open or create new file
try:
csvfile = open(filelocation , 'w+', 777)
except:
print ("Cannot open file.")
# To keep track of rows added to file
rows = 0
try:
# Create file writer
filewriter = csv.writer(csvfile, delimiter=',')
filewriter.writerow(['date','ad_name', 'adset_id', 'adset_name', 'campaign_id', 'campaign_name', 'clicks', 'impressions', 'spend'])
except Exception as err:
print(err)
# Iterate through all accounts in the business account
ads = account.get_insights(params={'time_range': {'since':date_, 'until':date_}, 'level':'ad' }, fields=[AdsInsights.Field.ad_name, AdsInsights.Field.adset_id, AdsInsights.Field.adset_name, AdsInsights.Field.campaign_id, AdsInsights.Field.campaign_name, AdsInsights.Field.clicks, AdsInsights.Field.impressions, AdsInsights.Field.spend ])
for ad in ads:
# Set default values in case the insight info is empty
date = date_
adsetid = ""
adname = ""
adsetname = ""
campaignid = ""
campaignname = ""
clicks = ""
impressions = ""
spend = ""
# Set values from insight data
if ('adset_id' in ad) :
adsetid = ad[AdsInsights.Field.adset_id]
if ('ad_name' in ad) :
adname = ad[AdsInsights.Field.ad_name]
if ('adset_name' in ad) :
adsetname = ad[AdsInsights.Field.adset_name]
if ('campaign_id' in ad) :
campaignid = ad[AdsInsights.Field.campaign_id]
if ('campaign_name' in ad) :
campaignname = ad[AdsInsights.Field.campaign_name]
if ('clicks' in ad) : # This is stored strangely, takes a few steps to break through the layers
clicks = ad[AdsInsights.Field.clicks]
if ('impressions' in ad) : # This is stored strangely, takes a few steps to break through the layers
impressions = ad[AdsInsights.Field.impressions]
if ('spend' in ad) :
spend = ad[AdsInsights.Field.spend]
# Write all ad info to the file, and increment the number of rows that will display
filewriter.writerow([date_, adname, adsetid, adsetname, campaignid, campaignname, clicks, impressions, spend])
rows += 1
csvfile.close()
# Print report
print (str(rows) + " rows added to the file " + filename)
print(check_limit(), 'reached of rate limit')
## write to GCS and BQ
blob = bucket.blob('fb_2/fb_%s.csv'%date_compact)
blob.upload_from_filename(filelocation)
load_job_config = bigquery.LoadJobConfig()
table_name = '0_fb_ad_stats_%s' % date_compact
load_job_config.write_disposition = 'WRITE_TRUNCATE'
load_job_config.skip_leading_rows = 1
# The source format defaults to CSV, so the line below is optional.
load_job_config.source_format = bigquery.SourceFormat.CSV
load_job_config.field_delimiter = ','
load_job_config.autodetect = True
uri = 'gs://my-project/fb_2/fb_%s.csv'%date_compact
load_job = bq_client.load_table_from_uri(
uri,
dataset.table(table_name),
job_config=load_job_config) # API request
print('Starting job {}'.format(load_job.job_id))
load_job.result() # Waits for table load to complete.
print('Job finished.')
if (check_limit()>=75):
print('75% Rate Limit Reached. Cooling Time 5 Minutes.')
logging.debug('75% Rate Limit Reached. Cooling Time Around 3 Minutes And Half.')
time.sleep(225)
这确实可以很好地工作，但是请注意，如果您打算提取3年的数据，该脚本将需要大量时间来运行！
我要感谢LucyTurtle和Ashish
Baid的脚本在我的工作中对我有帮助！
如果您需要更多详细信息，或者需要提取一天中不同广告帐户的数据，请参阅此帖子：
