我写了这个脚本，返回一个包含他们统计数据的广告列表，但显然我只获得有效广告的见解而不是暂停的 - 对于暂停的广告，我只是获取广告系列名称及其ID！
我尝试使用如下所示的过滤，但它不起作用：
''
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)
这是我得到的输出样本：
{'data': [{'created_time': '2019-02-15T17:24:29+0100', 'name': '20122301-FB-BOOST-EVENT-CC SDSDSD', 'effective_status': 'PAUSED', 'id': '6118169436761'}
只有广告系列的名称，而不是见解！有没有人确实在之前检索暂停广告/广告系列的统计信息/见解？
谢谢 ！
请查看我的python脚本的另一篇文章：I can't fetch stats for all my facebook campaigns using Python and Facebook Marketing API
经过几天的挖掘，我终于想出了一个脚本，我已经开始提取3年的facebook广告洞察力，避免了facebook API的速率限制。
首先，我们导入我们需要的lib：
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，在提取了这些见解之后，我将它们保存在Google Cloud存储上，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)
这确实很有效，但请注意，如果您计划提取3年的数据，脚本将花费大量时间来运行！
我要感谢LucyTurtle和Ashish Baid在我的工作中帮助我的脚本！
如果您需要更多详细信息，或者您需要为不同的广告帐户提取一天的数据，请参阅此帖子：
Facebook Marketing API - Python to get Insights - User Request Limit Reached
您可以结合使用更多过滤条件作为示例，对于过滤暂停的广告系列，该名称包含字符串name，并从您可以使用的1月3日开始：
act_105433210/campaigns?filtering=[{'field':'effective_status','operator':'IN','value':['PAUSED']},{'field':'name','operator':'CONTAIN','value':'name'},{'field':'created_time','operator':'GREATER_THAN','value':'1551444673'}]&fields=created_time,name,effective_status,insights{spend,impressions,clicks}
时间戳应该是一个纪元时间戳，例如：
大纪元时间戳：1551444673人类时间（格林尼治标准时间）：2019年3月1日星期五12:51:13 PM