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社区首页 >问答首页 >Python函数，它从Google获得响应并对其进行排序/聚合

问Python函数，它从Google获得响应并对其进行排序/聚合
EN

Code Review用户
提问于 2022-10-12 20:27:53
回答 1查看 72关注 0票数 3

我在这里处理这个函数，它产生所需的输出。我只想确定我是以一种明智的方式去做事情。

它只是对数据进行有效的排序，同时考虑到不同公司可能发起的活动。然后，它聚合数据。

代码语言：javascript
复制
from collections import defaultdict

def aggregate_ads(ad_data, labels=list(), default_advertiser='internal'):    
    # Creates dicts to hold data, structured to provide code-readability
    ads_data = defaultdict(
        lambda: defaultdict(
            lambda: defaultdict(int)
        ))
    
    # Lowercases all labels
    labels = map(str.lower, labels)
    
    # Sorts each instance into its channel and adds
    for adgroup in ad_data:
        # Cleans and standardizes campaign name
        campaign_name = adgroup['campaign']['name']
        campaign_name = campaign_name.replace('-', ' ').replace('_', ' ').lower()
        
        # Handling where ad_group type is not provided
        if not adgroup['ad_group'].get('type_'):
            adgroup['ad_group']['type_'] = 'MIXED'
            
        # Collects channel and metrics
        channel = adgroup['ad_group']['type_']
        metrics = dict(
            impressions= int(adgroup['metrics']['impressions']),
            clicks     = int(adgroup['metrics']['clicks']),
            # Converts cost in microns to usd
            cost       = round(int(adgroup['metrics']['cost_micros'])/1000000, 2),
        )
            
        # Checks for labels in campaign name and defaults to specified default
        advertisers = set(labels).intersection(campaign_name.split())
        if not advertisers:
            advertisers.add(default_advertiser)
        
        # Adds the variables to ads_data
        for advertiser in advertisers:
            ads_data[advertiser][channel]['impressions'] += metrics['impressions']
            ads_data[advertiser][channel]['clicks']      += metrics['clicks']
            ads_data[advertiser][channel]['cost']        += metrics['cost']
    
    # Converts into regular dict on return
    return dict(
        (advertiser, dict((ad_type, dict(metrics)) 
                          for ad_type, metrics in ad_data.items())) 
        for advertiser, ad_data in ads_data.items())

这是我的输出：

代码语言：javascript
复制
{'internal': {'DISPLAY_STANDARD': {'clicks': 163,
                                   'cost': 11.8,
                                   'impressions': 6785},
              'MIXED': {'clicks': 6, 'cost': 0.1, 'impressions': 434},
              'SEARCH_STANDARD': {'clicks': 2,
                                  'cost': 5.89,
                                  'impressions': 151}},
 'play': {'MIXED': {'clicks': 5, 'cost': 0.05, 'impressions': 242}}}

和一些样本输入：

代码语言：javascript
复制
example = [
 {'campaign': {'resource_name': 'blahblahbah',
   'serving_status': 'SERVING',
   'name': 'Google Play Market-USA/Canada-2022-08',
   'start_date': '2022-07-20',
   'end_date': '2037-12-30'},
  'ad_group': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'type_': 'MIXED'},
  'metrics': {'clicks': '5', 'cost_micros': '54238', 'impressions': '242'},
  'ad_group_ad': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'ad': {'resource_name': 'blahblahbah'}},
  'segments': {'date': '2022-10-11'}},
 {'campaign': {'resource_name': 'blahblahbah',
   'serving_status': 'SERVING',
   'name': 'Google Play Market-USA/Canada-2022-08',
   'start_date': '2022-07-20',
   'end_date': '2037-12-30'},
  'ad_group': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'type_': 'MIXED'},
  'metrics': {'clicks': '3', 'cost_micros': '53943', 'impressions': '217'},
  'ad_group_ad': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'ad': {'resource_name': 'blahblahbah'}},
  'segments': {'date': '2022-10-11'}},
 {'campaign': {'resource_name': 'blahblahbah',
   'serving_status': 'SERVING',
   'name': 'Google Play Market-USA/Canada-2022-08',
   'start_date': '2022-07-20',
   'end_date': '2037-12-30'},
  'ad_group': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'type_': 'MIXED'},
  'metrics': {'clicks': '3', 'cost_micros': '53943', 'impressions': '217'},
  'ad_group_ad': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'ad': {'resource_name': 'blahblahbah'}},
  'segments': {'date': '2022-10-11'}},
 {'campaign': {'resource_name': 'blahblahbah',
   'serving_status': 'SERVING',
   'name': 'Display-Global-Desktop-202208',
   'start_date': '2022-07-21',
   'end_date': '2037-12-30'},
  'ad_group': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'type_': 'DISPLAY_STANDARD'},
  'metrics': {'clicks': '95', 'cost_micros': '6036546', 'impressions': '4186'},
  'ad_group_ad': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'ad': {'resource_name': 'blahblahbah'}},
  'segments': {'date': '2022-10-11'}},
 {'campaign': {'resource_name': 'blahblahbah',
   'serving_status': 'SERVING',
   'name': 'Search-USA/NOTES',
   'start_date': '2022-08-30',
   'end_date': '2037-12-30'},
  'ad_group': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'type_': 'SEARCH_STANDARD'},
  'metrics': {'clicks': '2', 'cost_micros': '5890000', 'impressions': '151'},
  'ad_group_ad': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'ad': {'resource_name': 'blahblahbah'}},
  'segments': {'date': '2022-10-11'}},
 {'campaign': {'resource_name': 'blahblahbah',
   'serving_status': 'SERVING',
   'name': 'Display-Global--Desktop-Files',
   'start_date': '2022-09-02',
   'end_date': '2037-12-30'},
  'ad_group': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'type_': 'DISPLAY_STANDARD'},
  'metrics': {'clicks': '68', 'cost_micros': '5757098', 'impressions': '2599'},
  'ad_group_ad': {'resource_name': 'blahblahbah',
   'status': 'ENABLED',
   'ad': {'resource_name': 'blahblahbah'}},
  'segments': {'date': '2022-10-11'}}
]
代码语言：javascript
复制
labels = ['play'] # In reality, this is partner labels they'd put in the campaign name

aggregate_ads(example, labels)
python
python-3.x
google-api
EN

回答 1

Code Review用户

回答已采纳

发布于 2022-10-12 21:34:28

你有个严重的错误。您将一个map分配给labels，但它从未实现，因此会被消耗，随后看起来就像一个空集合。

我建议您放弃大部分实现，用Pandas替换它，这非常适合您的情况。入口点将通过pd.json_normalize放置示例代码，这将为示例数据提供一个有六行的数据框架。

用Pandas to_dict替换dict生成器，并以矢量化的方式执行和，而不是在循环中执行求和，代码可能如下所示：

代码语言：javascript
复制
from pprint import pprint

import pandas as pd


def aggregate_ads(ad_data: dict, labels: set[str], default_advertiser: str = 'internal') -> pd.DataFrame:
    df = pd.json_normalize(ad_data).astype({
        'metrics.clicks': int,
        'metrics.cost_micros': int,
        'metrics.impressions': int,
    })
    df['channel'] = df['ad_group.type_'].fillna('MIXED')
    df['metrics.cost'] = df['metrics.cost_micros'] / 1e6

    campaign_fragments = (
        df['campaign.name']
        .str.replace('-', ' ').str.replace('_', ' ').str.lower().str.split()
        .apply(set).apply(labels.__and__)
    )
    campaign_fragments[campaign_fragments == set()] = default_advertiser
    df['advertisers'] = campaign_fragments

    return (
        df.explode('advertisers')
        .groupby(['advertisers', 'channel'])
        ['metrics.impressions', 'metrics.clicks', 'metrics.cost']
        .sum()
        .rename(columns={
            'metrics.impressions': 'impressions',
            'metrics.clicks': 'clicks',
            'metrics.cost': 'cost',
        })
    )


def ads_to_json(groups: pd.DataFrame) -> dict:
    inners = groups.groupby(level=0).apply(
        lambda df: df.droplevel(0).to_dict('index'))
    return inners.to_dict()


def test() -> None:
    example = [
        {'campaign': {'resource_name': 'blahblahbah',
                      'serving_status': 'SERVING',
                      'name': 'Google Play Market-USA/Canada-2022-08',
                      'start_date': '2022-07-20',
                      'end_date': '2037-12-30'},
         'ad_group': {'resource_name': 'blahblahbah',
                      'status': 'ENABLED',
                      'type_': 'MIXED'},
         'metrics': {'clicks': '5', 'cost_micros': '54238', 'impressions': '242'},
         'ad_group_ad': {'resource_name': 'blahblahbah',
                         'status': 'ENABLED',
                         'ad': {'resource_name': 'blahblahbah'}},
         'segments': {'date': '2022-10-11'}},
        {'campaign': {'resource_name': 'blahblahbah',
                      'serving_status': 'SERVING',
                      'name': 'Google Play Market-USA/Canada-2022-08',
                      'start_date': '2022-07-20',
                      'end_date': '2037-12-30'},
         'ad_group': {'resource_name': 'blahblahbah',
                      'status': 'ENABLED',
                      'type_': 'MIXED'},
         'metrics': {'clicks': '3', 'cost_micros': '53943', 'impressions': '217'},
         'ad_group_ad': {'resource_name': 'blahblahbah',
                         'status': 'ENABLED',
                         'ad': {'resource_name': 'blahblahbah'}},
         'segments': {'date': '2022-10-11'}},
        {'campaign': {'resource_name': 'blahblahbah',
                      'serving_status': 'SERVING',
                      'name': 'Google Play Market-USA/Canada-2022-08',
                      'start_date': '2022-07-20',
                      'end_date': '2037-12-30'},
         'ad_group': {'resource_name': 'blahblahbah',
                      'status': 'ENABLED',
                      'type_': 'MIXED'},
         'metrics': {'clicks': '3', 'cost_micros': '53943', 'impressions': '217'},
         'ad_group_ad': {'resource_name': 'blahblahbah',
                         'status': 'ENABLED',
                         'ad': {'resource_name': 'blahblahbah'}},
         'segments': {'date': '2022-10-11'}},
        {'campaign': {'resource_name': 'blahblahbah',
                      'serving_status': 'SERVING',
                      'name': 'Display-Global-Desktop-202208',
                      'start_date': '2022-07-21',
                      'end_date': '2037-12-30'},
         'ad_group': {'resource_name': 'blahblahbah',
                      'status': 'ENABLED',
                      'type_': 'DISPLAY_STANDARD'},
         'metrics': {'clicks': '95', 'cost_micros': '6036546', 'impressions': '4186'},
         'ad_group_ad': {'resource_name': 'blahblahbah',
                         'status': 'ENABLED',
                         'ad': {'resource_name': 'blahblahbah'}},
         'segments': {'date': '2022-10-11'}},
        {'campaign': {'resource_name': 'blahblahbah',
                      'serving_status': 'SERVING',
                      'name': 'Search-USA/NOTES',
                      'start_date': '2022-08-30',
                      'end_date': '2037-12-30'},
         'ad_group': {'resource_name': 'blahblahbah',
                      'status': 'ENABLED',
                      'type_': 'SEARCH_STANDARD'},
         'metrics': {'clicks': '2', 'cost_micros': '5890000', 'impressions': '151'},
         'ad_group_ad': {'resource_name': 'blahblahbah',
                         'status': 'ENABLED',
                         'ad': {'resource_name': 'blahblahbah'}},
         'segments': {'date': '2022-10-11'}},
        {'campaign': {'resource_name': 'blahblahbah',
                      'serving_status': 'SERVING',
                      'name': 'Display-Global--Desktop-Files',
                      'start_date': '2022-09-02',
                      'end_date': '2037-12-30'},
         'ad_group': {'resource_name': 'blahblahbah',
                      'status': 'ENABLED',
                      'type_': 'DISPLAY_STANDARD'},
         'metrics': {'clicks': '68', 'cost_micros': '5757098', 'impressions': '2599'},
         'ad_group_ad': {'resource_name': 'blahblahbah',
                         'status': 'ENABLED',
                         'ad': {'resource_name': 'blahblahbah'}},
         'segments': {'date': '2022-10-11'}}
    ]

    grouped = aggregate_ads(example, labels={'play'})
    js = ads_to_json(grouped)
    pprint(js)


if __name__ == '__main__':
    test()
票数 2
EN
页面原文内容由Code Review提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：

https://codereview.stackexchange.com/questions/280395

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