协助连接指挥探照灯API的客户
searchlight-api的Python项目详细描述
searchlight api客户端python
searchlight api客户机是一个python包，它使得通过conductor searchlightapi进行身份验证和从中检索数据变得非常容易。
开始
安装
最新发布的版本可以通过python包索引安装。
pip install searchlight-api
依赖性
- Requests:2.19.1或更高版本
- Simplejson:3.11.1或更高版本
认证
Searchlight API客户端需要知道API密钥和API密钥才能使用Searchlight进行身份验证它可以通过以下两种方式之一找到它们：
- 将凭据添加到环境变量（首选）
- macos/linux：编辑.bash_配置文件以包含以下行
- export searchlight_api_key=xxxxx
- 导出searchlight_shared_secret=xxxxxx
- windows：在Environment Variables in the Advanced system settings中添加searchlight_api_键和searchlight_shared_secret
- macos/linux：编辑.bash_配置文件以包含以下行
- 在实例化API客户端时传递凭据 从searchlight_api.client导入AccountService 帐户服务=帐户服务（api_key=xxxxx，secret=xxxxx）
如果没有API密钥和机密，请从http://developers.conductor.com/请求一个你应该在一周内收到一封包含你的证书的电子邮件。
使用探照灯api
Searchlight API包有两个主要功能：客户端和分析
- client提供了一个围绕searchlight rest api的sdk，使您可以很容易地从您的帐户请求所需的数据。
- 分析包括使用Searchlight API聚合数据（排名和搜索量）的函数
示例
客户
搜索灯光服务
SearchLightService客户端提供用于获取SearchLight和配置文件配置数据的包装器
from searchlight_api import client
# instantiate Searchlight client
ss = client.SearchlightService()
# Retrieve all Searchlight accounts you have access to.
# You can use this to retrieve Account IDs to instantiate the AccountService client
my_accounts = ss.get_accounts()
print(my_accounts)
[{'accountId': 'XXXX',
'isActive': False,
'name': 'Account 1',
'webProperties': 'https://api.conductor.com/v3/accounts/XXXX/web-properties'},
{'accountId': 'YYYY',
'isActive': True,
'name': 'Account 2',
'webProperties': 'https://api.conductor.com/v3/accounts/YYYY/web-properties'}]
# Account IDs can also be found in Searchlight URLs: https://searchlight.conductor.com/YYYY/insight-stream
在searchlight中，关键字可以跨不同的排名源（或搜索引擎）、位置和设备进行跟踪。 您可以检索以下支持的内容：
rank_sources = ss.get_rank_sources()
locations = ss.get_locations()
devices = ss.get_devices()
会计服务
AccountService客户端提供从Searchlight帐户检索报告数据的功能，同时保留在SearchlightService客户端中找到的所有功能。实例化时需要searchlight帐户ID。
from searchlight_api import client
# instantiate the AccountService client
account_service = client.AccountService(YYYY)
# retrieve all web properties tracked in the account
web_properties = account_service.get_web_properties()
web_property_id = web_properties[0]['webPropertyId']
# the web_property_id can be used to get the name of the corresponding domain
domain_name = account_service.get_domain_name(web_property_id)
# use the web_property_id to get all tracked searches in the account
tracked_searches = account_service.get_tracked_searches(web_property_id)
# get a rank_source_id that the web_property is tracked against
rank_source_id = web_properties[0]['rankSourceInfo'][0]['rankSourceId']
# retrieve rank data for searches tracked against a web property and rank source for a given date within a reporting interval.
# by default the current date is used.
ranks = account_service.get_ranks(web_property_id, rank_source_id, date='2019-01-12')
# retrieve search volume data for searches tracked against a web property and rank source for a given date within a reporting interval.
# by default the current date is used.
search_volume = account_service.get_volume(web_property_id, rank_source_id, date='2019-01-12')
分析
通过分析，您可以使用AccountService客户端在给定日期内聚合整个帐户的报告数据
from searchlight_api.client import AccountService
from searchlight_api import analysis
account_service = AccountService(YYYY)
rank_data = analysis.rank_data(account_service, date='2019-01-12')
search_volume = analysis.search_volume(account_service)
要请求其他聚合方法，请访问dgoodman@conductor.com或pmurphy@conductor.com
与Searchlight API客户端一起使用熊猫
from searchlight_api import client, analysis
import pandas as pd
# instantiate AccountService object
account_service = client.AccountService(YYYY)
# get rank sources, locations and devices and turn to dfs
rank_source_df = pd.DataFrame(account_service.get_rank_sources())
location_df = pd.DataFrame(account_service.get_locations())
device_df = pd.DataFrame(account_service.get_devices())
# get the tracked searches and turn into a df
tracked_search_df = pd.DataFrame(analysis.all_tracked_searches(account_service))
# turn search_volume and rank_data to Data Frames
search_volume_df = pd.DataFrame(analysis.search_volume(account_service))
rank_data_df = pd.DataFrame(analysis.rank_data(account_service))
report = pd.merge(rank_data_df, search_volume_df, on=['trackedSearchId', 'rankSourceId', 'webPropertyId'])
report = report.merge(rank_source_df, on='rankSourceId', how='left')
print(report.head())
itemType rankSourceId standardRank target targetDomainName \
0 ANSWER_BOX 1 3.0 example.com
1 IMAGE_RESULT 1 NaN example.com
2 LOCAL_RESULT 1 NaN www.organiccompetitor.com
3 LOCAL_RESULT 1 NaN www.organiccompetitor.com
4 LOCAL_RESULT 1 NaN www.organiccompetitor.com
targetUrl targetWebPropetyId \
0 https://www.example.com/subfolder1/ 43162.0
1 https://www.example.com/subfolder2/... 43162.0
2 http://www.organiccompetitor.com/subfolder... NaN
3 http://www.organiccompetitor.com/subfolder/... NaN
4 http://www.organiccompetitor.com/subfolder/... NaN
trackedSearchId trueRank webPropertyId averageVolume \
0 7188291 6 43162 135000
1 7188291 62 43162 135000
2 7188291 4 43162 135000
3 7188291 2 43162 135000
4 7188291 3 43162 135000
volumeItems baseDomain \
0 [{'volume': 165000, 'month': 11, 'year': 2018}... google.com
1 [{'volume': 165000, 'month': 11, 'year': 2018}... google.com
2 [{'volume': 165000, 'month': 11, 'year': 2018}... google.com
3 [{'volume': 165000, 'month': 11, 'year': 2018}... google.com
4 [{'volume': 165000, 'month': 11, 'year': 2018}... google.com
description name deviceId isActive locationId \
0 Google (US / English) GOOGLE_EN_US 1 True 1
1 Google (US / English) GOOGLE_EN_US 1 True 1
2 Google (US / English) GOOGLE_EN_US 1 True 1
3 Google (US / English) GOOGLE_EN_US 1 True 1
4 Google (US / English) GOOGLE_EN_US 1 True 1
preferredUrl queryPhrase
0 http://www.example.com/ example phrase
1 http://www.example.com/ example phrase
2 http://www.example.com/ example phrase
3 http://www.example.com/ example phrase
4 http://www.example.com/ example phrase
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