blocks|key|2345148|text|这对我很有效：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2345149|import+requests
import+csv
from+simple_salesforce+import+Salesforce
import+pandas+as+pd


sf+=+Salesforce(username=your_username,+password=your_password,+security_token+=+your_token)


login_data+=+{'username':+your_username,+'password':+your_password_plus_your_token}


with+requests.session()+as+s:
++++d+=+s.get("https://your_instance.salesforce.com/{}?export=1&enc=UTF-8&xf=csv".format(reportid),+headers=sf.headers,+cookies={'sid':+sf.session_id})|code-block|syntax|javascript|2345150|d.content将包含一个逗号分隔值字符串，您可以使用csv模块读取该字符串。|offset|length|style|CODE|2345151|我从那里将数据放入pandas，因此有了函数名和import+pandas。我删除了将数据放到DataFrame中的函数的其余部分，但是如果您对这是如何完成的感兴趣，请告诉我。|2345152|entityMap^0|0|0|0|9|S|3|0|9|6|O|D|1B|9|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@$I|T|J|U|K|L]|$I|V|J|W|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|X|8|@$I|Y|J|Z|K|L]|$I|10|J|11|K|L]|$I|12|J|13|K|L]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|P|$]]

This worked for me:

<pre><code>import requests
import csv
from simple_salesforce import Salesforce
import pandas as pd


sf = Salesforce(username=your_username, password=your_password, security_token = your_token)


login_data = {'username': your_username, 'password': your_password_plus_your_token}


with requests.session() as s:
 d = s.get("https://your_instance.salesforce.com/{}?export=1&amp;enc=UTF-8&amp;xf=csv".format(reportid), headers=sf.headers, cookies={'sid': sf.session_id})
</code></pre>

<code>d.content</code> will contain a string of comma separated values which you can read with the <code>csv</code> module.

I take the data into <code>pandas</code> from there, hence the function name and <code>import pandas</code>. I removed the rest of the function where it puts the data into a <code>DataFrame</code>, but if you're interested in how that's done let me know.

blocks|key|2340205|text|如果它是有用的，我想写出我用来回答这个问题的步骤现在(2018年8月-2018年)，基于Obol的评论。作为参考，我遵循了salesforce_reporting包在https://github.com/cghall/force-retrieve/blob/master/README.md上的自述文件说明。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2340206|要连接到Salesforce：|2340207|from+salesforce_reporting+import+Connection,+ReportParser

sf+=+Connection(username='your_username',password='your_password',security_token='your_token')|code-block|syntax|javascript|2340208|然后，要将我想要的报告放入Pandas+DataFrame：|2340209|report+=+sf.get_report(your_reports_id)
parser+=+salesforce_reporting.ReportParser(report)
report+=+parser.records_dict()
report+=+pd.DataFrame(report)|2340210|如果您愿意，也可以将上面的四行简化为一行，如下所示：|2340211|report+=+pd.DataFrame(salesforce_reporting.ReportParser(sf.get_report(your_reports_id)).records_dict())|2340212|我在自述文件中遇到的一个不同之处是，sf.get_report('report_id',+includeDetails=True)抛出了一个声明get_report()+got+an+unexpected+keyword+argument+'includeDetails'的错误。简单地删除它似乎会使代码工作得很好。|style|CODE|2340213|report现在可以通过report.to_csv('report.csv',index=False)导出，也可以直接操作。|2340214|编辑:将parser.records()更改为parser.records_dict()，因为这允许DataFrame具有已经列出的列，而不是对它们进行数字索引。|2340215|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/cghall/force-retrieve/blob/master/README.md^0|2B|1Q|0|0|0|0|0|0|0|0|I|1B|20|1S|0|0|6|C|13|0|4|G|N|L|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@$A|19|B|1A|1|1B]]|C|$]]|$1|D|3|E|5|6|7|1C|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|1D|8|@]|9|@]|C|$I|J]]|$1|K|3|L|5|6|7|1E|8|@]|9|@]|C|$]]|$1|M|3|N|5|H|7|1F|8|@]|9|@]|C|$I|J]]|$1|O|3|P|5|6|7|1G|8|@]|9|@]|C|$]]|$1|Q|3|R|5|H|7|1H|8|@]|9|@]|C|$I|J]]|$1|S|3|T|5|6|7|1I|8|@$A|1J|B|1K|U|V]|$A|1L|B|1M|U|V]]|9|@]|C|$]]|$1|W|3|X|5|6|7|1N|8|@$A|1O|B|1P|U|V]|$A|1Q|B|1R|U|V]]|9|@]|C|$]]|$1|Y|3|Z|5|6|7|1S|8|@$A|1T|B|1U|U|V]|$A|1V|B|1W|U|V]]|9|@]|C|$]]|$1|10|3|-4|5|6|7|1X|8|@]|9|@]|C|$]]]|11|$12|$5|13|14|15|C|$16|17]]]]

In case it is helpful, I wanted to write out the steps I used to answer this question now (Aug-2018), based on Obol's comment. For reference, I followed the README instructions at <a href="https://github.com/cghall/force-retrieve/blob/master/README.md" rel="nofollow noreferrer">https://github.com/cghall/force-retrieve/blob/master/README.md</a> for the salesforce_reporting package.

To connect to Salesforce:

<pre><code>from salesforce_reporting import Connection, ReportParser

sf = Connection(username='your_username',password='your_password',security_token='your_token')
</code></pre>

Then, to get the report I wanted into a Pandas DataFrame:

<pre><code>report = sf.get_report(your_reports_id)
parser = salesforce_reporting.ReportParser(report)
report = parser.records_dict()
report = pd.DataFrame(report)
</code></pre>

If you were so inclined, you could also simplify the four lines above into one, like so:

<pre><code>report = pd.DataFrame(salesforce_reporting.ReportParser(sf.get_report(your_reports_id)).records_dict())
</code></pre>

One difference I ran into from the README is that <code>sf.get_report('report_id', includeDetails=True)</code> threw an error stating <code>get_report() got an unexpected keyword argument 'includeDetails'</code>. Simply removing it out seemed result in the code working fine.

<code>report</code> can now be exported via <code>report.to_csv('report.csv',index=False)</code>, or manipulated directly.

EDIT: <code>parser.records()</code> changed to <code>parser.records_dict()</code>, as this allows the DataFrame to have the columns already listed, rather than indexing them numerically.

blocks|key|5563203|text|下面的代码相当长，可能只适用于我们的用例，但其基本思想如下：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5563204|找出日期间隔长度和额外的需要过滤，永远不会运行到“超过2,000”的限制。在我的例子中，我可以使用每周日期范围过滤器，但需要应用一些额外的过滤器|5563205|然后像这样运行它：|5563206|report_id+=+'00O4…'
sf+=+SalesforceReport(user,+pass,+token,+report_id)
it+=+sf.iterate_over_dates_and_filters(datetime.date(2020,2,1),
+++++'Invoice__c.InvoiceDate__c',+'Opportunity.CustomField__c',+
+++++[('a',+'startswith'),+('b',+'startswith'),+…])
for+row+in+it:
++#+do+something+with+the+dict|code-block|syntax|javascript|5563207|自2020-02-01以来，迭代器每周执行一次(如果您需要每日迭代器或每月迭代程序，则需要更改代码，但更改应最小)，并应用过滤器CustomField__c.startswith('a')，然后应用CustomField__c.startswith('b')，…作为一个生成器，所以你不需要自己去处理过滤器循环。|5563208|如果有一个返回超过2000行的查询，迭代器就会抛出异常，以确保数据不完整。|5563209|这里有一个警告:+SF有每小时最多500个查询的限制。比方说，如果你有一年的52周和10个额外的过滤器，你已经达到了这个限制。|5563210|下面是这个类(依赖于simple_salesforce)|offset|length|style|CODE|5563211|import+simple_salesforce
import+json
import+datetime

"""
helper+class+to+iterate+over+salesforce+report+data
and+manouvering+around+the+2000+max+limit
"""

class+SalesforceReport(simple_salesforce.Salesforce):
++def+__init__(self,+username,+password,+security_token,+report_id):
++++super(SalesforceReport,+self).__init__(username=username,+password=password,+security_token=security_token)
++++self.report_id+=+report_id
++++self._fetch_describe()

++def+_fetch_describe(self):
++++url+=+f'{self.base_url}analytics/reports/{self.report_id}/describe'
++++result+=+self._call_salesforce('GET',+url)
++++self.filters+=+dict(result.json()['reportMetadata'])

++def+apply_report_filter(self,+column,+operator,+value,+replace=True):
++++"""
++++adds/replaces+filter,+example:
++++apply_report_filter('Opportunity.InsertionId__c',+'startsWith',+'hbob').
++++For+date+filters+use+apply_standard_date_filter.

++++column:+++needs+to+correspond+to+a+column+in+your+report,+AND+the+report
++++++++++++++needs+to+have+this+filter+configured+(so+in+the+UI+the+filter
++++++++++++++can+be+applied)
++++operator:+equals,+notEqual,+lessThan,+greaterThan,+lessOrEqual,
++++++++++++++greaterOrEqual,+contains,+notContain,+startsWith,+includes
++++++++++++++see+https://sforce.co/2Tb5SrS+for+up+to+date+list
++++value:++++value+as+a+string
++++replace:++if+set+to+True,+then+if+there's+already+a+restriction+on+column
++++++++++++++this+restriction+will+be+replaced,+otherwise+it's+added+additionally
++++"""
++++filters+=+self.filters['reportFilters']
++++if+replace:
++++++filters+=+[f+for+f+in+filters+if+not+f['column']+==+column]
++++filters.append(dict(
++++++column=column,+
++++++isRunPageEditable=True,+
++++++operator=operator,+
++++++value=value))
++++self.filters['reportFilters']+=+filters

++def+apply_standard_date_filter(self,+column,+startDate,+endDate):
++++"""
++++replace+date+filter.+The+date+filter+needs+to+be+available+as+a+filter+in+the
++++UI+already

++++Example:+apply_standard_date_filter('Invoice__c.InvoiceDate__c',+d_from,+d_to)

++++column:+needs+to+correspond+to+a+column+in+your+report
++++startDate,+endDate:+instance+of+datetime.date
++++"""
++++self.filters['standardDateFilter']+=+dict(
++++++column=column,
++++++durationValue='CUSTOM',
++++++startDate=startDate.strftime('%25Y-%25m-%25d'),
++++++endDate=endDate.strftime('%25Y-%25m-%25d')
++++)

++def+query_report(self):
++++"""
++++return+generator+which+yields+one+report+row+as+dict+at+a+time
++++"""
++++url+=+self.base_url+%2B+f"analytics/reports/query"
++++result+=+self._call_salesforce('POST',+url,+data=json.dumps(dict(reportMetadata=self.filters)))
++++r+=+result.json()
++++columns+=+r['reportMetadata']['detailColumns']
++++if+not+r['allData']:
++++++raise+Exception('got+more+than+2000+rows!+Quitting+as+data+would+be+incomplete')
++++for+row+in+r['factMap']['T!T']['rows']:
++++++values+=+[]
++++++for+c+in+row['dataCells']:
++++++++t+=+type(c['value'])
++++++++if+t+==+str+or+t+==+type(None)+or+t+==+int:
++++++++++values.append(c['value'])
++++++++elif+t+==+dict+and+'amount'+in+c['value']:
++++++++++values.append(c['value']['amount'])
++++++++else:
++++++++++print(f"don't+know+how+to+handle+{c}")
++++++++++values.append(c['value'])
++++++yield+dict(zip(columns,+values))

++def+iterate_over_dates_and_filters(self,+startDate,+date_column,+filter_column,+filter_tuples):
++++"""
++++return+generator+which+iterates+over+every+week+and+applies+the+filters+
++++each+for+column
++++"""
++++date_runner+=+startDate
++++while+True:
++++++print(date_runner)
++++++self.apply_standard_date_filter(date_column,+date_runner,+date_runner+%2B+datetime.timedelta(days=6))
++++++for+val,+op+in+filter_tuples:
++++++++print(val)
++++++++self.apply_report_filter(filter_column,+op,+val)
++++++++for+row+in+self.query_report():
++++++++++yield+row
++++++date_runner+%2B=+datetime.timedelta(days=7)
++++++if+date_runner+>+datetime.date.today():
++++++++break|5563212|entityMap^0|0|0|0|0|0|0|0|A|H|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|11|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|12|8|@]|9|@]|A|$]]|$1|F|3|G|5|H|7|13|8|@]|9|@]|A|$I|J]]|$1|K|3|L|5|6|7|14|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|15|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|16|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|17|8|@$S|18|T|19|U|V]]|9|@]|A|$]]|$1|W|3|X|5|H|7|1A|8|@]|9|@]|A|$I|J]]|$1|Y|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|Z|$]]

The code below is rather long and might be just for our use case but the basic idea is the following:

Find out date interval length and additional needed filtering to never run into the "more the 2'000" limit. In my case I could have weekly date range filter but would need to apply some additional filters

Then run it like this:

<pre><code>report_id = '00O4…'
sf = SalesforceReport(user, pass, token, report_id)
it = sf.iterate_over_dates_and_filters(datetime.date(2020,2,1),
 'Invoice__c.InvoiceDate__c', 'Opportunity.CustomField__c', 
 [('a', 'startswith'), ('b', 'startswith'), …])
for row in it:
 # do something with the dict
</code></pre>

The iterator goes through every week (if you need daily iterators or monthly then you'd need to change the code, but the change should be minimal) since 2020-02-01 and applies the filter CustomField__c.startswith('a'), then CustomField__c.startswith('b'), … and acts as a generator so you don't need to mess with the filter cycling yourself.

The iterator throws an Exception if there's a query which returns more than 2000 rows, just to be sure that the data is not incomplete.

One warning here: SF has a limit of max 500 queries per hour. Say if you have one year with 52 weeks and 10 additional filters you'd already run into that limit.

Here's the class (relies on <code>simple_salesforce</code>)

<pre><code>import simple_salesforce
import json
import datetime

"""
helper class to iterate over salesforce report data
and manouvering around the 2000 max limit
"""

class SalesforceReport(simple_salesforce.Salesforce):
 def __init__(self, username, password, security_token, report_id):
 super(SalesforceReport, self).__init__(username=username, password=password, security_token=security_token)
 self.report_id = report_id
 self._fetch_describe()

 def _fetch_describe(self):
 url = f'{self.base_url}analytics/reports/{self.report_id}/describe'
 result = self._call_salesforce('GET', url)
 self.filters = dict(result.json()['reportMetadata'])

 def apply_report_filter(self, column, operator, value, replace=True):
 """
 adds/replaces filter, example:
 apply_report_filter('Opportunity.InsertionId__c', 'startsWith', 'hbob').
 For date filters use apply_standard_date_filter.

 column: needs to correspond to a column in your report, AND the report
 needs to have this filter configured (so in the UI the filter
 can be applied)
 operator: equals, notEqual, lessThan, greaterThan, lessOrEqual,
 greaterOrEqual, contains, notContain, startsWith, includes
 see https://sforce.co/2Tb5SrS for up to date list
 value: value as a string
 replace: if set to True, then if there's already a restriction on column
 this restriction will be replaced, otherwise it's added additionally
 """
 filters = self.filters['reportFilters']
 if replace:
 filters = [f for f in filters if not f['column'] == column]
 filters.append(dict(
 column=column, 
 isRunPageEditable=True, 
 operator=operator, 
 value=value))
 self.filters['reportFilters'] = filters

 def apply_standard_date_filter(self, column, startDate, endDate):
 """
 replace date filter. The date filter needs to be available as a filter in the
 UI already

 Example: apply_standard_date_filter('Invoice__c.InvoiceDate__c', d_from, d_to)

 column: needs to correspond to a column in your report
 startDate, endDate: instance of datetime.date
 """
 self.filters['standardDateFilter'] = dict(
 column=column,
 durationValue='CUSTOM',
 startDate=startDate.strftime('%Y-%m-%d'),
 endDate=endDate.strftime('%Y-%m-%d')
 )

 def query_report(self):
 """
 return generator which yields one report row as dict at a time
 """
 url = self.base_url + f"analytics/reports/query"
 result = self._call_salesforce('POST', url, data=json.dumps(dict(reportMetadata=self.filters)))
 r = result.json()
 columns = r['reportMetadata']['detailColumns']
 if not r['allData']:
 raise Exception('got more than 2000 rows! Quitting as data would be incomplete')
 for row in r['factMap']['T!T']['rows']:
 values = []
 for c in row['dataCells']:
 t = type(c['value'])
 if t == str or t == type(None) or t == int:
 values.append(c['value'])
 elif t == dict and 'amount' in c['value']:
 values.append(c['value']['amount'])
 else:
 print(f"don't know how to handle {c}")
 values.append(c['value'])
 yield dict(zip(columns, values))

 def iterate_over_dates_and_filters(self, startDate, date_column, filter_column, filter_tuples):
 """
 return generator which iterates over every week and applies the filters 
 each for column
 """
 date_runner = startDate
 while True:
 print(date_runner)
 self.apply_standard_date_filter(date_column, date_runner, date_runner + datetime.timedelta(days=6))
 for val, op in filter_tuples:
 print(val)
 self.apply_report_filter(filter_column, op, val)
 for row in self.query_report():
 yield row
 date_runner += datetime.timedelta(days=7)
 if date_runner &gt; datetime.date.today():
 break
</code></pre>

blocks|key|432972|text|对于任何想要将报告下载到DataFrame中的人来说，这就是如何做到这一点的(我添加了一些注释和链接以便于澄清)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|432973|import+pandas+as+pd
import+csv
import+requests
from+io+import+StringIO
from+simple_salesforce+import+Salesforce

#+Input+Salesforce+credentials:
sf+=+Salesforce(
++++username='johndoe@mail.com',+
++++password='<password>',+
++++security_token='<security_token>')+#+See+below+for+help+with+finding+token+

#+Basic+report+URL+structure:
orgParams+=+'https://<INSERT_YOUR_COMPANY_NAME_HERE>.my.salesforce.com/'+#+you+can+see+this+in+your+Salesforce+URL
exportParams+=+'?isdtp=p1&export=1&enc=UTF-8&xf=csv'

#+Downloading+the+report:
reportId+=+'reportId'+#+You+find+this+in+the+URL+of+the+report+in+question+between+"Report/"+and+"/view"
reportUrl+=+orgParams+%2B+reportId+%2B+exportParams
reportReq+=+requests.get(reportUrl,+headers=sf.headers,+cookies={'sid':+sf.session_id})
reportData+=+reportReq.content.decode('utf-8')
reportDf+=+pd.read_csv(StringIO(reportData))|code-block|syntax|javascript|432974|您可以按照this+page底部的说明获取令牌|offset|length|432975|entityMap|0|LINK|mutability|MUTABLE|url|https://help.salesforce.com/articleView?id=user_security_token.htm&type=5^0|0|0|5|9|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@$I|V|J|W|1|X]]|A|$]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|L|$M|$5|N|O|P|A|$Q|R]]]]

For anyone just trying to download a report into a DataFrame this is how you do it (I added some notes and links for clarifications):
<pre><code>import pandas as pd
import csv
import requests
from io import StringIO
from simple_salesforce import Salesforce

# Input Salesforce credentials:
sf = Salesforce(
 username='johndoe@mail.com', 
 password='&lt;password&gt;', 
 security_token='&lt;security_token&gt;') # See below for help with finding token 

# Basic report URL structure:
orgParams = 'https://&lt;INSERT_YOUR_COMPANY_NAME_HERE&gt;.my.salesforce.com/' # you can see this in your Salesforce URL
exportParams = '?isdtp=p1&amp;export=1&amp;enc=UTF-8&amp;xf=csv'

# Downloading the report:
reportId = 'reportId' # You find this in the URL of the report in question between &quot;Report/&quot; and &quot;/view&quot;
reportUrl = orgParams + reportId + exportParams
reportReq = requests.get(reportUrl, headers=sf.headers, cookies={'sid': sf.session_id})
reportData = reportReq.content.decode('utf-8')
reportDf = pd.read_csv(StringIO(reportData))
</code></pre>
You can get your token by following the instructions at the bottom of <a href="https://help.salesforce.com/articleView?id=user_security_token.htm&amp;type=5" rel="nofollow noreferrer">this page</a>

I am new to sfdc . I have report already created by user . I would like to use python to dump the data of the report into csv/excel file.
I see there are couple of python packages for that. But my code gives an error

<pre><code>from simple_salesforce import Salesforce
sf = Salesforce(instance_url='https://cs1.salesforce.com', session_id='')
sf = Salesforce(password='xxxxxx', username='xxxxx', organizationId='xxxxx')
</code></pre>

Can i have the basic steps for setting up the API and some example code

Importing salesforce report data using python

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我是sfdc的新手。我已经由用户创建了报告。我想用python把报告的数据转储到csv/excel文件中。我看到有几个python包可以做到这一点。但是我的代码给出了一个错误from simple_salesforce import Salesforcesf = Salesforce(instance_url='htt...

问使用python导入salesforce报表数据
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问使用python导入salesforce报表数据EN