Python Pandas / Sales force SOQL:如何使用 for 循环将列表传递给 SOQL 查询?

Python Pandas / Sales force SOQL: how to pass list to SOQL query using for loop?

我想做一个刺激列表的代码,并在 SOQL 查询中使用它来获取输出并附加到 excel sheet.

这是我的代码,有没有更简洁的方法来做到这一点。

 ExlReport=pd.read_excel(ExlReportPath,sheet_name=ExlSheetName)
 CaseNumberList = [] IdList = [] IdList = ExlReport['Id']

 for Id in IdList:
             results = sf.query_all ("SELECT LastModifiedDate,Case,Id FROM Case_Note  WHERE Case = '%s' ORDER BY LastModifiedDate ASC" %
 soqlEscape(Id)) sf_df =
 pd.DataFrame(results['records']).drop(columns='attributes')

预期输出:

  LastModifiedDate             Case           Id 
0  2020-02-19T23:31:35.000+0000  xxxxxxxxxxxx  yyyyyyyyyyyy  
1  2020-02-19T23:31:43.000+0000  xxxxxxxxxxxx  yyyyyyyyyyyy 
2  2020-03-11T20:48:54.000+0000  xxxxxxxxxxxx  yyyyyyyyyyyy  

我得到的输出:

0  2020-02-19T23:31:35.000+0000  xxxxxxxxxxxx  yyyyyyyyyyyy  
1  2020-02-19T23:31:43.000+0000  xxxxxxxxxxxx  yyyyyyyyyyyy 
2  2020-03-11T20:48:54.000+0000  xxxxxxxxxxxx  yyyyyyyyyyyy  

 new_axis = axis.drop(labels, errors=errors)
  File "C:\xxxxxxx", line 5018, in drop
    raise KeyError(f"{labels[mask]} not found in axis")
KeyError: "['attributes'] not found in axis"
for Id in IdList:
         results = sf.query_all ("SELECT LastModifiedDate,Case,"+Id+" FROM Case_Note  WHERE Case = '%s' ORDER BY LastModifiedDate ASC" % soqlEscape(Id))

之后我将从每个 ID 向 df 追加数据:

df.append(results)

我使用 dropna 解决了这个问题,以便能够将数据帧整洁地传递给 excel sheet。

sf_df=(sf_df).dropna(axis=0,how='any')
Appended_df=pd.DataFrame((sf_df).drop(columns='attributes'))

检查此 link for dropna

完整代码:

    import pandas as pd
    ExlReport=pd.read_excel(ExlReportPath,sheet_name=ExlSheetName)
    CaseNumberList = []
    IdList = [] 
    IdList = ExlReport['Id']
    Appended_df = pd.DataFrame()
    sf_df=pd.DataFrame()


    for Id in IdList:
           results = sf.query_all ("SELECT LastModifiedDate,Case,Id FROM Case_Note  WHERE Case='%s' ORDER BY LastModifiedDate ASC" % soqlEscape(Id)) 
           sf_df =pd.DataFrame(results['records']).drop(columns='attributes')
           df = pd.DataFrame(results['records'])
           sf_df = sf_df.append(df)

    sf_df=(sf_df).dropna(axis=0,how='any')
    Appended_df=pd.DataFrame((sf_df).drop(columns='attributes'))