veevatools

Introduction

This python package is a set of Salesforce.com, Veeva Network, Veeva Vault, and Veeva Nitro libraries, scripts, and functions used to help expedite the development of Veeva Tools.

Installation / Requirements

Ensure you have at least Python version 3.10 installed. To Check your installation version, type the following commands in the terminal (MacOs) / command prompt (Windows):

python --version

To install python, go to https://www.python.org/ then navigate to the download page of your Operating System.

You will need to have Packager Installer for Python (pip) installed. To install pip, run the following command in the terminal (MacOs) / command prompt (Windows):

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python3 get-pip.py

To install the Veeva Tools library:

pip install veevatools

To upgrade to the latest version of Veeva Tools library:

pip install veevatools --upgrade

Overview

The Veeva Tools package currently contains 3 major components:

Salesforce library

Authentication:

from salesforce import Sf
from pandas import pd
sf = Sf()
sf.authenticate(
    sfUsername='[e-mail]',
    sfPassword='password123',
    sfOrgId='00D2C0000008jIK',
    isSandbox= false
    )

Sidenote on Pandas DataFrames:
Pandas DataFrame (pd) is used to prepare the data for import (i.e. create, update methods) and additional export methods such as pd.to_excel() in order to save the output into an Excel file.
Additionally, Complex data manipuation (joins, merges, groupbys, filters)and data analytics (describe, statistical analysis) can all be performed using Pandas.
To learn more about Pandas DataFrames, go to the Pandas documentation <br > Or just Google tutorials on Pandas DataFrames. This YouTube playlist by Corey Schafer provides an excellent starting point into the world of Pandas!

Data methods:

The salesforce class (Sf) contains methods that can help you interact with data and metadata components:

Query

account_recordtypes = sf.query("SELECT Id, Name, SobjectType from RecordType WHERE SobjectType = 'Account'")]

account_recordtypes

Return -> pd.DataFrame():

	Id	Name	SobjectType
0	012f4000001ArT3AAK	Professional_vod	Account
1	012f4000001ArT4AAK	Institution_vod	Account
2	012f4000001ArT5AAK	MCO_vod	Account
3	012f4000001ArT6AAK	Organization_vod	Account
4	012f4000001ArWzAAK	Hospital_vod	Account

Sidenote:
You can use any Pandas (pd) methods on the return value of the query output. For example
account_recordtypes.to_excel("Account RecordTypes.xlsx")
Will save the results of the DataFrame into an Excel file.

Create

## Takes a DataFrame of CRM records and creates records in CRM

account_records = pd.DataFrame([{'FirstName': 'Test', 'LastName': 'Account'}, {'FirstName': 'Test2', 'LastName': 'Account2'}])

result = sf.create('Account', account_records)

result

Return -> pd.DataFrame():

	success	created	Id
0	True	True	0010r00000tF7L1AAK
1	True	True	0010r00000tF7L2AAK

Update

### Takes a dataframe that contains at least the Id column
### and any other column to be updated, for example, FirstName
update_account_name = pd.DataFrame(
    [{'FirstName': 'Updated', 'Id': '0010r00000tF7L1AAK'},
     {'FirstName': 'Name', 'Id': '0010r00000tF7L2AAK'}]
    )

result = sf.update('Account', update_account_name)

result

Return -> pd.DataFrame()

	success	created	Id
0	True	False	0010r00000tF7L1AAK
1	True	False	0010r00000tF7L2AAK

Upsert

### Takes a dataframe that contains an external ID column
### and any other column to be updated, for example, Name
### if the external ID matches an existing record,
### the account is updated, otherwise, a new record is created

upsert_account = pd.DataFrame(
    [{'NET_External_Id__c': '242977178138969088', 'Name': 'Updated Hospital Name'},
     {'NET_External_Id__c': '555579769212255555', 'Name': 'Create New Hospital'}]
    )

result = sf.upsert(object_api='Account', external_id_field_api='NET_External_Id__c', record_dataframe=upsert_account)

result

Return -> pd.DataFrame()

	success	created	id
0	True	False	001f400000PKOrwAAH
1	True	True	0010r00000tF7stAAC

Delete

### Takes a dataframe that contains the Id column
### deletes records listed based on their SFID.

delete_account = pd.DataFrame([{'Id': '0010r00000tF7stAAC'}, {'Id': '001f400000PKOrwAAH'}])

result = sf.delete(object_api='Account', record_dataframe=delete_account)

result

Return -> pd.DataFrame()

	success	created	Id
0	True	False	0010r00000tF7stAAC
1	True	False	001f400000PKOrwAAH

Read Metadata

###

Create Metadata

###

Update Metadata

###

Rename Metadata

###

Delete Metadata

###

List MetaData

###