Helpful Datetime Resources

A list of helpful resources when working with datetime/dates from Salesforce

Convert SFDC Datetime to Datetime or Date object

import datetime
# Formatting to SFDC datetime
formatted_datetime = datetime.datetime.strptime(x, "%Y-%m-%dT%H:%M:%S.%f%z")

# Formatting to SFDC date
formatted_date = datetime.datetime.strptime(x, "%Y-%m-%d")

Helpful Pandas Resources

A list of helpful resources when working with Pandas and simple-salesforce

Generate list for SFDC Query “IN” operations from a Pandas Dataframe

import pandas as pd

df = pd.DataFrame([{'Id':1},{'Id':2},{'Id':3}])
def dataframe_to_sfdc_list(df, column):
    df_list = df[column].unique()
    df_list = [str(x) for x in df_list]
    df_list = ','.join("'" + item + "'" for item in df_list)
    return df_list

sf.query(format_soql(
    "SELECT Id, Email FROM Contact WHERE Id IN ({})",
    dataframe_to_sfdc_list(df, column)
))

Generate Pandas Dataframe from SFDC API Query (ex.query,query_all)

import pandas as pd

sf.query("SELECT Id, Email FROM Contact")

df = pd.DataFrame(data['records']).drop(['attributes'],axis=1)

Generate Pandas Dataframe from SFDC API Query (ex.query,query_all) and append related fields from query to data frame

import pandas as pd

def sf_api_query(data):
    df = pd.DataFrame(data['records']).drop('attributes', axis=1)
    listColumns = list(df.columns)
    for col in listColumns:
        if any (isinstance (df[col].values[i], dict) for i in range(0, len(df[col].values))):
            df_no_col = df.drop(columns=[col])
            expanded_col = df[col].apply(pd.Series, dtype=df[col].dtype)
            expanded_col = expanded_col.drop('attributes', axis=1)
            expanded_col = expanded_col.add_prefix(col + '.')
            df = pd.concat([df_no_col, expanded_col], axis=1)
            new_columns = np.setdiff1d(df.columns, listColumns)
            for i in new_columns:
                listColumns.append(i)
    return df

df = sf_api_query(sf.query("SELECT Id, Email, ParentAccount.Name FROM Contact"))

Generate Pandas Dataframe from SFDC Bulk API Query (ex.bulk.Account.query)

import pandas as pd

sf.bulk.Account.query("SELECT Id, Email FROM Contact")
df = pd.DataFrame.from_dict(data,orient='columns').drop('attributes',axis=1)

YouTube Tutorial

Here is a helpful YouTube tutorial which shows how you can manage records in bulk using a jupyter notebook, simple-salesforce and pandas.

This can be a effective way to manage records, and perform simple operations like reassigning accounts, deleting test records, inserting new records, etc…