import os
import datetime
import pandas as pd
from salesforce_bulk import SalesforceBulk

# this is the format needed for batch updating
data = {
    "id": ["xxxxxxxxxx", "xxxxxxxx"],
    # the __c is needed for custom fields
    "field1__c": [1.26, 1.26],
    "field2__c": ["Test", "Test"],
    "field3__c": [
        datetime.datetime.utcnow().strftime(
            format="%Y-%m-%dT%H:%M:%S.%fZ"),
        datetime.datetime.utcnow().strftime(
            format="%Y-%m-%dT%H:%M:%S.%fZ")],
}

# create the bulk instance
bulk = SalesforceBulk(
  # set these env vars
  username=os.environ["SALESFORCE_ID"],
  password=os.environ["SALESFORCE_PASSWORD"],
  security_token=os.environ["SALESFORCE_TOKEN"],
  # if you want to hit your sfdc test env
  sandbox=True
)


# turn the data into a dataframe
df = pd.DataFrame(data)

# create an update job
job = bulk.create_update_job("Lead", contentType="CSV", concurrency="Parallel")

# post the batch
batch = bulk.post_batch(job, df.to_csv(header=True, index=False))

# this will pause while the batch resolves (success or failure)
bulk.wait_for_batch(job, batch)

# if you want to check the status of the batch
bulk.batch_status(batch_id=batch, job_id=job)

# optional: the response produced by processing the batch, handy for debugging
# if the batch fails
bulk.get_batch_results(batch_id=batch, job_id=job)

# close the batch job
bulk.close_job(job)