text: import pandas as pd
  from google.cloud import bigquery
  # 2. Run
  def readStorage():
      df_01 = pd.read_csv("gs://your_bucket/data/retail-data/by-day/2010-12-01.csv")
      transformData(df_01)
  
  def transformData(df_01):
      # Calculamos la nueva variable
      df_01["Total01"] = df_01["Quantity"]*df_01["UnitPrice"]
      # Agrupamos y generamos el nuevo dataframe
      df_01_agg = pd.DataFrame(df_01.groupby(["Description"])["Total01"].agg(sum)).reset_index()
      df_01_agg.to_csv("gs://your_bucket/data/tmp/enrichedData_jose.csv",index=False)
  
  def writeBq():
      df = pd.read_csv("gs://your_bucket/data/tmp/enrichedData_jose.csv")
  
      table_id = 'fact_project.upsales_byday_jose'
      # Since string columns use the "object" dtype, pass in a (partial) schema to ensure the correct BigQuery data type.
      client = bigquery.Client()
  
      job_config = bigquery.LoadJobConfig(schema=[
          bigquery.SchemaField("Description", "STRING"),
          bigquery.SchemaField("Total01", "FLOAT")
      ])
  
      job = client.load_table_from_dataframe(
          df, table_id, job_config=job_config
      )
  
      job.result()