@kmodelew The issue is that your volume path is missing the leading forward slash. Also, the library isn't loading properly.

Quick Fix

# Correct path format for Volumes
location = '/Volumes/catalog/schema/volume_name/file.xlsx' ## Note the leading

# For DBR 16.4, use pandas instead (built-in, no library needed)
import pandas as pd

df_pandas = pd.read_excel(f"/Volumes/{catalog}/{schema}/{volume}/file.xlsx")
df = spark.createDataFrame(df_pandas)

If You Must Use spark-excel

1. Install the library correctly: # In notebook cell
   %pip install spark-excel
   dbutils.library.restartPython()
   2. Then read with the proper path:
   location = '/Volumes/catalog/schema/volume/file.xlsx'
   df = (spark.read.format("com.crealytics.spark.excel")
   .option("header", "true")
   .option("inferSchema", "true")
   .option("dataAddress", "'Sheet1'!A1") # Specify sheet if needed
   .load(location))
   3. Alternative: Native Approach (Most Reliable)
   

   # Using openpyxl directly for better control
   import openpyxl
   from pyspark.sql import Row

   file_path = "/Volumes/catalog/schema/volume/file.xlsx"
   workbook = openpyxl.load_workbook(file_path, read_only=True)
   sheet = workbook.active

   # Convert to Spark DataFrame
   rows = []
   headers = [cell.value for cell in sheet[1]]
   for row in sheet.iter_rows(min_row=2, values_only=True):
   rows.append(Row(**dict(zip(headers, row))))

   df = spark.createDataFrame(rows)

   Security Note: Volumes are already secure with ACL controls. Your approach is correct for confidential data.

   Which catalog/schema/volume are you using? The exact path structure matters.