Programmatically interact with workspace files

You can interact with workspace files stored in Databricks programmatically. This enables tasks such as:

  • Storing small data files alongside notebooks and code.

  • Writing log files to directories synced with Git.

  • Importing modules using relative paths.

  • Creating or modifying an environment specification file.

  • Writing output from notebooks.

  • Writing output from execution of libraries such as Tensorboard.

You can programmatically create, edit, and delete workspace files in Databricks Runtime 11.2 and above.


To disable writing to workspace files, set the cluster environment variable WSFS_ENABLE_WRITE_SUPPORT=false. For more information, see Environment variables.


In Databricks Runtime 14.0 and above, the the default current working directory (CWD) for code executed locally is the directory containing the notebook or script being run. This is a change in behavior from Databricks Runtime 13.3 LTS and below. See What is the default current working directory?.

Read the locations of files

Use shell commands to read the locations of files, for example, in a repo or in the local filesystem.

To determine the location of files, enter the following:

%sh ls
  • Files aren’t in a repo: The command returns the filesystem /databricks/driver.

  • Files are in a repo: The command returns a virtualized repo such as /Workspace/Repos/

Read data workspace files

You can programmatically read small data files such as .csv or .json files from code in your notebooks. The following example uses Pandas to query files stored in a /data directory relative to the root of the project repo:

import pandas as pd
df = pd.read_csv("./data/winequality-red.csv")

You can use Spark to read data files. You must provide Spark with the fully qualified path.

  • Workspace files in Repos use the path file:/Workspace/Repos/<user-folder>/<repo-name>/path/to/file.

  • Workspace files in your personal directory use the path: file:/Workspace/Users/<user-folder>/path/to/file.


Spark cannot directly interact with workspace files on compute configured with shared access mode.

You can copy the absolute or relative path to a file from the dropdown menu next to the file:

file drop down menu

The example below shows the use of {os.getcwd()} to get the full path.

import os"csv").load(f"file:{os.getcwd()}/my_data.csv")

To learn more about files on Databricks, see Work with files on Databricks.

Programmatically create, update, and delete files and directories

In Databricks Runtime 11.2 and above, you can directly manipulate workspace files in Databricks. The following examples use standard Python packages and functionality to create and manipulate files and directories.

# Create a new directory


# Create a new file and write to it

with open('dir1/new_file.txt', "w") as f:
    f.write("new content")

# Append to a file

with open('dir1/new_file.txt', "a") as f:
    f.write(" continued")

# Delete a file


# Delete a directory