How to fix Python ModuleNotFoundError in multi-project workspaces: stable imports across sibling packages using __file__ and sys.path

Learn how to fix Python ModuleNotFoundError in multi-project workspaces by anchoring imports with __file__ and sys.path. Works with PySpark and IDE runners.

Fixing Python imports across sibling packages in a multi-project workspace can be unexpectedly tricky. A typical symptom is ModuleNotFoundError even though the file clearly exists in a neighboring folder. The root cause usually isn’t the code itself, but where the program is launched from — the current working directory. Below is a practical walkthrough to make such imports reliable in a setup where one script lives in testing_framework/main_scripts and needs to import another module from testing_framework/user_functions.

Minimal failing scenario

Consider a script in testing_framework/main_scripts/main_script.py that attempts to reach testing_framework/user_functions/config_reader.py. A direct import looks natural, but it may fail depending on how your IDE or runner sets the working directory:

from user_functions import config_reader

If the application is executed with a working directory that doesn’t include testing_framework on sys.path, Python won’t find user_functions and will raise ModuleNotFoundError.

Why it breaks

Python resolves imports by scanning entries in sys.path. Many IDEs, including VSCode, can launch the script with a different current working directory than the script’s directory. Relying on a relative location like .. becomes ambiguous because it depends on where the process starts, not where the file lives. Checking os.getcwd() quickly reveals if the process is started from an unexpected location. The fix is to derive an absolute path from the running file (using __file__), move to the intended parent folder, and add it to sys.path before importing. If needed, you can explicitly append the full path to testing_framework to sys.path before any import that references user_functions.

Working approach

The idea is to anchor paths at the script’s location and make the parent folder discoverable by the import system. Below is a robust example that ensures the import works from testing_framework/main_scripts/main_script.py when targeting testing_framework/user_functions/config_reader.py.

The example uses pyspark exactly as in the working version of the scenario and reads a CSV configuration by delegating to config_reader.

Fixed main script

import os, sys
from pyspark.sql import SparkSession
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
project_root = os.path.abspath(os.path.join(SCRIPT_DIR, ".."))
sys.path.append(project_root)
from user_functions import config_reader as cfg_reader
spark_ctx = SparkSession.builder.appName("validation").master("local").getOrCreate()
# config_dir and config_name are expected to point to your config location and file
settings_data = cfg_reader.fetch_config(spark_ctx, config_dir, config_name)
print(settings_data)

Module that provides the config loading

import os
from pyspark.sql import SparkSession
def fetch_config(session: SparkSession, cfg_dir, cfg_file):
    cfg_path = os.path.join(cfg_dir, cfg_file)
    if cfg_file.endswith('.csv'):
        cfg_df = session.read.format('csv') \
            .option('header', True) \
            .load(cfg_path)
    return cfg_df.collect()

What changed and why it works

The import is stabilized by computing an absolute path to the script’s directory, moving up to the parent folder that contains both main_scripts and user_functions, and appending that parent to sys.path before the import happens. This avoids any dependency on how the program was launched and where os.getcwd() happens to point. If necessary, the same effect can be achieved by appending the full path to testing_framework to sys.path prior to importing from user_functions.

Why you should care

Data pipelines and Spark jobs are commonly executed from different drivers, notebooks, or IDE tasks where working directories vary. If imports hinge on the current working directory, the same code may behave differently across environments. By anchoring imports to the script’s actual location, you eliminate a class of non-deterministic failures and make your code portable across tools and runners.

Takeaways

Always ensure that the path containing your target package is present on sys.path before the import. Derive this path from the script’s location using os.path.abspath(__file__) and walk to the intended parent directory. When debugging, print os.getcwd() to confirm the current working directory used by your runner. If needed, add the full path to testing_framework to sys.path before importing modules from user_functions. With these guardrails, Python will discover your packages consistently, and your Spark configuration loader will work as expected.