pathpilot 0.5.0

Simplifies file and folder manipulation

pathpilot

pathpilot is a Python package that makes file and folder manipulation simple and intuitive.

Installation

pip install pathpilot

Main Features

• file_factory → Function that routes new file instances to the appropriate child class. Some file types are supported natively such as: .xlsx, .csv, .txt, .parquet, etc. The mapping of file extensions to their child class counterparts is managed using the config.extension_mapping dictionary. Unmapped extensions are routed to the File base class by default.
• Folder → Class that handles folder operations. It is important to be mindful of the read_only parameter which, if True, allows folders to be created or deleted.

Example Usage

Please note the examples below represent a small fraction of the functionality offered by pathpilot. Please refer to the intra-code documentation more information.

Imports

from pathpilot import Folder, file_factory

Folders

Create a Folder instance. Passing read_only=False will create the folder if it does not already exist.

# initiate a folder instance
folder = Folder(r'C:\Users\MyID\Documents\MyFolder', read_only=False)

The join method is used to access subfolders. If read_only=False, the subfolders are created automatically.

# create subfolders (i.e. C:\Users\MyID\Documents\MyFolder\Year\2025\Month\)
month_folder = folder.join('Year', '2025', 'Month')

Alternatively, you can access subfolders by attribute.

# create a new subfolder called "January" by accessing it via attribute
january_folder = month_folder.january

Joining to a file name will return a file object instead.

new_years_file = january_folder.join('Happy New Year.txt')

Files

Create an instance of the ExcelFile class using the file_factory function. This occurs automatically by virtue of the .xlsx file extension.

# create ExcelFile instance
file = file_factory(r'C:\Users\MyID\Documents\MyFolder\MyFile.xlsx')

Next, let's check if the file exists. If not, let's save a pandas DataFrame as an Excel file.

# export a pd.DataFrame to the file, if it does not already exist
if not file.exists:
  df = pd.DataFrame({'id': [1, 2, 3], 'data': ['a', 'b', 'c']})
  file.save(df)

MyFile.xlsx:
    • Wrote 72.00 B to sheet 'Sheet1' in 0.0 seconds.
    • Wrote 80.00 B to sheet 'Sheet1' in 0.0 seconds.

Now let's read the file we created as a DataFrame.

# read the file we created as a pd.DataFrame
df = file.read()

On second thought, let's delete the file.

# delete the file we created
file.delete()