A pretty simple solution would be to run a couple of sub process calls to export the files into CSV format:
import subprocess
# Global variables for directory being mapped
location = '.' # Enter the path here.
pattern = '*.py' # Use this if you want to only return certain filetypes
rootDir = location.rpartition('/')[-1]
outputFile = rootDir + '_directory_contents.csv'
# Find the requested data and export to CSV, specifying a pattern if needed.
find_cmd = 'find ' + location + ' -name ' + pattern + ' -fprintf ' + outputFile + ' "%Y%M,%n,%u,%g,%s,%A+,%P\n"'
subprocess.call(find_cmd, shell=True)
That command produces comma-separated values that can be easily analyzed in Excel.
f-rwxrwxrwx,1,cathy,cathy,2642,2021-06-01+00:22:00.2970880000,content-audit.py
The resulting CSV file doesn't have a header row, but you can use a second command to add them.
# Add headers to the CSV
headers_cmd = 'sed -i.bak 1i"Permissions,Links,Owner,Group,Size,ModifiedTime,FilePath" ' + outputFile
subprocess.call(headers_cmd, shell=True)
Depending on how much data you get back, you can massage it further using Pandas. Here are some things I found useful, especially if you're dealing with many levels of directories to look through.
Add these to your imports:
import numpy as np
import pandas as pd
Then add this to your code:
# Create DataFrame from the CSV file created above.
df = pd.read_csv(outputFile)
# Format columns
# Get the filename and file extension from the filepath
df['FileName'] = df['FilePath'].str.rsplit("/", 1).str[-1]
df['FileExt'] = df['FileName'].str.rsplit('.', 1).str[1]
# Get the full path to the files. If the path doesn't include a "/" it's the root directory
df['FullPath'] = df["FilePath"].str.rsplit("/", 1).str[0]
df['FullPath'] = np.where(df['FullPath'].str.contains("/"), df['FullPath'], rootDir)
# Split the path into columns for the parent directory and its children
df['ParentDir'] = df['FullPath'].str.split("/", 1).str[0]
df['SubDirs'] = df['FullPath'].str.split("/", 1).str[1]
# Account for NaN returns, indicates the path is the root directory
df['SubDirs'] = np.where(df.SubDirs.str.contains('NaN'), '', df.SubDirs)
# Determine if the item is a directory or file.
df['Type'] = np.where(df['Permissions'].str.startswith('d'), 'Dir', 'File')
# Split the time stamp into date and time columns
df[['ModifiedDate', 'Time']] = df.ModifiedTime.str.rsplit('+', 1, expand=True)
df['Time'] = df['Time'].str.split('.').str[0]
# Show only files, output includes paths so you don't necessarily need to display the individual directories.
df = df[df['Type'].str.contains('File')]
# Set columns to show and their order.
df = df[['FileName', 'ParentDir', 'SubDirs', 'FullPath', 'DocType', 'ModifiedDate', 'Time', 'Size']]
filesize = [] # Create an empty list to store file sizes to convert them to something more readable.
# Go through the items and convert the filesize from bytes to something more readable.
for items in df['Size'].items():
filesize.append(convert_bytes(items[1]))
df['Size'] = filesize
# Send the data to an Excel workbook with sheets by parent directory
with pd.ExcelWriter("scripts_directory_contents.xlsx") as writer:
for directory, data in df.groupby('ParentDir'):
data.to_excel(writer, sheet_name = directory, index=False)
# To convert sizes to be more human readable
def convert_bytes(size):
for x in ['b', 'K', 'M', 'G', 'T']:
if size < 1024:
return "%3.1f %s" % (size, x)
size /= 1024
return size
