(required for some monitors with Flexible aspect ratio option.)
#DEEPVACUUM NOT GRABBING SUBDIRECTORIES WINDOWS#
Fixed : will make all windows display centered on screen.Curved : will horizontally & vertically stretch the desktop, with edges reduced to one 16th of screen height and 16th of screen width.Auto detect : Works for most monitors, will automatically configure the screen.With pd.ExcelWriter("scripts_directory_contents.xlsx") as writer:įor directory, data in df.groupby('ParentDir'):ĭata.My User Display Settings supports the following settings: # Send the data to an Excel workbook with sheets by parent directory # Go through the items and convert the filesize from bytes to something more readable. # Show only files, output includes paths so you don't necessarily need to display the individual directories.ĭf=df]įilesize= # Create an empty list to store file sizes to convert them to something more readable. # Split the time stamp into date and time columnsĭf] = df.('+', 1, expand=True)ĭf = df.str.split('.').str # Determine if the item is a directory or file.ĭf = np.where(df.str.startswith('d'), 'Dir', 'File') # Account for NaN returns, indicates the path is the root directoryĭf = np.where(df.('NaN'), '', df.SubDirs) # Split the path into columns for the parent directory and its childrenĭf = df.str.split("/",1).str If the path doesn't include a "/" it's the root directoryĭf = np.where(df.str.contains("/"), df, rootDir) # Get the filename and file extension from the filepathĭf = df.str.rsplit("/",1).strĭf = df.str.rsplit('.',1).str Then add this to your code: # Create DataFrame from the csv file created above. Here are some things I found useful, especially if you're dealing with many levels of directories to look through.Īdd these to your imports: import numpy as np Headers_cmd = 'sed -i.bak 1i"Permissions,Links,Owner,Group,Size,ModifiedTime,FilePath" ' + outputFileĭepending on how much data you get back, you can massage it further using Pandas. The resulting CSV file doesn't have a header row, but you can use a second command to add them. That command produces comma separated values that can be easily analyzed in Excel. # Find the requested data and export to CSV, specifying a pattern if needed.įind_cmd = 'find ' + location + ' -name ' + pattern + ' -fprintf ' + outputFile + ' "%Y%M,%n,%u,%g,%s,%A+,%P\n"' OutputFile = rootDir + '_directory_contents.csv' Pattern = '*.py' # Use this if you want to only return certain filetypes # Global variables for directory being mapped Pretty simple solution would be to run a couple of sub process calls to export the files into CSV format: import subprocess So as you can see for yourself, the listdir version is much more efficient.
Print("Time taken: %.2fs"%(time.time()-start)) # 0.42sįor i in range(100): files = listFiles4("src") # walk and join Print("Time taken: %.2fs"%(time.time()-start)) # 0.28sįor i in range(100): files = listFiles2("src") # listdir and join Since every example here is just using walk (with join), i'd like to show a nice example and comparison with listdir: import os, timeįolder = walk.pop(0)+"/" items = os.listdir(folder) # items = folders + filesįor i in items: i=folder+i (walk if os.path.isdir(i) else allFiles).append(i)ĭef listFiles2(root): # listdir/join (takes ~1.4x as long) (and uses '\\' instead)įolder = walk.pop(0) items = os.listdir(folder) # items = folders + filesįor i in items: i=os.path.join(folder,i) (walk if os.path.isdir(i) else allFiles).append(i)ĭef listFiles3(root): # walk (takes ~1.5x as long)įor folder, folders, files in os.walk(root):įor file in files: allFiles+= # folder+"\\"+file still ~1.5xĭef listFiles4(root): # walk/join (takes ~1.6x as long) (and uses '\\' instead)įor file in files: allFiles+=įor i in range(100): files = listFiles1("src") # warm upįor i in range(100): files = listFiles1("src") # listdir
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