Introduction It is difficult to write a python script that does not have some interaction with the file system. The activity could be as simple as reading a data file into a pandas DataFrame or as complex as parsing thousands of files in a deeply nested directory structure. Python’s standard library has several helpful functions for these tasks - including the pathlib module. The pathlib module was first included in python 3.4 and has been enhanced in each of the subsequent releases. Pathlib is an object oriented interface to the filesystem and provides a more intuitive method to interact with the filesystem in a platform agnostic and pythonic manner. I recently had a small project where I decided to use pathlib combined with pandas to sort and manage thousands of files in a nested directory structure. Once it all clicked, I really appreciated the capabilities that pathlib provided and will definitely use it in projects going forward. That project is the inspiration for this post. In order to help others, I have created a pathlib cheat sheet that I hope will make it easier to use this great library. Later in this post, I include an example of building out a pandas DataFrame based on a directory structure. This is a useful tool for reviewing and analyzing a large number of files - especially on a Windows system where the breadth of shell tools is not readily available.

Getting Started with Pathlib The pathlib library is included in all versions of python >= 3.4. I recommend using the latest version of python in order to get access to all the latest updates. For this article, I will use python 3.6. One of the useful features of the pathlib module is that it is more intuitive to build up paths without using os.joindir . For example, when I start small projects, I create in and out directories as subdirectories under the current working directory (using os.getcwd() ). I use those directories to store the working input and output files. Here’s what that code would look like: import os in_dir = os . path . join ( os . getcwd (), "in" ) out_dir = os . path . join ( os . getcwd (), "out" ) in_file = os . path . join ( in_dir , "input.xlsx" ) out_file = os . path . join ( out_dir , "output.xlsx" ) This works but it is a little clunky. For instance, if I wanted to define just the input and output files without defining the directories, it looks like this: import os in_file = os . path . join ( os . path . join ( os . getcwd (), "in" ), "input.xlsx" ) out_file = os . path . join ( os . path . join ( os . getcwd (), "out" ), "output.xlsx" ) Hmmm. That’s not complex but it is certainly not pretty. Let’s see what it looks like if we use the pathlib module. from pathlib import Path in_file_1 = Path . cwd () / "in" / "input.xlsx" out_file_1 = Path . cwd () / "out" / "output.xlsx" Interesting. In my opinion this is much easier to mentally parse. It’s a similar thought process to the os.path method of joining the current working directory (using Path.cwd() ) with the various subdirectories and file locations. It is much easier to follow because of the clever overriding of the / to build up a path in a more natural manner than chaining many os.path.joins together. Additionally, if you don’t like the syntax above, you can chain multiple parts together using joinpath : in_file_2 = Path . cwd () . joinpath ( "in" ) . joinpath ( "input.xlsx" ) out_file_2 = Path . cwd () . joinpath ( "out" ) . joinpath ( "output.xlsx" ) This is a little clunkier in my opinion but still much better than the os.path.join madness above. Finally, there is one other trick you can use to build up a path with multiple directories: parts = [ "in" , "input.xlsx" ] in_file_3 = Path . cwd () . joinpath ( * parts ) Regardless of the method you use, these approaches work for building a path to a file or a directory. The added benefit of these methods is that you are creating a Path object vs. just a string representation of the path. Look at the difference between printing the in_file compared to in_file_1 print ( in_file ) print ( type ( in_file )) /home/chris/src/pbpython/code/notebooks/in/input.xlsx <class 'str'> The output of the os.path.join is a normal string. Compare this to the various pathlib approaches: print ( in_file_1 ) print ( type ( in_file_1 )) /home/chris/src/pbpython/code/notebooks/in/input.xlsx <class 'pathlib.PosixPath'> The actual string representation is the same but the variable type is a pathlib.PosixPath The fact that the path is an object means we can do a lot of useful actions on the object. It’s also interesting that the path object “knows” it is on a Linux system (aka Posix) and internally represents it that way without the programmer having to tell it. The benefit is that the code will run the same on a Windows machine and that the underlying library will take care of (m)any Windows eccentricities.

Working with Path objects Now that you know the basics of creating a Path object, let’s see what we can do with the object. For this article, I will use a simple nested structure that has a mix of CSV and Excel files and is stored on an external USB drive. Here is what it looks like on a Linux system: To get the examples started, create the Path to the data_analysis directory: from pathlib import Path dir_to_scan = "/media/chris/KINGSTON/data_analysis" p = Path ( dir_to_scan ) This example shows how to use a full string to create a path object. In this case, I am passing the full path to the USB drive. Let’s see what we can do with the p object. p . is_dir () True p . is_file () False p . parts ('/', 'media', 'chris', 'KINGSTON', 'data_analysis') p . absolute () PosixPath('/media/chris/KINGSTON/data_analysis') p . anchor '/' p . as_uri () 'file:///media/chris/KINGSTON/data_analysis' p . parent PosixPath('/media/chris/KINGSTON') I think you’ll agree that it is pretty straightforward to use and interpret the results from this object. There are many other functions available through this API. Outside of interrogating the path in various manners, a very common need is to parse all the files and directories within a given directory. The python standard library has several methods to walk through all the files and subdirectories in a path. I will describe those next.

Walking Directories The first approach I will cover is to use the os.scandir function to parse all the files and directories in a given path and build a list of all the directories and all the files. folders = [] files = [] for entry in os . scandir ( p ): if entry . is_dir (): folders . append ( entry ) elif entry . is_file (): files . append ( entry ) print ( "Folders - {} " . format ( folders )) print ( "Files - {} " . format ( files )) Folders - [<DirEntry 'Scorecard_Raw_Data'>] Files - [<DirEntry 'HS_ARCHIVE9302017.xls'>] The key items to remember with this approach is that it does not automatically walk through any subdirectories and the returned items are DirEntry objects. This means that you manually need to convert them to Path objects if you need that functionality. If you need to parse through all the subdirectories, then you should use os.walk Here is an example that shows all the directories and files within the data_analysis folder. for dirName , subdirList , fileList in os . walk ( p ): print ( 'Found directory: %s ' % dirName ) for fname in fileList : print ( ' \t %s ' % fname ) Found directory: /media/chris/KINGSTON/data_analysis HS_ARCHIVE9302017.xls Found directory: /media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data MERGED1996_97_PP.csv MERGED1997_98_PP.csv MERGED1998_99_PP.csv <...> MERGED2013_14_PP.csv MERGED2014_15_PP.csv MERGED2015_16_PP.csv Found directory: /media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/Crosswalks_20170806 CW2000.xlsx CW2001.xlsx CW2002.xlsx <...> CW2014.xlsx CW2015.xlsx Found directory: /media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/Crosswalks_20170806/tmp_dir CW2002_v3.xlsx CW2003_v1.xlsx CW2000_v1.xlsx CW2001_v2.xlsx This approach does indeed walk through all the subdirectories and files but once again returns a str instead of a Path object. These two approaches allow a lot of manual control around how to access the individual directories and files. If you need a simpler approach, the path object includes some additional options for listing files and directories that are compact and useful. The first approach is to use glob to list all the files in a directory: for i in p . glob ( '*.*' ): print ( i . name ) HS_ARCHIVE9302017.xls As you can see, this only prints out the file in the top level directory. If you want to recursively walk through all directories, use the following glob syntax: for i in p . glob ( '**/*.*' ): print ( i . name ) HS_ARCHIVE9302017.xls MERGED1996_97_PP.csv <...> MERGED2014_15_PP.csv MERGED2015_16_PP.csv CW2000.xlsx CW2001.xlsx <...> CW2015.xlsx CW2002_v3.xlsx <...> CW2001_v2.xlsx There is another option to use the rglob to automatically recurse through the subdirectories. Here is a shortcut to build a list of all of the csv files: list ( p . rglob ( '*.csv' )) [PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED1996_97_PP.csv'), PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED1997_98_PP.csv'), PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED1998_99_PP.csv'), <...> PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED2014_15_PP.csv'), PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED2015_16_PP.csv')] This syntax can also be used to exclude portions of a file. In this case, we can get everything except xlsx extensions: list ( p . rglob ( '*.[!xlsx]*' )) [PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED1996_97_PP.csv'), PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED1997_98_PP.csv'), PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED1998_99_PP.csv'), <...> PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED2014_15_PP.csv'), PosixPath('/media/chris/KINGSTON/data_analysis/Scorecard_Raw_Data/MERGED2015_16_PP.csv')] There is one quick note I wanted to pass on related to using glob. The syntax may look like a regular expression but it is actually a much more limited subset. A couple of useful resources are here and here.

Combining Pathlib and Pandas On the surface, it might not seem very beneficial to bring file and directory information into a pandas DataFrame. However, I have found it surprisingly useful to be able to take a complex directory structure and dump the contents into a pandas DataFrame. From the DataFrame, it is easy to format the results as Excel. Which in turn makes it very easy for non-technical users to identify missing files or do other analysis that might be difficult to automate. The other positive benefit is that you can use all the pandas string, numeric and datetime functions to more thoroughly analyze the file and directory structure data. I have done some looking and have not found a simpler way to get thousands of files into a formatted Excel file. For this example, I will go through all the files in the data_analysis directory and build a DataFrame with the file name, parent path and modified time. This approach is easily extensible to any other information you might want to include. Here’s the standalone example: import pandas as pd from pathlib import Path import time p = Path ( "/media/chris/KINGSTON/data_analysis" ) all_files = [] for i in p . rglob ( '*.*' ): all_files . append (( i . name , i . parent , time . ctime ( i . stat () . st_ctime ))) columns = [ "File_Name" , "Parent" , "Created" ] df = pd . DataFrame . from_records ( all_files , columns = columns ) df . head () File_Name Parent Created 0 HS_ARCHIVE9302017.xls /media/chris/ KINGSTON /data_analysis Sat Nov 11 13:14:57 2017 1 MERGED1996_97_PP.csv /media/chris/ KINGSTON /data_analysis/Scorecard_… Sat Nov 11 13:14:57 2017 2 MERGED1997_98_PP.csv /media/chris/ KINGSTON /data_analysis/Scorecard_… Sat Nov 11 13:14:57 2017 3 MERGED1998_99_PP.csv /media/chris/ KINGSTON /data_analysis/Scorecard_… Sat Nov 11 13:14:57 2017 4 MERGED1999_00_PP.csv /media/chris/ KINGSTON /data_analysis/Scorecard_… Sat Nov 11 13:14:57 2017 This code is relatively simple but is very powerful when you’re trying to get your bearings with a lot of data files. If the from_records creation does not make sense, please refer to my previous article on the topic. Once the data is in a DataFrame, dumping it to Excel is as simple as doing df.to_excel("new_file.xlsx")

Additional Functionality The pathlib module is very rich and provides a lot of other useful functionality. I recommend looking at the documentation on the python site as well as this excellent article on the Python 3 Module of the Week. Finally, since I find a visual reference useful, here is a pathlib cheatsheet I created based on this article.