Hacks, Leaks, and Revelations

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Chapter 8: Working with Data in Python

The basics of Python are behind you, but there’s still a lot to learn. In this chapter, you’ll expand your programming skills and start to directly investigate datasets, including BlueLeaks and chat logs leaked from a pro-Putin ransomware gang after Russia invaded Ukraine in 2022.

We’ll go over some more advanced Python topics, like how to use modules, how to traverse the filesystem, and how to create your own command line programs in Python. You’ll write programs that look through all of the files in a folder, including the hundreds of thousands of files in the BlueLeaks dataset, and learn to add arguments to your programs. You’ll also start working with a new type of variable in Python, the dictionary, which will prove handy for working with data that’s too complex to store in simple lists. As with the previous chapter, future chapters rely on your understanding of the topics covered here.


As you learned in Chapter 7, functions are reusable blocks of code that you can run as many times as you want without having to rewrite any code. Python modules are similar, but instead of making a single block of code reusable, they make an entire Python file (or multiple files) reusable. You can think of a module as a separate Python file that you can load into the file you’re currently working on.

Python includes a wealth of features, but most of them aren’t available to every Python script by default. Instead, they’re stored in built-in modules, those that come with Python. Once you import a module into your script using an import statement, you can access all of the functions, variables, and other Python objects defined in that module using the syntax module_name .``item_name.

For example, the time module includes the function time.sleep() (pronounced “time dot sleep”), which makes your program wait a given number of seconds before continuing to the next line of code. Run the following commands to import the time module and then have it tell Python to wait five seconds:

>>> import time
>>> time.sleep(5)

Your Python interpreter should wait five seconds before the prompt appears again.

Here are a few of the built-in modules I use the most:

os Includes useful functions for browsing the filesystem, like os.listdir() and os.walk(). It also includes the submodule os.path, which is full of functions to inspect files. For example, it includes os.path.isfile() and os.path.isdir(), which help determine whether a specific path is a file or a folder.

csv Lets you work with CSV spreadsheet data.

json Lets you work with JSON data.

datetime Includes useful Python features for working with dates and times. For example, it allows you to convert strings like February 24, 2022 5:07:20 UTC+3 (the exact time that Russia invaded Ukraine) into a timestamp that Python can understand and compare with other timestamps, then convert it back into strings of any format you choose.

You’ll use the os module extensively later in this chapter, the csv module in Chapter 9, and the json module in Chapter 11. You’ll briefly see how datetime works later in this chapter when you take a look at chat logs from a ransomware gang, as well as in the Chapter 14 case study, where you’ll analyze leaked neo-Nazi chat logs.

As your programs get more complex, you might find it useful to split them up into multiple files, with each file containing a different part of your code. When you do this, you’re creating your own modules. The name of the module is the same as its filename. For example, if you define some functions in a file called helpers.py, another Python file can access those functions by importing the helpers module. The helpers.py file could contain the following code:

def get_tax(price, tax_rate):
    return price * tax_rate

def get_net_price(price, tax_rate):
    return price + get_tax(price, tax_rate)

This module contains two functions for calculating sales tax, get_tax() and get_net_price(). The following Python script, price.py, imports it like so:

import helpers
total_price = helpers.get_net_price(50, 0.06)
print(f"A book that costs $50, and has 6% sales tax, costs ${total_price}")

The first line, import helpers, makes the functions defined in the helpers module accessible to this script. The second line calls the helpers.get_net _price() function from that module and stores the return value in the variable total_price. The third line displays the value of total_price.

Here’s what it looks like when I run this script:

micah@trapdoor module % python3 price.py
A book that costs $50, and has 6% sales tax, costs $53.0

Running the price.py script executes the code defined in the helpers module. Inside that module, the get_net_price() function calls get_tax() and uses its return value to calculate the net price, then returns that value back into the price.py script.

Before you write your first advanced Python script in Exercise 8-1, let’s look at the best way to start new Python scripts.

Python Script Template

I use the same basic template for all my Python scripts, putting my code into a function called main(), then calling that function at the bottom of the file. This isn’t required (you didn’t do this for any of the scripts you wrote in Chapter 7, after all), but it’s a good way to organize your code. Here’s what it looks like:

def main():

if __name__ == "__main__":

The template defines the main() function with a pass statement that tells Python, “Skip this line.” I later replace pass with the real body of the script.

Next, the if statement tells Python under which conditions it should run main(). Python automatically defines the __name__ variable, and the definition differs depending on what Python file is being run. If you’re running the currently executing Python file directly, then Python sets the value of __name__ to the __main__ string. But if you imported the currently executing Python file from another script, Python sets the value of __name__ to the name of the imported module. Using the example from the previous section, if you run the helpers.py script directly, the value of __name__ inside that script will be __main__, but if you run the price.py script, then the value of __name__ will be __main__ inside price.py and the value of __name__ will be helpers inside helpers.py.

In short, if you run your script directly, the main() function will run. But if you import your script as a module into another script or into the Python interpreter, the main() function won’t run unless you call it yourself. This way, if you have multiple Python scripts in the same folder, you can have one script import another script to call the functions defined within it without worrying about calling the latter script’s main() function.

After I create this template script, I start filling in the main() function with whatever I want the script to do. Putting the main logic of your script inside a function allows you to use the return statement to end main() early, which will quit the script early. You can’t use return when you’re not in a function.

In the following exercise, you’ll put this into practice by writing a script to start investigating BlueLeaks.

Exercise 8-1: Traverse the Files in BlueLeaks

To efficiently investigate datasets, you need to be able to write code that looks through large collections—sometimes thousands or millions—of files for you. In this exercise, you’ll learn various ways to traverse the filesystem in Python using functions in the os module, working with the BlueLeaks dataset. You’ll also rely on the foundational skills you learned in Chapter 7, like using variables, for loops, and if statements.

As you read along and run the scripts, feel free to modify the code however you’d like and try running those versions too. You might discover revelations I didn’t think to look for.

List the Filenames in a Folder

Start by using os.listdir() to list the files in the BlueLeaks-extracted folder. In your text editor, create a file called list-files1.py and enter this short script (or copy and paste it from https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/list-files1.py):

import os

def main():
    blueleaks_path = "/Volumes/datasets/BlueLeaks-extracted"
    for filename in os.listdir(blueleaks_path):

if __name__ == "__main__":

First, the script imports the os module. It then defines the variable blueleaks_path with the path of the BlueLeaks-extracted folder (update the script to include the path of this folder on your own computer). The os.listdir() function takes the path to the folder as an argument and returns a list of filenames in that folder. The code uses a for loop to loop through the output of os.listdir(blueleaks_path), displaying each filename.

NOTE Windows paths include the backslash character (\), which Python strings consider an escape character. For example, if your BlueLeaks-extracted folder is located at D:\BlueLeaks-extracted, Python will misinterpret the string "D:\BlueLeaks-extracted", assuming that \B is a special character. To escape your backslashes for any Windows path you store as a string, use \\ instead of \. In this case, set the blueleaks_path string to "D:\\BlueLeaks-extracted".

Run this script. Here’s what the output looks like on my computer:

micah@trapdoor chapter-8 % python3 list-files1.py

Next, you’ll try something slightly more advanced. Instead of just listing the filenames in BlueLeaks, you’ll check each filename to see whether it’s a folder, and if so, you’ll open each of those folders and count how many files and subfolders they contain.

Count the Files and Folders in a Folder

Create a file called list-files2.py and enter the following code (or copy and paste it from https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/list-files2.py):

import os

def main():
    blueleaks_path = "/Volumes/datasets/BlueLeaks-extracted"
   for bl_folder in os.listdir(blueleaks_path):
        bl_folder_path = os.path.join(blueleaks_path, bl_folder)

       if not os.path.isdir(bl_folder_path):

       files_count = 0
        folders_count = 0
       for filename in os.listdir(bl_folder_path):
            filename_path = os.path.join(bl_folder_path, filename)

           if os.path.isfile(filename_path):
                files_count += 1

            if os.path.isdir(filename_path):
                folders_count += 1

       print(f"{bl_folder} has {files_count} files, {folders_count} folders")

if __name__ == "__main__":

This script counts the number of files and folders it finds within each BlueLeaks folder. It starts like list-files1.py does, importing os and defining the blueleaks_path variable (remember to update the variable’s value to match the correct path on your computer).

The first for loop cycles through the filenames in your BlueLeaks-extracted folder, this time saving each filename in the bl_folder variable, so its value will be something like miacx or ncric ❶. The script then sets the value of the new bl_folder_path variable accordingly. The os.path.join() function connects filenames together to make complete paths. Its first argument is the starting path, and it adds all other arguments to the end of that path. For example, if the value of bl_folder is miacx, then this function will return the string /Volumes/datasets/BlueLeaks-extracted/miacx on my computer (the output will be different if your blueleaks_path is different or if you’re using Windows and your filenames use backslashes instead of slashes).

Since you want to look inside bl_folder_path and count the number of files and folders it contains, the script needs to check that it’s actually a folder and not a file, using the os.path.isdir() function ❷. If bl_folder_path isn’t a folder, the script runs the continue statement. This statement, which can run only inside of loops, tells Python to immediately continue on to the next iteration of the loop. In short, if the script comes across a file instead of a folder, it ignores it and moves on.

The script then prepares to count the number of files and folders within each individual BlueLeaks folder as the code loops by defining the variables files_count and folders_count with a value of 0 ❸.

A second for loop loops through the files in the BlueLeaks folder from the first for loop, saving each filename in the filename variable ❹. Inside this loop, the script defines filename_path as the absolute path for the filename under consideration. For instance, if the value of filename is a string like Directory.csv, then the value of filename_path would be a string like /Volumes/datasets/BlueLeaks-extracted/211sfbay/Directory.csv.

The script then checks to see if this absolute path is a file or a folder, using the os.path.isfile() and os.path.isdir() functions ❺. If the path is a file, the script increments the files_count variable by 1; if it’s a folder, the script increments folders_count by 1. When the second for loop finishes running, these two variables should contain the total count of files and folders for the BlueLeaks folder you’re currently looping through in the first for loop. Finally, the script displays an f-string that shows these numbers ❻.

Try running the script. The output should show how many files and folders are contained in each BlueLeaks folder, potentially with the list of folders in a different order:

micah@trapdoor chapter-8 % python3 list-files2.py
bostonbric has 506 files, 10 folders
terrorismtip has 207 files, 0 folders
ociac has 216 files, 1 folders
usao has 0 files, 84 folders
alertmidsouth has 512 files, 10 folders
chicagoheat has 499 files, 10 folders

So far, you’ve combined various functions in the os module to make a list of filenames in your BlueLeaks folder and check whether each name actually refers to a file or to another folder. Now it’s time to learn to write code that can also traverse the BlueLeaks folder’s nested folders.

Traverse Folders with os.walk()

Let’s say you want to write a program that displays all of the files in a folder and its subfolders, and its subsubfolders, and so on. When you have nested folders but don’t actually know how deep the folder structure goes, listing all of the filenames just by using os.listdir(), os.path.isfile(), and os.path .isdir() isn’t so simple. Python’s os.walk() function solves this problem.

The os.walk() function takes a path to a folder as an argument and returns a list of tuples, or multiple values contained in a single value. To define a tuple, you place all of the values, separated by commas, within parentheses. For example, (3, 4) is a tuple, as is ("cinco", "seis", "siete"). Tuples can also contain mixed types like (1, "dos") and can contain any number of values.

The os.walk() function returns a list of tuples where each tuple contains three values:

(dirname, subdirnames, filenames)

where dirname is a string, subdirnames is a list of strings, and filenames is a list of strings. For example, the following code loops through the return value of os.walk(path):

for dirname, subdirnames, filenames in os.walk(path):
    print(f"The folder {dirname} has subfolders: {subdirnames} and files: {filenames}")

When you use for loops to loop through lists, you normally assign just a single variable to each item in the list. However, since each item is a tuple, you can assign three variables to it: dirname, subdirnames, and filenames. In each loop, the values for this set of variables will be different: the value of dirname is the path to a folder, the value of subdirnames is a list of subfolders inside that folder, and the value of filenames is a list of files inside that folder.

For example, suppose you have a folder called example that contains these subfolders and files:

├── downloads
│   ├── screenshot.png
│   └── paper.pdf
└── documents
    ├── work
    │   └── finances.xlsx
    └── personal

This folder has two subfolders: downloads (containing screenshot.png and paper.pdf) and documents. The documents folder has its own subfolders: work (containing finances.xlsx) and personal.

The following commands loop through the return value of os.walk ("./example"), where ./example is the path to this example folder, to find the values of dirname, subdirnames, and filenames for each loop:

>>> for dirname, subdirnames, filenames in os.walk("./example"):
...     print(f"The folder {dirname} has subfolders: {subdirnames} and files: {filenames}")

Running this command returns the following output:

The folder ./example has subfolders: ['documents', 'downloads'] and files: []
The folder ./example/documents has subfolders: ['personal', 'work'] and files: []
The folder ./example/documents/personal has subfolders: [] and files: []
The folder ./example/documents/work has subfolders: [] and files: ['finances.xlsx']
The folder ./example/downloads has subfolders: [] and files: ['paper.pdf', 'screenshot.png']

This code loops once for each folder, including all subfolders, with the path to that folder stored in dirname. The list of subfolders in that folder is stored in subdirnames, and the list of files is stored in filenames. Once you’ve looped through the folder and all of its subfolders, the for loop ends.

Any time you need to traverse all of the files in a dataset that contains lots of nested folders, you’ll want to use os.walk(). With a single for loop, you’ll be able to write code that inspects each file in the entire dataset. The os.walk() function has many uses, including figuring out which files are the largest or smallest, as you’ll see next.

Exercise 8-2: Find the Largest Files in BlueLeaks

In this exercise, you’ll use os.walk() to write a script that looks through all the files, folders, and subfolders in BlueLeaks; measures the size of each file; and displays the filenames for files over 100MB. This code allows you to loop through all of the files in a folder, no matter how deep the folder structure.

Create a file called find-big-files.py and enter the following code (or copy and paste it from https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/find-big-files.py):

import os

def main():
    blueleaks_path = "/Volumes/datasets/BlueLeaks-extracted"
    for dirname, subdirnames, filenames in os.walk(blueleaks_path):
        for filename in filenames:
            absolute_filename = os.path.join(dirname, filename)
            size_in_bytes = os.path.getsize(absolute_filename)
            size_in_mb = int(size_in_bytes / 1024 / 1024)
            if size_in_mb >= 100:
                print(f"{absolute_filename} is {size_in_mb}MB")

if __name__ == "__main__":

Inside the main() function, the script first defines the blueleaks_path variable as the path of the BlueLeaks-extracted folder and loops through all of the files in the entire BlueLeaks dataset using the os.walk() function. Inside each loop in the first for loop are the dirname, subdirnames, and filenames variables. Each item in the list that os.walk() returns represents a different folder or subfolder in the BlueLeaks dataset, so by the time this loop finishes, the code will have traversed the entire dataset.

To find the biggest files, the next step is to look at each file with another for loop, this time looping through filenames. Inside this second for loop, the script defines absolute_filename to be the absolute path to the filename. Since dirname tells the script which folder it’s looking in, and filename tells the script which file it’s looking at, the script passes these values into os.path.join() to combine them, creating the absolute path to the filename.

A new function, os.path.getsize(), returns the size, in bytes, of the file under consideration and stores it in the variable size_in_bytes. The script then converts this value from bytes to megabytes (storing that in the variable size_in_mb) and checks if it’s greater than or equal to 100MB. If it is, the output displays its filename and file size in megabytes with the print() function.

Try running the script. It will take longer than the previous scripts in this chapter, because this time, you’re measuring the size of every single file in BlueLeaks. Here’s what the output looks like when I run it (your output may be displayed in a different order):

micah@trapdoor chapter-8 % python3 find-big-files.py
/Volumes/datasets/BlueLeaks-extracted/usao/usaoflntraining/files/VVSF00000/001.mp4 is 644MB
/Volumes/datasets/BlueLeaks-extracted/chicagoheat/html/ZA-CHICAGO HEaT_LR-20160830-034_Final
Files.pdf is 102MB
/Volumes/datasets/BlueLeaks-extracted/nmhidta/files/RFIF300000/722.pdf is 148MB
/Volumes/datasets/BlueLeaks-extracted/nmhidta/files/RFIF200000/543.pdf is 161MB
/Volumes/datasets/BlueLeaks-extracted/nmhidta/files/RFIF100000/723.pdf is 206MB
/Volumes/datasets/BlueLeaks-extracted/fbicahouston/files/VVSF00000/002.mp4 is 145MB
/Volumes/datasets/BlueLeaks-extracted/fbicahouston/files/PSAVF100000/009.mp4 is 146MB
/Volumes/datasets/BlueLeaks-extracted/fbicahouston/files/PSAVF100000/026.mp4 is 105MB

The script should display the absolute paths of the 101 files in BlueLeaks that are at least 100MB, along with each file’s size.

Third-Party Modules

In addition to built-in modules, Python also supports third-party modules that you can easily incorporate into your own code. Most Python scripts that I write, even simple ones, rely on at least one third-party module (when a Python program depends on third-party modules, they’re called dependencies). In this section, you’ll learn how to install third-party modules and use them in your own scripts.

The Python Package Index (PyPI) contains hundreds of thousands of third-party Python packages, or bundles of Python modules, and subpackages. Pip, which stands for Package Installer for Python, is a package manager similar to Ubuntu’s apt or macOS’s Homebrew used to install packages hosted on PyPI. You can search for packages on PyPI’s website (https://pypi.org), then install a package by running the python3 -m pip install package_name command.

For example, I frequently use a package called Click, which stands for Command Line Interface Creation Kit. The click Python module makes it simple to add command line arguments to your scripts. To see what happens when you try importing this module before you’ve installed it, open a Python interpreter and run import click. Assuming you don’t already have the package installed, you should see a ModuleNotFoundError error message:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'click'

Now exit the Python interpreter and install click with pip by running the following command:

micah@trapdoor ~ % python3 -m pip install click
Collecting click
  Using cached click-8.1.3-py3-none-any.whl (96 kB)
Installing collected packages: click
Successfully installed click-8.1.3

Open the Python interpreter again and try importing click once more:

>>> import click

If no error messages pop up, you’ve successfully imported the click module, and its additional features are now available for you to use.

The command to uninstall a package is python3 -m pip uninstall package_name. Try uninstalling click:

micah@trapdoor ~ % python3 -m pip uninstall click
Found existing installation: click 8.1.3
Uninstalling click-8.1.3:
  Would remove:
Proceed (Y/n)? y
  Successfully uninstalled click-8.1.3

As you can see, when I ran this command, the output listed the files that pip would need to delete to uninstall the click module, then asked if I wanted to proceed. I entered y and pressed ENTER, and the files were deleted and the module uninstalled.

You can install multiple Python packages at once like so:

python3 -m pip install package_name1 package_name2 package_name3

The same is true of uninstalling.

It’s common to define the Python packages that your script requires inside a file called requirements.txt, then install all of them at once with the python3 -m pip install -r requirements.txt command. For example, suppose in addition to using click, you want to use the HTTP client httpx to load web pages inside Python and the sqlalchemy module to work with SQL databases. To include all three in your Python script, first create a requirements .txt file with each package name on its own line:


Then run the following command to install them simultaneously:

micah@trapdoor chapter-8 % python3 -m pip install -r requirements.txt
Collecting click
  Using cached click-8.1.3-py3-none-any.whl (96 kB)
Collecting httpx
  Using cached httpx-0.23.0-py3-none-any.whl (84 kB)
Successfully installed anyio-3.6.1 certifi-2022.9.24 click-8.1.3 h11-0.12.0 httpcore-0.15.0
httpx-0.23.0 idna-3.4 rfc3986-1.5.0 sniffio-1.3.0 sqlalchemy-1.4.41

As you can see, this command installs more than just those three Python packages: rfc3986, certifi, sniffio, and so on are also included. That’s because click, httpx, and sqlachemy have dependencies of their own. For example, httpcore is a dependency of the httpx package, so it installs that as well. To summarize, the requirements.txt file defines your project’s dependencies, each of which might depend on its own list of packages.

NOTE To learn more about how to use httpx and other Python modules to automate interacting with websites, check out Appendix B. I recommend waiting until you complete Chapters 7, 8, 9, and 11, however, since the instructions covered in Appendix B rely on the skills you’ll pick up in those chapters.


It’s not unusual to have multiple versions of Python and multiple versions of the same dependencies for different projects installed on the same computer. If you routinely install Python packages with pip for various projects, this can get very messy over time. For example, different projects might depend on different versions of the same module to work, but you can’t have two versions of a module installed at the same time—at least not without virtual environments, which are like stand-alone folders containing your Python dependencies for a specific project. This way, different projects’ dependencies won’t trip each other up.

To keep things simple, this book doesn’t use virtual environments, and it uses only pip to install Python packages. As long as you don’t have multiple Python projects requiring specific versions of the few third-party modules this book uses, you should be fine without using a virtual environment.

You can learn more about virtual environments at https://docs.python.org/3/tutorial/venv.html. For larger Python projects, you might also consider using Python package management programs such as Poetry (https://python-poetry.org) or Pipenv (https://github.com/pypa/pipenv), which handle the complicated parts of keeping track of Python packages and virtual environments for you.

Now that you know how to install third-party modules, you’ll practice using Click.

Exercise 8-3: Practice Command Line Arguments with Click

As you learned in the previous section, the Click package makes it simple to add command line arguments to your scripts. You can use it to define variables to pass into your main() function from the terminal, without having to define those variables in your code. In this exercise, you’ll learn how to use Click by writing a sample script in preparation for using this module in later exercises.

First, install the Click package with pip again by running python3 -m pip install click. Next, open your text editor and enter the following Python script, exercise-8-3.py (or copy and paste it from https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/exercise-8-3.py):

import click

def main(name):
    """Simple program that greets NAME"""
    print(f"Hello {name}!")

if __name__ == "__main__":

First, the script imports the click module. It then runs a few decorators, function calls that begin with @ and add functionality to another function you’re about to define—the main() function, in this case. The @click.command() decorator tells Click that main() is a command, and the @click .argument("name") decorator tells Click that this command has an argument called name.

Next, the script defines the main() function, which takes name as an argument. This function has a docstring, Simple program that greets NAME. Click uses this docstring for its commands when it builds the output for --help, as you’ll see shortly. The main() function simply displays a string with the name you passed in as an argument.

Finally, the script calls the main() function. Notice that even though main() requires an argument (name), the script doesn’t explicitly pass that argument in when calling the function. This is where the magic of the Click decorators comes in. When the script calls main(), Click will figure out what arguments it needs to pass in, find their values from the CLI arguments, and pass them in for you.

Run the script as follows:

micah@trapdoor chapter-8 % python3 exercise-8-3.py
Usage: click-example.py [OPTIONS] NAME
Try 'click-example.py --help' for help.

Error: Missing argument 'NAME'.

When you run the program, if you don’t pass in the correct CLI arguments, Click tells you what you did wrong. As you can see, you’re missing the required NAME argument. Click also tells you that you can get help by running the script again with the --help argument.

Try running the --help command:

micah@trapdoor chapter-8 % python3 exercise-8-3.py --help
Usage: click-example.py [OPTIONS] NAME

  Simple program that greets NAME

  --help  Show this message and exit.

This time, the output shows a description of the program based on the docstring. Any CLI program that uses Click will display the docstring for the command when you run it with --help.

Try running the command again, this time passing in a name. For example, here’s what happens when I pass in Eve as the name:

micah@trapdoor chapter-8 % python3 exercise-8-3.py Eve
Hello Eve!

NOTE You can read more about using Click at https://click.palletsprojects.com.

Avoiding Hardcoding with Command Line Arguments

As you’ve seen in previous chapters, CLI arguments let you run the same program in many different ways, targeting different data. For example, in Chapter 4, you used the du command to estimate the disk space of a folder by adding the folder’s path as an argument. In du -sh --apparent-size path, the arguments are -sh, --apparent-size, and path.

The du command would be much less useful if it could measure disk space for only a single hardcoded folder. Hardcoding means embedding information, like a path, directly into source code. You can avoid hardcoding anything in your CLI programs by having the user provide this information as arguments when running them.

Passing paths into scripts, rather than hardcoding them, makes for a better user experience. In previous exercises in this chapter, you hardcoded the path to your copy of the BlueLeaks dataset into your Python scripts. If you were to pass the appropriate path in as an argument, however, other people could use your script without editing it—they could just pass in their path when they ran it.

Using arguments rather than hardcoding can also make your scripts more universally useful. For example, in Exercise 8-2, you wrote a script to find all of the files that are at least 100MB in the BlueLeaks dataset. Using CLI arguments, you could make this script work for any dataset you get your hands on, not just BlueLeaks, and for any minimum file size, allowing you to run it in a variety of situations. You’d just need to pass in the dataset path and the minimum file size as CLI arguments. You’ll try this out in the next exercise.

Exercise 8-4: Find the Largest Files in Any Dataset

In this exercise, you’ll modify the script you wrote in Exercise 8-2 to make it work for any dataset, and for any minimum file size, using CLI arguments. In the following chapters, you’ll write simple Python scripts that use Click for CLI arguments, so you can provide the paths to the datasets you’ll be working with.

Create a new file called exercise-8-4.py, and copy and paste the exercise-8-2 .py code into it. Next, make the following modifications to the code, highlighted in bold (or find the full modified script at https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/exercise-8-4.py):

import os
import click

@click.argument("min_file_size", type=click.INT)
def main(path, min_file_size):
    """Find files in PATH that are at least MIN_FILE_SIZE MB big"""
    for dirname, subdirnames, filenames in os.walk(path):
        for filename in filenames:
            absolute_filename = os.path.join(dirname, filename)
            size_in_bytes = os.path.getsize(absolute_filename)
            size_in_mb = int(size_in_bytes / 1024 / 1024)
            if size_in_mb >= min_file_size:
                  print(f"{absolute_filename} is {size_in_mb}MB")

if __name__ == "__main__":

This code imports the click module at the top of the file. Next, it adds Click decorators before the main() function: @click.command() makes the main() function a Click command, and @click.argument() adds path and min_file_size as arguments. The script specifies with type=click.INT that the min_file_size argument should be an integer, or a whole number, as opposed to a string. Then it adds path and min_file_size as arguments to the main() function and adds a docstring that describes what this command does.

The new script uses arguments instead of hardcoded values. It deletes the line that defines the blueleaks_path variable, and in the os.walk() function call, it changes blueleaks_path to just path, which is the argument. Finally, it changes 100 in size_in_mb >= 100 to min_file_size.

You can now use this program to find big files in any folder in the BlueLeaks dataset or elsewhere. For example, here’s what it looks like when I search for all files that are at least 500MB in /Applications on my Mac:

micah@trapdoor chapter-8 % python3 exercise-8-4.py /Applications 500
/Applications/Dangerzone.app/Contents/Resources/share/container.tar.gz is 668MB
/Applications/Docker.app/Contents/Resources/linuxkit/services.iso is 602MB

As you can see, I have only two apps installed that include files this big: Dangerzone and Docker Desktop.

Now that you’ve seen how to add CLI arguments to your Python scripts using Click, you should be able to avoid hardcoding information like dataset paths in your future programs.

Next, we’ll switch gears and explore a new powerful type of Python variable called dictionaries.


In the course of your investigations, sometimes you’ll need to keep track of data with more structure than a simple list. To do so, you can use Python dictionaries. Instead of a collection of items, a dictionary (dict for short) is a collection of keys that map to values. Keys are labels that you use to save or retrieve information in a dictionary, and values are the actual information being saved or retrieved. Nearly every Python script I write that deals with data uses dictionaries. In this section, you’ll learn how to define dictionaries, get values from them, add values to them, and update existing values in them.

Defining Dictionaries

Dictionaries are defined using braces ({and }), sometimes referred to as curly brackets. Inside the braces is a list of key-value pairs in the format key: value, where each pair is separated from the next by commas—for example, {"country": "Italy", "drinking_age": 18}. For longer dictionaries, you can make your code more readable by putting each key-value pair on its own line.

Listing 8-1 shows an example dictionary stored in the variable capitals.

capitals = {
    "United States": "Washington, DC",
    "India": "New Delhi",
    "South Africa": "Cape Town",
    "Brazil": "Brasília",
    "Germany": "Berlin",
    "Russia": "Moscow",
    "China": "Beijing"  

Listing 8-1: A dictionary stored in the capitals variable

In this case, the keys are country names and the values are the capitals of those countries.

Each key in a dictionary can have only one value. If you try to set the same key more than once, Python will save the version you last set. For example, if you define a dictionary and use the name key more than once, the dictionary will overwrite the previous value with the most recent one:

>>> test_dict = {"name": "Alice", "name": "Bob", "hobby": "cryptography"}
>>> print(test_dict)
{'name': 'Bob', 'hobby': 'cryptography'}

However, you can also use lists, or other dictionaries, as values:

>>> test_dict = {"names": ["Alice", "Bob"], "hobby": "cryptography"}
>>> print(test_dict)
{'names': ['Alice', 'Bob'], 'hobby': 'cryptography'}

In this case, the value for the key names is ['Alice', 'Bob'], which itself is a list. You can use a combination of lists and dictionaries to organize pretty much any type of data, no matter how complicated, allowing you to more easily work with it in Python.

Getting and Setting Values

To retrieve an item you’ve stored inside a dictionary, add square brackets containing the item’s key to the end of the dictionary name. If you try to use a key you haven’t defined, your script will crash with a KeyError. For example, here’s how to look up the capitals of certain countries in the capitals dictionary:

>>> capitals["United States"]
'Washington, DC'
>>> capitals["China"]
>>> capitals["Kenya"]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: 'Kenya'

When you run capitals["Kenya"], Python throws the error message KeyError: 'Kenya'. This means that Kenya isn’t a valid key in the capitals dictionary. You can see that the only keys defined in Listing 8-1 are United States, India, South Africa, Brazil, Germany, Russia, and China. Because Kenya isn’t a key in this dictionary, you can’t retrieve its value.

You can add new key-value pairs to a dictionary, or update an existing one, like this:

>>> capitals["Kenya"] = "Nairobi"
>>> capitals["United States"] = "Mar-a-Lago"
>>> print(capitals)
{'United States': 'Mar-a-Lago', 'India': 'New Delhi', 'South Africa': 'Cape Town', 'Brazil': 'Brasília', 'Germany': 'Berlin', 'Russia': 'Moscow', 'China': 'Beijing', 'Kenya': 'Nairobi'}

This code defines a new key, Kenya, with the value Nairobi. It also updates an existing key, United States, to have the value Mar-a-Lago, overwriting its old value, which used to be Washington, DC.

Navigating Dictionaries and Lists in the Conti Chat Logs

You can combine dictionaries and lists in a single flexible data structure that allows you to represent a wide variety of information. If you’re writing Python code to work with datasets, chances are you’re going to need both. You might directly load the data in this format, or you might create your own dictionaries and lists to store aspects of the data.

To describe how to use data structures that include a combination of dictionaries and lists, I’ll use an example from a real dataset. The day after Russia invaded Ukraine on February 24, 2022, the notorious Russian ransomware gang Conti, known for hacking companies around the world and extorting millions of dollars from them, published a statement on its website throwing its full support behind the Russian government. It threatened any “enemy” who launched cyberattacks against Russia with retaliation against their “critical infrastructure.” Three days later, a Ukrainian security researcher anonymously leaked 30GB of internal data from Conti: hacking tools, training documentation, source code, and chat logs. The Conti chat logs originally came in the form of JSON files, which is structured data. When you load JSON files into Python, they’ll automatically be loaded as a combination of dictionaries and lists.

In this section, you’ll look through some of these chat logs in order to practice working with real leaked data stored in dictionaries and lists. Using Python code, you’ll learn how to navigate these structures to access specific pieces of data as well as how to quickly loop through the chat logs and select just the parts you’re interested in.

Exploring Dictionaries and Lists Full of Data in Python

You can download the complete Conti dataset from https://ddosecrets.com/wiki/Conti_ransomware_chats. However, for this section, you’ll use just one file from the dataset, 2022-02-24-general.json, which the Ukranian security researcher extracted from a chat system called RocketChat.

Download 2022-02-24-general.json from https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/2022-02-24-general.json. Open a terminal, change to the folder where you stored this file, and open a Python interpreter. Load this file into a dictionary with the following commands:

>>> import json
>>> with open("2022-02-24-general.json") as f:
...     data = json.load(f)

This code uses the json module and loads the data from 2022-02-24-general.json into the data variable. The chat logs from this file are too long to display in their entirety, but Listing 8-2 shows a snippet of the value of the data dictionary that demonstrates its structure.

    "messages": [
            "_id": "FmFZbde9ACs3gtw27",
            "rid": "GENERAL",
            "msg": "Некоторые американские сенаторы предлагают помимо соцсетей блокировать в
Россииещё и PornHub!",
            "ts": "2022-02-24T22:02:38.276Z",
            "u": {"_id": "NKrXj9edAPWNrYv5r", "username": "thomas", "name": "thomas"},
            "urls": [],
            "mentions": [],
            "channels": [],
            "md": [
                    "type": "PARAGRAPH",
                    "value": [
                            "type": "PLAIN_TEXT",
                            "value": "Некоторые американские сенаторы предлагают помимо
соцсетейблокировать в России ещё и PornHub!",
            "_updatedAt": "2022-02-24T22:02:38.293Z",
    "success": True 

Listing 8-2: Conti chat logs from RocketChat

The data variable is a dictionary with two keys, messages and success. You access the value of the messages key, which is a list of dictionaries, using the expression data["messages"] ❶. You can tell that the value of data["messages"] is a list because it’s enclosed in square brackets ([ and ]), and you can tell that the items inside it are dictionaries because they’re enclosed in braces ({and }). Almost all of the data in this file is stored in this list.

Each dictionary in the data["messages"] list describes a chat message. This snippet of code includes only one of the dictionaries, the ninth chat message in the list (I snipped out the first eight messages, so you can’t tell that it’s the ninth without looking at the original file). You can access the dictionary that contains that specific chat message using the expression data["messages"][8]. (Remember, in programming we start counting at 0, not 1, so the first item is at index 0, the second item is at index 1, and so on.) If you run the command print(data["messages"][8]) to display the dictionary for the ninth message, the output should match the message in the listing. Notice that just as you place index numbers within brackets to select from lists, you place keys within brackets to select from dictionaries, like ["messages"] or ["success"].

You can also access the value of the success key with data["success"]. Its value is the Boolean True ❷. I’m not entirely sure what this means, but I suspect that the success key was left over from whatever system the Ukrainian researcher used to export these chat messages from RocketChat, confirming that exporting the data was successful and that there were no errors.

The file from which I loaded this code contained 604 different chat messages, each in its own dictionary, that were sent in Conti’s #general RocketChat channel on February 24, 2022. I discovered that this list has 604 items by measuring its length with the len() function, like this:

>>> len(data["messages"])

The dictionary for each chat message has many keys: _id, rid, msg, u, urls, and so on.

You can find out what types of data these keys contain using the for key_variable in dictionary syntax, and you can determine a variable’s data type using the type() function. Try this out using the following commands:

>>> for key in data["messages"][8]:
...     print(f"{key}: {type(data['messages'][8][key])}")

This command loops through the data["messages"][8] dictionary and stores each key in the key variable. Then, using the print() function and an f-string, it displays the key (key) and the type of data stored in that key, as shown in the following output:

_id: <class 'str'>
rid: <class 'str'>
msg: <class 'str'>
ts: <class 'str'>
u: <class 'dict'>
urls: <class 'list'>
mentions: <class 'list'>
channels: <class 'list'>
md: <class 'list'>
_updatedAt: <class 'str'>

In the output, the values at the _id, rid, msg, ts, and _updatedAt keys are all strings. The value at the u key is a dictionary. The value at the urls, mentions, channels, and md keys are lists.

You can get the value of the data at the key using data['messages'][8][key]. Remember that to retrieve the value of a key in a dictionary, you put the key in square brackets. In this case, the key itself is stored in the variable key, so you can get its value by putting key inside the square brackets. To find out what type of data that is, then, just pass the value into the type() function.

Selecting Values in Dictionaries and Lists

When working with datasets, you often end up with structures like this: a mess of dictionaries and lists that you need to make sense of. Being able to select the exact values you’re looking for is an important skill. To practice navigating through dictionaries and lists, take a closer look at the value of just one of these keys, the md key, by running the following command:

>>> print(data["messages"][8]["md"])

In the output, you can tell that this value is a list because it’s surrounded by square brackets:

[{'type': 'PARAGRAPH', 'value': [{'type': 'PLAIN_TEXT', 'value': 'Некоторые американские сенаторы предлагают помимо соцсетей блокировать в России ещё и PornHub!'}]}]

The list’s single item is a dictionary, which is surrounded by braces. The dictionary has a type key whose value is PARAGRAPH, as well as a value key. The value of value is another list with one item containing another dictionary; that dictionary itself contains type and value keys, where the value of type is PLAIN_TEXT.

These data structures can have as many sublists and subdictionaries as you’d like. To select specific values, after the data variable keep adding square brackets containing an index (if it’s a list) or a key (if it’s a dictionary) until you get to the value you’re looking for. For example, use the following command to access the value of the value key in the inner dictionary within the inner list, which is in another value key in the outer dictionary in the outer list:

>>> print(data["messages"][8]["md"][0]["value"][0]["value"])

You already know that data["messages"][8] is a dictionary that represents a chat message. To find the value of the md key in that dictionary, you include["md"] in the command. As you can tell from inspecting the structure in Listing 8-2, this is a list with one item, so adding [0] selects that item. This item is a dictionary, and you select the value of its value key by adding ["value"]. This item is another list with one item, so you again add [0] to select that one item. This is yet another dictionary, so you can select the value of the final inner value key by adding another ["value"].

You should get the following output:

Некоторые американские сенаторы предлагают помимо соцсетей блокировать в России ещё и PornHub!

In English, the message that you just displayed says, “Some American Senators suggest blocking PornHub in Russia in addition to social networks!” It was posted right after Russia started its invasion of Ukraine, and US and European leaders immediately began imposing economic sanctions on Russia. After invading Ukraine, the Russian government censored access to Twitter and Facebook from the Russian internet. Rumors spread that PornHub, a popular American porn website, would block access to Russian users (though this didn’t happen). This same user followed up their first post with “That’s it, we’re done,” and then “They will take away our last joys!”

Analyzing Data Stored in Dictionaries and Lists

Whenever I work with any sort of structured data, I find myself looping through a list of dictionaries and selecting specific pieces of data. As long as you understand its structure, you can write your own similar code to quickly pull out the relevant information, no matter what dataset you’re working with. For example, you might want to view the chat logs in the format timestamp username``: message in order to hide the unimportant sections of data so that you can directly copy and paste the relevant parts into machine translation systems like DeepL or Google Translate. Run the following commands to display all of the messages in data["messages"] in that format:

>>> for message in data["messages"]:
...     print(f"{message['ts']} {message['u']['username']}: {message['msg']}")

You should get the following output:

2022-02-24T22:02:49.448Z thomas: последние радости у нас заберут
2022-02-24T22:02:44.463Z thomas: ну все, приплыли)
2022-02-24T22:02:38.276Z thomas: Некоторые американские сенаторы предлагают помимо соцсетей
блокировать в России ещё и PornHub!
2022-02-24T22:00:00.347Z thomas:
2022-02-24T21:58:56.152Z rags: угу :(

Since data["messages"] is a list, each time the for loop in this command runs, it updates the value of the message variable to a different item in that list. In this case, each item is a different dictionary. Inside the for loop, the print() function displays three values: the timestamp (message['ts']), the username (message['u']['username']), and the message itself (message['msg']).

You can change this command to display whatever information you’d like from each message. Maybe you’re interested is the user’s ID rather than their username. In that case, you could display message['u']['_id'].

The previous output shows the same messages about PornHub just discussed, as well as a message posted just before that from another user, rags. If you’re interested in seeing only the messages posted by rags, view those by running the following commands:

>>> for message in data["messages"]:
...     if message["u"]["username"] == "rags":
...         print(f"{message['ts']} {message['u']['username']}: {message['msg']}")

This code is similar to the previous example. A for loop loops through each message in data["messages"], and then a print() statement displays specific pieces of information from that message. This time, though, each loop also contains an if statement. Each time the code finds another message, it checks to see if the username is rags and, if so, displays the message. Otherwise, it moves on to the next message. You should get the following output:

2022-02-24T22:08:49.684Z rags: давай бро спокойной ночи
2022-02-24T22:03:50.131Z rags: сча посмотрю спасиб =)
2022-02-24T21:58:56.152Z rags: угу :(

Finally, suppose you want to figure out how many messages each person posted, perhaps to find the most active poster in the #general chatroom on this day. The simplest way to do this is to create a new empty dictionary yourself and then write code to fill it up. Run the following command to create an empty dictionary called user_posts:

>>> user_posts = {}

The keys in this dictionary will be usernames, and the values will be the number of posts from that user. Fill up the user_posts dictionary with the following code:

>>> for message in data["messages"]:
...     username = message["u"]["username"]
...     if username not in user_posts:
...         user_posts[username] = 1
...     else:
...         user_posts[username] += 1

Again, this code uses a for loop to loop through the messages. Next, it defines the username variable as message["u"]["username"], the username of the person who posted the message the code is currently looping through. Next, using an if statement, the code checks to see if this username is already a key in the user_posts dictionary. (It’s not checking to see if the string username is a key, but rather if the value of the username variable, like thomas or rags, is a key.)

If this user doesn’t exist in the user_posts dictionary, the program adds a key to this dictionary and sets the value at that key to 1, with the line user_posts[username] = 1. Otherwise, it increases the value by 1, with user_posts[username] += 1. By the time the for loop finishes running, the user_posts dictionary should be complete. The keys should be all of the usernames found in the messages, and the values should be the total number of messages for that user.

Use the following code to display the information inside the user_posts dictionary, viewing the data you just collected:

>>> for username in user_posts:
...     print(f"{username} posted {user_posts[username]} times")

You should get the following output:

weldon posted 64 times
patrick posted 62 times
rags posted 38 times
thomas posted 58 times
ryan posted 2 times
kermit posted 151 times
biggie posted 39 times
stanton posted 12 times
angelo posted 102 times
Garfield posted 61 times
jaime posted 2 times
grem posted 5 times
jefferson posted 1 times
elijah posted 6 times
chad posted 1 times

These are the users who posted in Conti’s #general chatroom, in their RocketChat server, on the day Russia invaded Ukraine in 2022. The user kermit posted 151 times, more than any other user.

In these examples, you looped through hundreds of chat messages, but the same concepts would work with millions or billions of messages or with data representing any sort of information.


This dataset includes far more chat logs than just a few messages worrying about a porn site getting blocked. The example I used in this section included the chat logs for the #general channel for a single day, but the logs for this RocketChat server span from July 24, 2021, to February 26, 2022. The leak also includes many logs from the chat service known as Jabber, including some where Conti hackers discuss hacking a contributor to the OSINT-based investigative journalism group Bellingcat. The hackers were hoping to find information about Alexei Navalny, the imprisoned Russian opposition leader who survived an FSB assassination attempt.

The anonymous Ukrainian researcher who leaked the Conti dataset told CNN, “I cannot shoot anything, but I can fight with a keyboard and mouse.” According to CNN, a few weeks after leaking the data, the researcher successfully slipped out of Ukraine during Russia’s invasion, laptop in hand.

From reading the chat logs, I learned that many of the Conti hackers are Russian ultranationalists. Many of them believe Putin’s conspiratorial lies about Ukraine, like that it’s run by a “neo-Nazi junta,” while at the same time making antisemitic comments about Volodymyr Zelenskyy, Ukraine’s Jewish president. You can see my full reporting on this dataset at https://theintercept.com/2022/03/14/russia-ukraine-conti-russian-hackers/.

In this section, you learned how to work with flexible data structures that combine dictionaries and lists, including how to pick out specific elements that you’re interested in and how to quickly traverse them by looping through them. These skills will often prove useful when you’re writing Python scripts to help you analyze data.

Now that you’re familiar with data structures that combine dictionaries and lists, it’s time to create your own to map out the CSV files in BlueLeaks.

Exercise 8-5: Map Out the CSVs in BlueLeaks

Each folder in BlueLeaks includes data from a single hacked law enforcement website in the form of hundreds of CSV files. These files contain some of the most interesting information in all of BlueLeaks, such as the contents of bulk email that fusion centers sent to local cops, or “suspicious activity reports.” In this exercise, you’ll construct a map of the contents of the dataset.

By manually looking in different BlueLeaks folders, I noticed that each folder seems to have a file called Company.csv (each containing different content), but only one folder, ncric, has a file called 911Centers.csv. Clearly, not all of the BlueLeaks sites have the same data. Which CSV files are in every folder in BlueLeaks, which are in some folders, and which are unique to a single folder? Let’s write a Python script to find out.

As with most programming problems, there are multiple ways you could write a script that answers this question. If you feel comfortable enough with Python by now that you’d like a challenge, try writing one on your own. Otherwise, follow along with this exercise. Either way, the program must meet the following requirements:

In each step that follows, I’ll quote a snippet of code, explain how it works, and give you a chance to run it as is. You’ll then add more features to that code and run it again. It’s good practice to write code in small batches, pausing frequently to test that it works as you expect. This will help you catch bugs early, making the process of debugging much simpler.

Accept a Command Line Argument

Create an exercise-8-5.py file and enter the Python template:

def main():

if __name__ == "__main__":

Next, instead of hardcoding the path to the BlueLeaks data like you did in Exercise 8-2, let’s use Click to pass in the path as a command line argument, blueleaks_path. To do so, make the following modifications to your code (the added syntax is highlighted in bold):

import click

def main(blueleaks_path):
    """Map out the CSVs in BlueLeaks"""
    print(f"blueleaks_path is: {blueleaks_path}")

if __name__ == "__main__":

This code modifies the template to import the click module, adds the correct decorators before the main() function, adds the blueleaks_path argument to the main() function, and adds a simple docstring to the main() function so that running this script with --help will be more useful. Finally, it includes a line to display the value of blueleaks_path, so that you can confirm the code is working when you run it.

Try running your script with --help to see if the help text works, and with a value for blueleaks_path to see if the argument is successfully sent to the main() function:

micah@trapdoor chapter-8 % python3 exercise-8-5.py --help
Usage: exercise-8-4.py [OPTIONS] BLUELEAKS_PATH

  Map out the CSVs in BlueLeaks

  --help  Show this message and exit.
micah@trapdoor chapter-8 % python3 exercise-8-5.py test-path
blueleaks_path is: test-path

If your output looks like this, everything is working correctly so far.

Loop Through the BlueLeaks Folders

Now that you can use the blueleaks_path CLI argument, make the following modifications to your code to have it loop through all of the folders it finds in that path:

import click
import os

def main(blueleaks_path):
    """Map out the CSVs in BlueLeaks"""
    for folder in os.listdir(blueleaks_path):
        blueleaks_folder_path = os.path.join(blueleaks_path, folder)

        if os.path.isdir(blueleaks_folder_path):
            print(f"folder: {folder}, path: {blueleaks_folder_path}")

if __name__ == "__main__":

First, you import the os module in order to be able to list all of the files in the BlueLeaks-extracted folder using the os.listdir() function. Inside the main() function, a for loop loops through the return value of os.listdir (blueleaks_path), the list of filenames inside the folder at blueleaks_path.

Inside the loop, the code defines blueleaks_folder_path as the path of the specific BlueLeaks folder for the current loop. For example, if the value of blueleaks_path is /Volumes/datasets/BlueLeaks-extracted, and at this point in the for loop, the value of folder is icefishx, then the value of blueleaks_folder_path will be /Volumes/datasets/BlueLeaks-extracted/icefishx.

You want to look inside subfolders in the BlueLeaks-extracted folder, not inside files. If there are any files in that folder, you want to skip them. To meet these requirements, the code includes an if statement that checks whether blueleaks_folder_path is actually a folder. Finally, the code displays the current value of folder and blueleaks_folder_path.

Run your script again. This time, pass in the real path to your BlueLeaks -extracted folder:

micah@trapdoor chapter-8 % python3 exercise-8-5.py /Volumes/datasets/BlueLeaks-extracted
folder: bostonbric, path: /Volumes/datasets/BlueLeaks-extracted/bostonbric
folder: terrorismtip, path: /Volumes/datasets/BlueLeaks-extracted/terrorismtip
folder: ociac, path: /Volumes/datasets/BlueLeaks-extracted/ociac

The output should show that the folder variable holds just the name of the folder, like bostonbric, and the blueleaks_folder_path variable includes the full path to that folder, like /Volumes/datasets/BlueLeaks-extracted/bostonbric. When you run this on your own computer, you may see these values in a different order than what’s shown here.

Fill Up the Dictionary

You now have a script that accepts blueleaks_path as an argument and then loops through every folder in that path. Adding the code in bold creates the csv_to_folders dictionary and starts to fill it up with data:

import click
import os

def main(blueleaks_path):
    """Map out the CSVs in BlueLeaks"""
    csv_to_folders = {}

    for folder in os.listdir(blueleaks_path):
        blueleaks_folder_path = os.path.join(blueleaks_path, folder)

        if os.path.isdir(blueleaks_folder_path):
            for filename in os.listdir(blueleaks_folder_path):
                if filename.lower().endswith(".csv"):
                    if filename not in csv_to_folders:
                        csv_to_folders[filename] = []


if __name__ == "__main__":

Your goal with this script is to map out which CSV files are in which BlueLeaks folders. To store this data, the code creates the empty dictionary csv_to_folders at the top of the main() function. The next step is to fill up that dictionary.

The code loops through all of the filenames in blueleaks_path, checking each to see if it’s a folder. Removing the print() statement in the previous iteration of the code, this code instead adds a second for loop that loops through all of the files in that specific BlueLeaks folder.

In this second for loop, an if statement checks whether the filename ends in .csv. This if statement calls the lower() method on the filename string, which returns a lowercase-only version of the string. The code then calls the endswith() method on that lowercase string, which returns a Boolean describing whether the string ends with the string that was passed in. If the string filename ends with .csv, .CSV, or .cSv, the lower() method will convert the file extension to .csv, and endswith() will return True. If filename ends with anything else, like .docx, then endswith() will return False.

Each time the code following this if statement runs, it means the program has found a CSV (called filename) in the current BlueLeaks folder (called folder). You want csv_to_folders to be a dictionary where the keys are CSV filenames and the values are lists of folders. This code checks to see if the key filename has been created in csv_to_folders, and if it hasn’t, creates it and set its value to an empty list ([]). Finally, after the code has confirmed that the filename key has been created and is a list, it appends the value of folder to that list.

These last lines are tricky, so let’s dig in a little more. The first time the script comes across a CSV filename (like CatalogRelated.csv), the script sets the value of that key in csv_to_folders to an empty list. If the same filename exists in another BlueLeaks folder later on, the expression filename not in csv_to_folders will evaluate to False (meaning csv_to_folders["CatalogRelated .csv"] already exists), so the code following the if statement won’t run. Finally, the code appends folder, the name of the BlueLeaks folder it’s currently looking in, to the list of folders that include that filename.

Pause and try running the script so far:

micah@trapdoor chapter-8 % python3 exercise-8-5.py /Volumes/datasets/BlueLeaks-extracted

This should take a moment to run but displays nothing, since you’re not yet using the print() function anywhere. The code is simply creating the csv_to_folders dictionary and filling it up with data.

Display the Output

By the time the previous version of the script runs, the csv_to_folders dictionary should contain a complete set of CSV filenames, mapped to the BlueLeaks sites where they were found. The following code should show you what the program found:

import click
import os

def main(blueleaks_path):
    """Map out the CSVs in BlueLeaks"""
    csv_to_folders = {}

    for folder in os.listdir(blueleaks_path):
        blueleaks_folder_path = os.path.join(blueleaks_path, folder)

          if os.path.isdir(blueleaks_folder_path):
            for filename in os.listdir(blueleaks_folder_path):
                if filename.lower().endswith(".csv"):
                    if filename not in csv_to_folders:
                        csv_to_folders[filename] = []


    for filename in csv_to_folders:
        print(f"{len(csv_to_folders[filename])} folders | {filename}")

if __name__ == "__main__":

The added code in bold loops through all of the keys (each a CSV filename) in csv_to_folders, then displays the number of BlueLeaks folders that contain that file (len(csv_to_folders[filename])) along with the filename itself.

You can find this final script at https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/exercise-8-5.py. When you run it, the output should look like this:

micah@trapdoor chapter-8 % python3 exercise-8-5.py /Volumes/datasets/BlueLeaks-extracted
161 folders | CatalogRelated.csv
161 folders | Blog.csv
161 folders | EmailBuilderOptions.csv
1 folders | HIDTAAgentCategory.csv
1 folders | Lost.csv
1 folders | AgencyContacts.csv

Since this script displays the number of folders at the beginning of each line of output, you can pipe the output into sort -n to sort it numerically in ascending order, like so:

micah@trapdoor chapter-8 % python3 exercise-8-5.py /Volumes/datasets/BlueLeaks-extracted | sort
1 folders | 1Cadets.csv
1 folders | 1Mentors.csv
1 folders | 1Unit.csv
161 folders | VideoDownload.csv
161 folders | VideoHistory.csv
161 folders | VideoOptions.csv

Most of the CSV files are in either a single folder or all 161 folders. However, there are a few exceptions: Donations.csv should be in 10 folders, SARs.csv should be in 25, and so on. This information would have taken you many hours of busywork to find manually.

At this point, you’ve learned the basics of navigating the filesystem in Python. You’ve seen how to loop through folders using os.listdir(), loop through entire folder structures using os.walk(), and look up information about the files and folders you find. In the next section, you’ll learn how to actually read the contents of a file you find and create new files yourself.

Reading and Writing Files

To follow the rest of this book, you’ll need to know one more major Python concept: how to read and write files. During a data investigation, you’ll almost certainly need to read the contents of files, especially CSV and JSON files. You’ll also probably want to be able to create new files, by calculating some data of your own and saving it to a spreadsheet, for example. In this section you’ll learn how to open files and write or read content to them.

In programming, to work with a file, you first need to open it and specify the mode—that is, whether you’re planning on reading from or writing to this file. To open an existing file and access its contents, open it for reading using mode r. To create a new file and put data in it, open it for writing using mode w.

Opening Files

To prepare to work with a file, whether for writing or reading, you use the built-in Python function open(). To open it for reading, you use the following code:

with open("some_file.txt", "r") as f:
    text = f.read()

This code uses a with statement, which tells Python that after the open() function is done running, it should set the variable f to that function’s return value. The f variable is a file object, a type of variable that allows you to read or write data to a file. The first argument to the open() function is a path, and the second argument is the mode, which in this example is "r" for reading.

In the code block after the with statement, you can call methods on f to interact with the file. For example, f.read() will read all of the data in the file and return it—in this case, storing it in the text variable.

To open a file for writing, you set the mode to "w" like so:

with open("output.txt", "w") as f:
    f.write("hello world")

The open() function returns the file object f. To write data into the file, you can use the f.write() method. Here, this code is opening a file called output.txt and writing the string hello world to it.

In the next two sections, you’ll learn more about using f.write() to write to files and f.read() and f.readlines() to read from files.

Writing Lines to a File

Text files are made up of a series of individual characters. Consider a text file with these contents:

Hello World
Hola Mundo

You could also represent the entire contents of this file as a Python string:

"Hello World\nHola Mundo\n"

The first character of the string is H, then e, then l, and so on. The 12th character (counting the space), \n, is a special character known as a newline that represents a break between lines. As with shell scripting, the backslash is the escape character in Python, so a backslash followed by another character represents a single special character.

Newlines are used to write lines to a file. Try running these commands in your Python interpreter:

>>> with open("output.txt", "w") as f:
...     f.write("Hello World\n")
...     f.write("Hola Mundo\n")

The 12 and 11 in the output represent the number of bytes written. The first f.write() call wrote 12 bytes, because the string Hello World takes 11 bytes of memory: it has 11 characters, plus 1 for the newline character. The second call wrote 11 bytes, since Hola Mundo takes 10 bytes of memory, plus 1 for the newline character.

In your terminal, use the following command to view the file you just wrote:

micah@trapdoor ~ % cat output.txt
Hello World
Hola Mundo

If you had written the same code but without the newlines, the output would have been Hello WorldHola Mundo, with no line breaks.

Reading Lines from a File

Run the following command to read the file you just created:

>>> with open("output.txt", "r") as f:
...     text = f.read()

This code reads all of the data from the file and saves it in the string text. In fact, this might look familiar: earlier in this chapter, in the Exploring Dictionaries and Lists Full of Data in Python section, we used similar code to load the leaked Conti chat logs into a Python dictionary.

Since splitting text files into multiple lines is so common, file objects also have a convenient method called readlines(). Instead of reading all of the data into a file, it reads only one line at a time, and you can loop over the lines in a for loop. Try this out by running the following commands:

>>> with open("/tmp/output.txt", "r") as f:
...     for line in f.readlines():
...         print(line)
Hello World

Hola Mundo

This code opens the file for reading, then loops through each line in the file. Each line is stored in the variable line, then displayed with the print() function. Because the line variable in each loop ends in \n (for example, the first line is Hello World\n, not Hello World), and the print() function automatically adds an extra \n, the output shows an extra hard return after each line.

If you don’t want to display these extra newlines, you can use the strip() method to get rid of any whitespace (spaces, tabs, or newlines) from the beginning and end of the string. Run the same code, but this time strip out the newline characters on each line:

>>> with open("/tmp/output.txt", "r") as f:
...     for line in f.readlines():
...         line = line.strip()
...         print(line)
Hello World
Hola Mundo

You’ll practice the basics of how to read and write files in Python in the following exercise.

Exercise 8-6: Practice Reading and Writing Files

In Exercise 7-5, you wrote a function that converts a string to an alternating caps version, like This book is amazing to ThIs bOoK Is aMaZiNg. To practice your newfound reading and writing files, in this exercise, you’ll write a script to create an alternating caps version of all of the text in an entire text file.

If you’d like a challenge, you can try programming your own script to meet the following requirements:

Otherwise, follow along with my explanation of the following code, which implements this iNcReDiBlY uSeFuL command line program.

Start by copying the alternating_caps() function that you wrote in Exercise 7-5 into a new Python script called exercise-8-6.py. Next, make the modifications highlighted in bold here (or copy the final script at https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/exercise-8-6.py):

import click

def alternating_caps(text):
    """Returns an aLtErNaTiNg cApS version of text"""
    alternating_caps_text = ""
    should_be_capital = True

    for character in text:
        if should_be_capital:
            alternating_caps_text += character.upper()
            should_be_capital = False
            alternating_caps_text += character.lower()
            should_be_capital = True

    return alternating_caps_text

def main(input_filename, output_filename):
    """Converts a text file to an aLtErNaTiNg cApS version"""
    with open(input_filename, "r") as f:
        text = f.read()

    with open(output_filename, "w") as f:

if __name__ == "__main__":

This code first imports the click module, used for the arguments, and then defines the alternating_caps() function. Again, the main() function is a Click command, but this time it takes two arguments, input_filename and output_filename.

Once the main() function runs, the section for reading and writing files runs. The code opens input_filename for reading and loads all of the contents of that file into the string text. It then opens output_filename for writing and saves the alternating caps version of that string into the new file. It does so by running alternating_caps(text), which takes text as an argument and returns its alternating caps version, and then passes that return value directly into f.write(), writing it to the file.

To demonstrate how this script works, try running it on the famous “To be, or not to be” soliloquy from Hamlet. First, save a copy of the soliloquy found at https://github.com/micahflee/hacks-leaks-and-revelations/blob/main/chapter-8/shakespeare.txt to a file called shakespeare.txt. Here are the original contents of shakespeare.txt, displayed using the cat command:

micah@trapdoor chapter-8 % cat shakespeare.txt
To be, or not to be, that is the question:
Whether 'tis nobler in the mind to suffer
The slings and arrows of outrageous fortune,
Or to take Arms against a Sea of troubles,
And by opposing end them: to die, to sleep
No more; and by a sleep, to say we end

Next, pass that filename into your script to create an alternating caps version of that file. Here’s what happens when I do it:

micah@trapdoor chapter-8 % python3 exercise-8-5.py shakespeare.txt shakespeare-mocking.txt
micah@trapdoor chapter-8 % cat shakespeare-mocking.txt
To bE, oR NoT To bE, tHaT Is tHe qUeStIoN:
wHeThEr 'TiS NoBlEr iN ThE MiNd tO SuFfEr
tHe sLiNgS AnD ArRoWs oF OuTrAgEoUs fOrTuNe,
Or tO TaKe aRmS AgAiNsT A SeA Of tRoUbLeS,
aNd bY OpPoSiNg eNd tHeM: tO DiE, tO SlEeP
No mOrE; aNd bY A SlEeP, tO SaY We eNd

First, I ran the script, passing in shakespeare.txt as input_filename and shakespeare-mocking.txt as output_filename. The script itself displayed no output (it doesn’t include any print() statements), but it did create a new file. I then used cat to display the contents of that new file, which is indeed an alternating caps version of Hamlet’s soliloquy.


Congratulations on making it through a crash course in the fundamentals of Python programming! You’ve learned how to bring extra functionality to your scripts with built-in and third-party Python modules. You’ve also learned how to make your own CLI programs using Click, how to write code that traverses the filesystem, how to work with structured data using dictionaries and lists, and how to read and write files.

You’ll use these skills throughout the following chapters as you dig through various datasets, uncovering revelations you’d never discover otherwise. In the next chapter, you’ll write Python programs that loop through rows in the BlueLeaks CSV spreadsheets, transforming the data into a more workable format. You’ll get practice writing the content of law enforcement bulk email messages to files, and you’ll use Python to create your own CSV spreadsheets.

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