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Extracting 2023 Enchantments Lottery PDF-to-CSV

by Jarrett Retz

April 2nd, 2024 python data analysis the enchantments

Published Data Formats

Since publishing this article, the National Forest Service has published the 2023 data in .csv and .xlsx formats. Therefore, the conversion covered in this article is no longer required.

However, the published tabular data formats do not include the Processing Sequence. If you want that data you'll have to use this conversion.


Recently, I investigated the 2021 and 2022 Enchantments Lottery data published on the USFS website to calculate probabilities of winning given your application selections. I wanted to test out some common advice and discover if the advice was true.

Analyzing the data for 2021 and 2022 was fairly straightforward because data for those years is available on the USFS website in a data-friendly format (.xlsx, .csv). Unfortunately, they provide the 2023 application data in the Portable Document Format (.pdf). I requested the data in similar formats to previous years but after not hearing back I decided to extract the data myself.

This article is a brief overview of that process using Python.

The Program

I knew there were libraries for extracting text from PDF files but I didn't know there was such a variety. I tried textract, but ran into problems immediately. After reading a few SO posts, I tried pypdf and it worked immediately.

Source Code

View the repository on Github

The steps in the program are simple:

  • Extract the text from the pdf file (fseprd1162873.pdf)
  • Make corrections or format the text for CSV storage
  • Store the text data in a temporary CSV document
  • Read the temporary file
  • Make corrections to the data by adding cells and combining cells that should be one data cell (i.e., "British,Columbia" -> "British Columbia")
  • Save the modified CSV data to a new CSV file

The program code is below:

import csv
import os
import re

from pypdf import PdfReader

TITLE_TEXT = "Enchantments Lottery 2023 Application Data"

# Text to remove from the PDF
text_to_remove = [TITLE_TEXT]

# Regex pattern that matches any single digit number followed immediately by a letter, excluding forward slashes until the of the line
PATTERN = r"(\d)([A-Za-z]+)(?![/])"

    "Eightmile/Caroline Zone,(stock)",
    "Stuart Zone,(stock)",


# Combine the zone names and column names into single list

# File name in local direction
FILE_NAME = "fseprd1162873.pdf"

# Read the PDF file
reader = PdfReader(FILE_NAME)
text = ""

for page in reader.pages:
    page_text = page.extract_text()
    # Remove unwanted text
    for item in text_to_remove:
        page_text = page_text.replace(item, "")

    # Split the awarded group size and the state code with a space
    page_text = re.sub(PATTERN, r"\1 \2", page_text)

    # Separate 0Cancelled and 0Awarded with a space
    page_text = re.sub(r"(\d)(Cancelled|Awarded)", r"\1 \2", page_text)

    # Add the page text to the text variable
    text += page_text + "\n"

# Save text to temporary CSV file
with open("temp.csv", "w", newline="") as file:
    writer = csv.writer(file, quotechar=None)

# Open the input file in read mode and output file in write mode
with open("temp.csv", "r") as input_file, open(
    "2023_results_w_pdf_totals.csv", "w"
) as output_file:
    # Read each line from the input file
    for line in input_file:
        # Replace spaces with commas
        line = line.replace(" ", ",")

        # Check for zone names and replace commas with spaces
        for zone_name in CORRECTION_NAMES:
            line = line.replace(zone_name, zone_name.replace(",", " "))

        # Add four commas after Unsuccessful or Cancelled to fill missing cells
        line = re.sub(r"(Unsuccessful|Cancelled)", r"\1,,,,", line)

        # Count the number of commas in the line
        num_commas = line.count(",")

        # Check if the num_commas is less than 18
        if num_commas < 18:
            # Store the number of commas to add to the line
            num_commas_to_add = 18 - num_commas

            # Regex that matches any series of digits followed by a comma and a result status (Unsuccessful, Cancelled, or Awarded)
            # This pattern is used to add before the processing sequence column for missing entry cells
            pattern = r"(\d+),((Unsuccessful|Cancelled|Awarded))"

            # Add the number of commas infront of the series of digits in the line
            line = re.sub(pattern, r"," * num_commas_to_add + r"\1" + "," + r"\2", line)

        # Match the entire row before the 18th comma
        before_comma_pattern = r"^(.*?,){18}"
        # Store everything before the 18th comma in a variable
        before_18th_comma = re.match(before_comma_pattern, line)

        if before_18th_comma:
            before_18th_comma =

            # Match everything after the 18th comma, keeping everything before the 18th comma and after the 18th comma
            # but substitude the commas after the 18th comma with spaces
            # This pattern is used to collect the "State" column values (some have spaces in them)
            # and replace the commas with spaces so the word is not split into separate columns
            after_comma_pattern = r"^(.*?,){18}(.*)$"
            replacement_after_18th_comma = re.sub(
                lambda x:",", " "),

            # Combine the before and after 18th comma
            line = before_18th_comma + replacement_after_18th_comma

        # Write the modified line to the output file

# Remove the temporary CSV file

I ran into a series of problems when parsing the text for CSV storage.


First, not all spaces are replaceable with a comma because some data points in the file have spaces (i.e., "Core Enchantment Zone" or "British Columbia").

Second, some of the data that should be in separate cells is parsed as one cell (i.e., "2Awarded" should be "2,Awarded" denoting the group size and awarded status).

Finally, and the most complex when parsing the data, no value appears for an "empty" cell. Applicants are only required to enter one application entry option, but they're allowed to apply for three different options. Therefore, some application rows had fewer cells than other full rows. This same problem manifested between awarded and unsuccessful permits. The awarded permits had awarded permit data (i.e., awarded group size), but all the unsuccessful permits had no such data.

PDF Statistic Totals

The USFS, or someone else, put the awarded permit totals broken down by zone near the top of the PDF file next to the first ten rows. What that meant was ~99.75% of this pdf is normal, but ten rows that are not.

Instead of writing custom code to remove those data rows, I decided that I would go into the document after parsing and remove them which is what I did.


I'm appreciative that the USFS provides the Enchantments lottery data on their website. I think it's interesting and fun to look at. The 2023 data has new columns ("Processing Sequence" and "State") which makes this year's data even more exciting.

Now, I'm eagerly anticipating the 2024 results.

Jarrett Retz

Jarrett Retz is a freelance web application developer and blogger based out of Spokane, WA.

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