It is possible to reproduce one of the key claims in this post -- the "Russian tail" in the early voting tallies -- straight from the raw data hosted on the Clark County, NV website. This code can be run in a Colab notebook:

  # Download and extract zip file
  import requests
  import zipfile
  import io

  # Get raw data from Clark County website
  zip_url = "https://elections.clarkcountynv.gov/electionresultsTV/cvr/24G/24G_CVRExport_NOV_Final_Confidential.zip"

  # Download the zip file
  response = requests.get(zip_url)
  zip_file = zipfile.ZipFile(io.BytesIO(response.content))

  # Extract to the current working directory
  zip_file.extractall()

  # Close the zip file
  zip_file.close()

  import pandas as pd
  import matplotlib.pyplot as plt
  import numpy as np

  # Read the actual data, skipping the first three header rows and excluding downballot races
  df = pd.read_csv('/content/24G_CVRExport_NOV_Final_Confidential.csv', skiprows=3, usecols=range(21), low_memory=False)

  # Find the Trump and Harris columns
  trump_col = "REP"
  harris_col = "DEM"

  # Convert to numeric
  df[trump_col] = pd.to_numeric(df[trump_col], errors='coerce')
  df[harris_col] = pd.to_numeric(df[harris_col], errors='coerce')

  # Filter for early voting
  early_voting = df[df['CountingGroup'] == 'Early Voting']

  # Group by tabulator and calculate percentages
  tabulator_stats = early_voting.groupby('TabulatorNum').agg({
      harris_col: 'sum',
      trump_col: 'sum'
  }).reset_index()

  # Calculate total votes and percentages
  tabulator_stats['total_votes'] = tabulator_stats[harris_col] + tabulator_stats[trump_col]
  tabulator_stats['harris_pct'] = tabulator_stats[harris_col] / tabulator_stats['total_votes'] \* 100
  tabulator_stats['trump_pct'] = tabulator_stats[trump_col] / tabulator_stats['total_votes'] \* 100

  # Create subplots
  fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))

  # Plot Harris histogram
  ax1.hist(tabulator_stats['harris_pct'], bins=50, edgecolor='black', color='blue', alpha=0.7)
  ax1.set_title('Distribution of Harris Votes by Tabulator (Early Voting Only)')
  ax1.set_xlabel('Percentage of Votes for Harris')
  ax1.set_ylabel('Number of Tabulators')

  # Plot Trump histogram
  ax2.hist(tabulator_stats['trump_pct'], bins=50, edgecolor='black', color='red', alpha=0.7)
  ax2.set_title('Distribution of Trump Votes by Tabulator (Early Voting Only)')
  ax2.set_xlabel('Percentage of Votes for Trump')
  ax2.set_ylabel('Number of Tabulators')

  plt.tight_layout()
  plt.show()

This produces a figure identical (up to histogram bucketing) to the one at the end of the linked article.