CPE

Common Platform Enumeration (CPE) is a structured naming scheme for information technology systems, software, and packages. Based upon the generic syntax for Uniform Resource Identifiers (URI), CPE includes a formal name format, a method for checking names against a system, and a description format for binding text and tests to a name. This method of naming is known as a well-formed CPE name (WFN)

You can see this notebook directly via:

Generation time

[1]:

from datetime import datetime, timezone, timedelta

timezone_offset = 0.0
tzinfo = timezone(timedelta(hours=timezone_offset))
generation_time = datetime.now(tzinfo).strftime('%Y-%m-%d %H:%M:%S %z')
print(generation_time)

2026-05-10 16:53:17 +0000

Creative Commons

This notebook and generated diagrams are released with Creative Commons liecense (CC BY 4.0).

CC BY 4.0

[2]:

import requests
import urllib3

urllib3.disable_warnings()

urls = ['https://mirrors.creativecommons.org/presskit/icons/cc.xlarge.png',
       'https://mirrors.creativecommons.org/presskit/icons/by.xlarge.png']
for url in urls:
    file_name = url.split("/")[-1:][0]
    print(file_name)

    file = requests.get(url, verify=False)
    open(file_name, 'wb').write(file.content)

cc.xlarge.png
by.xlarge.png

CPE data downloading

All CPE stats are taken from nvd.nist.gov/products/cpe/statistics

[3]:

import requests
from bs4 import BeautifulSoup, SoupStrainer

def get_data_with_requests(url):
    headers = {
        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
    }
    response = requests.get(url, headers=headers)
    response.raise_for_status()
    product = SoupStrainer('table')
    soup = BeautifulSoup(response.text, "html.parser", parse_only=product)
    return soup

url = "https://nvd.nist.gov/products/cpe/statistics"
try:
    data = get_data_with_requests(url)
    print("Data fetched successfully.")
    print(len(data))
except requests.exceptions.RequestException as e:
    print(f"Request failed: {e}")

Data fetched successfully.
18

New CPE entries

CPE data parsing

[4]:

import pandas as pd

def pars(data):
    data_table = []

    for table in data:
        table_id = table['id']
        table_year = table_id[-4:]
        table_rows = table.find_all("tr")
        number_of_new_cpe_entries_yearly = 0
        number_of_new_cpe_entries_list = []
        data_row = []
        for table_row in table_rows:
            data = table_row.find_all("td")

            if data:
                number_of_new_cpe_entries = int(data[1].string.replace(",",""))

                number_of_new_cpe_entries_list.append(number_of_new_cpe_entries)

                number_of_new_cpe_entries_yearly += number_of_new_cpe_entries

        while len(number_of_new_cpe_entries_list) < 12:
            number_of_new_cpe_entries_list.append(0)

        data_row.append(table_year)
        data_row.append(number_of_new_cpe_entries_yearly)
        data_row = data_row + number_of_new_cpe_entries_list

        data_table.append(data_row)

    data_columns = ['Year', 'Summary', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
    df = pd.DataFrame (data_table, columns = data_columns)
    df.sort_values(by=['Year'], inplace=True)
    df.reset_index(drop=True, inplace=True)
    df.index += 1
    return df

parsed_data = pars(data)

parsed_data.style.bar(subset=['Summary'], color='#FF6200')

[4]:
  Year Summary January February March April May June July August September October November December
1 2009 5786 181 422 830 639 612 209 241 110 330 102 1960 150
2 2010 11094 990 234 509 1106 803 886 426 1252 707 1336 1026 1819
3 2011 9263 702 925 583 2252 818 682 430 390 423 470 737 851
4 2012 23235 1148 1003 1622 2501 3228 2259 3124 2887 1583 1830 1110 940
5 2013 15671 1460 1105 1138 1271 932 989 1623 1359 1140 1941 1327 1386
6 2014 16714 1552 1151 1644 2103 2175 1447 1606 886 885 260 1380 1625
7 2015 6503 758 654 849 838 429 736 446 564 81 216 144 788
8 2016 11074 2450 535 871 699 732 1471 321 762 979 452 696 1106
9 2017 18650 746 888 1070 1935 1494 1210 1364 1889 1516 1596 2585 2357
10 2018 56509 2273 2404 1571 2525 10489 5160 3707 3895 3722 10862 6564 3337
11 2019 272322 6810 7197 10466 12979 13181 32837 45459 23763 47195 26717 22731 22987
12 2020 171681 15960 18668 20159 16306 14157 11250 10659 14459 13077 10921 9293 16772
13 2021 189563 11253 14969 18562 15476 13997 18730 20791 16531 14022 16164 17159 11909
14 2022 175776 11297 12125 13565 10410 10261 14428 13652 18251 14395 15559 14888 26945
15 2023 222949 18296 16482 22053 14261 17588 19858 18925 20938 16826 21329 18292 18101
16 2024 130132 20233 16164 2052 2926 514 7636 12531 12495 15996 18115 14856 6614
17 2025 193533 12540 12811 10686 15989 15607 15147 17688 15314 21625 15091 14789 26246
18 2026 170703 34581 34974 52711 41260 7177 0 0 0 0 0 0 0

CPE data saving

CSV file is available in GitHub repository, see:

[5]:

csv_filename = 'cpe-number-of-new-entries.csv'

parsed_data.to_csv(csv_filename, index=False)

CPE data ploting

PNG files are available in GitHub repository with two background versions, see:

[6]:

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import ticker

df = pd.read_csv(csv_filename)

# Set style and colors
plt.style.use('seaborn-v0_8-whitegrid')
bar_color = '#FF6200'

# Create figure and axes
fig, ax = plt.subplots()

bars = ax.bar(df['Year'], df['Summary'], color=bar_color, width=0.6)

# Add value labels above bars
for bar in bars:
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width() / 2,
            height,
            f'{int(height)}',
            ha='center', va='bottom',
            fontsize=9, fontweight='light', color='#333333')

# Disable default x-axis ticks
ax.set_xticks([])

# Add labels below bars
for bar, label in zip(bars, df['Year']):
    ax.text(bar.get_x() + bar.get_width() / 2,
            0,
            str(label),
            ha='center', va='top',
            rotation=45,
            fontsize=9, color='#333333')

# Labels and title
ax.set_xlabel('Year', fontsize=12, labelpad=30)
ax.set_ylabel('Number of CPE', fontsize=12)
ax.set_title('Number of CPE per Year', fontsize=14, fontweight='bold')

# Tweak spines and grid
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(axis='y', linestyle='--', alpha=0.5)

# Format Y-axis with thousands separator
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{int(x):,}'))

# Adjust layout for rotated labels
plt.subplots_adjust(bottom=0.15)
plt.tight_layout()

# Legend and footer
plt.legend(['CPE'])
plt.figtext(0.14, 0.02,
            f"Generated on {generation_time} thanks to limberduck.org based on source: nvd.nist.gov/products/cpe/statistics",
            ha="left", fontsize=8)

# Figure background
fig.set_size_inches(10, 6)
fig.patch.set_facecolor('white')

# License icons
img_cc = plt.imread('cc.xlarge.png')
newax_cc = fig.add_axes([0.88, 0.0, 0.05, 0.05], anchor='NE', zorder=-1)
newax_cc.imshow(img_cc)
newax_cc.axis('off')

img_by = plt.imread('by.xlarge.png')
newax_by = fig.add_axes([0.92, 0.0, 0.05, 0.05], anchor='NE', zorder=-1)
newax_by.imshow(img_by)
newax_by.axis('off')

# Save images
plt.savefig('cpe-number-of-new-entries-bg-white.png', dpi=300, facecolor='white')
plt.savefig('cpe-number-of-new-entries-bg-transparent.png', dpi=300, transparent=True)
../../_images/notebooks_cpe_cpe_18_0.png