Sahara3のAI副業

AI副業でどこまでいけるのか?

AI副業:WindowsのビルドバージョンとKBの対比表をPythonで作りたい。

「さはら3」です。

AI副業でどこまでいけるのか?をテーマに頑張っていきたいと思います。



頭の体操

  • 1か所だけ異なる漢字が入っています。(解答は一番最後に掲載)

問題


本編

本業で業務をしていると、Windowsのビルド番号はわかるけど

KBがどこまで適用されているか?がわからない事が多々あります。

そこで、Pythonを使用して、MS公式情報から、情報を収集する事を行いたいと思います。

情報元

https://learn.microsoft.com/en-us/windows/release-health/windows11-release-information

  • 対比表をバージョンアップした記事は以下になります。

ai-sahara-navi.com

成果物

全部出すと長いので、2023年8月以降分のみにしています。

(本記事の末にすべての一覧を掲載いたします。)

Availability date Build KB article
2023/10/10 22621.2428 KB5031354
2023/10/10 22000.2538 KB5031358
2023/10/10 19045.357 KB5031356
2023/10/10 19044.357 KB5031356
2023/10/10 17763.4974 KB5031361
2023/10/10 14393.6351 KB5031362
2023/10/10 10240.20232 KB5031377
2023/9/26 22621.2361 KB5030310
2023/9/26 22000.2482 KB5030301
2023/9/26 19045.3516 KB5030300
2023/9/12 22621.2283 KB5030219
2023/9/12 22000.2416 KB5030217
2023/9/12 19045.3448 KB5030211
2023/9/12 19044.3448 KB5030211
2023/9/12 17763.4851 KB5030214
2023/9/12 14393.6252 KB5030213
2023/9/12 10240.20162 KB5030220
2023/8/22 22621.2215 KB5029351
2023/8/22 22000.236 KB5029332
2023/8/22 19045.3393 KB5029331
2023/8/8 22621.2134 KB5029263
2023/8/8 22000.2295 KB5029253
2023/8/8 19045.3324 KB5029244
2023/8/8 19044.3324 KB5029244
2023/8/8 17763.4737 KB5029247
2023/8/8 14393.6167 KB5029242
2023/8/8 10240.20107 KB5029259

Amazon アソシエイト


スクリプト

import requests
from bs4 import BeautifulSoup
import csv
import os
import pandas as pd

def get_web_data(URL):
    response = requests.get(URL)
    web_source = response.text

    # Clean up the web source
    web_source = web_source.replace("\n", "").replace("\r", "")
    for _ in range(100):
        web_source = web_source.replace("> ", ">").replace(">\t", ">").replace(" <", "<").replace("\t<", "<").replace("\t", "")
    web_source = web_source.replace("><", ">\r\n<")

    with open("c:\\temp\\OS_Build_temp.csv", "w", encoding="utf-8") as f:
        writer = csv.writer(f, delimiter="\t")
        writer.writerow(["Servicing option", "Availability date", "Build", "KB article"])

    soup = BeautifulSoup(web_source, "html.parser")
    for i in range(100):
        table = soup.find("table", {"id": f"historyTable_{i}"})
        if table:
            rows = table.find_all("tr")
            for row in rows:
                cols = row.find_all(["td", "th"])
                cols = [ele.text.strip() for ele in cols]
                with open("c:\\temp\\OS_Build_temp.csv", "a", encoding="utf-8") as f:
                    writer = csv.writer(f, delimiter="\t")
                    writer.writerow(cols)

    os_build_versions = []
    for line in web_source.split("\r\n"):
        if "OS Build" in line:
            os_build_version = BeautifulSoup(line, "html.parser").get_text()
            os_build_version = os_build_version.replace("- End of servicing", "").replace("Version ", "").replace("(RTM) ", "").replace("(OS build ", "\t").replace(")", "").replace(" ", "")
            os_build_versions.append(os_build_version)

    rep_os_build_versions = []
    for version in os_build_versions:
        parts = version.split("\t")
        if parts[0]:
            os_ver = "Windows 10 " + parts[0]  # Assuming WinName is "Windows 10"
            rep_os_build_versions.append([os_ver, parts[1]])

    with open("C:\\temp\\OS_Build_Version.csv", "a", encoding="utf-8") as f:
        writer = csv.writer(f, delimiter="\t")
        writer.writerow(["Version", "Build"])
        writer.writerows(rep_os_build_versions)

    # この関数の最後で、データフレームを返すように変更
    df = pd.read_csv("c:\\temp\\OS_Build_temp.csv", delimiter="\t", encoding="utf-8")
    return df


def remove_duplicates_and_save(dataframes):
    # 2つのデータフレームを結合
    combined_df = pd.concat(dataframes, ignore_index=True)

    # 重複を排除
    combined_df = combined_df.drop_duplicates()

    # 指定されたカラムだけを持つデータフレームを作成
    combined_df = combined_df[["Availability date", "Build", "KB article"]]

    # 'Availability date' で降順にソート
    combined_df = combined_df.sort_values(by=["Availability date", "Build"], ascending=[False, False])

    # "Availability date" が "Availability date" である行を削除
    combined_df = combined_df[combined_df["Availability date"] != "Availability date"]

    # CSVファイルとして保存
    combined_df.to_csv("c:\\temp\\OS_Build.csv", index=False, encoding="utf-8", header=True)

def delete_files(file_paths):
  """
  指定されたファイルパスのファイルを削除します。

  Args:
    file_paths: 削除するファイルパスのリスト。
  """
  for file_path in file_paths:
    os.remove(file_path)

if __name__ == "__main__":
    dataframes = []  # 各URLからのデータを格納するリスト

    URL = 'https://learn.microsoft.com/en-us/windows/release-health/release-information'
    WinName = "Windows 10"
    dataframes.append(get_web_data(URL))

    URL = 'https://learn.microsoft.com/en-us/windows/release-health/windows11-release-information'
    WinName = "Windows 11"
    dataframes.append(get_web_data(URL))

    # 2つのデータフレームを合算して処理
    remove_duplicates_and_save(dataframes)
    
    file_paths = [r"C:\temp\OS_Build_temp.csv", r"C:\temp\OS_Build_Version.csv"]
    delete_files(file_paths)


AI関連は日進月歩、日々之精進でございます。

最後まで読んで頂きありがとうございました。

AIさはら


頭の体操:解答

  • わかりましたか?

解答


本日のAI着物美女

AI着物美女

Instagram

良かったらInstagramのフォローをお願いします。

https://www.instagram.com/ai_kimono_bijo/

非アダルトで運営しておりますので、職場でも安心して堪能いただけます。

ビルドバージョン全て

  • 長いです。
Availability date Build KB article
2023/10/10 22621.2428 KB5031354
2023/10/10 22000.2538 KB5031358
2023/10/10 19045.357 KB5031356
2023/10/10 19044.357 KB5031356
2023/10/10 17763.4974 KB5031361
2023/10/10 14393.6351 KB5031362
2023/10/10 10240.20232 KB5031377
2023/9/26 22621.2361 KB5030310
2023/9/26 22000.2482 KB5030301
2023/9/26 19045.3516 KB5030300
2023/9/12 22621.2283 KB5030219
2023/9/12 22000.2416 KB5030217
2023/9/12 19045.3448 KB5030211
2023/9/12 19044.3448 KB5030211
2023/9/12 17763.4851 KB5030214
2023/9/12 14393.6252 KB5030213
2023/9/12 10240.20162 KB5030220
2023/8/22 22621.2215 KB5029351
2023/8/22 22000.236 KB5029332
2023/8/22 19045.3393 KB5029331
2023/8/8 22621.2134 KB5029263
2023/8/8 22000.2295 KB5029253
2023/8/8 19045.3324 KB5029244
2023/8/8 19044.3324 KB5029244
2023/8/8 17763.4737 KB5029247
2023/8/8 14393.6167 KB5029242
2023/8/8 10240.20107 KB5029259
2023/7/26 22621.207 KB5028254
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2023/7/25 19045.3271 KB5028244
2023/7/11 22621.1992 KB5028185
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2023/6/28 22000.2124 KB5027292
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2023/6/23 14393.5996 KB5028623
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2023/6/13 22621.1848 KB5027231
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2023/5/24 22621.1778 KB5026446
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2023/5/9 22621.1702 KB5026372
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