え〜と確認です １）本件原因の恐喝被害少年に対し少年①が性懲りなく恐喝・（今回は1.4万円、PayPayで）巻き上げですか? ２）ｽｸｼｮのやり取りは誰と誰の会話でしょうか？（お母様と恐喝被害少年?）PayPayの送信画面をｽｸｼｮ＋送信履歴をCSVfileでﾀﾞｳﾝﾛｰﾄﾞ→お母様に→警察&学校へ通報で

"import csv with open('anak2_pacil.csv', newline='', encoding='utf-8') as csvfile: reader = csv.DictReader(csvfile) for row in reader: name = row.get('Name', '').strip() prodi = row.get('Prodi', '').strip() print(f"{name} {prodi}")" ahh naming

Will Lua read my CSV file if I say 'please' in multiple languages? Source: devhubby.com/thread/how-to-r… #DevCommunity #SoftwareDevelopment #CodeTutorial #Lua #csvfile #files

A script is available to generate a CSV file containing all MQL5 Trade Signals' properties. This CSV can be opened with spreadsheet software like Excel for sorting and analysis to facilitate copy trading decisions. The script covers MT5 signals and includes up to 1000 entries. For comprehensive data, ensure the Signals tab is open in the terminal, enabling data download. The generated file is located in the terminal's MQL5\Files folder. To access, navigate via MetaTrader's File menu to Open Data Folder. Note, code publications are accessible in the Public Projects tab of MetaEditor under FMIC. #MQL5 #MT5 #script #CSVfile mql5.com/en/code/39071?utm_s…

In the Hadoop vs. CSV column staring contest, who blinks first? Source: devhubby.com/thread/how-to-r… #Coding #CloudComputing #DataPipeline #MapReduce #hadoop #csvfile

【本件解決済】 ChatGPTのAPIに詳しい人はいませんか？ コードかけない人間なのでGPTの暴力で組んでいるのですが "gpt-4o-mini"が使えるコードができません。 ウェブサイトのURLを読み込ませて、そこから答えさせあるのは難しいのですが 現在の最新のopenaiを使って、gpt-4o-miniを使うにはどう修正すればよろしいでしょうか？ 今のコードはここです ====以下コード==== import openai import csv import time import os # 1. OpenAI APIキーの設定 openai.api_key = 'APIHERE' # 2. Link.txt のパスを指定 link_file_path = 'Link.txt' # Question1,Question2等各行に質問がかいてある # 3. Link.txt が存在するか確認 if not os.path.exists(link_file_path): raise FileNotFoundError(f"'{link_file_path}' が見つかりません。ファイルのパスを確認してください。") # 4. Link.txt からリンクを読み込む with open(link_file_path, 'r', encoding='utf-8') as file: links = [line.strip() for line in file if line.strip()] # 5. レスポンスを保存するリスト responses = [] # 6. 各リンクに対してAPIリクエストを送信 for index, link in enumerate(links, start=1): print(f"Processing {index}/{len(links)}: {link}") messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": f"Please provide me Answer of the question\n{link}"} ] try: # ChatCompletionを使用してリクエストを送信 response = openai.ChatCompletion.create( model="gpt-4", # または "gpt-3.5-turbo" を使用可能 messages=messages, max_tokens=500, temperature=0.7, ) # レスポンスを取得 text_response = response.choices[0].message['content'].strip() # コンソールにレスポンスを出力 print(f"Response for {link}:\n{text_response}\n") # リストに追加 responses.append((link, text_response)) except Exception as e: print(f"Error processing link {link}: {e}") responses.append((link, f"Error: {e}")) # レート制限を考慮して1秒待機 time.sleep(1) # 7. 結果をCSVファイルに書き込む output_csv_path = 'output.csv' with open(output_csv_path, 'w', newline='', encoding='utf-8') as csvfile: writer = csv.writer(csvfile) writer.writerow(['Link', 'gptResponse']) writer.writerows(responses) print(f"処理が完了しました。結果は '{output_csv_path}' に保存されています。")

Day 62 of #100DaysOfCode I guess I'm back on track after many hurdles, ones beyond control...Let's finish what we started Advanced -#Python Flask, WTforms, Bootstrap and reading and saving to csvfile. And here's what we got

Lex Fridman的播客经常会采访一些大牛，搜到个Kaggle上的数据集，有300多期访谈CSV文件。 kaggle.com/datasets/rajneesh… 下载后让Claude写个Python程序生成300个txt文件，传到notebookLM使用。 生成文件Python代码： import csv import os import sys # 增加CSV字段大小限制 csv.field_size_limit(sys.maxsize) def create_txt_files_from_csv(csv_file_path): # 确保输出目录存在 output_dir = 'output_txt_files' os.makedirs(output_dir, exist_ok=True) # 读取CSV文件 with open(csv_file_path, 'r', newline='', encoding='utf-8') as csvfile: csv_reader = csv.DictReader(csvfile) # 遍历CSV的每一行 for row in csv_reader: # 获取title和text title = row['title'] text = row['text'] # 创建安全的文件名（移除不允许的字符） safe_title = "".join([c for c in title if c.isalpha() or c.isdigit() or c==' ']).rstrip() # 如果文件名为空，使用id作为文件名 if not safe_title: safe_title = f"file_{row['id']}" # 创建文件路径 file_path = os.path.join(output_dir, f"{safe_title}.txt") # 写入文本文件 with open(file_path, 'w', encoding='utf-8') as txtfile: txtfile.write(text) print(f"Created file: {file_path}") # 使用函数 csv_file_path = 'podcastdata_dataset.csv' # 替换为你的CSV文件路径 create_txt_files_from_csv(csv_file_path)

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Product uploading services are specialized platforms that assist businesses in creating and managing product listings on various online marketplaces, such as Amazon, eBay and more. #Productupload #Productlisting #Inventory #CSVfile #Addproduct fiverr: fiverr.com/s/m5d6LqP

エラーハンドリング、スッキリ書けた気がする！ 関数内の失敗時はreturn 1を返すようにしてあげて、 ||と{}を組み合わせたらシンプルに書けた気がする！！ ``` while read -a array; do not func1 || { echo "${err_msg}"; continue; } func2 done < <(tail -n 2 "${csvfile}") ```

Finance tip: to find where your biggest expenses are, try randomly sampling them proportional to the expense. E.g. this Python script processes a csv exported from a banking website: import csv import random rows = [] with open('expenses.csv', newline='') as csvfile: reader = csv.reader(csvfile, delimiter=',', quotechar='|') for row in reader: try: net = float(row[1]) if net < 0: rows.append((-net, row)) except: pass total_expense = sum(expense for expense, _ in rows) probabilities = [expense / total_expense for expense, _ in rows] for i in range(20): # Sample a row based on the probabilities sampled_row = random.choices(rows, weights=probabilities)[0][1] print(sampled_row)

I can't believe that my prof just made an activity about csv and what he puts in the csvfile was rick roll, genshin, and gawr gura 💀😆

🎥📝 I just created a complete video tutorial on how to make a CSV file in simple steps. This is an essential first step in creating your NFT collection on the @Apillon platform. 🚀💡 Join me and learn the easy process of preparing your CSV file for NFT creation. Let's unlock the world of digital assets together! 💪🌐 Check out the tutorial here: youtu.be/gact2jGrY6I #Tutorial #CSVFile #NFTCreation #ApillonPlatform #DigitalAssets #LearnWithApillon #NFTs

import requests import csv from datetime import datetime, timedelta # Twitter APIの認証情報 bearer_token = "YourAPIKEY" # ツイートID tweet_id = "1643445901015531522" # Twitter API v2のエンドポイント retweets_url = f"api.twitter.com/2/tweets/{tweet_id}/retweeted_by" retweeted_tweet_url = f"api.twitter.com/2/tweets/{tweet_id}" user_tweets_url = "api.twitter.com/2/users/{}/tweets" headers = { "Authorization": f"Bearer {bearer_token}", "Content-Type": "application/json" } def get_tweets(url, params=None): response = requests.get(url, headers=headers, params=params) if response.status_code != 200: raise Exception(f"Request returned an error: {response.status_code}, {response.text}") return response.json() # リツイートしたツイートの本文を取得 retweeted_tweet_data = get_tweets(retweeted_tweet_url, params={"tweet.fields": "text"}) retweeted_tweet_text = retweeted_tweet_data["data"]["text"][:20] # リツイートしたユーザーを取得 retweeters_data = get_tweets(retweets_url) tweet_fields = {"tweet.fields": "created_at"} # CSVファイルに出力 with open('output.csv', 'w', newline='', encoding='utf-8') as csvfile: csv_writer = csv.writer(csvfile) csv_writer.writerow(['Username', 'Tweet Text', 'Tweet URL']) for retweeter in retweeters_data["data"]: user_id = retweeter["id"] # ユーザーのツイートを取得 user_tweets_data = get_tweets(user_tweets_url.format(user_id), params={**tweet_fields, "max_results": 100}) rt_time = None for tweet in user_tweets_data["data"]: if retweeted_tweet_text in tweet["text"]: rt_time = datetime.fromisoformat(tweet["created_at"].replace("Z", " 00:00")) break if rt_time: for i, tweet in enumerate(user_tweets_data["data"]): tweet_time = datetime.fromisoformat(tweet["created_at"].replace("Z", " 00:00")) if rt_time < tweet_time: next_tweet_time = datetime.fromisoformat(user_tweets_data["data"][i 1]["created_at"].replace("Z", " 00:00")) # リツイート直後のツイートが3分以内で、リツイートでない場合、そのツイートのユーザー名、本文、URLをCSVに出力 if (next_tweet_time - rt_time <= timedelta(minutes=3)) and ("retweeted_status" not in tweet): username = retweeter['username'] tweet_text = tweet["text"] tweet_url = f"twitter.com/{username}/status/{tweet['id']}" csv_writer.writerow([username, tweet_text, tweet_url]) import csv # CSVファイルからURLを読み込む tweet_urls = [] with open('output.csv', 'r', newline='', encoding='utf-8') as csvfile: csv_reader = csv.reader(csvfile) next(csv_reader)  # ヘッダー行をスキップ for row in csv_reader: tweet_urls.append(row[2]) # ツイート埋め込みコードを生成する embed_html = "" for url in tweet_urls: embed_html = f'<blockquote class="twitter-tweet"><a href="{url}"></a></blockquote>\n' # Twitterウィジェットのスクリプトタグを追加 embed_html = '<script async src="platform.twitter.com/widgets…" charset="utf-8"></script>' # HTMLファイルに出力 with open('embedded_tweets.html', 'w', encoding='utf-8') as html_file: html_file.write(embed_html)

So I asked GPT4 to rewrite the code for exporting tweet js file to csv with 2 types of comments 1. Real life analogy 2. Movie analogy it found references to matrix, inception etc. it is not amazing but it is something what a fun time to learn programming isn't it e.g. Like Thor's hammer, Mjolnir, the CSV writer empowers us to record our findings. writer = csv.DictWriter(csvfile, fieldnames=fieldnames, quoting=csv.QUOTE_ALL)

learning python when ChatGPT is available is really weird... I have an idea and then ChatGPT gives me the code in a few seconds with good notes on it : ------ import ipaddress import csv # Read the input CSV file with open('input_ips.csv') as csvfile: reader = csv.reader(csvfile) ip_list = [row[0] for row in reader] # Convert the list of IP addresses to a list of IP objects ip_objects = [ipaddress.ip_address(ip) for ip in ip_list] # Sort the list of IP objects sorted_ips = sorted(ip_objects) # Group the IP addresses into CIDR ranges cidr_blocks = ipaddress.summarize_address_range(sorted_ips[0], sorted_ips[-1]) # Write the output CSV file with open('output_cidr_blocks.csv', 'w', newline='') as csvfile: writer = csv.writer(csvfile) for cidr in cidr_blocks: writer.writerow([str(cidr)]) ---

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