O3-Mini
>已證明是Openai的編碼和推理最先進的模型。在大多數標準的基準測試中,O3-Mini(高)模型單槍匹馬勝過其他現有模型,例如DeepSeek-R1和Claude 3.5。因此,由O3-Mini提供支持的Chatgpt現在已成為開發人員的日常伴侶。它為他們提供了一種解決編程挑戰的智能和有效方法,例如調試,代碼生成,文檔或數據清潔。本文列出了10個CHATGPT提示,可以幫助您解鎖O3-Mini在編碼任務中的全部潛力。所以,讓我們開始!
> 內容表
>改進生成正則表達式(REGEX)
>>協助進行數據清潔和處理 編寫自動化的shell腳本
>>常見問題 1。調試代碼錯誤
>假設您正在為Web應用程序創作Python腳本,突然間,您遇到了一個不了解的錯誤。追溯消息很長,令人困惑,您不確定如何修復它。 O3-Mini提供了一種快速調試問題並了解出了什麼問題的方法。<em>import pandas as pd # Sample data data = { "Product": ["Laptop", "Headphones", "Smartphone", "Monitor", "Mouse"], "Category": ["Electronics", "Electronics", "Electronics", "Accessories", "Accessories"], "Sales": ["00", "0", "0", "0", ""] # Sales values contain a '$' sign } df = pd.DataFrame(data) # Convert Sales column to float df["Sales"] = df["Sales"].astype(float) # ? This line throws a ValueError # Calculate total sales per category total_sales = df.groupby("Category")["Sales"].sum() print(total_sales)</em>
>>模板提示:
“我有一塊python代碼,該代碼正在丟棄錯誤:attributeError:'nontype'對像沒有屬性'group'。你能幫我調試嗎?
代碼的輸出import pandas as pd # Sample data data = { "Product": ["Laptop", "Headphones", "Smartphone", "Monitor", "Mouse"], "Category": ["Electronics", "Electronics", "Electronics", "Accessories", "Accessories"], "Sales": ["00", "0", "0", "0", ""] # Sales values contain a '$' sign } df = pd.DataFrame(data) # Convert Sales column to float after stripping '$' df["Sales"] = df["Sales"].str.replace("$", "").astype(float) # ✅ Fixed: Removed currency symbols # Calculate total sales per category total_sales = df.groupby("Category")["Sales"].sum() print(total_sales)
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2。創建代碼模板
>當您手頭有一個新項目時,通常會在從哪裡開始時會感到困惑。可能有不同的方法或涉及多個步驟,或者可能需要您不熟悉的庫。在這種情況下,O3-Mini可以幫助您開始使用基本的代碼模板,並指導您編寫整個代碼。
模板提示:“您可以提供一個結構化的模板和指南,用於編寫[task]的[語言]項目?
示例提示:
> o3-mini的響應:
>
模板提示:“在保持邏輯和功能時,您可以將此[源語言]代碼轉換為[目標語言]嗎?
示例提示:
“在保持其邏輯和功能時,您可以將此Python代碼轉換為JavaScript(Node.js)嗎? 代碼的輸出
:
<em>import pandas as pd # Sample data data = { "Product": ["Laptop", "Headphones", "Smartphone", "Monitor", "Mouse"], "Category": ["Electronics", "Electronics", "Electronics", "Accessories", "Accessories"], "Sales": ["00", "0", "0", "0", ""] # Sales values contain a '$' sign } df = pd.DataFrame(data) # Convert Sales column to float df["Sales"] = df["Sales"].astype(float) # ? This line throws a ValueError # Calculate total sales per category total_sales = df.groupby("Category")["Sales"].sum() print(total_sales)</em>
> o3-mini的響應:
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4。改善代碼可讀性
> >>模板提示:
“您可以為此[語言]函數生成docstrings and評論嗎?[插入代碼]
>或
“您可以在處理用戶輸入並更新數據庫的python函數中添加Docstrings和註釋嗎?
> o3-mini的響應:
5。編寫邊緣案例的代碼和錯誤處理 >您是在Python中提出API請求,但是您的代碼無法處理請求失敗的情況。您需要添加錯誤處理以使其更加健壯。
模板提示:
示例提示:
<em>from collections import Counter # Sample text input text = """ This is a sample text. This text is meant to test word frequency. Some words appear more than once, some words appear only once. """ # Convert to lowercase and split into words words = text.lower().split() # Count word occurrences word_counts = Counter(words) # Print results for word, count in word_counts.items(): print(f"{word}: {count}")</em>
“您可以在此Python函數中添加適當的錯誤處理嗎?
> o3-mini的響應:// Sample text input let text = ` This is a sample text. This text is meant to test word frequency. Some words appear more than once, some words appear only once. `; // Convert to lowercase and split into words let words = text.toLowerCase().replace(/[^\w\s]/g, "").split(/\s+/); // Count word occurrences let wordCounts = new Map(); words.forEach(word => { wordCounts.set(word, (wordCounts.get(word) || 0) + 1); }); // Print results wordCounts.forEach((count, word) => { console.log(`${word}: ${count}`); });
<em>import pandas as pd # Sample data data = { "Product": ["Laptop", "Headphones", "Smartphone", "Monitor", "Mouse"], "Category": ["Electronics", "Electronics", "Electronics", "Accessories", "Accessories"], "Sales": ["00", "0", "0", "0", ""] # Sales values contain a '$' sign } df = pd.DataFrame(data) # Convert Sales column to float df["Sales"] = df["Sales"].astype(float) # ? This line throws a ValueError # Calculate total sales per category total_sales = df.groupby("Category")["Sales"].sum() print(total_sales)</em>
>讓我們假設您的團隊正在部署Node.js應用程序,並且已分配了設置自動化CI/CD管道的任務。您需要配置GitHub操作以運行測試並在將更改推到主分支時部署應用程序。這是一個簡單的提示,您可以在O3-Mini上使用來設置部署管道。
>模板提示:>“您可以使用[CI/CD工具:GitHub Actions,Jenkins等]生成[技術堆棧:React,Mongodb,Node.js等]的CI/CD管道配置,以運行測試和部署[github Actions,Jenkins等]
示例提示:
“您可以使用github操作為node.js項目生成CI/CD管道配置
> o3-mini的響應:7。代碼優化和性能改進
您是否曾經在Python功能正常工作但在大型數據集上緩慢運行的情況?如果這是完成工作的障礙,那麼O3-Mini可以幫助您優化代碼以提高執行效率。 > >
>模板提示:import pandas as pd # Sample data data = { "Product": ["Laptop", "Headphones", "Smartphone", "Monitor", "Mouse"], "Category": ["Electronics", "Electronics", "Electronics", "Accessories", "Accessories"], "Sales": ["00", "0", "0", "0", ""] # Sales values contain a '$' sign } df = pd.DataFrame(data) # Convert Sales column to float after stripping '$' df["Sales"] = df["Sales"].str.replace("$", "").astype(float) # ✅ Fixed: Removed currency symbols # Calculate total sales per category total_sales = df.groupby("Category")["Sales"].sum() print(total_sales)
“您可以優化此Python代碼的性能嗎?目前,它在檢查質數時效率低下,並且在搜索大範圍內的素數(例如1至100,000)時會緩慢運行。
> o3-mini的響應:
代碼的輸出 :
<em>from collections import Counter # Sample text input text = """ This is a sample text. This text is meant to test word frequency. Some words appear more than once, some words appear only once. """ # Convert to lowercase and split into words words = text.lower().split() # Count word occurrences word_counts = Counter(words) # Print results for word, count in word_counts.items(): print(f"{word}: {count}")</em>
8。生成正則表達式(REGEX)
O3-Mini在代碼中生成正則表達式非常有幫助。假設您正在為網站構建表單驗證系統,並且需要一個正則模式來驗證電子郵件地址。只需要求O3 Mini使用此提示來生成正則模式。// Sample text input let text = ` This is a sample text. This text is meant to test word frequency. Some words appear more than once, some words appear only once. `; // Convert to lowercase and split into words let words = text.toLowerCase().replace(/[^\w\s]/g, "").split(/\s+/); // Count word occurrences let wordCounts = new Map(); words.forEach(word => { wordCounts.set(word, (wordCounts.get(word) || 0) + 1); }); // Print results wordCounts.forEach((count, word) => { console.log(`${word}: ${count}`); });
模板提示:“您能以[task]的語言]生成一個正則表達式?
示例提示:
> o3-mini的響應:
:
>在分析熊貓中的客戶銷售數據時,您經常以多種格式遇到混亂的數據集,缺失值,重複條目和數據。在運行分析之前,在O3-Mini上使用以下提示是清潔數據的有效方法。
>>模板提示:
“您能否幫助使用python和pandas?
> o3-mini的響應:
<em>import pandas as pd # Sample data data = { "Product": ["Laptop", "Headphones", "Smartphone", "Monitor", "Mouse"], "Category": ["Electronics", "Electronics", "Electronics", "Accessories", "Accessories"], "Sales": ["00", "0", "0", "0", ""] # Sales values contain a '$' sign } df = pd.DataFrame(data) # Convert Sales column to float df["Sales"] = df["Sales"].astype(float) # ? This line throws a ValueError # Calculate total sales per category total_sales = df.groupby("Category")["Sales"].sum() print(total_sales)</em>
代碼的輸出 :
>我敢肯定,當我說我們的許多工作本質上是重複性的時,您會同意我的看法 - 無論是經常部署應用程序還是備份文件。 O3-Mini可以通過編碼來幫助您自動化它們,而不是每次手動運行這些命令。它可以根據您的要求編寫您的shell腳本以自動化重複終端命令。
>>>模板提示:
“您可以編寫一個shell腳本以自動備份目錄(/home/home/user/documents),每天午夜使用cron?
> o3-mini的響應:1️。 shell腳本(backup.sh)
#定義源和目標目錄
#創建備份目錄
import pandas as pd # Sample data data = { "Product": ["Laptop", "Headphones", "Smartphone", "Monitor", "Mouse"], "Category": ["Electronics", "Electronics", "Electronics", "Accessories", "Accessories"], "Sales": ["00", "0", "0", "0", ""] # Sales values contain a '$' sign } df = pd.DataFrame(data) # Convert Sales column to float after stripping '$' df["Sales"] = df["Sales"].str.replace("$", "").astype(float) # ✅ Fixed: Removed currency symbols # Calculate total sales per category total_sales = df.groupby("Category")["Sales"].sum() print(total_sales)
<em>from collections import Counter # Sample text input text = """ This is a sample text. This text is meant to test word frequency. Some words appear more than once, some words appear only once. """ # Convert to lowercase and split into words words = text.lower().split() # Count word occurrences word_counts = Counter(words) # Print results for word, count in word_counts.items(): print(f"{word}: {count}")</em>
// Sample text input let text = ` This is a sample text. This text is meant to test word frequency. Some words appear more than once, some words appear only once. `; // Convert to lowercase and split into words let words = text.toLowerCase().replace(/[^\w\s]/g, "").split(/\s+/); // Count word occurrences let wordCounts = new Map(); words.forEach(word => { wordCounts.set(word, (wordCounts.get(word) || 0) + 1); }); // Print results wordCounts.forEach((count, word) => { console.log(`${word}: ${count}`); });
運行以下命令以提供腳本執行權限:
<em>import sqlite3 def update_user_balance(user_id, amount): conn = sqlite3.connect("database.db") cursor = conn.cursor() current_balance = cursor.execute("SELECT balance FROM users WHERE id = ?", (user_id,)).fetchone() if current_balance: new_balance = current_balance[0] + amount cursor.execute("UPDATE users SET balance = ? WHERE id = ?", (new_balance, user_id)) conn.commit() conn.close() return new_balance else: conn.close() return None</em>
安排每日備份
打開crontab編輯器:import sqlite3 def update_user_balance(user_id, amount): """ Updates the balance of a user in the database. Parameters: user_id (int): The unique ID of the user whose balance needs to be updated. amount (float): The amount to add to (or subtract from) the user's balance. Returns: float: The updated balance if the user exists. None: If the user does not exist in the database. """ # Connect to the SQLite database conn = sqlite3.connect("database.db") cursor = conn.cursor() # Fetch the current balance of the user current_balance = cursor.execute( "SELECT balance FROM users WHERE id = ?", (user_id,) ).fetchone() if current_balance: # Calculate the new balance new_balance = current_balance[0] + amount # Update the user's balance in the database cursor.execute( "UPDATE users SET balance = ? WHERE id = ?", (new_balance, user_id) ) # Commit the transaction conn.commit() # Close the connection conn.close() return new_balance # Return the updated balance else: # Close the connection if user does not exist conn.close() return None # Return None if user ID is not found
在最後添加此行:
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<em>import requests response = requests.get("https://api.example.com/data") data = response.json()</em>
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常見問題>
以上是10 O3米尼編碼提示可以幫助您完成所有編碼任務的詳細內容。更多資訊請關注PHP中文網其他相關文章!