How to use for loop to sum in python
You can use a for loop to implement cumulative summation in Python
for loop syntax:
for 变量 in range(x): 循环需要执行的代码
The following implements the summation from 1 to n:
def main(): sum = 0 # 定义变量做累加器 n = int(input('n=')) #从键盘上输入累加的范围 for x in range(n): sum += (x + 1) print(sum) if __name__ == '__main__': main()
For more Python-related technical articles, please visit the Python Tutorial column to learn!
The above is the detailed content of How to use for loop to sum in python. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Use subprocess.run() to safely execute shell commands and capture output. It is recommended to pass parameters in lists to avoid injection risks; 2. When shell characteristics are required, you can set shell=True, but beware of command injection; 3. Use subprocess.Popen to realize real-time output processing; 4. Set check=True to throw exceptions when the command fails; 5. You can directly call chains to obtain output in a simple scenario; you should give priority to subprocess.run() in daily life to avoid using os.system() or deprecated modules. The above methods override the core usage of executing shell commands in Python.

Use Seaborn's jointplot to quickly visualize the relationship and distribution between two variables; 2. The basic scatter plot is implemented by sns.jointplot(data=tips,x="total_bill",y="tip",kind="scatter"), the center is a scatter plot, and the histogram is displayed on the upper and lower and right sides; 3. Add regression lines and density information to a kind="reg", and combine marginal_kws to set the edge plot style; 4. When the data volume is large, it is recommended to use "hex"

Use httpx.AsyncClient to efficiently initiate asynchronous HTTP requests. 1. Basic GET requests manage clients through asyncwith and use awaitclient.get to initiate non-blocking requests; 2. Combining asyncio.gather to combine with asyncio.gather can significantly improve performance, and the total time is equal to the slowest request; 3. Support custom headers, authentication, base_url and timeout settings; 4. Can send POST requests and carry JSON data; 5. Pay attention to avoid mixing synchronous asynchronous code. Proxy support needs to pay attention to back-end compatibility, which is suitable for crawlers or API aggregation and other scenarios.

String lists can be merged with join() method, such as ''.join(words) to get "HelloworldfromPython"; 2. Number lists must be converted to strings with map(str, numbers) or [str(x)forxinnumbers] before joining; 3. Any type list can be directly converted to strings with brackets and quotes, suitable for debugging; 4. Custom formats can be implemented by generator expressions combined with join(), such as '|'.join(f"[{item}]"foriteminitems) output"[a]|[

Pythoncanbeoptimizedformemory-boundoperationsbyreducingoverheadthroughgenerators,efficientdatastructures,andmanagingobjectlifetimes.First,usegeneratorsinsteadofliststoprocesslargedatasetsoneitematatime,avoidingloadingeverythingintomemory.Second,choos

Install pyodbc: Use the pipinstallpyodbc command to install the library; 2. Connect SQLServer: Use the connection string containing DRIVER, SERVER, DATABASE, UID/PWD or Trusted_Connection through the pyodbc.connect() method, and support SQL authentication or Windows authentication respectively; 3. Check the installed driver: Run pyodbc.drivers() and filter the driver name containing 'SQLServer' to ensure that the correct driver name is used such as 'ODBCDriver17 for SQLServer'; 4. Key parameters of the connection string

shutil.rmtree() is a function in Python that recursively deletes the entire directory tree. It can delete specified folders and all contents. 1. Basic usage: Use shutil.rmtree(path) to delete the directory, and you need to handle FileNotFoundError, PermissionError and other exceptions. 2. Practical application: You can clear folders containing subdirectories and files in one click, such as temporary data or cached directories. 3. Notes: The deletion operation is not restored; FileNotFoundError is thrown when the path does not exist; it may fail due to permissions or file occupation. 4. Optional parameters: Errors can be ignored by ignore_errors=True

Use psycopg2.pool.SimpleConnectionPool to effectively manage database connections and avoid the performance overhead caused by frequent connection creation and destruction. 1. When creating a connection pool, specify the minimum and maximum number of connections and database connection parameters to ensure that the connection pool is initialized successfully; 2. Get the connection through getconn(), and use putconn() to return the connection to the pool after executing the database operation. Constantly call conn.close() is prohibited; 3. SimpleConnectionPool is thread-safe and is suitable for multi-threaded environments; 4. It is recommended to implement a context manager in combination with context manager to ensure that the connection can be returned correctly when exceptions are noted;
