Python for loop to read file line by line
Jul 14, 2025 am 02:47 AMUsing for loop to read files line by line is an efficient way to process large files. 1. The basic usage is to open the file with open() and automatically manage the closing. Combined with for line in file to traverse each line, line.strip() can remove line breaks and spaces; 2. If you need to record the line number, you can use enumerate(file, start=1) to start the line number; 3. When processing non-ASCII files, you should specify encoding parameters such as utf-8 to avoid encoding errors. These methods are concise and practical, and are suitable for most text processing scenarios.
When you want to use Python to read file contents line by line, for
loop is a very direct and efficient method. Compared to loading the entire file into memory at one time, reading line by line is more suitable for processing large files and saves resources.

The following common methods are the writing methods that are often used in actual development.
The most basic way to read for loops
The most commonly used method is this:

with open('example.txt', 'r') as file: for line in file: print(line.strip())
The advantage of this method is its simplicity and easy to read. You don't need to call read()
or close()
file manually, with
statement will automatically help you handle these things.
-
'r'
ofopen()
means to open the file in read-only mode. - The
file
object itself is an iterable object, and each iteration returns a row of content. -
line.strip()
can remove line breaks and before and after spaces at the end of each line, and can also be replaced withrstrip('\n')
as needed to just use line breaks.
If you just want to iterate through the file content and do simple processing on each line, such as finding keywords or statistics, this writing method is enough.

If you want to know which line is currently
Sometimes, not only reading content, but also want to record line numbers, you can use enumerate()
:
with open('example.txt', 'r') as file: for idx, line in enumerate(file, start=1): print(f"Line {idx}: {line.strip()}")
Here start=1
is to make the line number start from 1 instead of the default 0.
This method is very suitable for debugging or outputting logs, and can quickly locate the content of which line.
What to do if you encounter coding problems?
If you report an error when reading Chinese or other non-ASCII content, remember to add encoding
parameters:
with open('example.txt', 'r', encoding='utf-8') as file: for line in file: print(line.strip())
In addition to utf-8
, common encodings also include gbk
, latin-1
, etc., which depends on how your file is saved.
If you are not sure, you can try utf-8
first, and then try other things if you don't.
Basically that's it.
Reading files with for
loop is not complicated, but there are several points that are easy to ignore, such as forgetting to use with
causes the file to be closed, or failing to process newlines to affect subsequent judgments. Just pay attention to these details and you can process text files stably.
The above is the detailed content of Python for loop to read file line by line. 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)

To realize text error correction and syntax optimization with AI, you need to follow the following steps: 1. Select a suitable AI model or API, such as Baidu, Tencent API or open source NLP library; 2. Call the API through PHP's curl or Guzzle and process the return results; 3. Display error correction information in the application and allow users to choose whether to adopt it; 4. Use php-l and PHP_CodeSniffer for syntax detection and code optimization; 5. Continuously collect feedback and update the model or rules to improve the effect. When choosing AIAPI, focus on evaluating accuracy, response speed, price and support for PHP. Code optimization should follow PSR specifications, use cache reasonably, avoid circular queries, review code regularly, and use X

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"

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]|[

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

pandas.melt() is used to convert wide format data into long format. The answer is to define new column names by specifying id_vars retain the identification column, value_vars select the column to be melted, var_name and value_name, 1.id_vars='Name' means that the Name column remains unchanged, 2.value_vars=['Math','English','Science'] specifies the column to be melted, 3.var_name='Subject' sets the new column name of the original column name, 4.value_name='Score' sets the new column name of the original value, and finally generates three columns including Name, Subject and Score.

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

First, define a ContactForm form containing name, mailbox and message fields; 2. In the view, the form submission is processed by judging the POST request, and after verification is passed, cleaned_data is obtained and the response is returned, otherwise the empty form will be rendered; 3. In the template, use {{form.as_p}} to render the field and add {%csrf_token%} to prevent CSRF attacks; 4. Configure URL routing to point /contact/ to the contact_view view; use ModelForm to directly associate the model to achieve data storage. DjangoForms implements integrated processing of data verification, HTML rendering and error prompts, which is suitable for rapid development of safe form functions.

Introduction to Statistical Arbitrage Statistical Arbitrage is a trading method that captures price mismatch in the financial market based on mathematical models. Its core philosophy stems from mean regression, that is, asset prices may deviate from long-term trends in the short term, but will eventually return to their historical average. Traders use statistical methods to analyze the correlation between assets and look for portfolios that usually change synchronously. When the price relationship of these assets is abnormally deviated, arbitrage opportunities arise. In the cryptocurrency market, statistical arbitrage is particularly prevalent, mainly due to the inefficiency and drastic fluctuations of the market itself. Unlike traditional financial markets, cryptocurrencies operate around the clock and their prices are highly susceptible to breaking news, social media sentiment and technology upgrades. This constant price fluctuation frequently creates pricing bias and provides arbitrageurs with
