


How can I efficiently parse fixed-width file lines in Python?
Oct 30, 2024 pm 05:09 PMFast Parsing of Fixed Width File Lines
Parsing fixed width files, where each column occupies a specific number of characters in a line, can be a task requiring efficiency. Here's a discussion on how to achieve this efficiently:
The Problem
Consider a fixed-width file where the first 20 characters represent one column, followed by 21-30 for the second, and so on. Given a line with 100 characters, how can we effectively parse it into its respective columns?
Solutions
1. Struct Module:
Utilizing the Python standard library's struct module provides both simplicity and speed due to its C implementation. The code below demonstrates its usage:
<code class="python">import struct fieldwidths = (2, -10, 24) fmtstring = ' '.join('{}{}'.format(abs(fw), 'x' if fw < 0 else 's') for fw in fieldwidths) # Convert Unicode input to bytes and decode result. unpack = struct.Struct(fmtstring).unpack_from # Alias. parse = lambda line: tuple(s.decode() for s in unpack(line.encode())) # Parse a sample line. line = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\n' fields = parse(line) print('fields:', fields)</code>
Output:
fmtstring: '2s 10x 24s', record size: 36 chars fields: ('AB', 'MNOPQRSTUVWXYZ0123456789')
2. Optimized String Slicing:
While string slicing is commonly used, it can become cumbersome for large lines. Here's an optimized approach:
<code class="python">from itertools import zip_longest from itertools import accumulate def make_parser(fieldwidths): # Calculate slice boundaries. cuts = tuple(cut for cut in accumulate(abs(fw) for fw in fieldwidths)) # Create field slice tuples. flds = tuple(zip_longest(cuts, (0,)+cuts))[:-1] # Ignore final value. # Construct the parsing function. parse = lambda line: tuple(line[i:j] for i, j in flds) parse.size = sum(abs(fw) for fw in fieldwidths) parse.fmtstring = ' '.join('{}{}'.format(abs(fw), 'x' if fw < 0 else 's') for fw in fieldwidths) return parse # Parse a sample line. line = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\n' fieldwidths = (2, -10, 24) # Negative values indicate ignored padding fields. parse = make_parser(fieldwidths) fields = parse(line) print('fmtstring:', parse.fmtstring, ', record size:', parse.size, 'chars') print('fields:', fields)</code>
Output:
fmtstring: '2s 10x 24s', record size: 36 chars fields: ('AB', 'MNOPQRSTUVWXYZ0123456789')
The above is the detailed content of How can I efficiently parse fixed-width file lines 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)

The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

Assert is an assertion tool used in Python for debugging, and throws an AssertionError when the condition is not met. Its syntax is assert condition plus optional error information, which is suitable for internal logic verification such as parameter checking, status confirmation, etc., but cannot be used for security or user input checking, and should be used in conjunction with clear prompt information. It is only available for auxiliary debugging in the development stage rather than substituting exception handling.

InPython,iteratorsareobjectsthatallowloopingthroughcollectionsbyimplementing__iter__()and__next__().1)Iteratorsworkviatheiteratorprotocol,using__iter__()toreturntheiteratorand__next__()toretrievethenextitemuntilStopIterationisraised.2)Aniterable(like

TypehintsinPythonsolvetheproblemofambiguityandpotentialbugsindynamicallytypedcodebyallowingdeveloperstospecifyexpectedtypes.Theyenhancereadability,enableearlybugdetection,andimprovetoolingsupport.Typehintsareaddedusingacolon(:)forvariablesandparamete

A common method to traverse two lists simultaneously in Python is to use the zip() function, which will pair multiple lists in order and be the shortest; if the list length is inconsistent, you can use itertools.zip_longest() to be the longest and fill in the missing values; combined with enumerate(), you can get the index at the same time. 1.zip() is concise and practical, suitable for paired data iteration; 2.zip_longest() can fill in the default value when dealing with inconsistent lengths; 3.enumerate(zip()) can obtain indexes during traversal, meeting the needs of a variety of complex scenarios.

To create modern and efficient APIs using Python, FastAPI is recommended; it is based on standard Python type prompts and can automatically generate documents, with excellent performance. After installing FastAPI and ASGI server uvicorn, you can write interface code. By defining routes, writing processing functions, and returning data, APIs can be quickly built. FastAPI supports a variety of HTTP methods and provides automatically generated SwaggerUI and ReDoc documentation systems. URL parameters can be captured through path definition, while query parameters can be implemented by setting default values ??for function parameters. The rational use of Pydantic models can help improve development efficiency and accuracy.

To test the API, you need to use Python's Requests library. The steps are to install the library, send requests, verify responses, set timeouts and retry. First, install the library through pipinstallrequests; then use requests.get() or requests.post() and other methods to send GET or POST requests; then check response.status_code and response.json() to ensure that the return result is in compliance with expectations; finally, add timeout parameters to set the timeout time, and combine the retrying library to achieve automatic retry to enhance stability.

A virtual environment can isolate the dependencies of different projects. Created using Python's own venv module, the command is python-mvenvenv; activation method: Windows uses env\Scripts\activate, macOS/Linux uses sourceenv/bin/activate; installation package uses pipinstall, use pipfreeze>requirements.txt to generate requirements files, and use pipinstall-rrequirements.txt to restore the environment; precautions include not submitting to Git, reactivate each time the new terminal is opened, and automatic identification and switching can be used by IDE.
